Autism is a pervasive neuro-developmental disorder, primarily encompassing difficulties in the social,
language, and communicative domains. Because autism is a spectrum disorder, it affects each individual
differently and has varying degrees. There are three core aspects of impairment based upon the Diagnostic
and Statistical Manual of Mental Disorders (DSM-IV), namely impairment in socialization, impairment in
communication, and restricted repetitive activities or interests. This work describes the experiment aims at
expressing autistic traits through the use of self-organizing map. Works related to simulating autism
through self-organizing map is limited. This work compare and contrast the difference in attention index
for normal learning and marred attention shift learning ability. It was found that the attention index of
normal learning is 9 times better marred attention shift for both random and pre-fixed input data. In the
marred attention shift context, neurons adapt more towards the mean of both sources combined under
marred context while some neurons adapt towards mean of one source under normal context. The normal
learning ability produces maps with neurons orienting towards mean values of combined stimuli source.
Impairment in learning ability produces similar cortical maps compared to normal learning ability. The
major difference is in the attention index.
SIMULATING ATTENTION DISORDER IN AUTISTIC PATIENTS BASED ON A COMPUTATIONAL M...ijitcs
Autism is an advanced neurological disease that affect communication and social behaviors, including attention -one of the fundamental skills to learn about the world around us. Autistic people have difficulty moving their attention from one point to another fluently. Due to the high prevalence of autism and its increasing progression, and the need to address common disorders in patients, this study aimed to implement and simulate a computational model for attention deficit disorder in autistic patients using MATLAB. This computational model has three components: context-sensitive reinforcement learning, contextual processing, and automation that can teach a shift-shift task automatically. At first, the model functions like normal people, but its performance gets closer to autistic people after changing a single parameter. This study demonstrates that even a simple computational model can be used for normal and abnormal developmental cases using a neural network reinforcement learning approach and provide valuable insights into autism.
1) The study examined implicit learning of local context in individuals with autism spectrum disorder (ASD) using a contextual cueing task.
2) In the task, participants searched for a target shape within arrays of distractor shapes, and response times were faster for repeated versus novel contexts.
3) Previous research found intact implicit learning in ASD using this task. However, the current study found that exposure to contexts biasing attention to local rather than global displays made it difficult for those with ASD to adapt to new trials.
1 b. theories of intelligence elka shane dela peñaAhL'Dn Daliva
Cattell and Horn's theory of intelligence classified intelligence into three dimensions: fluid intelligence which is the ability to solve new problems, crystallized intelligence which uses previously learned methods to solve problems, and visual-spatial reasoning which uses visual images and relationships. Perkins' theory identified three components of IQ: neural intelligence referring to neurological efficiency, experiential intelligence from accumulated knowledge and experience, and reflective intelligence involving problem-solving strategies, learning approaches, and attitudes like persistence. The document discusses different theories of intelligence by Cattell and Horn, and Perkins.
This document describes a study that used eye tracking to examine how prior knowledge and color contrast affect the visual search processes of novice learners. The study aimed to understand how students interpret microscope slides in relation to their existing biology content knowledge and prior microscope experience. Students were split into high and low prior knowledge groups and tested on their ability to visually search microscope images with high and low color contrasts. The results could help improve the use of visual aids to support learning in biology education.
ESTABLISHMENT OF VIRTUAL POLICY BASED NETWORK MANAGEMENT SCHEME BY LOAD EXPER...IJCNCJournal
In the current Internet-based systems, there are many problems using anonymity of the network
communication such as personal information leak and crimes using the Internet systems. This is because
the TCP/IP protocol used in Internet systems does not have the user identification information on the
communication data, and it is difficult to supervise the user performing the above acts immediately. As a
solution for solving the above problem, there is the approach of Policy-based Network Management
(PBNM). This is the scheme for managing a whole Local Area Network (LAN) through communication
control of every user. In this PBNM, two types of schemes exist. The first is the scheme for managing the
whole LAN by locating the communication control mechanisms on the course between network servers and
clients. The second is the scheme of managing the whole LAN by locating the communication control
mechanisms on clients. As the second scheme, we have been studied theoretically about the Destination
Addressing Control System (DACS) Scheme. By applying this DACS Scheme to Internet system
management, we intend to realize the policy-based Internet system management finally. In the DACS
Scheme, the inspection is not done about compatibility to cloud environment with virtualization technology
that spreads explosively. As the result, the coverage of the DACS Scheme is limited only in physical
environment now. In this study, we inspect compatibility of the DACS Scheme for the cloud environment
with virtualization technology, and enlarge coverage of this scheme. With it, the Virtual DACS Scheme
(vDACS Scheme) is established.
A proposal to enhance cellular and wifiIJCNCJournal
WiFi offloading is becoming one of the key enablers to help the network operators dealing with the exponentially growing demand of mobile data. The idea of using WiFi to offload data traffic from cellular network has proposed for many years. However, the interoperability issue between the two networks needs to be enhanced so that WiFi can efficiently supplement for the cellular network in case of congestion or outage. In this paper, we propose a novel network roaming and selection scheme based on 3GPP TS 24.312 and IEEE 802.11k, u standards to enhance cellular and WiFi interworking. The proposed scheme is aimed at enhancing the network roaming and selection so that WiFi network can serve as a supplement and backup access network for the cellular not only for congestion control but also in case of unexpected network failure event. We also model and evaluate the proposed scheme in a typical HetNet with interworking WiFi access points and cellular base stations. The simulation result shows that our proposed scheme quickly detects unexpected network failure event and assists active UEs to perform handoff to preferable alternative point of access. As a result, service disruption is substantially reduced and quality of experience (downlink/uplink’s throughput) is improved. Therefore, our proposed scheme can be used for a more reliable HetNet in terms of congestion control and disruption tolerance.
GEOGRAPHIC MAPS CLASSIFICATION BASED ON L*A*B COLOR SYSTEMIJCNCJournal
Today any geographic information system (GIS) layers became vital part of any GIS system , and
consequently , the need for developing automatic approaches to extract GIS layers from different image
maps like digital maps or satellite images is very important.
Map classification can be defined as an image processing technique which creates thematic maps from
scanned paper maps or remotely sensed images. Each resultant theme will represent a GIS layer of the
images.
A new proposed approach to extract GIS layers (classes) automatically based on L*A*B colorsystem
selected from ( A and B ) is proposed in this paper, our experiments shows that the hsi color space gives
better than L*A*B.
SIMULATING ATTENTION DISORDER IN AUTISTIC PATIENTS BASED ON A COMPUTATIONAL M...ijitcs
Autism is an advanced neurological disease that affect communication and social behaviors, including attention -one of the fundamental skills to learn about the world around us. Autistic people have difficulty moving their attention from one point to another fluently. Due to the high prevalence of autism and its increasing progression, and the need to address common disorders in patients, this study aimed to implement and simulate a computational model for attention deficit disorder in autistic patients using MATLAB. This computational model has three components: context-sensitive reinforcement learning, contextual processing, and automation that can teach a shift-shift task automatically. At first, the model functions like normal people, but its performance gets closer to autistic people after changing a single parameter. This study demonstrates that even a simple computational model can be used for normal and abnormal developmental cases using a neural network reinforcement learning approach and provide valuable insights into autism.
1) The study examined implicit learning of local context in individuals with autism spectrum disorder (ASD) using a contextual cueing task.
2) In the task, participants searched for a target shape within arrays of distractor shapes, and response times were faster for repeated versus novel contexts.
3) Previous research found intact implicit learning in ASD using this task. However, the current study found that exposure to contexts biasing attention to local rather than global displays made it difficult for those with ASD to adapt to new trials.
1 b. theories of intelligence elka shane dela peñaAhL'Dn Daliva
Cattell and Horn's theory of intelligence classified intelligence into three dimensions: fluid intelligence which is the ability to solve new problems, crystallized intelligence which uses previously learned methods to solve problems, and visual-spatial reasoning which uses visual images and relationships. Perkins' theory identified three components of IQ: neural intelligence referring to neurological efficiency, experiential intelligence from accumulated knowledge and experience, and reflective intelligence involving problem-solving strategies, learning approaches, and attitudes like persistence. The document discusses different theories of intelligence by Cattell and Horn, and Perkins.
This document describes a study that used eye tracking to examine how prior knowledge and color contrast affect the visual search processes of novice learners. The study aimed to understand how students interpret microscope slides in relation to their existing biology content knowledge and prior microscope experience. Students were split into high and low prior knowledge groups and tested on their ability to visually search microscope images with high and low color contrasts. The results could help improve the use of visual aids to support learning in biology education.
ESTABLISHMENT OF VIRTUAL POLICY BASED NETWORK MANAGEMENT SCHEME BY LOAD EXPER...IJCNCJournal
In the current Internet-based systems, there are many problems using anonymity of the network
communication such as personal information leak and crimes using the Internet systems. This is because
the TCP/IP protocol used in Internet systems does not have the user identification information on the
communication data, and it is difficult to supervise the user performing the above acts immediately. As a
solution for solving the above problem, there is the approach of Policy-based Network Management
(PBNM). This is the scheme for managing a whole Local Area Network (LAN) through communication
control of every user. In this PBNM, two types of schemes exist. The first is the scheme for managing the
whole LAN by locating the communication control mechanisms on the course between network servers and
clients. The second is the scheme of managing the whole LAN by locating the communication control
mechanisms on clients. As the second scheme, we have been studied theoretically about the Destination
Addressing Control System (DACS) Scheme. By applying this DACS Scheme to Internet system
management, we intend to realize the policy-based Internet system management finally. In the DACS
Scheme, the inspection is not done about compatibility to cloud environment with virtualization technology
that spreads explosively. As the result, the coverage of the DACS Scheme is limited only in physical
environment now. In this study, we inspect compatibility of the DACS Scheme for the cloud environment
with virtualization technology, and enlarge coverage of this scheme. With it, the Virtual DACS Scheme
(vDACS Scheme) is established.
A proposal to enhance cellular and wifiIJCNCJournal
WiFi offloading is becoming one of the key enablers to help the network operators dealing with the exponentially growing demand of mobile data. The idea of using WiFi to offload data traffic from cellular network has proposed for many years. However, the interoperability issue between the two networks needs to be enhanced so that WiFi can efficiently supplement for the cellular network in case of congestion or outage. In this paper, we propose a novel network roaming and selection scheme based on 3GPP TS 24.312 and IEEE 802.11k, u standards to enhance cellular and WiFi interworking. The proposed scheme is aimed at enhancing the network roaming and selection so that WiFi network can serve as a supplement and backup access network for the cellular not only for congestion control but also in case of unexpected network failure event. We also model and evaluate the proposed scheme in a typical HetNet with interworking WiFi access points and cellular base stations. The simulation result shows that our proposed scheme quickly detects unexpected network failure event and assists active UEs to perform handoff to preferable alternative point of access. As a result, service disruption is substantially reduced and quality of experience (downlink/uplink’s throughput) is improved. Therefore, our proposed scheme can be used for a more reliable HetNet in terms of congestion control and disruption tolerance.
GEOGRAPHIC MAPS CLASSIFICATION BASED ON L*A*B COLOR SYSTEMIJCNCJournal
Today any geographic information system (GIS) layers became vital part of any GIS system , and
consequently , the need for developing automatic approaches to extract GIS layers from different image
maps like digital maps or satellite images is very important.
Map classification can be defined as an image processing technique which creates thematic maps from
scanned paper maps or remotely sensed images. Each resultant theme will represent a GIS layer of the
images.
A new proposed approach to extract GIS layers (classes) automatically based on L*A*B colorsystem
selected from ( A and B ) is proposed in this paper, our experiments shows that the hsi color space gives
better than L*A*B.
ADAPTIVE MULTI-TENANCY POLICY FOR ENHANCING SERVICE LEVEL AGREEMENT THROUGH R...IJCNCJournal
The appearance of infinite computing resources that available on demand and fast enough to adapt with
load surges makes Cloud computing favourable service infrastructure in IT market. Core feature in Cloud
service infrastructures is Service Level Agreement (SLA) that led seamless service at high quality of service
to client. One of the challenges in Cloud is providing heterogeneous computing services for the clients.
With the increasing number of clients/tenants in the Cloud, unsatisfied agreement is becoming a critical
factor. In this paper, we present an adaptive resource allocation policy which attempts to improve
accountable in Cloud SLA while aiming for enhancing system performance. Specifically, our allocation
incorporates dynamic matching SLA rules to deal with diverse processing requirements from
tenants.Explicitly, it reduces processing overheadswhile achieving better service agreement. Simulation
experiments proved the efficacy of our allocation policy in order to satisfy the tenants; and helps improve
reliable computing.
International Journal of Computer Networks & Communications (IJCNC)IJCNCJournal
This document summarizes the scope and contents of the International Journal of Computer Networks & Communications (IJCNC). IJCNC is a bi-monthly peer-reviewed journal that publishes articles on all aspects of computer networks and data communications. Topics of interest include network protocols, architectures, routing techniques, wireless networks, next generation networks, network operations and management, and more. The goal is to bring together researchers and industry practitioners to advance networking concepts and collaboration.
FLEXIBLE VIRTUAL ROUTING FUNCTION DEPLOYMENT IN NFV-BASED NETWORK WITH MINIMU...IJCNCJournal
In a conventional network, most network devices, such as routers, are dedicated devices that do not
have much variation in capacity. In recent years, a new concept of Network Functions
Virtualisation (NFV) has come into use. The intention is to implement a variety of network functions
with software on general-purpose servers and this allows the network operator to select any
capabilities and locations of network functions without any physical constraints.
This paper focuses on the deployment of NFV-based routing functions which are one of critical
virtual network functions, and present the algorithm of virtual routing function allocation that
minimize the total network cost. In addition, this paper presents the useful allocation policy of
virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy
takes the ratio of the cost of a routing function to that of a circuit and traffic distribution in the
network into consideration. Furthermore, this paper shows that there are cases where the use of
NFV-based routing functions makes it possible to reduce the total network cost dramatically, in
comparison to a conventional network, in which it is not economically viable to distribute smallcapacity
routing functions
Mobile paymentmethodbased on public keyIJCNCJournal
Mobile payment is defined as mobile money, which is considered as an attractive alternative for cash,
cheque, or credit. In this paper we propose a new secure mobile paymentmethod. This method is
summarized in three processes: firstly, the authentication process, which involves the authentication phases
for the applied customers. Secondly, the member recognition process which tests and ensures the customer
membership by the market server. Finally, payment processwhich will be done by ciphering the customer
information using public-key encryption cryptosystem (RSA), to be submitted over an insecure network to
the market server. Actually, this mobile payment methodis more efficient than otherpayment methods since
the customer can pay from his/her own mobilephone without any extra cost and effort. The RSA public-key
encryption system ensures the security of the proposed method. However, to prevent a brute force attack,
the choice of the key size becomes crucial.
SIMPLIFIED CBA CONCEPT AND EXPRESS CHOICE METHOD FOR INTEGRATED NETWORK MANAG...IJCNCJournal
This document proposes a simplified method for evaluating and selecting a network management system (NMS) for integration into an existing computer network. The method evaluates NMS options based on 3 criteria: 1) the level of integration risk, 2) the expected increase in network maintenance effectiveness, and 3) the level of management tasks completed by the system. Each criterion is evaluated on a standardized scale of 0 to 2. The scores are combined to calculate an overall value for each NMS, with the highest scoring option selected for integration. The method aims to provide a rapid evaluation that does not require extensive expertise, resources or time.
OMT: A DYNAMIC AUTHENTICATED DATA STRUCTURE FOR SECURITY KERNELSIJCNCJournal
We introduce a family of authenticated data structures — Ordered Merkle Trees (OMT) — and illustrate
their utility in security kernels for a wide variety of sub-systems. Specifically, the utility of two types of
OMTs: a) the index ordered merkle tree (IOMT) and b) the range ordered merkle tree (ROMT), are
investigated for their suitability in security kernels for various sub-systems of Border Gateway Protocol
(BGP), the Internet’s inter-autonomous system routing infrastructure. We outline simple generic security
kernel functions to maintain OMTs, and sub-system specific security kernel functionality for BGP subsystems
(like registries, autonomous system owners, and BGP speakers/routers), that take advantage of
OMTs.
GAME THEORY BASED INTERFERENCE CONTROL AND POWER CONTROL FOR D2D COMMUNICATIO...IJCNCJournal
With the current development of mobile communication services, people need personal communication of
high speed, excellent service, high quality and low latency,however, limited spectrum resources become
the most important factor to hamper improvement of cellular systems. As big amount of data traffic will
cause greater local consumption of spectrum resources, future networks are required to have appropriate
techniques to better support such forms of communication. D2D (Device-to-device) communication
technology in a cellular network makes full use of spectrum resources underlaying, reduces the load of the
base station, minimizes transmit power of the terminals and the base stations, thereby enhances the overall
throughput of the networks. Due to the use of multiplexing D2D UE (User equipment) resources and
spectrum, and the interference caused by the sharing of resources between adjacent cells, it has become a
major factor affecting coexisting of cellular subscribers and D2D users. When D2D communication
multiplexes the uplink resources, the base-stations are easily to be disturbed; when the downlink resources
are multiplexed, the users of downlink are susceptible to interference. In order to build a high-efficient
mobile network, we can meet the QoS requirements by controlling the power to suppress the interference
between the base station and a terminal user.
CONGESTION AWARE LINK COST ROUTING FOR MANETSIJCNCJournal
Due to the dynamic topology, self-configuration and decentralized nature of Mobile Ad hoc Network
(MANET), it provides many benefits in wireless networks and is easy to deploy. But the transmission of
data over ad hoc networks has elevated many technical issues for successful routing. Congestion is one of
the important issues which cause performance degradation of a network, due to long delay and high packet
loss. This paper proposes a Congestion aware Link Cost Routing for MANET where the protocol finds a
path with optimized linked cost based on SNR, Link delay, and the and remaining battery power. Along
with this optimization, in this protocol, every node finds its congestion status and participates in the route
discovery on the basis of its status. Data forwarding is also done based on the congestion status at the time
of forwarding. The protocol results in better performance in terms of packet delivery fraction, end to end
delay, throughput, and packet drop when compared to existing protocols.
LIGHT FIDELITY (LI-FI) BASED INDOOR COMMUNICATION SYSTEMIJCNCJournal
Indoor wireless communication is an essential part of next generation wireless communication system.For
an indoor communication number of users and their device are increasing very rapidly so as a result
capacity of frequency spectrum to accommodate further users in future is limited and also it would be
difficult for service providers to provide more user reliable and high speed communication so this short
come can be solve in future by using Li-Fi based indoor communication system. Li-Fi which is an emerging
branch of optical wireless communication can be useful in future as a replacement and backup of Wireless
Fidelity (Wi-Fi)for indoor communication because it can provide high data rate of transmission along with
high capacity to utilize more users as its spectrum bandwidth is much broader than the radio spectrum. In
this paper we will look at the different aspects of the Li-Fi based indoor communication system,summarizes
some of the research conducted so far andwe will also proposed a Li-Fi based communication model
keeping in mind coverage area for multiple user and evaluate its performance under different scenarios .
Fuzzy based clustering and energy efficientIJCNCJournal
Underwater Wireless Sensor Network (UWSN) is a particular kind of sensor networks which is
characterized by using acoustic channels for communication. UWSN is challenged by great issues specially
the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed
routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing
protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based
on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the
closeness to the center of the cluster, then the node having the higher residual energy elects itself as a
cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data
aggregation and transmits the data directly to the base station. The second algorithm uses the same
principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data
from cluster heads to the underwater sink (uw-sink). Furthermore the two proposed algorithms are tested
for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed
algorithms resulting in an extension of the network lifetime.
PROPOSED A HETEROGENEOUS CLUSTERING ALGORITHM TO IMPROVE QOS IN WSNIJCNCJournal
In this article it has presented leach extended hierarchical 3-level clustered heterogeneous and dynamics
algorithm. On suggested protocol (LEH3LA) with planning of selected auction cluster head, and
alternative cluster head node, problem of delay on processing, processing of selecting members, decrease
of expenses, and energy consumption, decrease of sending message, and receiving messages inside the
clusters, selecting of cluster heads in large sensor networks were solved. This algorithm uses hierarchical
heterogeneous network (3-levels), collective intelligence, and intra-cluster interaction for communications.
Also it will solve the problems of sending data in Multi-BS mobile networks, expanding inter-cluster
networks, overlap cluster, genesis orphan nodes, boundary change dynamically clusters, using backbone
networks, cloud sensor. Using sleep/wake scheduling algorithm or TDMA-schedule alternative cluster head
node provides redundancy, and fault tolerance. Local processing in cluster head nodes, and alternative
cluster head, intra-cluster and inter-cluster communications such as Multi-HOP cause increase on
processing speed, and sending data intra-cluster and inter-cluster. Decrease of overhead network, and
increase the load balancing among cluster heads. Using encapsulation of data method, by cluster head
nodes, energy consumption decrease during sending data. Also by improving quality of service (QoS) in
CBRP, LEACH, 802.15.4, decrease of energy consumption in sensors, cluster heads and alternative cluster
head nodes, cause increase on lift time of sensor networks.
Minimum Physical Hop (MPH) has been proposed as a peer selection algorithm for decreasing inter-AS (Autonomous System) traffic volume in P2P live streaming. In MPH, a newly joining peer selects a peer whose physical hop count (i.e., the number of ASes traversed on the content delivery path) from it is the minimum as its providing peer. However, MPH shows high inter-AS traffic volume when the number of joining peers is large. In this paper, we propose IMPH that tries to further decrease the inter-AS traffic volume by distributing peers with one logical hop count (i.e., the number of peers or origin streaming servers (OSSes) traversed on the content delivery path from an OSS to the peer) to many ASes and encouraging the following peers to find their providing peers within the same AS. Numerical examples show that IMPH achieves at the maximum of 64% lower inter-AS traffic volume than MPH.
PERFORMANCES OF ORTHOGONAL WAVELET DIVISION MULTIPLEX (OWDM) SYSTEM UNDER AWG...IJCNCJournal
Orthogonal Wavelet Division Multiplexing (OWDM) has been considered as an alternative of Orthogonal
Frequency Division Multiplexing (OFDM) in the recent years. OWDM has lower computational complexity
and higher flexibility compared to its OFDM counterpart. The core component of OWDM is wavelet.
Wavelet has been a much investigated and applied topic in digital image processing for a long time.
Recently, it has drawn considerable attention of the researchers working in communication field. In this
work we investigate the performances of OWDM under different channel conditions. We consider three
channel conditions namely Additive White Gaussian Noise (AWGN), Rayleigh, Ricean, and frequency
selective. We consider a number of wavelets namely Haar, Daubechies, Biorthogonal, Reverse
Biorthogonal, Coiflets, and Symlets in OWDM design. For system model we choose Digital Video
Broadcasting-Terrestrial (DVB-T). Originally DVB-T system was designed based on OFDM. In this work
we use OWDM instead. The simulation results show OWDM outperforms OFDM in terms of bit error rate
(BER), noise resiliency, and peak-to-average ration. The results also show that the Haar wavelet based
OWDM outperforms other wavelets based OWDM system under all three considered three channel
conditions.
The document proposes a clustering-based approach to dynamically allocate bandwidth in wireless networks. It extracts student data from a university's course timetable to predict user distributions over time. It then applies K-means clustering to group buildings into wireless nodes based on expected user loads. This clusters student devices and allows wireless nodes to adapt their bandwidth allocation according to predicted user demands at different times. The approach is tested on a university campus network, extracting student data to predict building loads and applying K-means clustering to allocate optimal bandwidth across wireless nodes over time.
Efficient management of bandwidth in wireless networks is a critical factor for a successful communication system. Special features of wireless networks such user mobility and growth of wireless applications and their high bandwidth intensity create a major challenge to utilize bandwidth resources optimally. In this research, we propose a model for an adaptable network bandwidth management method that combines bandwidth reservation and bandwidth adaptation to reduce call blocking and dropping probabilities. The model is an integer program that determines whether or not to accept new calls and decides how to allocate bandwidth optimally in a way to maximize user satisfaction. The results of a simulation study show that the proposed method outperforms an existing method with respect to key performance measures such as call blocking and dropping probabilities and call time survivability. This survivability indicator is a new measure that is introduced for the first time in this paper. We also present a second tradeoff model to allow the network manager to control call dropping probability. The results of a second simulation study show that network users are better off if a zero call dropping policy is adopted as proposed in the first model.
Screening Children with Autism by Developing Smart Toy CarsIJRES Journal
Autistic spectrum disorders are categorized under developmental disorders and lead into a wide range of symptoms in the patients. They are referred to as autism spectrum, since the intensity and the extent of their symptoms vary person to person based on disorder's intensity. In this study, a smart toy car was developed for screening children with autism. In making the toy car, field-programmable gate array was exploited which is an integrated circuit capable of high-speed planning. Appropriate selected features were the ones that identified repetitive and stereotypical motions in movement, which are normally comprised of 9 features. Finally, in the most appropriate conditions, the classification of aforementioned features was carried out using support vector machine algorithm with polynomial kernel with an order of 5. Autistic children screening was carried out with an accuracy of 100%
This document summarizes a research paper on mathematical skills in individuals with autism spectrum disorder (ASD). It discusses:
1. Autism is a complex disorder affecting social behavior, communication, and imagination. While many with ASD have intellectual disabilities, some have average or above average cognitive abilities.
2. Recent studies have explored numerical/computational skills in some low-functioning individuals with ASD, but mathematical competence in high-functioning ASD is less understood. A particular cognitive pattern in ASD may benefit development of "savant" skills like mathematics, but more research is needed.
3. The paper aims to examine the range of mathematical abilities in individuals with ASD and investigate
This document discusses a study on defining data relationships for autism spectrum disorders in an e-hospital setting. It aims to collect patient behavior data from multiple hospitals using a web service approach. The study focuses on collecting and analyzing autism behavior data using standardized questionnaires based on the DSM-IV criteria. It proposes a database design to store patient information and questionnaire data. A web service with authentication and encryption is designed to securely share data between connected hospitals in a standardized XML format. The goal is to pool clinical data from various sources to better analyze and classify autism disorders.
Disordered Brain Modeling Using Artificial Network SOFMSyeful Islam
Autism is known as a neurobiological developmental disorder which affects language,
communication, and cognitive skill. In the case of autism attention shift impairment and
strong familiarity preference are considered to be prime deficiencies. Attention shift
impairment is one of the most seen behavioral disorders found in autistic patients. We
have model this behavior by employing self-organizing feature map (SOFM).
Disordered Brain Modeling Using Artificial Network SOFMSyeful Islam
This document summarizes a research paper that models attention shift impairment in autistic individuals using an artificial neural network called Self Organizing Feature Map (SOFM). SOFM is an unsupervised learning algorithm that can train autistic people to recognize and learn objects. The document provides background on autism as a neurodevelopmental disorder characterized by social and communication difficulties. It describes research showing differences in brain structure and function in autistic individuals, pointing to the cortex being differently wired. It also summarizes treatment approaches and long-term outcomes for autism.
ADAPTIVE MULTI-TENANCY POLICY FOR ENHANCING SERVICE LEVEL AGREEMENT THROUGH R...IJCNCJournal
The appearance of infinite computing resources that available on demand and fast enough to adapt with
load surges makes Cloud computing favourable service infrastructure in IT market. Core feature in Cloud
service infrastructures is Service Level Agreement (SLA) that led seamless service at high quality of service
to client. One of the challenges in Cloud is providing heterogeneous computing services for the clients.
With the increasing number of clients/tenants in the Cloud, unsatisfied agreement is becoming a critical
factor. In this paper, we present an adaptive resource allocation policy which attempts to improve
accountable in Cloud SLA while aiming for enhancing system performance. Specifically, our allocation
incorporates dynamic matching SLA rules to deal with diverse processing requirements from
tenants.Explicitly, it reduces processing overheadswhile achieving better service agreement. Simulation
experiments proved the efficacy of our allocation policy in order to satisfy the tenants; and helps improve
reliable computing.
International Journal of Computer Networks & Communications (IJCNC)IJCNCJournal
This document summarizes the scope and contents of the International Journal of Computer Networks & Communications (IJCNC). IJCNC is a bi-monthly peer-reviewed journal that publishes articles on all aspects of computer networks and data communications. Topics of interest include network protocols, architectures, routing techniques, wireless networks, next generation networks, network operations and management, and more. The goal is to bring together researchers and industry practitioners to advance networking concepts and collaboration.
FLEXIBLE VIRTUAL ROUTING FUNCTION DEPLOYMENT IN NFV-BASED NETWORK WITH MINIMU...IJCNCJournal
In a conventional network, most network devices, such as routers, are dedicated devices that do not
have much variation in capacity. In recent years, a new concept of Network Functions
Virtualisation (NFV) has come into use. The intention is to implement a variety of network functions
with software on general-purpose servers and this allows the network operator to select any
capabilities and locations of network functions without any physical constraints.
This paper focuses on the deployment of NFV-based routing functions which are one of critical
virtual network functions, and present the algorithm of virtual routing function allocation that
minimize the total network cost. In addition, this paper presents the useful allocation policy of
virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy
takes the ratio of the cost of a routing function to that of a circuit and traffic distribution in the
network into consideration. Furthermore, this paper shows that there are cases where the use of
NFV-based routing functions makes it possible to reduce the total network cost dramatically, in
comparison to a conventional network, in which it is not economically viable to distribute smallcapacity
routing functions
Mobile paymentmethodbased on public keyIJCNCJournal
Mobile payment is defined as mobile money, which is considered as an attractive alternative for cash,
cheque, or credit. In this paper we propose a new secure mobile paymentmethod. This method is
summarized in three processes: firstly, the authentication process, which involves the authentication phases
for the applied customers. Secondly, the member recognition process which tests and ensures the customer
membership by the market server. Finally, payment processwhich will be done by ciphering the customer
information using public-key encryption cryptosystem (RSA), to be submitted over an insecure network to
the market server. Actually, this mobile payment methodis more efficient than otherpayment methods since
the customer can pay from his/her own mobilephone without any extra cost and effort. The RSA public-key
encryption system ensures the security of the proposed method. However, to prevent a brute force attack,
the choice of the key size becomes crucial.
SIMPLIFIED CBA CONCEPT AND EXPRESS CHOICE METHOD FOR INTEGRATED NETWORK MANAG...IJCNCJournal
This document proposes a simplified method for evaluating and selecting a network management system (NMS) for integration into an existing computer network. The method evaluates NMS options based on 3 criteria: 1) the level of integration risk, 2) the expected increase in network maintenance effectiveness, and 3) the level of management tasks completed by the system. Each criterion is evaluated on a standardized scale of 0 to 2. The scores are combined to calculate an overall value for each NMS, with the highest scoring option selected for integration. The method aims to provide a rapid evaluation that does not require extensive expertise, resources or time.
OMT: A DYNAMIC AUTHENTICATED DATA STRUCTURE FOR SECURITY KERNELSIJCNCJournal
We introduce a family of authenticated data structures — Ordered Merkle Trees (OMT) — and illustrate
their utility in security kernels for a wide variety of sub-systems. Specifically, the utility of two types of
OMTs: a) the index ordered merkle tree (IOMT) and b) the range ordered merkle tree (ROMT), are
investigated for their suitability in security kernels for various sub-systems of Border Gateway Protocol
(BGP), the Internet’s inter-autonomous system routing infrastructure. We outline simple generic security
kernel functions to maintain OMTs, and sub-system specific security kernel functionality for BGP subsystems
(like registries, autonomous system owners, and BGP speakers/routers), that take advantage of
OMTs.
GAME THEORY BASED INTERFERENCE CONTROL AND POWER CONTROL FOR D2D COMMUNICATIO...IJCNCJournal
With the current development of mobile communication services, people need personal communication of
high speed, excellent service, high quality and low latency,however, limited spectrum resources become
the most important factor to hamper improvement of cellular systems. As big amount of data traffic will
cause greater local consumption of spectrum resources, future networks are required to have appropriate
techniques to better support such forms of communication. D2D (Device-to-device) communication
technology in a cellular network makes full use of spectrum resources underlaying, reduces the load of the
base station, minimizes transmit power of the terminals and the base stations, thereby enhances the overall
throughput of the networks. Due to the use of multiplexing D2D UE (User equipment) resources and
spectrum, and the interference caused by the sharing of resources between adjacent cells, it has become a
major factor affecting coexisting of cellular subscribers and D2D users. When D2D communication
multiplexes the uplink resources, the base-stations are easily to be disturbed; when the downlink resources
are multiplexed, the users of downlink are susceptible to interference. In order to build a high-efficient
mobile network, we can meet the QoS requirements by controlling the power to suppress the interference
between the base station and a terminal user.
CONGESTION AWARE LINK COST ROUTING FOR MANETSIJCNCJournal
Due to the dynamic topology, self-configuration and decentralized nature of Mobile Ad hoc Network
(MANET), it provides many benefits in wireless networks and is easy to deploy. But the transmission of
data over ad hoc networks has elevated many technical issues for successful routing. Congestion is one of
the important issues which cause performance degradation of a network, due to long delay and high packet
loss. This paper proposes a Congestion aware Link Cost Routing for MANET where the protocol finds a
path with optimized linked cost based on SNR, Link delay, and the and remaining battery power. Along
with this optimization, in this protocol, every node finds its congestion status and participates in the route
discovery on the basis of its status. Data forwarding is also done based on the congestion status at the time
of forwarding. The protocol results in better performance in terms of packet delivery fraction, end to end
delay, throughput, and packet drop when compared to existing protocols.
LIGHT FIDELITY (LI-FI) BASED INDOOR COMMUNICATION SYSTEMIJCNCJournal
Indoor wireless communication is an essential part of next generation wireless communication system.For
an indoor communication number of users and their device are increasing very rapidly so as a result
capacity of frequency spectrum to accommodate further users in future is limited and also it would be
difficult for service providers to provide more user reliable and high speed communication so this short
come can be solve in future by using Li-Fi based indoor communication system. Li-Fi which is an emerging
branch of optical wireless communication can be useful in future as a replacement and backup of Wireless
Fidelity (Wi-Fi)for indoor communication because it can provide high data rate of transmission along with
high capacity to utilize more users as its spectrum bandwidth is much broader than the radio spectrum. In
this paper we will look at the different aspects of the Li-Fi based indoor communication system,summarizes
some of the research conducted so far andwe will also proposed a Li-Fi based communication model
keeping in mind coverage area for multiple user and evaluate its performance under different scenarios .
Fuzzy based clustering and energy efficientIJCNCJournal
Underwater Wireless Sensor Network (UWSN) is a particular kind of sensor networks which is
characterized by using acoustic channels for communication. UWSN is challenged by great issues specially
the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed
routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing
protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based
on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the
closeness to the center of the cluster, then the node having the higher residual energy elects itself as a
cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data
aggregation and transmits the data directly to the base station. The second algorithm uses the same
principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data
from cluster heads to the underwater sink (uw-sink). Furthermore the two proposed algorithms are tested
for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed
algorithms resulting in an extension of the network lifetime.
PROPOSED A HETEROGENEOUS CLUSTERING ALGORITHM TO IMPROVE QOS IN WSNIJCNCJournal
In this article it has presented leach extended hierarchical 3-level clustered heterogeneous and dynamics
algorithm. On suggested protocol (LEH3LA) with planning of selected auction cluster head, and
alternative cluster head node, problem of delay on processing, processing of selecting members, decrease
of expenses, and energy consumption, decrease of sending message, and receiving messages inside the
clusters, selecting of cluster heads in large sensor networks were solved. This algorithm uses hierarchical
heterogeneous network (3-levels), collective intelligence, and intra-cluster interaction for communications.
Also it will solve the problems of sending data in Multi-BS mobile networks, expanding inter-cluster
networks, overlap cluster, genesis orphan nodes, boundary change dynamically clusters, using backbone
networks, cloud sensor. Using sleep/wake scheduling algorithm or TDMA-schedule alternative cluster head
node provides redundancy, and fault tolerance. Local processing in cluster head nodes, and alternative
cluster head, intra-cluster and inter-cluster communications such as Multi-HOP cause increase on
processing speed, and sending data intra-cluster and inter-cluster. Decrease of overhead network, and
increase the load balancing among cluster heads. Using encapsulation of data method, by cluster head
nodes, energy consumption decrease during sending data. Also by improving quality of service (QoS) in
CBRP, LEACH, 802.15.4, decrease of energy consumption in sensors, cluster heads and alternative cluster
head nodes, cause increase on lift time of sensor networks.
Minimum Physical Hop (MPH) has been proposed as a peer selection algorithm for decreasing inter-AS (Autonomous System) traffic volume in P2P live streaming. In MPH, a newly joining peer selects a peer whose physical hop count (i.e., the number of ASes traversed on the content delivery path) from it is the minimum as its providing peer. However, MPH shows high inter-AS traffic volume when the number of joining peers is large. In this paper, we propose IMPH that tries to further decrease the inter-AS traffic volume by distributing peers with one logical hop count (i.e., the number of peers or origin streaming servers (OSSes) traversed on the content delivery path from an OSS to the peer) to many ASes and encouraging the following peers to find their providing peers within the same AS. Numerical examples show that IMPH achieves at the maximum of 64% lower inter-AS traffic volume than MPH.
PERFORMANCES OF ORTHOGONAL WAVELET DIVISION MULTIPLEX (OWDM) SYSTEM UNDER AWG...IJCNCJournal
Orthogonal Wavelet Division Multiplexing (OWDM) has been considered as an alternative of Orthogonal
Frequency Division Multiplexing (OFDM) in the recent years. OWDM has lower computational complexity
and higher flexibility compared to its OFDM counterpart. The core component of OWDM is wavelet.
Wavelet has been a much investigated and applied topic in digital image processing for a long time.
Recently, it has drawn considerable attention of the researchers working in communication field. In this
work we investigate the performances of OWDM under different channel conditions. We consider three
channel conditions namely Additive White Gaussian Noise (AWGN), Rayleigh, Ricean, and frequency
selective. We consider a number of wavelets namely Haar, Daubechies, Biorthogonal, Reverse
Biorthogonal, Coiflets, and Symlets in OWDM design. For system model we choose Digital Video
Broadcasting-Terrestrial (DVB-T). Originally DVB-T system was designed based on OFDM. In this work
we use OWDM instead. The simulation results show OWDM outperforms OFDM in terms of bit error rate
(BER), noise resiliency, and peak-to-average ration. The results also show that the Haar wavelet based
OWDM outperforms other wavelets based OWDM system under all three considered three channel
conditions.
The document proposes a clustering-based approach to dynamically allocate bandwidth in wireless networks. It extracts student data from a university's course timetable to predict user distributions over time. It then applies K-means clustering to group buildings into wireless nodes based on expected user loads. This clusters student devices and allows wireless nodes to adapt their bandwidth allocation according to predicted user demands at different times. The approach is tested on a university campus network, extracting student data to predict building loads and applying K-means clustering to allocate optimal bandwidth across wireless nodes over time.
Efficient management of bandwidth in wireless networks is a critical factor for a successful communication system. Special features of wireless networks such user mobility and growth of wireless applications and their high bandwidth intensity create a major challenge to utilize bandwidth resources optimally. In this research, we propose a model for an adaptable network bandwidth management method that combines bandwidth reservation and bandwidth adaptation to reduce call blocking and dropping probabilities. The model is an integer program that determines whether or not to accept new calls and decides how to allocate bandwidth optimally in a way to maximize user satisfaction. The results of a simulation study show that the proposed method outperforms an existing method with respect to key performance measures such as call blocking and dropping probabilities and call time survivability. This survivability indicator is a new measure that is introduced for the first time in this paper. We also present a second tradeoff model to allow the network manager to control call dropping probability. The results of a second simulation study show that network users are better off if a zero call dropping policy is adopted as proposed in the first model.
Screening Children with Autism by Developing Smart Toy CarsIJRES Journal
Autistic spectrum disorders are categorized under developmental disorders and lead into a wide range of symptoms in the patients. They are referred to as autism spectrum, since the intensity and the extent of their symptoms vary person to person based on disorder's intensity. In this study, a smart toy car was developed for screening children with autism. In making the toy car, field-programmable gate array was exploited which is an integrated circuit capable of high-speed planning. Appropriate selected features were the ones that identified repetitive and stereotypical motions in movement, which are normally comprised of 9 features. Finally, in the most appropriate conditions, the classification of aforementioned features was carried out using support vector machine algorithm with polynomial kernel with an order of 5. Autistic children screening was carried out with an accuracy of 100%
This document summarizes a research paper on mathematical skills in individuals with autism spectrum disorder (ASD). It discusses:
1. Autism is a complex disorder affecting social behavior, communication, and imagination. While many with ASD have intellectual disabilities, some have average or above average cognitive abilities.
2. Recent studies have explored numerical/computational skills in some low-functioning individuals with ASD, but mathematical competence in high-functioning ASD is less understood. A particular cognitive pattern in ASD may benefit development of "savant" skills like mathematics, but more research is needed.
3. The paper aims to examine the range of mathematical abilities in individuals with ASD and investigate
This document discusses a study on defining data relationships for autism spectrum disorders in an e-hospital setting. It aims to collect patient behavior data from multiple hospitals using a web service approach. The study focuses on collecting and analyzing autism behavior data using standardized questionnaires based on the DSM-IV criteria. It proposes a database design to store patient information and questionnaire data. A web service with authentication and encryption is designed to securely share data between connected hospitals in a standardized XML format. The goal is to pool clinical data from various sources to better analyze and classify autism disorders.
Disordered Brain Modeling Using Artificial Network SOFMSyeful Islam
Autism is known as a neurobiological developmental disorder which affects language,
communication, and cognitive skill. In the case of autism attention shift impairment and
strong familiarity preference are considered to be prime deficiencies. Attention shift
impairment is one of the most seen behavioral disorders found in autistic patients. We
have model this behavior by employing self-organizing feature map (SOFM).
Disordered Brain Modeling Using Artificial Network SOFMSyeful Islam
This document summarizes a research paper that models attention shift impairment in autistic individuals using an artificial neural network called Self Organizing Feature Map (SOFM). SOFM is an unsupervised learning algorithm that can train autistic people to recognize and learn objects. The document provides background on autism as a neurodevelopmental disorder characterized by social and communication difficulties. It describes research showing differences in brain structure and function in autistic individuals, pointing to the cortex being differently wired. It also summarizes treatment approaches and long-term outcomes for autism.
Investigating the Effects of Personality on Second Language Learning through ...CSCJournals
The aim of this research is to determine Second Language Acquisition and personality variable from affective factors analyzed by Artificial Neural Network in freshman class of both university students. This study presents an intelligent approach to the investigation of positive effects of personality on second language learning. For this purpose, watching TV, reading books, magazines, newspaper, listening to the radio, talking to a native English friend, and talking to people at school are investigated. The tool of our research is a survey (questionnaire) to collect a data in order to quantify students ‘personality traits based on affective factors. The questionnaire consists of two parts. The first part consists of Yes/ No questions while the second part uses a 4 point Likert scale with 5 items that indicates what helped students personally to learn English. The participants were 160 students from two private universities in Bosnia and Herzegovina, International Burch University (90 students) and International University of Sarajevo (70). The subjects’ major was English. The first part of the survey was analyzed using ANN, and the second part using statistical analysis. Both data analysis were processed by transferring answers to an Excel sheet. For each measure, mode, standard deviation, median were calculated to determine students’ personality factors. We used two different types of analysis in order to show that different kinds of analysis can be done.
Autism is a set of heterogeneous neurodevelopmental conditions characterized by difficulties in social communication and unusually restricted, repetitive behaviors and interests. The worldwide prevalence is about 1% and it affects more males than females. Individuals with autism have atypical cognitive profiles underpinned by atypical neural development. Genetics and early environmental factors both contribute to risk. Assessment needs to be multidisciplinary and early intervention can improve outcomes.
Autism spectrum disorder is a developmental disability that can cause significant social, communication and behavioural challenges. Parents of children on the spectrum find it difficult for their kids to communicate with them and other people, which makes it challenging for social interactions. Researchers have introduced different solutions such as Therapy Robot that Teaches Social Skills to Children with Autism. Additionally, Virtual reality was used to teach emotional and social skills to children with autism spectrum disorder. However, these solutions focus only on the person on the spectrum, neglecting the fact that the social challenges that people on the spectrum face are partly due to the lack of understanding on the neurotypicals' end. In this study, the solution introduced focuses on the neurotypical perspective; An advanced and interactive intelligent technology that can educate neurotypical people on how to communicate with people on the spectrum in different scenarios and environments. It also allows the learner to see the consequences of the different interactions from the point of view of a person on the spectrum, be aware of their actions, and fully engage in the scenarios through Virtual Reality (VR). Virtual Reality is a technology that simulates experiences that can be similar to the real world. The project aim was achieved by implementing a storyline game that is VR-based.
ALTRUISTIC ASD (AUTISM SPECTRUM DISORDER) VIRTUAL REALITY GAME ijma
This document describes the development of an altruistic virtual reality game aimed at educating neurotypical people on how to communicate with those on the autism spectrum. The game utilizes scenarios developed with specialist input to simulate interactions neurotypicals may have with those on the spectrum. Players make choices on how to respond and see the consequences from the perspective of someone with autism. The game is intended to raise awareness of the social and communication challenges faced by those on the spectrum in order to foster a more understanding and accepting social environment.
What lies beneath? Autism Spectrum DisorderVivek Misra
Disturbed patterns of neuronal activity underlying specific types of behavior correlating with specific genetic alleles thus linking gene to brain development to behavior. The programming of various brain networks is genetically modulated during neurodevelopment and mediated through a range of neuropeptides and interacting neurotransmitter systems.
Vlastos, D., Kyritsis, M., Papaioannou-Spiroulia, A., & Varela V.-A. (2017). ...Dimitris Vlastos
Oral Presentation, 22nd International Conference of the Association of Psychology & Psychiatry for Adults & Children (A.P.P.A.C.): Recent Advances in Neuropsychiatric, Psychological and Social Sciences in Psychological Research, 16th – 19th May 2017, Athens, Greece.
The study investigates the heritability of the anxious temperament (AT) phenotype in adolescent rhesus monkeys and its relationship to brain circuitry. Researchers found that the right dorsal amygdala, left hippocampus, and amygdalostriatal transition zone showed the highest predictability of AT from brain imaging data. Pedigree and phenotype analysis determined that the AT phenotype was heritable and metabolic activity in specific brain regions like the hippocampus was more heritable than in other regions like the amygdala.
Brain Imaging Abnormalities in Autism Disordersasclepiuspdfs
Background: Autism disorders are heterogeneous complex group of chronic disorders that have become increasingly known as pervasive developmental disorders since the 1980s. They include five main disorders associated with significant early impairment in socialization, communication, and behavior. Autism disorders have recently been called autism spectrum disorder mostly by the American Psychiatric Association, and the term pervasive developmental disorders have been used with the term autism spectrum disorder interchangeably. The association of autism disorders with significant brain imaging abnormalities has been infrequently reported. The aim of this paper is to report the association of brain imaging abnormalities in four autistic children. Patients and Methods: Four autistic patients (three boys and one girl) who had brain imaging abnormalities and observed at the Children Teaching Hospital of Baghdad Medical City are described. Results: Three patients had atypical autism with mental retardation, and one boy had Heller syndrome (childhood disintegrative disorder). The girl had right anterior basal temporal small arachnoid cyst on computed tomography (CT) scan. One of the boys with atypical autism also had mild cerebral palsy attributed to birth asphyxia and his CT scan showed evidence of slight brain atrophy with mild dilatation of the ventricular system. Conclusion: Brain imaging abnormalities in patients with autism disorders include arachnoid cyst, agenesis of the corpus callosum, evidence of vasculitis (in Heller syndrome), and brain imaging abnormalities related to a coexisting condition such as cerebral palsy.
IRJET-Artificial Neural Network and Fuzzy Logic Approach to Diagnose Autism S...IRJET Journal
This document describes a study that uses an artificial neural network and fuzzy logic approach to diagnose autism spectrum disorder early. The study aims to address the complexity of autism symptoms and the accuracy of information collected from parents, which current diagnosis methods struggle with. It proposes using a combination of artificial neural network and fuzzy logic to establish a system for early autism diagnosis. This approach has been shown to be effective in other fields like finance, geology and medicine. The study generates a sample dataset to test the proposed diagnosis model, which uses fuzzy inputs and weights to account for uncertainty in the data. The results indicate this approach has potential for use in autism research and diagnosis support systems.
The document provides information about Autism Spectrum Disorder (ASD) including:
- ASD is a complex developmental disability that causes problems with social interaction and communication. Symptoms usually start before age 3.
- Disorders included in the autism spectrum are Autistic Disorder, Asperger's Syndrome, and Pervasive Developmental Disorder Not Otherwise Specified.
- There are no known cures for ASD but treatments aim to lessen symptoms and help people gain life skills.
i was interested in Autism and this semester i find a good opportunity to make a presentation about autism because we are studying a subject called Psychology of Handicap.
I hope you find this presentation useful.
Yahya Fehdi , Psychology major.
This case study presents an adolescent with low-functioning autism named QC who has the rare ability of absolute pitch. QC underwent extensive testing to evaluate her pitch perception and processing abilities. She was found to have absolute pitch in identification and production without abnormalities in perceiving hierarchical properties of visual patterns or music. However, she showed deficits in cognitive flexibility and planning abilities across different materials. Both her short- and long-term memory were intact for verbal, non-verbal and musical information, though she had exceptional long-term memory for musical pieces when recalling them on the piano. This case suggests absolute pitch in autism may not result from a global processing deficit but rather from a lack of cognitive flexibility in someone with a
IBDisc3.0instructionswe will discuss how cultural issues coul.docxwilcockiris
IBDisc3.0instructions
we will discuss how cultural issues could impact different business situations. Address the following questions:
· Do you think the impact of cultural diversity is positive or negative? What, if anything, can management do mitigate any negative impact or build upon a positive impact?
· In your opinion, should the "outsider" change his/her behavior or should the "local" work to be understanding?
· When looking at language and communication as they apply to international management, which do you see as more important – language or communication? Should business people be fluent in a second language?
· How does understanding the communications context of countries impact our business relationships and meetings?
· Why would this be important to negotiations and other business transactions?
Please use at least 3 scholarly sources and cite using APA format. Make at least 400 words.
Assessment Key for Sections 1 through 4:
1
2
3
4
Unacceptable
Several pieces of key information were missing and many of the explanations were poor/unclear
Needs Work
One or two pieces of key information were omitted and/or some of the explanation was unclear
Good
All of the information was included and explained clearly with a minor omission
Superior
All information was included, was very clearly explained (and presentation was enhanced in some way)
Section 1. Introduction. This should include:
Presenters’ names
The title and author(s) of the article
A clear statement of the purpose of the study and statement of the authors' hypothesis or hypotheses
A brief review of previous research setting the stage for the present investigation
Definition of vocabulary that will be important for your audience in understanding the article
Overall quality of this section of presentation:
1 2 3 4
Unacceptable Acceptable Good Superior
Section 2. The research design and data collected. This section should include:
A description of the participants
A description of the tasks that participants were given (and why they were given them)
A description of the order of tasks and how they were administered
An explanation of the data that were collected
An explanation of why the study was designed this way (how it enables the researchers to test their
hypothesis/hypotheses)
Overall quality of this section of presentation:
1 2 3 4
Unacceptable Acceptable Good Superior
Section 3. Results. This section should include:
Presentation and explanation of the data
Graphs/tables/figures to aid in the explanation (make sure you EXPLAIN what these show)
Whether the hypothesis was supported and what that means
Overall quality of this section of presentation:
1 2 3 4
Unacceptable Acceptable Good Superior
Section 4. Conclusions/Discussion related to the article. This section should include:.
Similar to SIMULATING CORTICAL MAPS FOR ATTENTION SHIFT IN AUTISM (20)
Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post...IJCNCJournal
Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates post-disaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in post-disaster scenarios and contribute to life-saving efforts.
Blockchain Enforced Attribute based Access Control with ZKP for Healthcare Se...IJCNCJournal
The relationship between doctors and patients is reinforced through the expanded communication channels provided by remote healthcare services, resulting in heightened patient satisfaction and loyalty. Nonetheless, the growth of these services is hampered by security and privacy challenges they confront. Additionally, patient electronic health records (EHR) information is dispersed across multiple hospitals in different formats, undermining data sovereignty. It allows any service to assert authority over their EHR, effectively controlling its usage. This paper proposes a blockchain enforced attribute-based access control in healthcare service. To enhance the privacy and data-sovereignty, the proposed system employs attribute-based access control, zero-knowledge proof (ZKP) and blockchain. The role of data within our system is pivotal in defining attributes. These attributes, in turn, form the fundamental basis for access control criteria. Blockchain is used to keep hospital information in public chain but EHR related data in private chain. Furthermore, EHR provides access control by using the attributed based cryptosystem before they are stored in the blockchain. Analysis shows that the proposed system provides data sovereignty with privacy provision based on the attributed based access control.
EECRPSID: Energy-Efficient Cluster-Based Routing Protocol with a Secure Intru...IJCNCJournal
A revolutionary idea that has gained significance in technology for Internet of Things (IoT) networks backed by WSNs is the " Energy-Efficient Cluster-Based Routing Protocol with a Secure Intrusion Detection" (EECRPSID). A WSN-powered IoT infrastructure's hardware foundation is hardware with autonomous sensing capabilities. The significant features of the proposed technology are intelligent environment sensing, independent data collection, and information transfer to connected devices. However, hardware flaws and issues with energy consumption may be to blame for device failures in WSN-assisted IoT networks. This can potentially obstruct the transfer of data. A reliable route significantly reduces data retransmissions, which reduces traffic and conserves energy. The sensor hardware is often widely dispersed by IoT networks that enable WSNs. Data duplication could occur if numerous sensor devices are used to monitor a location. Finding a solution to this issue by using clustering. Clustering lessens network traffic while retaining path dependability compared to the multipath technique. To relieve duplicate data in EECRPSID, we applied the clustering technique. The multipath strategy might make the provided protocol more dependable. Using the EECRPSID algorithm, will reduce the overall energy consumption, minimize the End-to-end delay to 0.14s, achieve a 99.8% Packet Delivery Ratio, and the network's lifespan will be increased. The NS2 simulator is used to run the whole set of simulations. The EECRPSID method has been implemented in NS2, and simulated results indicate that comparing the other three technologies improves the performance measures.
Analysis and Evolution of SHA-1 Algorithm - Analytical TechniqueIJCNCJournal
A 160-bit (20-byte) hash value, sometimes called a message digest, is generated using the SHA-1 (Secure Hash Algorithm 1) hash function in cryptography. This value is commonly represented as 40 hexadecimal digits. It is a Federal Information Processing Standard in the United States and was developed by the National Security Agency. Although it has been cryptographically cracked, the technique is still in widespread usage. In this work, we conduct a detailed and practical analysis of the SHA-1 algorithm's theoretical elements and show how they have been implemented through the use of several different hash configurations.
Optimizing CNN-BiGRU Performance: Mish Activation and Comparative AnalysisIJCNCJournal
Deep learning is currently extensively employed across a range of research domains. The continuous advancements in deep learning techniques contribute to solving intricate challenges. Activation functions (AF) are fundamental components within neural networks, enabling them to capture complex patterns and relationships in the data. By introducing non-linearities, AF empowers neural networks to model and adapt to the diverse and nuanced nature of real-world data, enhancing their ability to make accurate predictions across various tasks. In the context of intrusion detection, the Mish, a recent AF, was implemented in the CNN-BiGRU model, using three datasets: ASNM-TUN, ASNM-CDX, and HOGZILLA. The comparison with Rectified Linear Unit (ReLU), a widely used AF, revealed that Mish outperforms ReLU, showcasing superior performance across the evaluated datasets. This study illuminates the effectiveness of AF in elevating the performance of intrusion detection systems.
An Hybrid Framework OTFS-OFDM Based on Mobile Speed EstimationIJCNCJournal
The Future wireless communication systems face the challenging task of simultaneously providing high-quality service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely adopted in 4G and 5G systems, it struggles to cope with a significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform named Orthogonal Time Frequency Space (OTFS). Designers aim to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on an estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile user’s speed. Additionally, we suggest retaining an Integrated Sensing and Communication (ISAC) radar approach for accurate Doppler estimation. This provides precise information to facilitate the waveform selection procedure. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar.Our proposed approach aims to enhance the performance of wireless communication systems in high mobility cases. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits.This hybrid system presents a promising solution for improving the performance of wireless communication systems in higher mobility.The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be illustrated.
Enhanced Traffic Congestion Management with Fog Computing - A Simulation-Base...IJCNCJournal
Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while still allowing communication with the cloud. Many applications rely on fog computing, including traffic management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog computing and tested in a crowdedCairo city. The results obtained indicate that the execution time of the simulation is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which are essential for evaluating the performance of the ITCMS. Our system model is also compared with other models to assess its performance. A comparison is made using two parameters, namely throughput and the total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency.
Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post...IJCNCJournal
Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates post-disaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in post-disaster scenarios and contribute to life-saving efforts.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
May 2024, Volume 16, Number 3 - The International Journal of Computer Network...IJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...IJCNCJournal
The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.
Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
April 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
High Performance NMF Based Intrusion Detection System for Big Data IOT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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Mind map of terminologies used in context of Generative AI
SIMULATING CORTICAL MAPS FOR ATTENTION SHIFT IN AUTISM
1. International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.4, July 2016
DOI: 10.5121/ijcnc.2016.8405 71
SIMULATING CORTICAL MAPS FOR ATTENTION
SHIFT IN AUTISM
L.-H. Tan, S.-Y. Cho and Y.-Y. Nguwi
School of Business (IT), James Cook University, Singapore
ABSTRACT
Autism is a pervasive neuro-developmental disorder, primarily encompassing difficulties in the social,
language, and communicative domains. Because autism is a spectrum disorder, it affects each individual
differently and has varying degrees. There are three core aspects of impairment based upon the Diagnostic
and Statistical Manual of Mental Disorders (DSM-IV), namely impairment in socialization, impairment in
communication, and restricted repetitive activities or interests. This work describes the experiment aims at
expressing autistic traits through the use of self-organizing map. Works related to simulating autism
through self-organizing map is limited. This work compare and contrast the difference in attention index
for normal learning and marred attention shift learning ability. It was found that the attention index of
normal learning is 9 times better marred attention shift for both random and pre-fixed input data. In the
marred attention shift context, neurons adapt more towards the mean of both sources combined under
marred context while some neurons adapt towards mean of one source under normal context. The normal
learning ability produces maps with neurons orienting towards mean values of combined stimuli source.
Impairment in learning ability produces similar cortical maps compared to normal learning ability. The
major difference is in the attention index.
KEYWORDS
self-organizing map, attention shift, autism, neural network
1. INTRODUCTION
Autism is a complex neurological illness characterized by intricate defects mainly in three
aspects: communication, social interaction and imagination [1]. The disease was initially
described by Kanner [2] and Asperger [3]. Asperger used the term autism to define the crux
elements of the disease. Inability to carry out normal nonverbal and verbal communication,
familiarity preference, extreme autistic loneliness and restricted repertoire of actions were four
basic traits of Autism examined by Kanner. In 1978, the idea of child autism as a prominent
neurological basic trait contributed to the commencement of research studies, clinical speculation
and progression of primary precise operationally diagnostic requirements [4, 5].
As many as 1.5 million Americans today are believed to have some form of autism, with many
more having related pervasive developmental disorders (PDD), such as Asperger’s disorder (AD)
[6]. Because autism is a spectrum disorder, it affects each individual differently and has varying
degrees. One model of autism, the weak central coherence account, addresses the fact that people
with autism often focus on component parts rather than wholes [7]. As a result, people with
autism often show superior performance on some visually presented tasks and other tasks that
favour such focus. However, tasks that require integration or discrimination among many sensory
stimuli or abstract concepts can be difficult to autistic patients. This can include computationally
heavy tasks such as envis ioning another’s state of mind in social settings or imaging future states
2. International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.4, July 2016
72
sufficiently to plan a task [8]. This can also put people with autism at risk for sensory overload,
and difficulty in functioning in the presence of many visual or auditory distracters.
Neuroanatomical studies [9] have shown that abnormal growth of brain are observed in
individuals suffered from autism. Such features were also observed in the magnetic resonance
imaging (MRI) studies by Courchesne et. al [10, 11]. It was demonstrated that significant
decrease in Purkinjie cells presence in cerebellum is frequently detected in autism brain [12] as
well. In addition, attention deficit, weak eye contact, known as "savant skills", sensory
hypersensitivities and hyposensitivities, stereotyped ritualistic behaviours, weak generalization
means, perseveration, hyperspecificity and attention to details rather than the whole[13, 14] were
some of the attributes associated with the illness as well. Mental retardation is the most
commonly associated trait of autism. In fact, a lack of eye contact is the most common feature of
autism and further confirmations are given in eye tracking studies [15] showing decreased
attention on the eyes and increased attention on the mouth or surrounding environment.
There are three core aspects of impairment [1] based upon the Diagnostic and Statistical Manual
of Mental Disorders (DSM-IV) are namely impairment in socialization, impairment in
communication, and restricted repetitive activities or interests.
Social difficulties may present in autistic individuals due to low levels of vasopressin, a
neuropeptide found in the brain. In a studies by Parker et. al., they begun a clinical trial to treat
individuals with vasopressin to reduce their social problems. The experiments demonstrated
improved social cognition and memory are observed in people who do not have autism [16].
Autistic individuals display weak intercommunication abilities which tend to be stilted and
mechanical when they do happen [17]. Children with autism are not able to establish and
maintain cooperation and social relationships due to the difference in their usages and
comprehension of gestures for socialization with others [1, 18]. Autism individuals were observed
to display severe social avoidance behaviour [19].
The second core trait of autism is the impairment in communication. Autistic individuals have
difficulties in understanding figurative language concepts [20]. They interpret spoken
conversation directly [21] and tend to reiterate what others said to them. Hermelin [22]
discovered that autistic child was incompetent of recoding details from sensory to abstract codes.
Consequently, people with autism are incapable of viewing and deciphering abstract and salient
elements of a scenario [7] compared to normal individuals. Moreover, the language used in
autistic individuals is mostly made up of needs requests rather than descriptive interaction [18]. It
is vital to explore this trait with the use of cortical map demonstrating these impairments in
autistic individuals for better understanding and counter-measure.
The third trait is restricted repetitive of activities or interests. Exhibition of restricted repetitive of
activities and interests were commonly observed in autism. Dawson et al. [23] found that autistic
children displayed attention shift deficits in the presence of social stimuli while Pascualvaca et al.
[12] discovered that people with autistic do not have general hindrance in shifting attention.
Moving on, Dawson and Lewy [24] realised that aversive reaction may arise when novelty is
introduced. People with autism are hypothesized to be compulsive and obsessive with the current
state of predicament they are in. Kootz et al. [25] also found out that child with autism response
to novelty with avoidance.
This paper is organized as follows: Section 2 provides background studies of self-organizing map.
Section 3 describes the use of self-organizing map for Autism Simulation, citing the related works
in this area. In section 4, we presents a framework for autism simulation. The framework includes
involve identifying simulating features, obtaining optimal base parameters, creating different
3. International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.4, July 2016
73
stimuli context, fine-tuning of cortical map, computation of attention shifts, and evaluating
analysis. Section 5 discusses the maps formation. Finally, conclusion is drawn in section 6.
2. SELF-ORGANIZING MAP BACKGROUND
Self-Organizing Map learns in an unsupervised fashion without feedback from a teacher. It is
extremely useful in visualizing data of high dimensionality using low dimensions. The neurons go
through competitive learning. An output neuron that wins the competition is called the winning
neuron. The goal of SOM is to transform an incoming signal pattern of arbitrary dimension into a
one- or two- dimensional discrete map, and to perform this transformation adaptively in a
topologically ordered fashion.
Kohonen [26] describes SOM as a nonlinear, ordered, smooth mapping of high-dimensional input
data manifolds onto the elements of a regular, low-dimensional array. Assume the set of input
variables { jξ
} is definable as a real vector 1 2[ , ,..., ]T n
nx ξ ξ ξ= ∈ℜ . With each element in the
SOM array, we associate a parametric real vector 1 2[ , ,..., ]T n
i i i inm µ µ µ= ∈ℜ that we call a
model. Assuming a general distance measure between x and im denoted
( , )id x m , the image of
an input vector x on the SOM array is defined as the array element cm that matches best with
x , i.e., that has the index
arg min{ ( , )}i
i
c d x m=
(1)
The task is to define im in such a manner that the mapping is ordered and descriptive of the
distribution of x . The data points that are projected to close-by locations on the map are close-by
also in the input space. The ability of self-organizing according to neuron’s neighbourhood
Euclidean distance is the key feature of SOM.
There are two types of maps [27, 28]: the cortical maps and maps of ocular dominance. Cortical
maps correspond to line association. Ocular dominance map denotes the influence of electrical
signal imposed on eyes. Both maps reveal the presence of hierarchical structure of
somatosensory, auditory, and visual maps in human brain. Self-organizing map can be applied in
a supervised or unsupervised manner. Unsupervised learning takes place when stimuli of the
same clusters and output activate the nodes spatially on the map. It produces outputs that enable
us to view the relationship exists among clusters in cortical maps.
3. SELF-ORGANIZING MAP FOR AUTISM SIMULATION
Self-Organizing Map (SOM) can be used to model biological sensory areas by modifying input
stimuli encoded in the topology of SOM. The behaviours of autistic and normal persons in the
focus-attention and shift-attention tasks were depicted in [18]. Generalization in autistic children
may be due to predispose of a form of neural network that disable them from manipulating
overlaps [29]. There are a number of different ways to explain autism such as increased
neurogenesis, decreased neuronal call death, abnormal myelin, decreased synaptic pruning and
increase production or non-neural tissues were made with neurobiological techniques which
require further exploration [30].
Autistic individuals are discovered to have the inability in recoding information from sensory to
abstract codes by Hermelin [22]. In turn, viewing features regarded as salient to normal children
4. International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.4, July 2016
74
were a challenge to autistic individuals [31]. An inadequate SOM, where correct grouping of
stimuli may not be completed, may be created during the learning process for a neural network.
Autism could be modelled as a consequence of ill-developed and highly discriminative cortical
SOM [32]. Apart from that, the familiarity preference in stimulus selection can also be shown as a
result of deficient SOM characteristics [33, 34].
Lennart et. al [35] investigated the effects of attention shift impairment and familiarity preference
in self-organizing map. Developed maps were discussed in details. The experiments demonstrated
that familiarity preference results in inadequate maps with characteristic impairments, supporting
the hypothesis of novelty avoidance being the primary reason of other autistic features.
Gerardo et. al. [36] examined abnormalities in neural development in the brain of autistic
children, sensory issues, the ability of generalization and the effects of noise communication
routes between neurons on the maps generated. The performance of SOMs generated was
measured in terms of effectiveness of stimuli coverage and map topology. Inferences proposed in
the work were supported by previous statistical analyses. Modelling of abnormal neural growth in
autism was done by manipulating the dimensions of the SOM-based network. A distinct negative
impact was observed in the unfolding of the neural network which changes with the intensity of
the aberrations. To analyze sensory issues, concept of attention functions was introduced. While
modelling hypersensitivity, it was deduced that an excess in specific aspects of excellent aptitude
may cause the dismissal of input to the hypersensitive domain. The authors [36] managed to
reproduce the tendency of autism personnel to concentrate on details than the whole. During the
process, it was stated that noise does not aid to explain the autism characteristic of focus to
details.
4. FRAMEWORK FOR AUTISM SIMULATION
In this section, we will discuss the framework for autism simulation. The steps (as depicted in
Figure 1) involve identifying simulating features, obtaining optimal base parameters, creating
different stimuli context, fine-tuning of cortical map, computation of attention shifts, and finally
evaluating analysis.
Stage 1: Identifying simulating features
Stage 2: Obtaining optimal base parameters
Stage 3: Creation of different stimuli context
Stage 4: Fine Tune of Cortical Maps
Stage 5: Computation of Attention Shifts
Stage 6: Evaluating and Analysis
Figure 1 Framework for Autism Simulation
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Stage 1 finds out the features appropriate for autism traits. We focus on the traits of novelty
avoidance (familiarity inclination) and concentration impairment.
In stage 2, we evaluate the most appropriate parameters values essential and suitable for cortical
map formation. The input data is formatted and processed with appropriate training length,
learning rate, and neighbouring function. We adopted two sets data input, first data format is
randomized while the second set of data is made up of pre-fixed inputs. Each set of data inputs
represent different source of provocation for displaying the interaction of neurons when presented
with different stimuli under dissimilar conditioned context during simulation. Training length
affects the cortical maps formation and map convergence issue. Different neighbouring functions
were tested like Bubble function, Gaussian function, Cut Gaussian function, and Epanechicov
function [22, 24].
Stage 3 involves the creation of context. We simulate three kinds of context: namely normal
learning, marring of learning aptitude and familiarity inclination [29]. In normal learning, neurons
adjust themselves towards new inputs in the maps concocted out. The competitive learning
behaviour of Self-Organizing map imitate the normal reaction expected when new stimuli is
perceived from the environment. In marring of learning aptitude, impairment of learning ability in
autism was formulated by changing the learning parameter. Reduction of learning parameter
consequently reduces neurons learning speed in the simulation, replicating the tendency of
autistic individuals who react slower or otherwise indifferently to novelty. The marring of
learning aptitude learning uses two data sets. The first data sets allows for neurons to adapt first,
then second data set is added as new source. The neurons will then be adapted more towards the
first dataset. The probability of association towards the second data set is greatly hindered by the
restricted fixed learning rate. It would take longer training time to achieve similar output than
normal learning mode. In familiarity inclination context, two steps are involved. Firstly, it takes
the output from normal learning mode and uses it in impairment of learning mode to create
familiarity inclination. Familiarity is achieved by averaging the distance between weights of
nodes closest to the source already existed.
Stage 4 fine tunes the generated maps. Upon completion of stage 1 and 2, cortical maps obtained
needed to be fine tuned for easy readings and better defined clusters. To do so, we could try
several ways introduced by Kohonen in his articles on Self-Organizing Maps [26]. Instead,
simulation of each different mode was done repetitively for at least fifty times till consistent
results is observed to abstract desired maps. Having repetitive simulation compensates the
minimum training length applied. Higher training length would give us better maps. However, as
stated in earlier section, training length was kept to the minimum to enhance efficiency of
experimental progress. Each output is measured and compared with those collected in [35].
Stage 5 is the computation of attention shifts. Attention shift calculated from maps generated
were the focus of this experiment. Input data set and neurons output position on generated maps
were extracted. Mean differences between the coordinates were computed. Average values of
each axis point were set as the combined average mean differences calculated. With the average
values computed, standard deviation is applied. Results formulated were multiply by a thousand.
Comparison between X and Y standard deviation were carry out and consistent largest value is
assigned as attention shift required for evaluation.
Stage 6 evaluates the analysis. The last stage of this experiment design involves comparing and
analysing of outputs constructed in stage 4 and 5 with cortical maps studied in [35]. Similar maps
were retrieved and attention shifts for each identified map were calculated.
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5. MAPS FORMATION
This work was implemented using Matlab, in particular the SOM toolbox [37]. Two sources of
data are adopted, source 1 (using randomised data) and source 2 (using adjusted input data). Each
simulation comprises of approximately 100 data inputs.
Figure 2 displays the trained map using (i) random data and (ii) adjusted data. The neurons
(blacked dots) scatters evenly around data (red x). Neurons adaptation or association appear to be
insignificant. Thus the need for using pre-fixed adjusted input data. The adjusted input distributes
around four corners. It can be observed from the figure that neurons adapt itself towards data
matrix moving towards the corner.
Figure 2 Trained map with (i) Random data (ii) Adjusted data
Figure 3 presents the map with (i) 1000 epoches and (ii) 10,000 epoches. The 10 times longer
training time does not really give rise to differentiated map. For training time beyond 10,000, the
training appears to slow down significantly and has problem converging.
Figure 3 Trained map with (i) 1000 epoches (ii) 10,000 epoches
Figure 4 depicts maps with different learning rate from 0.1 to 100. Learning rate controls how
well neurons associate themselves to input data within a given period. Low learning rate may
impede growth of association among neurons. While high learning rate is advisable, learning rate
might still reach a saturation point where further increments of learning value fails to produce
better maps. Overall, appropriate higher learning rate construes well defined cortical maps.
Various learning rate were applied. The first range employs values between 0.01 and 0.1 to see
the impact of setting small initial rate. After realizing the impact of low learning rate, 0.1 to 1.0
were further investigated to discover the minimum rate for a norm. The last range of studied
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values includes numbers from 1.0 to 10 and 100 to find out the optimal applicable optimal rate.
The first training graph (Figure 4(i)) resembles an X shape. The learning rate is too low to obtain
the respective cortical map. The second map generated Figure 4(ii) shows significantly better
“learnt” map. The map with learning rate of 100 (Figure 4 (iii)) shows slightly improved map
compare to Figure 4(ii). Saturation occurs when learning rate is 10.
(i) Learning rate = 0.1 (ii) Learning rate = 1.0 (iii) Learning rate = 100
Figure 4 Trained map with varying learning rate
The previous section (Section 4) discussed the three contexts in use, namely normal learning,
marring of learning aptitude formation, and familiarity inclination. We formulate the learning as
follows:
Table 1 Normal Learning Formulation
Steps for (I) Normal Learning Formulation
1. Construct two separate matrix data input representing two sources.
2. Fine-tune the right learning rate, training length, and neighbouring function.
3. Label neurons and input sources.
4. Maps slowly forms up until consistent construct is observed.
Table 2 Marring of Learning Aptitude Formulation
Steps for (II) Marring of Learning Aptitude Formulation
1. Construct two separate matrix data input representing two sources.
2. Fine-tune the right learning rate, training length, and neighbouring function.
3. Set the learning rate to be 0.3 to impede learning ability
4. Label neurons and input sources.
5. Maps slowly forms up until consistent construct is observed.
Table 3 Familiarity Inclination Formulation
Steps for (III) Familiarity Inclination Formulation
1. Construct two separate matrix data input representing two sources.
2. Fine-tune the right learning rate, training length, and neighbouring function.
3. Set the learning rate to be 0.3 to impede learning ability.
4. Label neurons and input sources.
5. Generate rate the map with one source of data.
6. Maps slowly forms up until consistent construct is observed.
7. Second source of data is added to existing map obtained in 5. Repeat step 6 until
the map converge.
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Attention index depicts the ability of normal and autistic person to relate to novelty when new
stimuli are introduced. Higher value of attention index implies higher adaptability. Attention
index is calculated based on the sum of differences of x-y coordinates of input and neurons,
followed by multiplying thousand to its standard deviation.
We arrange the data into 2 batches: first batch being a 3 by 3 mesh, second batch with lesser data
incorporated for one source. Figure 5 displays the cortical maps formed after training. The first
batch result shows two different input source as represented by red circle and blue cross.
Attention index for first batch is found to be 61190. The second batch simulates the map by
having one of its input source halved in number. The resultant map shows indifferent neurons
pattern and attentions index is also 61190. This shows that the learning ability of neurons in a
normal neural system is not compromised despite lesser information is received from new stimuli.
(i) First Batch (ii) Second Batch
Figure 5 Cortical maps for normal learning context
Figure 6 presents the cortical map for marred attention shift learning ability. It is observed that the
cortical output remain similar to normal learning context. The nodes adapts relatively better to
mean results of the input source. Thus, when presented with an alternative source, the neurons
will try to adapt to its means. This results in better contrast and node weight adjustment. The
attention index obtained is 6495 for first batch. The first batch appears to have similar result as
first batch. A closer look will reveal that neurons are more consistent in their adaptation
compared to normal learning condition. Neurons adapt more towards the mean of both sources
combined under marred context while some neurons are viewed to adapt towards mean of one
source under normal context. The second batch also has the same attention index of 6495. It is
noted that this drastic fall in attention shift compared to normal learning context is the basis of
this work where attention shift is lesser in marred learning compared to normal learning. The
attention index of normal learning (61190) is 9.4 times of marred attention shift learning (6495).
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(i) First Batch (ii) Second Batch
Figure 6 Cortical maps for marred attention shift learning ability
6. CONCLUSIONS AND FUTURE TRENDS
This paper described the experiment aims at expressing autistic traits through the use of self-
organizing map. The common features of autistic traits were discussed, namely socialization,
impairment in communication and restricted repetitive of activities and interest. Works related to
simulating autism through self-organizing map is limited. This work compare and contrast the
difference in attention index for normal learning and marred attention shift learning ability. It was
found that the attention index of normal learning is 9 times better than marred attention shift for
both random and pre-fixed input data. In the marred attention shift context, neurons adapt more
towards the mean of both sources combined under marred context while some neurons adapt
towards mean of one source under normal context. The normal learning ability produces maps
with neurons orienting towards mean values of combined stimuli source. Impairment in learning
ability produces similar cortical maps compared to normal learning ability. The major difference
is in the attention index. Similarities of maps obtained from marred and normal learning ability
can be explained by assuming neurons adapting themselves towards the mean of existing source
produce greater node weight changes that pull the nodes nearer to mean value of combined new
and existing sources.
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