This paper presents the applications of Eigenvalues and Eigenvectors (as part of spectral
decomposition) to analyze the bipartivity index of graphs as well as to predict the set of vertices
that will constitute the two partitions of graphs that are truly bipartite and those that are close
to being bipartite. Though the largest eigenvalue and the corresponding eigenvector (called the
principal eigenvalue and principal eigenvector) are typically used in the spectral analysis of
network graphs, we show that the smallest eigenvalue and the smallest eigenvector (called the
bipartite eigenvalue and the bipartite eigenvector) could be used to predict the bipartite
partitions of network graphs. For each of the predictions, we hypothesize an expected partition
for the input graph and compare that with the predicted partitions. We also analyze the impact
of the number of frustrated edges (edges connecting the vertices within a partition) and their
location across the two partitions on the bipartivity index. We observe that for a given number
of frustrated edges, if the frustrated edges are located in the larger of the two partitions of the
bipartite graph (rather than the smaller of the two partitions or equally distributed across the
two partitions), the bipartivity index is likely to be relatively larger.
Identification of isomorphism and detection of distinct mechanism of kinemati...eSAT Journals
Abstract The problem of isomorphism among kinematic chains and of these derived mechanisms has been a hot area of research being done from last several years. The researchers so far have proposed many methods which are mainly based on characteristic polynomial and on some code based method to test the isomorphism among kinematic chains. In the present communication a new structural invariant based on link-link path distance (shortest) has been developed for a kinematic chain. Each link of kinematic chain is assigned as a unique label using the invariant. The set of link labels are combined in the form a numerical label of kinematic chain, called kinematic chain label (KCL). KCL is proposed as unique identifier for detection of isomorphism among kinematic chains, and also used to find distinct mechanism of a kinematic chain. Method is tested on several cases of single degree of freedom plannar kinematic chain successfully with simple joints. Keywords: - Isomorphism, Kinematic chains, Mechanism, Invarient labeling, Distinct mechanism, path distance matrix.
Identification of isomorphism and detection of distinct mechanism of kinemati...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A start guide to the concepts and algorithms in machine learning, including regression frameworks, ensemble methods, clustering, optimization, and more. Mathematical knowledge is not assumed, and pictures/analogies demonstrate the key concepts behind popular and cutting-edge methods in data analysis.
Updated to include newer algorithms, such as XGBoost, and more geometrically/topologically-based algorithms. Also includes a short overview of time series analysis
Creates heuristic guidelines for classifying types of networks empirically through a series of network metrics. Introduces metrics and theoretical background of what those network metrics measure with respect to the graph.
Spatial correlation based clustering algorithm for random and uniform topolog...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Centrality Prediction in Mobile Social NetworksIJERA Editor
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, we propose a new method to predict centrality values of nodes in a dynamic environment. The proposed method is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities. The performance of the proposed method is evaluated through simulated predictions on data sets from real mobile networks. Results indicate significantly low prediction error rate occurs, with a suitable implementation of the proposed method.
Identification of isomorphism and detection of distinct mechanism of kinemati...eSAT Journals
Abstract The problem of isomorphism among kinematic chains and of these derived mechanisms has been a hot area of research being done from last several years. The researchers so far have proposed many methods which are mainly based on characteristic polynomial and on some code based method to test the isomorphism among kinematic chains. In the present communication a new structural invariant based on link-link path distance (shortest) has been developed for a kinematic chain. Each link of kinematic chain is assigned as a unique label using the invariant. The set of link labels are combined in the form a numerical label of kinematic chain, called kinematic chain label (KCL). KCL is proposed as unique identifier for detection of isomorphism among kinematic chains, and also used to find distinct mechanism of a kinematic chain. Method is tested on several cases of single degree of freedom plannar kinematic chain successfully with simple joints. Keywords: - Isomorphism, Kinematic chains, Mechanism, Invarient labeling, Distinct mechanism, path distance matrix.
Identification of isomorphism and detection of distinct mechanism of kinemati...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A start guide to the concepts and algorithms in machine learning, including regression frameworks, ensemble methods, clustering, optimization, and more. Mathematical knowledge is not assumed, and pictures/analogies demonstrate the key concepts behind popular and cutting-edge methods in data analysis.
Updated to include newer algorithms, such as XGBoost, and more geometrically/topologically-based algorithms. Also includes a short overview of time series analysis
Creates heuristic guidelines for classifying types of networks empirically through a series of network metrics. Introduces metrics and theoretical background of what those network metrics measure with respect to the graph.
Spatial correlation based clustering algorithm for random and uniform topolog...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Centrality Prediction in Mobile Social NetworksIJERA Editor
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, we propose a new method to predict centrality values of nodes in a dynamic environment. The proposed method is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities. The performance of the proposed method is evaluated through simulated predictions on data sets from real mobile networks. Results indicate significantly low prediction error rate occurs, with a suitable implementation of the proposed method.
Deep vs diverse architectures for classification problemsColleen Farrelly
Deep learning study, comparing deep learning methods with wide learning methods; applications include simulation data and real industry problems. Pre-print of paper found here: https://arxiv.org/ftp/arxiv/papers/1708/1708.06347.pdf
A short tutorial on Morse functions and their use in modern data analysis for beginners. Uses visual examples and analogies to introduce topological concepts and algorithms.
A comparative study of clustering and biclustering of microarray dataijcsit
There are subsets of genes that have similar behavior under subsets of conditions, so we say that they
coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can
be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of
utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes
that are coexpressed under clusters of conditions. This type of clustering is called biclustering.
Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate
this problem by finding suboptimal solutions. In this paper, we make a new survey on clustering and
biclustering of gene expression data, also called microarray data.
ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONA...ijiert bestjournal
In Natural Scene Image,Text detection is important tasks which are used for many content based image analysis. A maximally stable external region based method is us ed for scene detection .This MSER based method incl udes stages character candidate extraction,text candida te construction,text candidate elimination & text candidate classification. Main limitations of this method are how to detect highly blurred text in low resolutio n natural scene images. The current technology not focuses on any t ext extraction method. In proposed system a Conditi onal Random field (CRF) model is used to assign candidat e component as one of the two classes (text& Non Te xt) by Considering both unary component properties and bin ary contextual component relationship. For this pur pose we are using connected component analysis method. The proposed system also performs a text extraction usi ng OCR
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Presents a new type of statistical model developed by Quantopo, LLC, based on generalized linear modeling and Tweedie regression that leverages the power of quantum computing. Paper is being written and will be uploaded to arXiv while under review.
EVOLUTIONARY CENTRALITY AND MAXIMAL CLIQUES IN MOBILE SOCIAL NETWORKSijcsit
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
A Novel Clustering Method for Similarity Measuring in Text DocumentsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Predicting electricity consumption using hidden parametersIJLT EMAS
data mining technique to forecast power demand of a
biological region based on the metrological conditions. The value
forecast analytical data mining technique is implement with the
Hidden Marko Model. The morals of the factor such as heat,
clamminess and municipal celebration on which influence
operation depends and the everyday utilization morals compose
the data. Data mining operation are perform on this
chronological data to form a forecast model which is able of
predict every day utilization provide the meteorological
parameter. The steps of information detection of data process are
implemented. The data is preprocessed and fed to HMM for
guidance it. The educated HMM network is used to predict the
electricity demand for the given meteorological conditions.
Topology is the arrangement of the various elements of a computer network.
In communication networks, a topology is a usually schematic description of the arrangement of a network, including its nodes and connecting lines.
A computer network is made of computers which are linked to one another with communication lines (network cables, etc.) and hardware elements (network adapters, as well as other equipment for ensuring that data travels correctly.
An experimental evaluation of similarity-based and embedding-based link predi...IJDKP
The task of inferring missing links or predicting future ones in a graph based on its current structure
is referred to as link prediction. Link prediction methods that are based on pairwise node similarity
are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features,
and thus support the subsequent link prediction task in graph. This paper studies a selection of
methods from both categories on several benchmark (homogeneous) graphs with different properties
from various domains. Beyond the intra and inter category comparison of the performances of the
methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)-
based methods and heuristic ones as a means to alleviate the black-box well-known limitation.
Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.
Developing an arabic plagiarism detection corpuscsandit
A corpus is a collection of documents. It is a valuable resource in linguistics research to
perform statistical analysis and testing hypothesis for different linguistic rules. An annotated
corpus consists of documents or entities annotated with some task related labels such as part of
speech tags, sentiment etc One such task is plagiarism detection that seeks to identify if a given
document is plagiarized or not. This paper describes our efforts to build a plagiarism detection
corpus for Arabic. The corpus consists of about 350 plagiarized – source document pairs and
more than 250 documents where no plagiarism was found. The plagiarized documents consists
of students submitted assignments. For each of the plagiarized documents, the source document
was located from the Web and downloaded for further investigation. We report corpus statistics
including number of documents, number of sentences and number of tokens for each of the
plagiarized and source categories.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
The recent advances in computer technology and data networking have made videoconferencing
system a popular medium for users to interact with one another from remote locations. This
system offers communication between more than two users, who are able to interact through
their webcams, microphone and other components. The use of this system has been increased
recently due to many reasons, for one thing, progress in Internet access in different networks
like companies, universities and houses, with the increase of available bandwidth whereas the
decrease of delay in sending and receiving packets . On the other hand, the coming of Rich
Internet Applications (RIA) means that a large part of web application started to be
implemented on the web browsers. This paper discusses the conception of multiparty
videoconferencing systems using technologies of Web 2.0. For our conceptual
Videoconferencing Platform, we have developed many feature : live audio video, text
chat, video recording, user and room management and quality control. Videoconferencing
modules have been carried out using open source technologies Flex and J2EE.
Deep vs diverse architectures for classification problemsColleen Farrelly
Deep learning study, comparing deep learning methods with wide learning methods; applications include simulation data and real industry problems. Pre-print of paper found here: https://arxiv.org/ftp/arxiv/papers/1708/1708.06347.pdf
A short tutorial on Morse functions and their use in modern data analysis for beginners. Uses visual examples and analogies to introduce topological concepts and algorithms.
A comparative study of clustering and biclustering of microarray dataijcsit
There are subsets of genes that have similar behavior under subsets of conditions, so we say that they
coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can
be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of
utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes
that are coexpressed under clusters of conditions. This type of clustering is called biclustering.
Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate
this problem by finding suboptimal solutions. In this paper, we make a new survey on clustering and
biclustering of gene expression data, also called microarray data.
ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONA...ijiert bestjournal
In Natural Scene Image,Text detection is important tasks which are used for many content based image analysis. A maximally stable external region based method is us ed for scene detection .This MSER based method incl udes stages character candidate extraction,text candida te construction,text candidate elimination & text candidate classification. Main limitations of this method are how to detect highly blurred text in low resolutio n natural scene images. The current technology not focuses on any t ext extraction method. In proposed system a Conditi onal Random field (CRF) model is used to assign candidat e component as one of the two classes (text& Non Te xt) by Considering both unary component properties and bin ary contextual component relationship. For this pur pose we are using connected component analysis method. The proposed system also performs a text extraction usi ng OCR
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Presents a new type of statistical model developed by Quantopo, LLC, based on generalized linear modeling and Tweedie regression that leverages the power of quantum computing. Paper is being written and will be uploaded to arXiv while under review.
EVOLUTIONARY CENTRALITY AND MAXIMAL CLIQUES IN MOBILE SOCIAL NETWORKSijcsit
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
A Novel Clustering Method for Similarity Measuring in Text DocumentsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Predicting electricity consumption using hidden parametersIJLT EMAS
data mining technique to forecast power demand of a
biological region based on the metrological conditions. The value
forecast analytical data mining technique is implement with the
Hidden Marko Model. The morals of the factor such as heat,
clamminess and municipal celebration on which influence
operation depends and the everyday utilization morals compose
the data. Data mining operation are perform on this
chronological data to form a forecast model which is able of
predict every day utilization provide the meteorological
parameter. The steps of information detection of data process are
implemented. The data is preprocessed and fed to HMM for
guidance it. The educated HMM network is used to predict the
electricity demand for the given meteorological conditions.
Topology is the arrangement of the various elements of a computer network.
In communication networks, a topology is a usually schematic description of the arrangement of a network, including its nodes and connecting lines.
A computer network is made of computers which are linked to one another with communication lines (network cables, etc.) and hardware elements (network adapters, as well as other equipment for ensuring that data travels correctly.
An experimental evaluation of similarity-based and embedding-based link predi...IJDKP
The task of inferring missing links or predicting future ones in a graph based on its current structure
is referred to as link prediction. Link prediction methods that are based on pairwise node similarity
are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features,
and thus support the subsequent link prediction task in graph. This paper studies a selection of
methods from both categories on several benchmark (homogeneous) graphs with different properties
from various domains. Beyond the intra and inter category comparison of the performances of the
methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)-
based methods and heuristic ones as a means to alleviate the black-box well-known limitation.
Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.
Developing an arabic plagiarism detection corpuscsandit
A corpus is a collection of documents. It is a valuable resource in linguistics research to
perform statistical analysis and testing hypothesis for different linguistic rules. An annotated
corpus consists of documents or entities annotated with some task related labels such as part of
speech tags, sentiment etc One such task is plagiarism detection that seeks to identify if a given
document is plagiarized or not. This paper describes our efforts to build a plagiarism detection
corpus for Arabic. The corpus consists of about 350 plagiarized – source document pairs and
more than 250 documents where no plagiarism was found. The plagiarized documents consists
of students submitted assignments. For each of the plagiarized documents, the source document
was located from the Web and downloaded for further investigation. We report corpus statistics
including number of documents, number of sentences and number of tokens for each of the
plagiarized and source categories.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
The recent advances in computer technology and data networking have made videoconferencing
system a popular medium for users to interact with one another from remote locations. This
system offers communication between more than two users, who are able to interact through
their webcams, microphone and other components. The use of this system has been increased
recently due to many reasons, for one thing, progress in Internet access in different networks
like companies, universities and houses, with the increase of available bandwidth whereas the
decrease of delay in sending and receiving packets . On the other hand, the coming of Rich
Internet Applications (RIA) means that a large part of web application started to be
implemented on the web browsers. This paper discusses the conception of multiparty
videoconferencing systems using technologies of Web 2.0. For our conceptual
Videoconferencing Platform, we have developed many feature : live audio video, text
chat, video recording, user and room management and quality control. Videoconferencing
modules have been carried out using open source technologies Flex and J2EE.
The Influence of Economic Growth on Poverty, Investment, and Human Developmen...Suwandi, Dr. SE.,MSi
This paper discusses about the economic growth that has a direct impact on Human Development Index (HDI) and indirect one on the increase of investment absorption and decrease of poverty. Besides, we can know that economic growth has a direct impact on the increase of investment, as well as it directly affects the decrease of poverty level by using partial test quantitative analysis. To increase the economic growth and reduce poverty as well as to increase HDI, these are what to do (a) revitalizing the agriculture to help main sector of Fak Fak district (agriculture); (b) giving modal such as: banking soft loan with easy terms and revolving fund for the right target in the form of natura (cows, sheeps, etc.) that can accelerate the increase of economic; (c) regional government facilitates the linkage and partnership program with “win-win solution” concept.
Trinity Kings World Leadership: "Ambassador of Leadership" for United States ...Terrell Patillo
Proverbs 4:7
New English Translation (NET Bible)
7 Wisdom is supreme—so acquire wisdom,
and whatever you acquire, acquire understanding!
Proverbs 29:4
New English Translation (NET Bible)
4 A king brings stability to a land by justice,
Proverbs 28:2
The Message (MSG)
2 When the country is in chaos,
everybody has a plan to fix it—
But it takes a leader(king) of real understanding
to straighten things out.
The proposal of giving two receipts for voters to increase the security of el...csandit
Holding an election with aim of selecting only one person or approval / rejection of a state law,
is a special kind of election which every few years in the different countries going to happen.
Given the pervasiveness of this election, we must take special measures to provide high security
for the referendum. Using two receipts for each voter which one is named barcode receipt, a
secret indicator of vote and another is named key receipt that is a key to acknowledged the
voters information box, including: voter’s National Code, the candidate code which is voted by
this voter, code of election station and barcode information. In this paper is proposed to enable
people and social networks using data on bar code’s receipts without Intrusion into the privacy
of other voters, so they will put together their personal information from monitoring the election
process on a social network which can help to prevent any violation in election. The security of
the proposed scheme is based on the turnout in recount of votes
Data characterization towards modeling frequent pattern mining algorithmscsandit
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle
extremely huge amount of data, it is quite natural that the demand for the computational
environment to accelerates, and scales out big data applications increases. The important thing
is, however, the behavior of big data applications is not clearly defined yet. Among big data
applications, this paper specifically focuses on stream mining applications. The behavior of
stream mining applications varies according to the characteristics of the input data. The
parameters for data characterization are, however, not clearly defined yet, and there is no study
investigating explicit relationships between the input data, and stream mining applications,
either. Therefore, this paper picks up frequent pattern mining as one of the representative
stream mining applications, and interprets the relationships between the characteristics of the
input data, and behaviors of signature algorithms for frequent pattern mining.
Framework for developed simple architecture enterprise fdsaecsandit
In This article presents a framework for develop de Architecture enterprise based on the
articulation of emerging paradigms for architecture development of information enterprise [1].
The first one comes from the agile methods and it is inspired on the Scrum model which aim to
simplify the complex task of developing a quality software, the second the processes models
whose are oriented the development of Architectures Enterprise as Zachman and TOGAF in a
paradigm of the Model Driven and principles de reference de architecture de Software form the
paradigms Generation (MDG), these approaches are integrated eventually leading to the
formulation and presentation of an framework for developed simple architecture enterprise –
FDSAE- The goal is to present a simple, portable, understandable terms enabling, modeling
and design business information architecture in any organizational environment, in addition to
this, there are important aspects related to the unified Modeling Language UML 2.5 and the
Business Process Modeling BPMn that become tools to obtain the products in the FDSAE
Framework, This framework is an improved version of Framework MADAIKE [2] developed by
the same authors.
How to detect middleboxes guidelines on a methodologycsandit
Internet middleboxes such as VPNs, firewalls, and proxies can significantly change handling of
traffic streams. They play an increasingly important role in various types of IP networks. If end
hosts can detect them, these hosts can make beneficial, and in some cases, crucial improvements
in security and performance But because middleboxes have widely varying behavior and effects
on the traffic they handle, no single technique has been discovered that can detect all of them.
Devising a detection mechanism to detect any particular type of middlebox interference involves
many design decisions and has numerous dimensions. One approach to assist with the
complexity of this process is to provide a set of systematic guidelines. This paper is the first
attempt to introduce a set of general guidelines (as well as the rationale behind them) to assist
researchers with devising methodologies for end-hosts to detect middleboxes by the end-hosts.
The guidelines presented here take some inspiration from the previous work of other
researchers using various and often ad hoc approaches. These guidelines, however, are mainly
based on our own experience with research on the detection of middleboxes. To assist
researchers in using these guidelines, we also provide an example of how to bring them into
play for detection of network compression
The screening of chemical libraries is an important step in the drug discovery process. The
existing chemical libraries contain up to millions of compounds. As the screening at such scale
is expensive, the virtual screening is often utilized. There exist several variants of virtual
screening and ligand-based virtual screening is one of them. It utilizes the similarity of screened
chemical compounds to known compounds. Besides the employed similarity measure, another
aspect greatly influencing the performance of ligand-based virtual screening is the chosen
chemical compound representation. In this paper, we introduce a fragment-based
representation of chemical compounds. Our representation utilizes fragments to represent a
compound where each fragment is represented by its physico-chemical descriptors. The
representation is highly parametrizable, especially in the area of physico-chemical descriptors
selection and application. In order to test the performance of our method, we utilized an existing
framework for virtual screening benchmarking. The results show that our method is comparable
to the best existing approaches and on some data sets it outperforms them.
An important role performed by Zero Padding (ZP) in multi-band OFDM (MB-OFDM) System.
This role show for low-complexity in résistance against multipath interference by reducing
inter-carrier interference (ICI) and eliminating the inter-symbol interference (ISI) Also, zeropadded
suffix can be used to eliminate ripples in the power spectral density in order to conform
to FCC requirements.
At the receiver of MB-OFDM system needs to use of a technique called as overlap-and-add
(OLA). Which maintain the circular convolution property and take the multipath energy of the
channel.
In this paper, we proposed a method of performing overlap-and-add length for zero padded
suffixes. Then, we studied the effect of this method, dynamic optimization of overlap-and-add
(OLA) equalization, on the performance of MBOFDM system on Bit Error Rate (BER) with
AWGN channel and Saleh-Valenzuela (S-V) Multipath channel Model.
In the dynamic optimization OLA, the Length of ZP depends on length of channel impulse
response (CIR). These measures, based on SNR, insert the ZP according to the measurement.
Dynamic optimization of length of ZP improves the Performance of MBOFDM system. In fact
we developed a technique to select the length of ZP as function of SNR and CIR
estimate(repetition). In our simulation this technique improve to 3 dB at BER=10-2 with a
multipath channels CM4.
This paper deals with the optimization of the capacity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
A considerable interest has been given to Multiword Expression (MWEs) identification and treatment. The identification of MWEs affects the quality of results of different tasks heavily used in natural language processing (NLP) such as parsing and generation. Different
approaches for MWEs identification have been applied such as statistical methods which employed as an inexpensive and language independent way of finding co-occurrence patterns.Another approach relays on linguistic methods for identification, which employ information such as part of speech (POS) filters and lexical alignment between languages is also used and
produced more targeted candidate lists. This paper presents a framework for extracting Arabic
MWEs (nominal or verbal MWEs) for bi-gram using hybrid approach. The proposed approach starts with applying statistical method and then utilizes linguistic rules in order to enhance the results by extracting only patterns that match relevant language rule. The proposed hybrid
approach outperforms other traditional approaches.
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on possibilistic networks are limited by the size of the possibility distributions.
Generally, these approaches are based on possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG into a secondary structure, known as the junction trees. This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation
of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of normalization in the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.
The recruitment of new personnel is one of the most essential business processes which affect the quality of human capital within any company. It is highly essential for the companies to ensure the recruitment of right talent to maintain a competitive edge over the others in the
market. However IT companies often face a problem while recruiting new people for their ongoing projects due to lack of a proper framework that defines a criteria for the selection process. In this paper we aim to develop a framework that would allow any project manager to
take the right decision for selecting new talent by correlating performance parameters with the
other domain-specific attributes of the candidates. Also, another important motivation behind this project is to check the validity of the selection procedure often followed by various big companies in both public and private sectors which focus only on academic scores, GPA/grades
of students from colleges and otheracademic backgrounds. We test if such a decision will produce optimal results in the industry or is there a need for change that offers a more holistic approach to recruitment of new talent in the software companies. The scope of this work extends beyond the IT domain and a similar procedure can be adopted to develop a recruitment
framework in other fields as well. Data-mining techniques provide useful information from the historical projects depending on which the hiring-manager can make decisions for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining
framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for quality objectives.
In the area of network security, the fundamental security principles and security practice skills are both required for students’ understanding. Instructors have to emphasize both; the theoretical part and practices of security. However, this is a challenging task for instructors’
teaching and students’ learning. For this reason, researchers are eager to support the lecture
lessons by using interactive visualization tools. The learning tool CrypTool 2 is one of these tools that mostly cover all of the above. In fact, the evaluations of the effectiveness of the tools in teaching and learning are limited. Therefore, this paper provides an overview of an empirical evaluation for assessing CrypTool 2 tool. The effectiveness of this tool was tested using an empirical evaluation method. The results show that this visualization tool was effective in
meeting its learning objectives.
Nowadays we can observe the change of the structure of energy resources, which leads to the
increasing fraction of a renewable energy sources. Traditional underground coal mining loses
its significance in a total but there are countries, including Poland, which economy is still coal
based. A decreasing coal resources imply an exploitation a becoming harder accessible coal
beds what is connected with the increase of the safety of the operation. One of the most
important technical factor of the safety of underground coal mining is the diagnostic state o a
longwall powered roof support. It consists of dozen (or hundreds) of units working in a row. The
diagnostic state of a powered roof supports depends on the diagnostic state of all units. This
paper describes the possibility of unit diagnostic state analysis based on the biclustering
methods.
Cryptography is a security technique that must be applied in both communication sides to protect
the data during its transmission through the network from all kinds of attack. On the sender
side, the original data will be changed into different symbols or shapes by using a known key;
this is called encryption. On the other communication side, the decryption process will be done
and the data will be returned to its former shape by using the agreed key. The importance of
cryptography is to fulfil the communication security requirements. Real time applications (RTA)
are vulnerable for the moment because of their big size. However, some of the current algorithms
are not really appropriate for use with these kinds of information. In this paper, a novel
symmetric block cipher cryptography algorithm has been illustrated and discussed. The system
uses an 8x8x8 cube, and each cell contains a pair of binary inputs. The cube can provide a huge
number of combinations that can produce a very strong algorithm and a long key size. Due to
the lightweight and fast technique used in this idea, it is expected to be extremely rapid compared
to the majority of current algorithms, such as DES and AES.
Using spectral radius ratio for node degreeIJCNCJournal
In this paper, we show that the spectral radius ratio for node degree could be used to analyze the variation of node degree during the evolution of complex networks. We focus on three commonly studied models of complex networks: random networks, scale-free networks and small-world networks. The spectral radius ratio for node degree is defined as the ratio of the principal (largest) eigenvalue of the adjacency matrix of a network graph to that of the average node degree. During the evolution of each of the above three categories of networks (using the appropriate evolution model for each category), we observe the spectral radius ratio for node degree to exhibit high-very high positive correlation (0.75 or above) to that of the
coefficient of variation of node degree (ratio of the standard deviation of node degree and average node degree). We show that the spectral radius ratio for node degree could be used as the basis to tune the operating parameters of the evolution models for each of the three categories of complex networks as well as analyze the impact of specific operating parameters for each model.
O N T HE D ISTRIBUTION OF T HE M AXIMAL C LIQUE S IZE F OR T HE V ERTICES IN ...csandit
The high-level contributions of this paper are as f
ollows: We modify an existing branch-and-
bound based exact algorithm (for maximum clique siz
e of an entire graph) to determine the
maximal clique size that the individual vertices in
the graph are part of. We then run this
algorithm on six real-world network graphs (ranging
from random networks to scale-free
networks) and analyze the distribution of the maxim
al clique size of the vertices in these graphs.
We observe five of the six real-world network graph
s to exhibit a Poisson-style distribution for
the maximal clique size of the vertices. We analyze
the correlation between the maximal clique
size and the clustering coefficient of the vertices
, and find these two metrics to be poorly
correlated for the real-world network graphs. Final
ly, we analyze the Assortativity index of the
vertices of the real-world network graphs and obser
ve the graphs to exhibit positive
assortativity with respect to maximal clique size a
nd negative assortativity with respect to node
degree; nevertheless, we observe the Assortativity
index of the real-world network graphs with
respect to both the maximal clique size and node de
gree to increase with decrease in the
spectral radius ratio for node degree, indicating a
positive correlation between the maximal
clique size and node degree.
Distribution of maximal clique size of theIJCNCJournal
Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the well-known Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of
the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category
of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the
vertices with respect to maximal clique size and node degree.
Energy Efficient Power Failure Diagonisis For Wireless Network Using Random G...idescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighboring nodes. An algorithm has been described in which the power failure diagnosis is
made and solved. This paper also involves proper selection of the ideal networks by the
concepts of Random Graph theory. The paper involves the mathematics of complete fault
diagnosis including solving the problem and continuing the process flow.
Complex systems,
Software systems,
Database systems,
Operating systems,
Bioinformatics systems,
Social Systems,
Service Oriented Systems,
Cloud Systems,
Ubiquitous systems,
Distributed Version Control Systems (GitHub), and
Software Container Systems (DockerHub and Google App Engine).
Energy Efficient Modeling of Wireless Sensor Networks using Random Graph Theoryidescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighbouring nodes. An algorithm has been described in which the power failure diagnosis
is made and solved. The concepts of Electromagnetics, Wave Duality, Energy model of an
atom is linked with wireless networks. A model is prepared in which the positioning of
nodes of sensors are decided. Also the model is made more efficient regarding the energy
consumption, power delivery etc. using the concepts of graph theory concepts, probability
distribution.
Distribution of maximal clique size underijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER THE WATTS-STROGATZ MODEL OF EVOLUTI...ijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly
A NEW GENERALIZATION OF EDGE OVERLAP TO WEIGHTED NETWORKSijaia
Finding the strength of an edge in a network has always been a big demand. In the context of social networks, it allows to estimate the relationship strength between users. The best-known method to compute edge strength is the Neighbourhood Overlap. It computes the ratio of common neighbours to all neighbours of an edge terminal nodes. This method has been initially proposed for unweighted networks and later extended for weighted ones. These two versions of the method are not mathematically equivalent: In fact, an unweighted network is commonly considered as weighted with all edge weights equal to one. Using both existent versions of Neighbourhood Overlap on such network produce completely different values. In this paper, we tackle this problem and propose a new generalization for Neighbourhood Overlap that works equally for unweighted and weighted networks. Experiment performed on networks with various parameters showed similar performance of our measure to the existing measures.
A NEW GENERALIZATION OF EDGE OVERLAP TO WEIGHTED NETWORKSgerogepatton
Finding the strength of an edge in a network has always been a big demand. In the context of social networks, it allows to estimate the relationship strength between users. The best-known method to compute edge strength is the Neighbourhood Overlap. It computes the ratio of common neighbours to all neighbours of an edge terminal nodes. This method has been initially proposed for unweighted networks and later extended for weighted ones. These two versions of the method are not mathematically equivalent: In fact, an unweighted network is commonly considered as weighted with all edge weights equal to one. Using both existent versions of Neighbourhood Overlap on such network produce completely different values. In this paper, we tackle this problem and propose a new generalization for Neighbourhood Overlap that works equally for unweighted and weighted networks. Experiment performed on networks with various parameters showed similar performance of our measure to the existing measures.
ABSTRACT
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
GRAPH ALGORITHM TO FIND CORE PERIPHERY STRUCTURES USING MUTUAL K-NEAREST NEIG...ijaia
Core periphery structures exist naturally in many complex networks in the real-world like social,
economic, biological and metabolic networks. Most of the existing research efforts focus on the
identification of a meso scale structure called community structure. Core periphery structures are another
equally important meso scale property in a graph that can help to gain deeper insights about the
relationships between different nodes. In this paper, we provide a definition of core periphery structures
suitable for weighted graphs. We further score and categorize these relationships into different types based
upon the density difference between the core and periphery nodes. Next, we propose an algorithm called
CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from
weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN).
Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed
core periphery structures.
Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neig...gerogepatton
Core periphery structures exist naturally in many complex networks in the real-world like social,
economic, biological and metabolic networks. Most of the existing research efforts focus on the
identification of a meso scale structure called community structure. Core periphery structures are another
equally important meso scale property in a graph that can help to gain deeper insights about the
relationships between different nodes. In this paper, we provide a definition of core periphery structures
suitable for weighted graphs. We further score and categorize these relationships into different types based
upon the density difference between the core and periphery nodes. Next, we propose an algorithm called
CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from
weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN).
Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed
core periphery structures.
Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neig...gerogepatton
Core periphery structures exist naturally in many complex networks in the real-world like social, economic, biological and metabolic networks. Most of the existing research efforts focus on the identification of a meso scale structure called community structure. Core periphery structures are another equally important meso scale property in a graph that can help to gain deeper insights about the relationships between different nodes. In this paper, we provide a definition of core periphery structures suitable for weighted graphs. We further score and categorize these relationships into different types based upon the density difference between the core and periphery nodes. Next, we propose an algorithm called CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN). Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed core periphery structures.
Paper Explained: Understanding the wiring evolution in differentiable neural ...Devansh16
Read my Explanation of the Paper here: https://medium.com/@devanshverma425/why-and-how-is-neural-architecture-search-is-biased-778763d03f38?sk=e16a3e54d6c26090a6b28f7420d3f6f7
Abstract: Controversy exists on whether differentiable neural architecture search methods discover wiring topology effectively. To understand how wiring topology evolves, we study the underlying mechanism of several existing differentiable NAS frameworks. Our investigation is motivated by three observed searching patterns of differentiable NAS: 1) they search by growing instead of pruning; 2) wider networks are more preferred than deeper ones; 3) no edges are selected in bi-level optimization. To anatomize these phenomena, we propose a unified view on searching algorithms of existing frameworks, transferring the global optimization to local cost minimization. Based on this reformulation, we conduct empirical and theoretical analyses, revealing implicit inductive biases in the cost's assignment mechanism and evolution dynamics that cause the observed phenomena. These biases indicate strong discrimination towards certain topologies. To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
Analysis of the Use of Universal Distribution Factors in SEC Power Gridresearchinventy
Distribution factors have been extensively used in many power system analysis and planning studies. In recent power system studies, the AC distribution factors are insensitive to the operating point and relatively sensitive at certain degree to changes in network topology. These factors are linear approximations of sensitivities of variables with various inputs. This paper presents the calculation of the universal distribution factors (UDF’s) applies them on several practical scenarios of Saudi Electricity Company (SEC) power grid. The results are analyzed and evaluated considering various system conditions of SEC load. The results show that the accuracy of the used approach is acceptable compared with exact method. This is practically beneficial to SEC in computing its grid complex power flows using UDF's at the base case without the need to recalculate UDF’s which save efforts and time.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
2. 222 Computer Science & Information Technology (CS & IT)
of the network graph G essentially captures the presence of edges between any two vertices. For
any two vertices i and j in graph G, the entry in the ith
row and jth
column of A(G) = 1 is 1 if there
is an edge from vertex i to vertex j and 0 otherwise. Depending on the nature of the interactions,
the edges could be undirected (symmetric adjacency matrix) or directed (non-symmetric
adjacency matrix). We encounter undirected edges when we model networks where two-way
interaction is the de facto standard for any association between two nodes in a network (for
example: Protein-protein interaction networks, Power grid, Science collaboration networks,
Internet, Actor network, etc). Directed edges are encountered when the interactions need not be in
both directions of the link between any two nodes (for example: Phone call network, Email
network, World wide web, etc). Sometimes, there could be both directed and undirected edges in
a network graph (e.g., metabolic networks where certain chemical reactions are reversible while
certain reactions proceed in only one direction).
In this paper, we present spectral decomposition (eigenvalues and eigenvectors of the adjacency
matrix of the graph) based analysis of network graphs to detect whether they are bipartite or
close-to being bipartite or not and if found to be either of the two cases, we show how to predict
the two partitions of the "true" or "close-to" bipartite graph. A bipartite graph is a graph wherein
the set of vertices in the graph could be partitioned to two disjoint partitions and the edges of the
graph connect the vertices across the partitions. In a "true" bipartite graph, there are no edges
connecting the vertices within a partition. In a "close-to" bipartite graph, there may be one or few
edges (called the frustrated edges) connecting the vertices within a partition.
Spectral decomposition consists of generating a continuous multi-dimensional representation (a
set of eigenvalues and the corresponding eigenvectors) of the adjacency matrix of the network
graph. An eigenvector is referred to as the vector of coordinates of the points along each axis of
the multi-dimensional space and the corresponding eigenvalue is the length of the projection on
the particular dimension. Depending on the underlying network characteristic that is to be
studied, we identify the set of axes (eigenvectors) that essentially capture the variability in the
data (in this paper, we make use of the smallest eigenvalue and its corresponding eigenvector):
the first axis corresponds to the direction of greatest variability in the data; the second axis
(orthogonal to the first axis) captures the direction of the greatest remaining variability and etc.
Though the number of dimensions in the spectrum is the number of vertices in the graph, most of
the variations could be captured in the first few dimensions of the coordinate system represented
by the eigenvalues and the eigenvectors.
Related Work: Most of the work in the literature has focused on developing algorithms that
minimize the number of edges (i.e., the frustrated edges) that need to be deleted from a graph to
extract a bipartite spanning graph. Though this is an NP-hard problem for general graphs [1], for
fullerene graphs (cubic 3-connected planar graph with exactly 12 pentagonal faces and an
optional number of hexagonal faces), it has been found that there exists a polynomial-time
algorithm [2] to determine the minimum set of edges that could be removed from fullerene graphs
to extract a bipartite spanning graph. In [3], the authors developed a mathematical programming
model and a genetic algorithm to determine the minimum number of frustrated edges to be
removed from fullerene to extract a bipartite subgraph. In [4], the authors compute the bipartite
edge frustration of a polybuckyball (a fullerene polymer) by extending the splice and link
operations on the two partitions of the graph. In [5], the authors derive theoretical bounds on the
maximum frustration index of a complete graph with a set of l and r vertices constituting the two
partitions. Thus, most of the work in the literature is focused on minimizing the number of
frustrated edges that need to be removed from selected graphs (mostly chemical compounds) to
obtain a bipartite spanning graph. Our contributions in this paper are to predict the two bipartite
partitions of any given graph that is hypothesized to be "true" bipartite or "close-to" bipartite as
well as to analyze the impact of the distribution of the frustrated edges on the bipartivity index.
3. Computer Science & Information Technology (CS & IT) 223
Roadmap of the Paper: The rest of the paper is organized as follows: Section 2 explains the
eigenvalues and eigenvectors in greater detail and illustrates their computation with a simple
example. Section 3 presents the calculation of bipartivity index on undirected graphs. Section 4
illustrates the prediction of the partitions for undirected "true" and "close-to" bipartite graphs.
Section 5 illustrates the prediction of the partitions for directed "true" and "close-to" bipartite
graphs. Section 6 concludes the paper.
2. EIGENVALUES AND EIGENVECTORS
Spectral decomposition is a standard method to handle multivariate data in statistics and identify
the directions of maximum variability [6]. The directions are called the eigenvectors and the
relative importance to be given for each direction is represented by the eigenvalues. The spectrum
is the collection of all the (eigenvalues, eigenvector) pairs of the multivariate data represented as
a matrix. In this paper, we show that spectral decomposition of a unit-weight adjacency matrix
(where the entries are either 0 or 1) of a network graph could be conducted to extract information
on the extent of bipartivity in an underlying network as well as to predict the two partitions
constituting the network graph.
We now illustrate an example to determine the calculation of eigenvalues and eigenvectors.
Figure 1 illustrates the computation of the characteristic polynomial of an adjacency matrix for
the network graph shown. The roots of the characteristic polynomial (i.e., roots of the equation |
A - λ I | = 0) are the eigenvalues. Accordingly, we solve the characteristic polynomial λ
4
- 4λ
2
-
2λ + 1 = 0; the roots are λ = {2.17; 0.31; -1; -1.48}. The eigenvector X for an eigenvalue λ is the
one that satisfies (A - λI) X = 0 [7]. Note that X is a column vector with n rows where n is the
dimension of the adjacency matrix A.
Figure 1. Characteristic Polynomial for the Adjacency Matrix of the Network Graph
4. 224 Computer Science & Information Technology (CS & IT)
We illustrate the computation of the eigenvector for eigenvalue 2.17 in Figure 2. Note that 2.17 is
the largest eigenvalue for the adjacency matrix and it is called the principal eigenvalue and its
corresponding eigenvector is called the principal eigenvector. For the calculation of the
eigenvalues and eigenvectors of adjacency matrices used in this paper (including Figure 2), we
use the website: http://www.arndt-bruenner.de/mathe/scripts/engl_eigenwert.htm. A screenshot of
the results obtained for the adjacency matrix of Figure 1 is shown below in Figure 3.
Figure 2. Calculation of the Principal Eigenvector for the Network Graph in Figure 1
Figure 3. Online Calculator for Eigenvalues and Eigenvectors for an Adjacency Matrix
3. BIPARTIVITY INDEX
A graph G = (V, E) is said to be bipartite if the vertex set V could be divided into two disjoint sets
V1 and V2 such that there are no edges connecting vertices within the two subsets and every edge
5. Computer Science & Information Technology (CS & IT) 225
in E only connects a vertex in V1 to a vertex in V2 or vice-versa (if the graph is directed) [8].
More formally, G = (V, E) is said to be bi-partite if the two partitions V1 and V2 of the vertex set
V and the edge set E are related as follows:
(i) V1Υ V2 = V and V1 Ι V2 = Φ (empty set)
(ii) ∀ (i, j)∈E, either i∈V1 and j∈V2 or i∈V2 and j∈V1
Figure 4.1 illustrates a bipartite graph that has no edges within its two vertex set partitions. In
reality, it may not be possible to find network graphs that are truly bipartite. There may be few
edges between the vertices within the same partition. Such edges are called frustrated edges.
Figure 4.2 illustrates a graph that is close to being bipartite, with the majority of the edges
connecting the vertices across the two partitions but there are two frustrated edges. The
eigenvalues of the adjacency matrix can be used to determine the extent of bipartitivity of a
network graph G in the form of a metric called the bipartitivity index, bS(G), calculated as
follows. Let λ1, λ2, λ3, ... , λn be the eigenvalues of the adjacency matrix of G.
Bipartivity Index of graph G,
∑∑
∑
==
=
+
= n
j
j
n
j
j
n
j
j
S Gb
11
1
)sinh()cosh(
)cosh(
)(
λλ
λ
Figure 4.1. A "True" Figure 4.2. A "Close-to"
Bipartite Graph Bipartite Graph
Figure 4. Examples of "True" and "Close-to" Bipartite Graphs
The calculation of the bipartivity index for a "true" bipartite graph and for a "close-to" bipartite
graph are shown in Figures 5 and 6 respectively. We can notice that for a "true" bipartite graph,
the sinh(λj) values in the formula for the bipartivity index add to 0, resulting in the bipartivity
index being 1 for such graphs. On the other hand, for a non-bipartite graph, the sum of the sinh(λj)
values contribute a positive value - leading to an increase in the value of the denominator
compared to the numerator in the formula for bipartivity index. Thus, the bipartivity index for a
non-bipartite graph is always less than 1; if the bS(G) values of graph G is closer to 1, we call
such graphs as "close-to" bipartite, as the one in Figure 6 (where edge 2 - 4 is removed from the
graph in Figure 5 and edge 1 - 4 is added as a frustrated edge).
6. 226 Computer Science & Information Technology (CS & IT)
Figure 5. Bipartivity Index Calculations for a "truly" Bipartite Graph
Figure 7 illustrates the impact of the number of frustrated edges and their location on the
bipartivity index values for several sample network graphs. The bipartivity index of the graphs
decreases with increase in the number of frustrated edges that connect vertices within the same
partition. We can observe that for a given number of frustrated edges, a larger value of the
bipartivity index is observed for graphs that have a relatively larger number of frustrated edges in
the larger partition vis-á-vis the smaller partition.
Figure 6. Bipartivity Index Calculations for a "close-to" Bipartite Graph
Figure 7. Impact of the Number of Frustrated Edges and their Location on the Bipartivity Index of Network
Graphs
7. Computer Science & Information Technology (CS & IT) 227
4. PREDICTIONS OF THE PARTITIONS IN A UNDIRECTED BIPARTITE
GRAPH
We now illustrate how to predict the two partitions of a "true" or "close-to" bipartite graph. For
this purpose, we will make use of the smallest of the eigenvalues and its corresponding
eigenvector, hereafter referred to as the bipartite eigenvalue and the bipartite eigenvector
respectively. The bipartite eigenvalue is most likely a negative value in "true" or "close-to"
bipartite graphs. The bipartite eigenvector is likely to comprise of both positive and negative
entries. The node IDs whose entries in the bipartite eigenvector are of the positive sign constitute
one of the two partitions and those of the negative sign constitute the other partition. The above
approach has been found to accurately predict the two partitions of a "true" bipartite graph, as
shown in Figure 8. However, for "close-to" bipartite graphs, the partitions predicted (using the
smallest eigenvalue and its corresponding eigenvector) may not be the same as the partitions
expected (hypothetical partitions) of the input graph whose adjacency matrix had been used to
determine the eigenvalue and the eigenvector. Nevertheless, the predicted partitions of the "close-
to" bipartite graphs and the hypothetical partitions of the original input graph contribute to the
same bipartivity index value. This shows that two "close-to" bipartite graphs that physically look
similar (i.e., same set of vertices and edges connecting the vertices), but are logically different
(i.e., differ in the partitions) would still have the same bipartivity index; the difference gets
compensated in the number of vertices that form the two partitions and/or the distribution of the
frustrated edges across the two partitions. Note that the predictions of the partitions get less
accurate as the bipartivitiy index gets far lower than 1.
Figure 8. "True" Bipartite Graph: Predicted Partitions Match with the Hypothetical Partitions of the Input
Graph
Figure 9. "Close-to" Bipartite Graph: Predicted Partitions Appear to be the Same as that of the Input Graph
8. 228 Computer Science & Information Technology (CS & IT)
Figure 9 illustrates the prediction of the partitions of a "close-to" bipartite graph that looks
complex enough for one to initially hypothesize the two partitions; hence, we assume the
predicted partitions are the same as what is as expected of the original input graph. However,
Figure 10 illustrates an example where the predicted partitions of a "close-to" bipartite graph are
of unequal sizes with two frustrated edges (whereas the input graph is hypothesized to have two
equal-sized partitions with one frustrated edge, as shown); but, both the graphs have the same
bipartitivity index. The predicted "close-to" bipartite graph consists of a larger partition with four
vertices and a smaller partition with two vertices; there are two frustrated edges in the larger
partition and none in the smaller partition. The input "close-to" bipartite graph for this illustration
has three vertices in each of the two partitions, with a frustrated edge in one of the two partitions.
This example reiterates our earlier assertion that for two "close-to" bipartite graphs that
physically look the same and have the same bipartivity index, there could be logically different
sets of partitions: a topology with equal number of vertices in both the partitions and fewer
frustrated edges could offset for a topology with a larger partition containing relatively larger
number of frustrated edges.
Figure 10. "Close-to" Bipartite Graph: Predicted Partitions do not Match with the Hypothetical Partitions of
the Input Graph
Figure 11. Predicting the Partitions of a "True" Bipartite Directed Graph: Predicted Partitions Match with
the Hypothetical Partitions of the Input Graph
9. Computer Science & Information Technology (CS & IT) 229
Figure 12. Predicting the Partitions of a "Close-to" Bipartite Directed Graph: Predicted Partitions do not
Match with the Hypothetical Partitions of the Input Graph
5. PREDICTION OF THE PARTITIONS IN A DIRECTED BIPARTITE
GRAPH
To predict the two partitions of a directed bipartite graph, we can still use the same approach as
explained above. We need to transform the directed graph to an undirected graph (replace all the
directed edges with undirected edges), determine the bipartite eigenvalue and bipartite
eigenvector of the undirected graph, and predict the constituent vertices of the two partitions
based on the sign of the entries (corresponding to these vertices) in the bipartite eigenvector.
Finally, we restore the directions of the edges. If the input directed graph is "truly" bipartite, the
predicted partitions will be the same as that hypothesized for the input graph (refer the example
shown in Figure 11). However, for "close-to" bipartite directed graphs, the predicted partitions
need not be the same as that of the hypothetical partitions of the input graph; but, as long as the
set of vertices and edges (including the directions of the edges) remain unaltered, the bipartivity
index of the "close-to" bipartite graph will remain the same with both the hypothetical expected
partitions and the predicted partitions. Figure 12 illustrates an example wherein the set of vertices
constituting the predicted partitions is observed to be different from the hypothetical partitions
expected of the input graph. The hypothetical partitions contributed to two frustrated edges,
whereas the predicted partitions contributed to only one frustrated edge. Nevertheless, since the
set of vertices and the set of edges are the same for both the input graph and the graph based on
the predicted partitions, the bipartivity index value remains the same.
6. CONCLUSIONS
The paper demonstrates the application of the eigenvalues and eigenvectors to analyze the
bipartivity of both undirected and directed graphs. We observe that for a given number of
frustrated edges, the bipartivity index is more likely to be larger if more of these edges are located
in the larger of the two partitions of the bipartite graph. For "close-to" bipartite graphs, we
observe the predicted partitions of the vertices to be different from that of the hypothetical
partitions of the input graph; but nevertheless, since the set of vertices and set of edges
constituting the bipartite graphs do not change, the bipartivity index remains the same for both the
input and predicted graphs. In other words, for a given number of vertices and edges, there could
be more than one instance of a bipartite graph (i.e., there could exist one or more combinations of
the two partitions) that could have the same bipartivity index value. The above argument holds
good for both directed and undirected bipartite graphs.
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