Eric Nyberg's Presentation "From Jeopardy! To Cognitive Agents: Effective Learning in the Wild" on Cognitive Systems Institute Group Speaker Series July 9, 2015
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
EARLY STAGE SOFTWARE DEVELOPMENT EFFORT ESTIMATIONS – MAMDANI FIS VS NEURAL N...cscpconf
Accurately estimating the software size, cost, effort and schedule is probably the biggest
challenge facing software developers today. It has major implications for the management of
software development because both the overestimates and underestimates have direct impact for
causing damage to software companies. Lot of models have been proposed over the years by
various researchers for carrying out effort estimations. Also some of the studies for early stage
effort estimations suggest the importance of early estimations. New paradigms offer alternatives
to estimate the software development effort, in particular the Computational Intelligence (CI)
that exploits mechanisms of interaction between humans and processes domain
knowledge with the intention of building intelligent systems (IS). Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation. In this paper neural network models and Mamdani
FIS model have been used to predict the early stage effort estimations using the student dataset.
It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in
comparison to the neural network models based models.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
EARLY STAGE SOFTWARE DEVELOPMENT EFFORT ESTIMATIONS – MAMDANI FIS VS NEURAL N...cscpconf
Accurately estimating the software size, cost, effort and schedule is probably the biggest
challenge facing software developers today. It has major implications for the management of
software development because both the overestimates and underestimates have direct impact for
causing damage to software companies. Lot of models have been proposed over the years by
various researchers for carrying out effort estimations. Also some of the studies for early stage
effort estimations suggest the importance of early estimations. New paradigms offer alternatives
to estimate the software development effort, in particular the Computational Intelligence (CI)
that exploits mechanisms of interaction between humans and processes domain
knowledge with the intention of building intelligent systems (IS). Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation. In this paper neural network models and Mamdani
FIS model have been used to predict the early stage effort estimations using the student dataset.
It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in
comparison to the neural network models based models.
Survey Based Reviewof Elicitation ProblemsIJERA Editor
Any software development process is the combination of multiple development activities and each activity has a
vital role in the software development cycle. Requirement Engineering is the main and basic branch of Software
Engineering, it has many phases but the most initial phase is Requirement Elicitation. In this phase requirements
are gathered for system development.
This paper provides a literature review of the requirements engineering processes performed in traditional and
modern development processes and analyses the problems in the requirements elicitation phase. This problem
analysis is based on a survey which was conducted in University. A questionnaire posing questions regarding
the problems in requirement elicitation was given to final year computer science graduate students who are
working on their final year project as a requirement for their degree. The theoretical analysis of the
questionnaire further clarifies the problems. This problems analysis will help to find out the main problems
which are faced by the perspective software developers
Software requirements prioritization is a
recognized practice in requirements engineering (RE)
that facilitates the management of stakeholders’
subjective views as specified in their requirements
listing. Since RE process is naturally collaborative in
nature, the intensiveness from both knowledge and
human perspectives opens up the problem of decision
making on requirements, which can be facilitated by
requirements prioritization. However, due to the large
volume of requirements elicited when considering an
ultra-large-scale system, existing prioritization
techniques proposed so far suffer some setbacks in
terms of efficiency, effectiveness and scalability. This
paper employed the use of a more efficient ranking
algorithm for requirements prioritization based on the
limitations of existing techniques. The major objective
is to provide a well-defined ranking procedure through
analysis, suitable for prioritizing software requirements.
An empirical evaluation of the proposed technique was
made using a typical scenario of the Pharmacy
Information System at the Obafemi Awolowo
University Teaching Hospital Complex (OAUTHC) as a
case study. The results showed the computation of the
positive ideal solution (PIS) and negative ideal solution
(NIS), as well as the closeness coefficient (CC) for 4
requirements across 3 stakeholders. The CC showed the
final ranks of requirements, where R4 with 2.09 point is
the most valued requirements, while R1 and R2 with
CC of 1.37 and 1.05 were next in the order of priority
respectively. The CC provides the medium through
which problems of multiple criteria decision making
can be handled, so as to determine the order of priority
of the available alternatives. The paper conveyed
encouraging evidence for the software engineering
community that is capable of resolving redundant
specified requirements, thereby providing the potential
that will facilitate effective and efficient decision
making in handling the differences amongst
requirements that have been prioritized. Thus,
prioritizing software requirements with the
recommended ranking procedure during software
development is crucial and vital in order to reduce
development cost.
A Survey of Building Robust Business Models in Pervasive ComputingOsama M. Khaled
Pervasive computing is one of the most challenging and difficult computing domains nowadays. It includes many architectural challenges like context awareness, adaptability, mobility, availability, and scalability. There are currently few approaches which provide methodologies to build suitable architectural models that are more suited to the nature of the pervasive domain. This area still needs a lot of enhancements in order to let the software business analyst (BA) cognitively handle pervasive applications by using suitable tasks and tools. Accordingly, any proposed research topic that would attempt to define a development methodology can greatly help BAs in modeling pervasive applications with high efficiency. In this survey paper we address some of the most significant and current software engineering practices that are proving to be most effective in building pervasive systems.
For citation:
Osama M. Khaled and Hoda M. Hosny. A Survey of Building Robust Business Models in Pervasive Computing. An accepted paper in the 2014 World Congress in Computer Science Computer Engineering and Applied Computing
This presentation briefly discusses the following topics:
What is Artificial Intelligence ?
Aim of AI
Need for AI
What is intelligence?
Objectives of AI research
AI research Scope
Role of Tools in AI
Multi and Cross disciplinary approach
Applications of AI
An Elite Model for COTS Component Selection ProcessIJEACS
Component-based software development (CBD) promises development of high-quality trustworthy software systems within specified budget and deadline. The selection of the most appropriate component based on specific requirement plays a vital role for high-quality software product. Multi-Agent software (MAS) engineering approach played a crucial role for selection of the most appropriate component based on a specific requirement in a distributed environment. In this paper, multi agent technique is used for component selection. A semi-automated solution to COTS component selection is proposed. It is evident from the result that (MAS) plays an essential role and is suitable for component selection in a distributed environment keeping in view of the system design and testing strategies.
Affective Metacognitive Scaffolding and User Model Augmentation for Experient...Adam Moore
The ImREAL project (http://www.imreal-project.eu) is researching how to meaningfully augment and extend existing experiential training simulators. The services developed support self-regulated, goal-, and application-oriented learning in adult training. We present results from a study evaluating a medical interview training simulator that has been augmented by an affective metacognitive scaffolding service and by user modelling exploiting social digital traces. Data from 152 medical students participating in this user trial were compared to the results of a prior trial on an earlier technology version. Findings show that students perceived the learning simulator positively and that the enhanced simulator led to increased feelings of success, less frustration, higher technical flow, and more reflection on learning. Interestingly, this cohort of users proved reluctant to provide their open social IDs to enrich their user models.
Management of time uncertainty in agileijseajournal
The rise of the use of mobile technologies in the world, such as smartphones and tablets, connected to
mobile networks is changing old habits and creating new ways for the society to access information and
interact with computer systems. Thus, traditional information systems are undergoing a process of
adaptation to this new computing context. However, it is important to note that the characteristics of this
new context are different. There are new features and, thereafter, new possibilities, as well as restrictions
that did not exist before. Finally, the systems developed for this environment have different requirements
and characteristics than the traditional information systems. For this reason, there is the need to reassess
the current knowledge about the processes of planning and building for the development of systems in this
new environment. One area in particular that demands such adaptation is software estimation. The
estimation processes, in general, are based on characteristics of the systems, trying to quantify the
complexity of implementing them. Hence, the main objective of this paper is to present a proposal for an
estimation model for mobile applications, as well as discuss the applicability of traditional estimation
models for the purpose of developing systems in the context of mobile computing. Hence, the main objective
of this paper is to present an effort estimation model for mobile applications.
Survey Based Reviewof Elicitation ProblemsIJERA Editor
Any software development process is the combination of multiple development activities and each activity has a
vital role in the software development cycle. Requirement Engineering is the main and basic branch of Software
Engineering, it has many phases but the most initial phase is Requirement Elicitation. In this phase requirements
are gathered for system development.
This paper provides a literature review of the requirements engineering processes performed in traditional and
modern development processes and analyses the problems in the requirements elicitation phase. This problem
analysis is based on a survey which was conducted in University. A questionnaire posing questions regarding
the problems in requirement elicitation was given to final year computer science graduate students who are
working on their final year project as a requirement for their degree. The theoretical analysis of the
questionnaire further clarifies the problems. This problems analysis will help to find out the main problems
which are faced by the perspective software developers
Software requirements prioritization is a
recognized practice in requirements engineering (RE)
that facilitates the management of stakeholders’
subjective views as specified in their requirements
listing. Since RE process is naturally collaborative in
nature, the intensiveness from both knowledge and
human perspectives opens up the problem of decision
making on requirements, which can be facilitated by
requirements prioritization. However, due to the large
volume of requirements elicited when considering an
ultra-large-scale system, existing prioritization
techniques proposed so far suffer some setbacks in
terms of efficiency, effectiveness and scalability. This
paper employed the use of a more efficient ranking
algorithm for requirements prioritization based on the
limitations of existing techniques. The major objective
is to provide a well-defined ranking procedure through
analysis, suitable for prioritizing software requirements.
An empirical evaluation of the proposed technique was
made using a typical scenario of the Pharmacy
Information System at the Obafemi Awolowo
University Teaching Hospital Complex (OAUTHC) as a
case study. The results showed the computation of the
positive ideal solution (PIS) and negative ideal solution
(NIS), as well as the closeness coefficient (CC) for 4
requirements across 3 stakeholders. The CC showed the
final ranks of requirements, where R4 with 2.09 point is
the most valued requirements, while R1 and R2 with
CC of 1.37 and 1.05 were next in the order of priority
respectively. The CC provides the medium through
which problems of multiple criteria decision making
can be handled, so as to determine the order of priority
of the available alternatives. The paper conveyed
encouraging evidence for the software engineering
community that is capable of resolving redundant
specified requirements, thereby providing the potential
that will facilitate effective and efficient decision
making in handling the differences amongst
requirements that have been prioritized. Thus,
prioritizing software requirements with the
recommended ranking procedure during software
development is crucial and vital in order to reduce
development cost.
A Survey of Building Robust Business Models in Pervasive ComputingOsama M. Khaled
Pervasive computing is one of the most challenging and difficult computing domains nowadays. It includes many architectural challenges like context awareness, adaptability, mobility, availability, and scalability. There are currently few approaches which provide methodologies to build suitable architectural models that are more suited to the nature of the pervasive domain. This area still needs a lot of enhancements in order to let the software business analyst (BA) cognitively handle pervasive applications by using suitable tasks and tools. Accordingly, any proposed research topic that would attempt to define a development methodology can greatly help BAs in modeling pervasive applications with high efficiency. In this survey paper we address some of the most significant and current software engineering practices that are proving to be most effective in building pervasive systems.
For citation:
Osama M. Khaled and Hoda M. Hosny. A Survey of Building Robust Business Models in Pervasive Computing. An accepted paper in the 2014 World Congress in Computer Science Computer Engineering and Applied Computing
This presentation briefly discusses the following topics:
What is Artificial Intelligence ?
Aim of AI
Need for AI
What is intelligence?
Objectives of AI research
AI research Scope
Role of Tools in AI
Multi and Cross disciplinary approach
Applications of AI
An Elite Model for COTS Component Selection ProcessIJEACS
Component-based software development (CBD) promises development of high-quality trustworthy software systems within specified budget and deadline. The selection of the most appropriate component based on specific requirement plays a vital role for high-quality software product. Multi-Agent software (MAS) engineering approach played a crucial role for selection of the most appropriate component based on a specific requirement in a distributed environment. In this paper, multi agent technique is used for component selection. A semi-automated solution to COTS component selection is proposed. It is evident from the result that (MAS) plays an essential role and is suitable for component selection in a distributed environment keeping in view of the system design and testing strategies.
Affective Metacognitive Scaffolding and User Model Augmentation for Experient...Adam Moore
The ImREAL project (http://www.imreal-project.eu) is researching how to meaningfully augment and extend existing experiential training simulators. The services developed support self-regulated, goal-, and application-oriented learning in adult training. We present results from a study evaluating a medical interview training simulator that has been augmented by an affective metacognitive scaffolding service and by user modelling exploiting social digital traces. Data from 152 medical students participating in this user trial were compared to the results of a prior trial on an earlier technology version. Findings show that students perceived the learning simulator positively and that the enhanced simulator led to increased feelings of success, less frustration, higher technical flow, and more reflection on learning. Interestingly, this cohort of users proved reluctant to provide their open social IDs to enrich their user models.
Management of time uncertainty in agileijseajournal
The rise of the use of mobile technologies in the world, such as smartphones and tablets, connected to
mobile networks is changing old habits and creating new ways for the society to access information and
interact with computer systems. Thus, traditional information systems are undergoing a process of
adaptation to this new computing context. However, it is important to note that the characteristics of this
new context are different. There are new features and, thereafter, new possibilities, as well as restrictions
that did not exist before. Finally, the systems developed for this environment have different requirements
and characteristics than the traditional information systems. For this reason, there is the need to reassess
the current knowledge about the processes of planning and building for the development of systems in this
new environment. One area in particular that demands such adaptation is software estimation. The
estimation processes, in general, are based on characteristics of the systems, trying to quantify the
complexity of implementing them. Hence, the main objective of this paper is to present a proposal for an
estimation model for mobile applications, as well as discuss the applicability of traditional estimation
models for the purpose of developing systems in the context of mobile computing. Hence, the main objective
of this paper is to present an effort estimation model for mobile applications.
Albania america community twining project 01 club twinAvi Dey
Albania is a small nation of 3 million people on the eastern Mediterranean. Republic of Albania has a long historical heritage of classical Greece, Rome and Ottoman Empire anchored at modern city of Istanbul to a modern parliamentary democracy of the 21st Century & ethnic voices. It is a land of incredible natural beauty of high mountains, rivers & coastal sea.
Birgit Plietzsch “RDM within research computing support” SALCTG June 2013SALCTG
An overview of Research Data Management: the research process from developing ideas to preservation of data; funder perspectives, the impact on the wider service, Data Asset Frameworks, preservation and access, and cost implications.
As a recently graduated Petroleum and Gas Engineer and currently obtaining a Post-Grad in Work Safety Engineering, I pride myself in work ethics, teamwork and leadership. I have 3 citizenships and speak 3 different languages, giving me vast communication skills and travel experience. I have administrative and interpersonal management capabilities that have permitted me to negotiate international business contracts on the company's behalf.
Proactive and results driven, I always seek to excel in what I do. As a leader, part of a team or individually, I tackle obstacles head on and value all those who are by my side to get the job done.
In the engineering field, I have worked on revamping a Natural Gas Cooling Unit, trained in thermal and vibrational predictive maintenance, am presently assisting in the elaboration of a Natural Gas Distribution Base and Thermoelectric Plant and am headed the pilot project implementation of virtual Natural Gas distribution to over 6000 residences in the interior of the state of Rio Grande do Norte (HDPE pipe grid, compression, storage, distribution, sales, maintenance).
Today, as an Engineer at the State Public Service Regulatory Agency, I regulate the Natural Gas distribution in the state of Rio Grande do Norte. I am also part of the Energy Regulation department (Wind, Solar and Thermoelectric energy), where I am part of the field inspection team and assist in the regulation of energy generation and distribution in the State.
CRITERION BASED AUTOMATIC GENERATION OF QUESTION PAPERvivatechijri
In any educational course curriculum, the courses are defined with learning objectives. Teachers conduct assessments to know if students have achieved certain learning objectives or not. The Proposed System provides a solution to choose challenging, well framed questions and make it easy for the user to generate it within a short period of time. The existing tools are rigid and support very basic or limited parameters. In our system we allow admin and user to input a set of questions and mark them with parameters such as difficulty level, complexity, type of question, module, min and max weightage. It contains two modules namely admin module and user module and the question management makes it an effortless task. From the entered input the paper is generated and saved as a .pdf file which can be kept for own or distributed as per the user or admin requirements. The required software and hardware are easily available and easy to work with. The goal is to simplify its current manual method, by means of computerised equipment and complete computer applications, in order to meet its needs, so that its important data/information can be stored for a longer period of time with easy access and manipulation. Basically the project describes how to manage for good performance and better services for the clients.
Role of artificial intellligence in construction engg & managementKundan Sanap
What is Artificial Intelligence (AI)?
Branches of Artificial Intelligence
AI in Construction Engineering & Management
Roles of AI in Construction Engineering & Management
Smart optimization for mega construction projects
Case study- Bispevika, a building Project in Oslo, Norway
Human-AI Collaboration
Applications of AI in Construction Industry
Future Application of AI Presented by Team Bispevika
References
Online Exams System fulfils the requirements of the institutes to conduct the exams online. They do not have to go to any software developer to make a separate site for being able to conduct exams online. They just have to register on the site and enter the exam details and the lists of the students which can appear in the exam.
This presentation introduces the concept of Machine Learning and then discusses how Machine Learning is being used in the Predictive Maintenance domain.
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
Digital Security by Design: ISCF Digital Security by Design Research Projects...KTN
KTN ran a collaborators' workshop on 26 September 2019 in London to explain more about the Digital Security by Design Challenge announced by the government.
The Digital Security by Design challenge has been recently announced by the Department for Business, Energy & Industrial Strategy (BEIS). This challenge, amounting to £70 million of government funding over 5 years, was delivered by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF).
This Collaborators' Workshop provides an opportunity to hear more details of the challenge and forthcoming competitions.
A Scoping Workshop for this challenge was held on 30th May: http://ow.ly/oz6230pHlGl
Find out more about the Defence and Security Interest Group at https://ktn-uk.co.uk/interests/defence-security
Join the Defence and Security Interest Group at https://www.linkedin.com/groups/8584397 or Follow KTN_UK Defence group on Twitter https://twitter.com/KTNUK_Defence
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
Teaching cognitive computing with ibm watsondiannepatricia
Ralph Badinelli, Lenz Chair in the Department of Business Information Technology, Pamplin College of Business of Virginia Tech. presented "Teaching Cognitive Computing with IBM Watson" as part of the Cognitive Systems Institute Speaker Series.
Cognitive systems institute talk 8 june 2017 - v.1.0diannepatricia
José Hernández-Orallo, Full Professor, Department of Information Systems and Computation at the Universitat Politecnica de València, presentation “Evaluating Cognitive Systems: Task-oriented or Ability-oriented?” as part of the Cognitive Systems Institute Speaker Series.
Building Compassionate Conversational Systemsdiannepatricia
Rama Akkiraju, Distinguished Engineer and Master Inventor at IBM, presention "Building Compassionate Conversational Systems" as part of the Cognitive Systems Institute Speaker Series.
“Artificial Intelligence, Cognitive Computing and Innovating in Practice”diannepatricia
Cristina Mele, Full Professor of Management at the University of Napoli “Federico II”, presentation as part of Cognitive Systems Institute Speaker Series
Eric Manser and Will Scott from IBM Research, presentation on "Cognitive Insights Drive Self-driving Accessibility" as part of the Cognitive Systems Institute Speaker Series
Roberto Sicconi and Malgorzata (Maggie) Stys, founders of TeleLingo, presented "AI in the Car" as part of the Cognitive Systems Institute Speaker Series.
“Semantic PDF Processing & Document Representation”diannepatricia
Sridhar Iyengar, IBM Distinguished Engineer at the IBM T. J. Watson Research Center, presention “Semantic PDF Processing & Document Representation” as part of the Cognitive Systems Institute Group Speaker Series.
Joining Industry and Students for Cognitive Solutions at Karlsruhe Services R...diannepatricia
Gerhard Satzger, Director of the Karlsruhe Service Research Institute and two former students and IBMers, Sebastian Hirschl and Kathrin Fitzer, presention"Joining Industry and Students for Cognitive Solutions at Karlsruhe Services Research Center" as part of the Cognitive Systems Institute Speaker Series.
170330 cognitive systems institute speaker series mark sherman - watson pr...diannepatricia
Dr. Mark Sherman, Director of the Cyber Security Foundations group at CERT within CMU’s Software Engineering Institute. , presention “Experiences Developing an IBM Watson Cognitive Processing Application to Support Q&A of Application Security Diagnostics” as part of the Cognitive Systems Institute Speaker Series.
“Fairness Cases as an Accelerant and Enabler for Cognitive Assistance Adoption”diannepatricia
Chuck Howell, Chief Engineer for Intelligence Programs and Integration at the MITRE Corporation, presentation “Fairness Cases as an Accelerant and Enabler for Cognitive Assistance Adoption” as part of the Cognitive Systems Institute Speaker Series.
From complex Systems to Networks: Discovering and Modeling the Correct Network"diannepatricia
From complex Systems to Networks: Discovering and Modeling the Correct Network" by Nitesh Chawla as part of the Cognitive Systems Institute Speaker Series
Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and director of the research center on network and data sciences (iCeNSA) at the University of Notre Dame.
Developing Cognitive Systems to Support Team Cognitiondiannepatricia
Steve Fiore from the University of Central Florida presented “Developing Cognitive Systems to Support Team Cognition” as part of the Cognitive Systems Institute Speaker Series
Kevin Sullivan from the University of Virginia presented: "Cyber-Social Learning Systems: Take-Aways from First Community Computing Consortium Workshop on Cyber-Social Learning Systems" as part of the Cognitive Systems Institute Speaker Series.
“IT Technology Trends in 2017… and Beyond”diannepatricia
William Chamberlin, IBM Distinguished Market Intelligence Professional, presented “IT Technology Trends in 2017… and Beyond” as part of the Cognitive Systems Institute Speaker Series on January 26, 2017.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
Ibm colloquium 070915_nyberg
1. From Jeopardy! To Cognitive Agents:
Effective Learning in the Wild
Eric Nyberg
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
2. History & Strengths:
Architecture for Info Systems
• Developed advanced service-oriented architectures for
information systems as part of IARPA AQUAINT [1]
• Contributed to the development of the Unstructured
Information Management Architecture (w/IBM) [2]
• Establish a framework for open advancement of Question
Answering systems (w/IBM) [3]
• Participated in the Jeopardy! Challenge (w/IBM) [4]
• Established OAQA Consortium at CMU for practical
applications of Question Answering (2012-)
– Sponsored by Boeing, Roche, Singapore DoD
• Joined IBM’s Cognitive Systems Institute in 2013 [5]
• Piloted Watson Challenge Course at CMU (F’14)
2
3. CMU’s Contributions to Watson & OAQA
Read more about CMU and Watson: http://www.cs.cmu.edu/~ehn/
• Modular architecture for QA systems
• Tools & process for error analysis
• Information retrieval for question answering
• Statistical machine learning for answer scoring
• How to find supporting evidence for answers
Dave Ferrucci and Watson visit CMU (3/11) Faculty & students receive Allan Newell
Award for Research Excellence (2/12)
4. IARPA AQUAINT Program
JAVELIN I JAVELIN II JAVELIN III
Book chapter
on advanced QA
architectures
CMU
adopts
UIMA
Roadmap
for QA R&D
(LREC 2002)
Ephyra I Ephyra II OpenEphyra
CMU joins Watson effort
(5 internships in 3 years)
OAQA defines common
framework, process, metrics
OAQA
Feb 2011: Watson
wins Jeopardy! Challenge
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
IBM Open Collaborative Research Awards
BlueJ / Watson
Research Sponsor
Key
Project @ Uni Karlsruhe
Project @ CMU
Project @ IBM
QA Research @ CMU:
The First 10 Years
(Oct. 2001 – Feb. 2011)
5. CMU QA Team: Core Collaborators (2001-2011)
Jamie Callan
Teruko Mitamura
Jaime Carbonell
Eric Nyberg
• Probabilistic Models for Answer Scoring
• Object type system / component architecture
• Source Expansion approach used by Watson
• Foundational work in machine learning for
answer extraction and answer scoring
• Tools for rapid development of QA apps
• Language-independent architecture
• Answer-scoring algorithms used by Watson
• Important extensions to the INDRI/Lemur
search engine used by Watson
6. What did we learn from Watson?
• QA systems can be fast, accurate, and confident enough to
perform in the real world
– Scalable, parallel architecture
– Plenty of training data available
– Agile, open advancement process
• Next big challenge: rapid domain adaptation
– Automatic configuration optimization: Given a labeled dataset
of inputs and expected outputs, automatically find the best
performing composition of existing analytics / agents to provide
a solution
– In-task learning : Cognitive agents improve performance
through proactive interaction with their users and other
external sources of knowledge (human/machine),
before/during/after performing a task
– Combine automatic configuration & optimization with in-task
learning to provide a set of personalized cognitive agents and
agent brokers to interact with end users
7. Automatic Optimization of QA
for TREC Genomics Questions
CSE Framework: Support automatic evaluation / optimization
of information systems using UIMA; part of the OAQA project [6]
8. Results of Automatic Optimization
CSE Framework found a significantly better configuration of
components compared to the prior published state of the art,
in 24 hours of clock time using a modest 30-node cluster. [7]
9. Other domains:QA4MRE
• Question Answering for Machine Reading
Evaluation
• Configuration space:
– 12 UIMA components were first developed
– Replace UIMA descriptors with ECD
• CSE
– 46 configurations
– 1,040 combinations
– 1,322 executions
The best trace identified by CSE
achieved 59.6% performance
gain over the original pipeline.
[Building Optimal Question Answering System Automatically using Configuration
Space Exploration (CSE) for QA4MRE 2013 Tasks Alkesh Patel, Zi Yang, Eric Nyberg
and Teruko Mitamura]
10. Leveraging Pre-Competitive, Open-Source
Development for Proprietary R&D
CMU
Student &
Advisor
Pre-Competitive
Requirements &
Data
Proprietary
Requirements
& Data
Open Source
Framework,
Modules &
Data
Proprietary
Modules &
Data
Industry
Sponsor
OA Consortium
Agreement
Non-Disclosure &
Employment
Agreements
proprietary extensions to
open-source software
11. Open Source Projects
• Repository Location: https://github.com/oaqa
• 18 public / 18 private project repositories
• 33 members (13 active committers)
12. QUADS: Question Answering
for Decision Support
Zi Yang1, Ying Li2, James Cai2, Eric Nyberg1
1) Carnegie Mellon University {ziy, ehn}@cs.cmu.edu
2) Roche Innovation Center {ying_l.li, james.cai}@roche.com
07/09/2014 at SIGIR 2014
13. Decision Making: Product
Recommendation from Review Text
Design and
usability
Brand
Functionality
Carrier
Operating
system
Weight
Thickness
Resolution
Keyboard
Decision decomposition Evidence gathering from Web
Synthesis
Brand Carrier Decision
aaa xxx Good
bbb yyy OK
ccc zzz Bad 13
14. Decision Making: Target Validation
Modulation
the activity
Expression in
tissues
Mutation
Clinical trials
Side effects
In vivo
In vitro
Normal
tissues
Disease
tissues
Decision decomposition Evidence gathering from
public/proprietary documents
Synthesis
In vivo Side effect Decision
Yes No Good
Yes Yes OK
No Yes Bad 14
15. Question Answering for Decision Support
• Decompose an end-user decision process into
weighted decision factors
• Values of atomic decision factors determined
by automatic QA system
• Overall decision value combines atomic
decision factors according to learned weights
• Significant improvement over baseline
methods for gene targeting, product rating [8]
16. 10/02/2013: IBM Announces New
Collaboration with CMU
• Focus: “How systems should be architected to
support intelligent, natural interaction with all
kinds of information in support of complex
human tasks.” [5]
17. Vision
• Automatically learn and improve new analytics through
independent interaction with humans
• Examples:
1. Learn to code medical records for insurance payment
from a human expert
2. Learn to detect fraudulent transactions (e.g. insurance
claims) from a human expert
3. Automatically improve intelligent information systems
with proactive learning and machine reading
4. Learn and refine decision-making processes for accident
management & fault prediction that combine
information written in policy and procedure documents
will real-time sensor data, e.g. for mobile robot control
17
18. Conceptual Architecture
First phase
of framework
mostly complete
Perform
ReflectLearn
Automatically build and
execute analytic solutions
Proactively evaluate
task performance,
analyze errors, propose
learning tasks
Specification of required
analytic input/output types,
desired information sources,
example dataset.
1
23
Subject Matter
Experts (SMEs)
Analyst’s
Information
Need
Configure
Optimize
Measure
Train
Automatically execute
learning tasks, update
models, KBs, etc.
Machine Learning
Agents
• Targeted Machine
Reading
• E-R Extraction
• Set Extension
• Clarification Dialogs
• Type/instance
knowledge
• Concept learning
Crowd-Sourcing (e.g.
Amazon Mechanical Turk)
• Type instance
labeling
• Relevance
judgments
Proposed
work
20. History and Strengths:
Proactive Machine Learning
• An approach that is more effective for learning independently
from multiple sources (“oracles”) (Carbonell et. al)
20
Traditional Active
Learning
Proactive Learning
Number of Oracles Individual (only one) Multiple, with different
capabilities, costs and areas of
expertise
Reliability Infallible (100% right) Variable across oracles and
queries, depending on difficulty,
expertise, …
Reluctance Indefatigable (always
answers)
Variable across oracles and
queries, depending on
workload, certainty, …
Cost per query Invariant (free or constant) Variable across oracles and
queries, depending on
workload, difficulty, …
21. Technical Challenges
• Extracting domain-specific entities, relations
– Which ones are important?
– How to interpret output of general NLP tools?
• Modeling inference
– How to represent e.g. complex biological processes
– How to leverage existing ontologies, inference rules to
build complex representations from text
• Incorporating direct user feedback
– How to present system data to the user
– What kinds / how to gather feedback
– How can the system learn effectively
22. Related Educational Programs @ CMU
• Language Technologies (MS, PhD)
• Master of Computational Data Science (MCDS)
• Biotechnology Innovation & Computing (MS)
• Intelligent Information Systems (MS)
23. References
• [1] Nyberg, E., Burger, J.D., Mardis, S., Ferrucci, D.A.: Software Architectures for Advanced
QA. ;In New Directions in Question Answering (2004) 19-30.
• [2] https://www.oasis-open.org/news/pr/oasis-members-approve-open-standard-for-
accessing-unstructured-information
• [3] https://www.research.ibm.com/deepqa/question_answering.shtml
• [4] http://www.prnewswire.com/news-releases/ibm-announces-eight-universities-
contributing-to-the-watson-computing-systems-development-115892914.html
• [5] http://www-03.ibm.com/press/us/en/pressrelease/42118.wss
• [6] http://oaqa.github.io/
• [7] Yang, Z., Garduno, E., Fang, Y., Maiberg, A., McCormack, C. and Nyberg, E. (2013).
“Building Optimal Information Systems Automatically: Configuration Space Exploration
for Biomedical Information Systems”, Proceedings of the ACM Conference on Information
and Knowledge Management
• [8] Zi Yang, Ying Li, James Cai, and Eric Nyberg. QUADS: Question Answering for Decision
Support. In Proceedings of SIGIR’2014: the Thirty-seventh Annual International ACM SIGIR
Conference on Research and Development in Information Retrieval, 2014.
![History & Strengths:
Architecture for Info Systems
• Developed advanced service-oriented architectures for
information systems as part of IARPA AQUAINT [1]
• Contributed to the development of the Unstructured
Information Management Architecture (w/IBM) [2]
• Establish a framework for open advancement of Question
Answering systems (w/IBM) [3]
• Participated in the Jeopardy! Challenge (w/IBM) [4]
• Established OAQA Consortium at CMU for practical
applications of Question Answering (2012-)
– Sponsored by Boeing, Roche, Singapore DoD
• Joined IBM’s Cognitive Systems Institute in 2013 [5]
• Piloted Watson Challenge Course at CMU (F’14)
2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)