Facilitating Security and Trust Among Multiple Parties Through Blockchain TechniquesJoin the Buffalo Computer Society Chapter and Santa Clara Valley Computer Society Chapter in welcoming Distinguished Visitor, Dr. Yuhong Liu, on Thursday November 17, at 3 PM PT, when she gives a virtual presentation in the Blockchain Technical Series for 2022.
Video: https://santaclarauniversity.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f72cd85e-dfee-4a38-9bda-af510012ac66&start=0
Topic Summary:
A blockchain consists of a list of blocks that are linked based on cryptography hash and maintained by distributed network nodes, so that the information recorded in these blocks is non-repudiated. After its first conceptualization by Nakamoto in 2008, blockchain has attracted broad attention and been considered as a promising solution to establish a decentralized architecture with security. In this talk, we will discuss some key characteristics of blockchain and a few promising applications of blockchain that can facilitate security and trust among multiple parties. Some examples include applying blockchain to secure software updates for resource-constrained IoT networks; designing a secure and efficient multi-signature scheme to facilitate multi-party approval process on Fabric, an enterprise blockchain platform; and facilitating fair trading of digital-goods via a blockchain based proxy re-encryption scheme.
Speaker: Dr. Yuhong Liu
Santa Clara University
Quantifying Configuration Health of Software SystemsVideo: https://ieeetv.ieee.org/video/quantifying-configuration-health-of-software-systems
…DevOps, Software configuration health index, diagnosing configuration problems, ...
Speaker:
IEEE Fellow, Distinguished Visitor, and Past Program Director at the National Science Foundation (NSF),
Prof. Krishna Kant
Date: November 2, 2022, 9 AM (PT)
Virtual Event via Zoom and YouTube live
Free Registration: https://www.eventbrite.com/e/quantifying-configuration-health-of-software-systems-tickets-428096777987
Synopsis:
Misconfigurations have been Achilles heel in complex software systems and are often responsible for the downtimes and enablers of the cyber-attacks. With the recent rise of the devOps paradigm, configuration changes have become even more frequent, sometimes, multiple times a day, thereby underscoring the importance of correct configuration. In this talk, the speaker will discuss the notion of configuration health index to provide an assessment of how well the system is configured with respect to various objectives and how it can be quantified based on a combination of domain knowledge and available data. Prof. Kant will also discuss the challenges in extracting and quantifying the domain knowledge and the selection of tests for diagnosing configuration problems.
Speaker Biography:
Krishna Kant is a professor in the Computer and Information Science Department at Temple University in Philadelphia, PA. Earlier he was a research professor in the Center for Secure Information Systems (CSIS) at George Mason University. From 2008-2013 he served as a program director at the National Science Foundation (NSF) where he managed the computer systems research (CSR) program and was instrumental in the development and running of an NSF-wide sustainability initiative called SEES (science, engineering, and education for sustainability). Earlier, he served at Intel Corporation for 11 years working on a variety of data center architecture and technology issues. From 1991 to 1997, he held the consultant position at Ericsson (formerly Bellcore) and worked on many broadband and narrowband telecommunications technologies. Before 1991, he was an Associate Professor of Computer Science at the Pennsylvania State University with research contributions in performance modeling and distributed systems. He received his Ph.D. degree in Mathematical Sciences from the University of Texas at Dallas in 1981. He carries extensive experience in academia, industry, and government and has published in a wide variety of areas in computer science. He has authored a graduate textbook on performance modeling of computer systems and coedited several other books. He is a Fellow of the IEEE and an IEEE distinguished visitor.
#IEEEday Event 2: Exploring the math in Support Vector MachinesVideo: https://ieeetv.ieee.org/video/exploring-the-math-in-support-vector-machines
…Lagrange Multipliers, Convex Hulls, Orthogonal Functions, Hilbert Space, Duality, Cover Theorem, ...
Speaker:
Chapter Chair, Book Author, and SJSU Faculty Member,
Vishnu S. Pendyala, PhD
Date: October 4, 2022, 6:15 PM (PT)
Virtual Event via Zoom and YouTube live
Free Registration: https://www.eventbrite.com/e/exploring-the-math-in-support-vector-machines-tickets-425130124647
Synopsis:
“SVMs are a rare example of a methodology where geometric intuition, elegant mathematics, theoretical guarantees, and practical algorithms meet” – Bennet and Campbell
Support Vector Machines (SVMs) are used for supervised machine learning and have been successful in many applications including those like image classification that favor deep learning. SVM owes its power to the intriguing math involved in its fabrication. This talk will introduce SVM and cover some of that math. Topics covered will include constrained and unconstrained optimization, convexity, the general notion of a function space, minmax equilibrium, duality, Cover theorem, Kernels, and Mercer theorem.
Speaker Biography:
Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience with software industry leaders like Cisco and Synopsys in the Silicon Valley, USA. During his recent 3-year term as an ACM Distinguished speaker and before that as a researcher and industry expert, he gave numerous (50+) invited talks. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
Dr. Pendyala’s book, “Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness” is catalogued by several libraries, including those of MIT, Stanford, CMU, and internationally. His edited books, “Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities” and "Machine Learning for Societal Improvement, Modernization, and Progress" are also well-received and are indexed by prominent libraries all over the world. Dr. Pendyala served on the Board of Directors, Silicon Valley Engineering Council during 2018-2019. He received the Ramanujan memorial gold medal at State Math Olympiad and has been a successful leader during his undergrad years. He also played an active role in Computer Society of India and was the Program Secretary for its annual national convention. Marquis Who's Who has selected Pendyala’s biography for inclusion in multiple of its publications for multiple years. Dr. Pendyala spends his fast-vanishing spare time volunteering and has been a reviewer / judge for competitions like the Grace Hopper Celebration of Women in Computing, state level DECA, science fairs and other STEAM events for the last several years. He has traveled widely, covering ~30 states in the US and 23 countries. He resorts to yoga, spirituality, and listening to music, to unwind and be himself.
Safety for Autonomous Vehicles: Addressing Interoperability and Dependability Challenges with IEEE P2851Video: https://www.youtube.com/watch?v=84ucEVqC890
Speaker: Jyotika Athavale, NVIDIA
Date: Sunday, September 11, 2022, 6:00 PM (PT)
In-person at Santa Clara University and online via Zoom and YouTube live
Abstract:
The IEEE P2851 standard defines a dependability lifecycle of products with focus on interoperable activities related to functional safety and its interactions with reliability, cybersecurity, SOTIF (Safety of the intended functionality) and real time. The standard also describes methods, description languages, data models, and databases that have been identified as necessary or critical, to enable the exchange/interoperability of data across all steps of the lifecycle encompassing activities executed at IP, SoC, system and item levels, in a technology independent way across application domains such as automotive, industrial, medical and avionics safety critical systems. The speaker, who is the chair of the working group on the standard will provide more insights into the topic.
Keywords: IEEE P2851, SOTIF, SoC, Safety critical systems, Self-driving cars
Biography:
Jyotika Athavale is a senior technical leader in automotive functional safety at NVIDIA, driving capability development, safety architectures and methodologies, system safety engineering activities and pathfinding for safety critical markets such as autonomous driving and avionics. Prior to NVIDIA, Jyotika was Principal Engineer (Director) at Intel Corporation where she led functional safety platform architecture for Automotive and Avionics use cases and drove Corporate wide methodologies for radiation effects modeling and product qualification across market segments. She is a Senior IEEE Member and an influencer in emerging technologies and standardization initiatives. She was awarded the IEEE Computer Society Golden Core Award in 2022. Currently, an elected member of the Computer Society Board of Governors and Executive Committee, Jyotika also serves as the 2022 Secretary and Distinguished Visitor of the Computer Society. She chairs the IEEE P2851 Standard on Functional Safety interoperability.
Jyotika is a core member of the IEEE Computer Society Functional Safety Standards Committee, Design Automation Standards Committee and Diversity and Inclusion Committee. In addition, she represents the Computer Society at the IEEE USA AI Policy Committee, IEEE SA Industry Connection Group on Mobility as a Service (MaaS) related standardization activities, the IEEE Women in Engineering (WIE) Committee and the IEEE Systems Council. Jyotika also serves on various technical conference committees and has authored patents and technical publications in various conferences and journals. For more information, please refer to jyotikaaathavale.com
23rd New Frontiers in Computing Conference (NFIC 2022): Emerging Trends in Applied Data Science (Virtual)Date: Saturday, August 13, 2022 from 4:00 PM – 8:00 PM (PDT)
Program Book: New Frontiers in Computing 2022 Program Book
Video: https://youtu.be/GuS6LqqB_hc
Cost: none
Speakers: Experts from USA and Taiwan (see below)
The first NFIC was held in 1999 and this is the 24th year. Organized by IEEE SCV Computer Society Chapter,
and North America Taiwanese Engineering and Science As
sociation (NATEA), Silicon Valley Chapter
View Video of Keynote Talk from 2021 NFIC: The State of Robotics@Google
OVERVIEW (full details further below)
Time (PST)
Presentation Title
Speaker
4:00 - 4:10pm
Opening Remarks
PC Chairs (NATEA and IEEE-CS)
4:10 - 4:55pm
Keynote Speech: Explainable AI for medical imaging informatics (including Q&A)
Prof. KC Santosh, Chair, Department of Computer Science, University of South Dakota
4:55 - 5:40pm
Machine Learning for Misinformation Containment: A Candid Assessment of the State of the Art
Dr. Vishnu S. Pendyala, SJSU
5:40 - 6:25pm
Yolo4 and Its Applications (including Q&A)
Prof. Mark Liao, NTUT
6:25 - 7:10pm
3-D Digital Pathology-Inspired AI for Precision Diagnosis (including Q&A)
Dr. Yen-Yin Lin and Dr. Tun-Wen Pai
7:10pm - 7:45pm
Trends in Securing AI Data Pipeline
Prakash Ramchandran
7:45 - 8:00pm
Closing Remarks
PC Chairs (NATEA and IEEE-CS)
Conference General Chair
-- Gary Ni, NATEASteering Committee
-- Kevin Cameron
-- Gary Ni, Google
-- John Yu, NATEA
-- Vishnu S. Pendyala, SJSU
Proceedings
-- Pat Fasang
Webmaster
-- Paul Wesling
Program Committee
-- Ray Sun
-- Vishnu Pendyala
-- Howard Ho
-- Prakash Ramachandran
-- Gary Ni
-- Rockwell Hsu
-- Pat Fasang
Introductions and Keynote: Active learning and explainable AI for medical imaging informatics – infectious disease outbreak
Synopsis: When we consider AI for healthcare, infectious disease outbreak is no exception. The talk will begin with machine learning models that help in not only predicting but also detecting abnormalities due to infectious diseases such as Pneumonia, TB, and Covid-19. Prof. KC Santosh will open my talk with infectious disease prediction models and unexploited data, where we will learn that predictive analytical tools are close to garbage-in garbage-out (at least for Covid19). He will then cover multimodal learning and representation based on both shallow learning (handcrafted features) as well as deep learning (deep features) that typically apply on medical imaging tools. Like in computer vision, Prof. KC will open an obvious question, how big data is big in addition to common techniques: data augmentation and transfer learning. With all these facts, as most of models are limited to education and training, he will end the talk with the statement “ML innovation should not be limited to building models.” What we need is #ExplainablableAI in #ActiveLearning framework.
Speaker: Professor KC Santosh is the Chair of the Department of Computer Science (CS) at the University of South Dakota (USD). Prior to that, he worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research center, Université de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also served as a research scientist at the INRIA Nancy Grand Est research center (France), where he received his PhD in Computer Science – Artificial Intelligence. His research projects, primarily in Applied AI, are funded (of more than $2m) by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He has demonstrated expertise (with 10 books, 220+ research articles, and 20+ journal edited issues, as of Dec. 2021) in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, and data mining with applications such as medical imaging informatics, document imaging, biometrics, forensics, and speech analysis. He completed leadership and training programs for Deans/Chairs (organized by the Councils of Colleges of Arts & Sciences (U.S. 21)) and PELI – President’s Executive Leadership Institute (USD 21). He is highly motivated/interested in academic leadership. To name a few, Prof. Santosh is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD 2021), the President’s Research Excellence Award (USD 2019) and the Ignite Award from the U.S. Department of Health & Human Services (HHS 2014).
Machine Learning for Misinformation Containment: A Candid Assessment of the State of the Art
Synopsis: Misinformation containment has been proven to be NP-hard more than a decade ago. It is undoubtedly a complex problem to solve and appropriately attracted plenty of attention from the research community. A wide variety of machine learning algorithms such as support vector machines and logistic regression, ensemble techniques like random forest and Adaboost, deep learning frameworks such as LSTM and GAN, language models like BOW / TF-IDF and BERT, and many more have been tried out in the attempts to solve the problem. In terms of feature engineering as well, no stone has been left unturned. Manual feature extraction, graph embeddings, and other approaches to representational learning have all been tried. Not just supervised and unsupervised learning, but various other types of learning such as few-shot learning, meta learning, transfer learning, self-supervised learning, semi-supervised learning, reinforcement learning, and active learning have been explored extensively for the problem. Despite the voluminous research literature purporting to solve the problem using machine learning methods, misinformation containment is largely unsolved and is in fact growing by the day. It is therefore pertinent to understand this huge disconnect between what is claimed in the literature and the actual reality. The talk will provide insights into the current state-of-the-art solutions and analyze why they are not helping enough. The talk will present some future directions that in the speaker’s opinion hold the promise and explain why there is hope.
Speaker: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and the chair of IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience with software industry leaders like Cisco and Synopsys in the Silicon Valley, USA. Dr. Pendyala served on the Board of Directors, Silicon Valley Engineering Council during 2018-2019. During his recent 3-year term as an ACM Distinguished speaker and before that as a researcher and industry expert, he gave numerous (50+) invited talks. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022. Dr. Pendyala’s book, “Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness” made it to several libraries, including those of MIT, Stanford, CMU, and internationally.
Yolo4 and Its Applications
Synopsis: YOLOv4 has been ranked first in the world object detection competition for two and a half months. It defeated the R&D teams of international companies such as Google, Amazon, Facebook, Microsoft, and Qualcomm. The birth of YOLOv4 is actually closely related to the project “Development of Smart Transportation System’’. This project is funded by the Ministry of Science and Technology, and it has led to a four-year cooperative relationship between the Academia Sinica and the listed company Elan Electronics. This speech will explain in detail the beginning and end of the implementation of this smart transportation project, and how to develop YOLOv4, the fastest and most accurate object detector in the world during the execution of the project.
Dr. Mark Liao received his Ph.D. degree in electrical engineering from Northwestern University in 1990. In July 1991, he joined the Institute of Information Science, Academia Sinica, Taiwan, and currently is a Distinguished Research Fellow and Director. He has worked in the fields of multimedia information processing, computer vision, pattern recognition, multimedia protection, and artificial intelligence for more than 30 years. He was appointed an Honorary Chair Professor of National Chiao-Tung University from 2016 to 2019. He received the Young Investigators' Award from Academia Sinica in 1998; the Distinguished Research Award from the National Science Council in 2003, 2010, and 2013; the Academia Sinica Investigator Award in 2010; the TECO Award from the TECO Foundation in 2016, and the 64th Academic Award from the Ministry of Education in 2020. His professional activities include: President, Image Processing and Pattern Recognition Society of Taiwan (2006-08); Editorial Board Member, ACM Computing Surveys (2018 – present), IEEE Signal Processing Magazine (2010-13); Associate Editor, IEEE Transactions on Image Processing (2009-13), IEEE Transactions on Information Forensics and Security (2009-12) and IEEE Transactions on Multimedia (1998-2001). He has been a Fellow of the IEEE since 2013.
3 D Digital Pathology Inspired AI for Precision Diagnosis
Synopsis: Digital medical images for training AI models have made major impact on precision diagnosis, among which digital pathology transforming glass slide stains to whole slide images (WSIs) facilitates systematic analysis of tissue morphology and biomarker distribution. Integrating thick tissue staining, 3D image scanning, software and deep learning algorithms, we have retrieved hundreds more high resolution digital images with spatial features from each clinical biopsy to develop novel AI models for precision diagnosis of morphology variation, tumor recognition, and biomarker expression. Our breakthrough technologies will support matching right patients to right treatment, contributing to precision medicine.
Dr. Tun-Wen Pai earned his Ph.D. in E&CE from Duke University, Durham, NC; MS in E&CF from John Hopkins University, Baltimore, MD USA. Presently, Dr. Pai is the Chairman of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan. Dr. Pai has published numbers of papers which are available upon request.
Dr. Yen-Yin Lin earned his Ph.D., MS degrees in Electrical Engineering , and BS degree in Nuclear Science from National Tsinghua University, Hsinchu, Taiwan. Presently, Dr. Lin is the CEO of JelloX biotech Inc., Hsinchu, Taiwan. His previous work experience includes: Chief Executive Officer, MOST Industrial Value Creation Program to develop AI and image inspection system for precision anatomy research Brain research center at National Tsinghua University in Hsinch, Taiwan; Visiting Research Fellow at Stanford University in California; R&D Consultant at Micotech Instruments in Oregan; Visiting Research Assistant at Brookhaven National Laboratory in New York. Dr. Lin’s publications include 47 papers in prestigious journals, more than 80+ conference papers (3 invited speeches in the first tier/+10000 participants conference, 1 worldwide webnair hosted by the Optical Society of America) and 16 issued USA or Taiwan patents. Dr. Lin received the following honors: 2022 Boehringer Ingelheim Grass Roots Award; 2021 The 20th Business Startup Award-MOEA in Taiwan; 2021 Gold Award-Entrepreneur Warrior Competition; Future Technology Award-MOST in Taiwan 2021 and Others. In 2007 Dr. Lin received the outstanding Ph.D. dissertation award from the Optical Engineering Society of the Republic of China in Taiwan. He originated several novel laser systems, such as, novel electro-optic laser Q-switch (USA Patent No. US20110075688 A1), quasi-phase-matching PDT/PDD laser sources (TW Patent No.196412), cascaded quasi-phase-matching QPM laser source (TW Patent No.I225948).
Trends in Securing AI Data Pipeline
Synopsis: ML & DL are part of AI. ML models apply labels to data or collect samples dynamically and curating them. Data needs be clean and relevant to building or optimizing models and delivering the analysis and inferences that lead to improving the accuracy of results with greater optimization and making than more human like intelligent yet applying MLOps, automation and efficient delivery for multiple domains and use cases. In the data pipeline from various sources they can be compromised and hence need to secure them and the speaker here will lead you through the causes and biases that can be controlled through use of best practices and share with you the trends in securing data pipeline in AI.
Prakash Ramchandran is leading Emerging Open Tech Foundation(eOTF) as Co-Founder and Secretary since 2020. The objectives of eOTF are helping the Indian subcontinent in its Digital and Operational Transformation. He is co-chair of INGR Edge Service working group focussed on Next Generation Networking beyond 5G since 2019 from Silicon Valley. As part of NFIC from IEEE CS SCV chapter and a constant promoter of IEEE & NATEA partnership over several years. With 40+ years of ICT industry experience in the US, EU, India and Asia-Pacific has led several startups and managed technically and innovated as ISP,ASP, CSP companies for decades. His insight into Data Analytics and AI is equally formidable and has several presentations in Open Source forums globally. He holds a Masters degree in EE from IIT Bombay.
ICADS 2022: International Conference on Applied Data Science… Fuzzy Loss functions for GANs, Learning Analytics, Next Generation AI and Sustainability, Deep Learning for Melodic Frameworks...
Full Papers and abstracts
Speakers:
Prof. Priti S. Sajja, Sardar Patel University, India
Prof. Elvira Popescu, University of Craiova, Romania
Dr. Celestine Iwendi, University of Bolton, UK
Dr. Vishnu S. Pendyala, San Jose State University, USA
Date: Tuesday, July 12, 2022, 8:00 AM - 12 noon (PST)
Virtual Event via Zoom and YouTube live
Free registration
8:00 am “Fuzzy Loss Functions for Generative Adversarial Neural Network” Prof. Priti Sajja, India (8:30 pm India time)
9:00 am “Applied Data Science for Education” Prof. Elvira Popescu, Romania (7 pm Romania time)
10:00 am “Next Generation Artificial Intelligence and Energy Sustainability” Dr. Celestine Iwendi, Bolton, UK (6 pm UK time)
11:00 am “Deep Learning for Melodic Frameworks” Dr. Vishnu S. Pendyala
Synopsis:
Fuzzy Loss Functions for Generative Adversarial Neural Network
Generative Adversarial neural Networks (GAN) are very popular for medical image analysis. The paper presents an innovative fuzzy loss function for the GAN in the domain of image analysis. The GAN architecture presented here uses two convolutional neural networks, one of which is a generator and the other is a discriminator. Besides the loss function, the training algorithm is also presented in the paper. The proposed approach is generic and can be used for many domains pertaining to medical image analysis and diagnosing. The proposed work with the modified loss function is experimented on the Covid-19 image set. The results achieved are discussed in brief. In the end, the paper presents limitations and future enhancements possible based on the work.
Applied Data Science in Education
Learning analytics (LA) is a growing research area, which aims at selecting, analyzing and reporting student data (in their interaction with the online learning environment), finding patterns in student behavior, displaying relevant information in suggestive formats; the end goal is the prediction of student performance, the optimization of the educational platform and the implementation of personalized interventions. The topic is highly interdisciplinary, including machine learning techniques, educational data mining, statistical analysis, social network analysis, natural language processing, but also knowledge from learning sciences, pedagogy and sociology.
Despite its increasing popularity, LA has been applied less in the context of social media-based environments; hence in this talk we focus especially on research in social learning analytics area. In particular, we explore academic performance predictors and the relationships between students’ learning styles and their social media use; we also investigate students’ collaboration patterns and the community of inquiry supported by social media tools. Four research directions are tackled: analytics dashboards, predictive analytics, social network analytics and discourse analytics. As far as analysis techniques are concerned, we apply various approaches, such as: classification, regression, clustering and PCA algorithms, textual complexity analysis, social network analysis techniques, content analysis based on Community of Inquiry. We thus address the “trinity” of methodological approaches: i) network analysis (representing actor-actor / social relations); ii) process-oriented analysis (based on action logs and pattern detection); iii) content-oriented analysis (based on learner created artefacts); hence a more comprehensive learning analytics perspective is provided.
Next Generation Artificial Intelligence and Energy Sustainability
The industry is on a mission to capture the business value that comes with the Next Generation Artificial Intelligence in the context of Industry 5.0 and Energy 5.0 as regarding the provision of renewable energy production facilities. This has greatly increased the need to address the cyber risk landscape with a secure, vigilant, and resilient response and system in place. This marriage of algorithms, processes, and ingenuity will enable humans and machines work hand-in-hand utilising human-centric design solutions in collaboration with human resources to enable sustainable, personalized and autonomous manufacturing through enterprise social networks. This talk will analyse how AI could engender better and more informed decision-making during crisis management scenarios. Furthermore, we shall elaborate how Industry 5.0 actively encourages and enhances the relationship between humans and robots in a cyber-physical domain and in the field of energy consumption, while also relying on advanced five-sense and hologram-based communications.
Deep Learning for Melodic Frameworks
Audio signals that appeal to the ear and create melody usually confirm to a structural framework. Audio is one of the four datatypes that deep learning works best on, the other three being image, video, and text. This talk will give insights into our deep learning experiments on audio signals particularly with respect to latent melodic frameworks within them.
Speaker Biography:
Prof. Priti Srinivas Sajja has been working at the Post Graduate Department of Computer Science, Sardar Patel University, India since 1994 and presently holds the post of Professor. She specializes in Artificial Intelligence and Systems Analysis & Design especially in knowledge-based systems, soft computing and multi-agent systems. She is author of Illustrated Computational Intelligence (Springer, 2020) and Essence of Systems Analysis and Design (Springer, 2017) published at Singapore and co-author of Intelligent Techniques for Data Science (Springer, 2016); Intelligent Technologies for Web Applications (CRC, 2012) and Knowledge-Based Systems (J&B, 2009) published at Switzerland and USA, and four books published in India. She is supervising work of a few doctoral research scholars while eight candidates have completed their Ph.D research under her guidance. She has served as Principal Investigator of a major research project funded by the University Grants Commission, India and as a member of Indo-Russian Joint Research Project. She has produced 219 publications in books, book chapters, journals, and in the proceedings of national and international conferences out of which five publications have won best research paper awards.
Prof. Elvira Popescu is a Full Professor at the Computers and Information Technology Department, University of Craiova, Romania. Her research interests include technology-enhanced learning, adaptive educational systems, learner modeling, computer-supported collaborative learning, learning analytics, and intelligent and distributed computing. She authored and co-authored over 100 publications, including two books, journal articles, book chapters, and conference papers. In addition, she co-edited six journal special issues, as well as 20 international conference proceedings. She participated in over 15 national and international research projects, three of which as a principal investigator. Prof. Popescu also serves as the Vice Chair for the IEEE Women in Engineering Romania Section Affinity Group and is a board member for the IEEE Technical Committee on Learning Technology and the International Association of Smart Learning Environments. She is also a Distinguished Speaker in IEEE Computer Society Distinguished Visitors Program (2020-2023). She received several scholarships and awards, including five best paper distinctions. She is actively involved in the research community by serving as associate editor for three journals (IEEE Transactions on Learning Technologies, Smart Learning Environments, Frontiers in Computer Science), member of five other journal editorial boards, organizing a series of international workshops (SPeL 2008–2020), serving as a conference chair, program committee chair and track chair for over 20 conferences.
Dr. CELESTINE IWENDI is an IEEE Brand Ambassador as well as a visiting Professor with many universities including Coal City University, Nigeria; School of Computing, Faculty of Engineering, BIHER, India; and the School of Computing & Engrg, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India. He also serves as an Honorary Adjunct Professor and Investigator in the Center of Excellence in Robotics and Mechatronics, ITM Baroda University, India; Adjunct Professor, Delta State Polytechnic, Nigeria; Honorary Professor in the School of Computing Science and Engineering, Galgotias University, India; and an Associate Professor (Senior Lecturer) in the School of Creative Technologies, University of Bolton, United Kingdom. He is a Fellow of the Higher Education Academy, United Kingdom and a Fellow of the Institute of Management Consultants. He authored over 60 peer-reviewed articles. He has delivered over 65 invited and keynote talks. He is a Distinguished Speaker, ACM; Senior Member of IEEE; Member, ACM; Member, IEEE Computational Intelligence Society; Senior Member, Swedish Engineers; Member, Nigeria Society of Engineers; and Member, Smart Cities Community, IEEE. Dr. Celestine is listed among the 2% top-scientist list. The list has been prepared by Stanford University and published by Elsevier.
Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience in the software industry in the Silicon Valley, USA. During his recent 3 year term as an ACM Distinguished speaker and before that as a researcher and industry expert, he gave numerous (50+) invited talks. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
Open House: Machine Learning, the mortar of modernization and State of the ChapterAn introduction / refresher on Machine Learning through exciting analogies, applications, and examples
Speaker:
Chapter Chair, Book Author, and SJSU Faculty Member,
Vishnu S. Pendyala, PhD
Date: June 14, 2022, 12:00 PM (PST)
Location: SEMI, 673 S Milpitas Blvd, Milpitas, CA 95035 (In-person at SEMI Milpitas and online)
Register (free) to attend
[Seating is limited. Please be kind to register for in-person attendance only if you are very sure to attend. Masks will be required indoors.]
Machine Learning is advancing the civilization and is one of the key drivers of economy today. Machine Learning literacy may one day become essential the way computer literacy is today. This talk intends to explain Machine Learning concepts to wider audiences by using easy to relate real world analogies and serves as a refresher to the already initiated. A fundamental concept in machine learning is similarity. The dot product that is ubiquitously present in machine learning is a measure for similarity. Similarity is key to human learning as well. We learn in delta increments by comparing and contrasting with what we already know. Analogies or similarities help
Machine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. This talk will use similarity to explain the underpinnings of machine learning to those who want to get insights into machine learning and data science. A number of concepts, techniques, and algorithms in machine learning will be explained relating them to real-world analogies in a way that audiences with little or no background in the area will also be able to appreciate.
Speaker: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE CS, Silicon Valley Chapter. He has over two decades of experience in the software industry and recently completed his 3 year term as an ACM Distinguished speaker. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
Meta-algorithms in Machine Learning...Ensemble methods; models that help win competitions; Bagging, Boosting, Stacking...
Speaker:
Chapter Chair, Book Author, and SJSU Faculty Member,
Vishnu S. Pendyala, PhD
Date: May 31, 2022, 6:00 PM (PST)
Virtual Event via Zoom and YouTube live
Free registration
Synopsis:
The word ‘meta’ indicates something beyond, a level up, or a higher layer. Meta-algorithms in Machine learning work on top of the known classification and regression algorithms such as Decision Trees, Logistic Regression, and Support Vector Machines to improve the performance substantially. It is often observed that these algorithms fetch top positions in the competition leaderboards and are now commonly used in the industry as well. This talk will cover some of the popular techniques of Meta-learning and explain why they generally work well. The techniques covered will include bagging, boosting, stacking, and algorithms within those broad categories such as random forest, adaboost, and gradient boosting. Time permitting, related concepts such as one-shot / few-shot learning, siamese neural network, and transfer learning will also be introduced.
Speaker Biography:
Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience in the software industry and recently completed his 3 year term as an ACM Distinguished speaker. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.
Ask Me Anything (AMA) session with Björn Schuller-- AI of Covid audio, reducing bias, affective computing, emotions, labeling, lie-detecting, ethics for AI, AEI, ML, deep learning, unsupervised & self-supervised learning, double-blind reviews, careers ...
Impact of Blockchain Technologies for Digital Transformation (1:02:10 + Q&A)
Blockchain technology is a secure decentralized distributed ledger. Blockchain can create and maintain a continuously growing data transaction blocks of cryptographically secured data records against fraudulence and tamper. Blockchain can also reduce the cost of data and unpredictability of working edge devices or connecting machines. Blockchain technology has potential to drive the digital transformation in different fields with securely, availability and distribution of information with the help of peer-to-peer communication. It can disrupt multiple industries by making transactions with the transparency, security, trusted and securely. It simplifies the development of cost-effective data transactions, where anything can be tracked and exchanged, without requiring a central body. This talk will provide an overview of the impact of blockchain technology in digital transformation with essential concepts of blockchain technology, initially exploring the features of blockchain such as consensus algorithm, distributed ledger, smart contract and cryptography in multiple fields. Audience will be equipped with strong foundation knowledge required to understand the how blockchain can make a difference in the digitalization era.
Dr. Madhusudan Singh is the Director of ReBlockchain Group in the department of AI & Big Data, ECIS, at Woosong University, South Korea. He previously worked in the R&D division at Samsung Display, S. Korea, and as a research professor at Seamless Transportation Lab, YICT, Yonsei University, Korea. Dr. Singh serves as a series editor of the Blockchain Technologies in Springer Nature, is a IEEE Computer Society, and ACM Distinguished speaker, a senior member of IEEE and also a Subject Matter Expert for the Automotive Cyber Security Course in IEEE e-learning sponsored by IEEE Computer Society. He is listed in top 2% of scientists published in a study conducted by researchers of ICSR Lab, Elsevier BV & Stanford University, USA. He is actively involved in entrepreneurial endeavors in Blockchain Technologies involving Artificial Intelligence, Information Security, and Autonomous vehicles.
