DISTINGUISHED SPEAKERS

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Dr. Tarek Sobh

President
Lawrence Technological University
USA
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Dr Björn W. Schuller

Professor
Imperial College
London, UK
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Dr. Keeley A Crockett

Professor
Manchester Metropolitan University
UK


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Prof. Dr. Jan Mendling

Einstein-Professor
Humboldt-University Berlin, Germany



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Dr. Seán McLoone

Director
Centre for Intelligent and Autonomous Manufacturing Systems
Queen's University Belfast
UK
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Dr. Jean-Paul Van Belle

Professor
University of Cape Town
South Africa
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Dr. Roger B. Dannenberg

Emeritus Professor of
Computer Science
Art & Music
Carnegie Mellon University
USA

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Dr. Zhongfei (Mark) Zhang

Professor
Binghamton University
USA
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Dr. Tzung-Pei Hong

Professor
National University of Kaohsiung
Taiwan


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Dr. Christian Blum

Senior Research Scientist
Artificial Intelligence Research Institute (IIIA)
Spanish National Research Council (CSIC)
Spain
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Dr.(Smt.) N. Ranjana

Director
Directorate of Systems & Technology
Defence Researh & Decvelopment organization(DSTA)

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Dr. B K Murthy

General Manager
NIXI-CSC
Data Service Ltd
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Dr. Vincenzo Piuri

Professor
University of Milan
Italy
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Dr. Mario Jose Divan

Sr. Solution Architect and Service Lead
Intel corporation
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Dr. Ashish Seth

Professor
Inha University Tashkent
Uzbekistan



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Dr. Dimitrios A. Karras

Professor
National and Kapodistrian University of Athens
Greece
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Dr. Raghu Ramakrishnan

Chief Architect & Technology Head
Government Business Unit
Tata Consultancy Services


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Dr. Michael Sheng

Professor
Macquarie University
Australia



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Dr. Sardar M N Islam

Professor
Victoria University
Australia



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Dr. Gautam Srivastava


Brandon University
Canada



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Prof.Dr. Alvaro Rocha

ISEG
University of Lisbon
Portugal


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Dr. Avdesh Mishra


Texas A&M University
Kingsville
USA


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Dr. Bhaskar Choubey

Professor
Siegen University
Germany



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Dr. Maleq Khan


Texas A&M University
Kingsville
USA


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Dr. Samaneh Kouchaki

Lecturer
University at Surrey
UK
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Dr. Ayush Goyal


Texas A&M University
Kingsville
USA
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Dr. Sandhya Samarasinghe

Head
Complex Systems
Big Data and Informatics Initiative (CSBII)
Lincoln University
New Zealand

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Dr. Vijay Mago

FSES Research Chair of
Department of Computer Science
Lakehead University
Thunder Bay, ON


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Dr. Vidhyashree Nagaraju

University of Tulsa
USA




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Dr. Marita Turpin


University of Pretoria
South Africa
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Dr. Adheesh Budree

University of Cape Town
South Africa




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Dr. Tanmoy Hazra


Indian Institute of Technology
Pune




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Mr. Rajiv Sariya


Vice President
Coforge
India
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Mr.Nikhil Arora


Director
Data Intelligence
S&P Global



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Ms.Kamiya Khatter

Editor
Applied Science & Engineering
Springer Nature India
New Delhi

Session on- Women in Engineering: "Your Innovative Journey as Woman Technocrat"


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Ms.Deepa Kaul

CIO
Sopra Steria




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Ms.Vertika Misra

Director of Public Policy
NASSCOM




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Ms.Sarbani Maiti

Vice President- Data Science
Veersa Technologies




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Ms.Smita Gupta

Asst. Gernal Manager
Engineers India Ltd.






Session on- Evolving Future of Work: "How to get yourself hired"


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Ms.Seema Bangia

VP & Cheif People Officer
Mahindra & Mahindra




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Ms.Priyanka Priyadarshini

Head HR APAC & Gulf
Monster India



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Mr.Kanishka Mallik

Senior Vice President-
Talent Solutions
Coffee Mug


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Mr.Kanish Gaur

Founder & Chief Executive Officer
India Future Foundation




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Mr.Sanjeev Kaushik

Head Cybersecurity/
CISO- Airports BU
Adani



Session on- ChatGPT: "A Threat to Human Jobs or Tool Towards Excellence"


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Mr. Rohit Bharath Das

Partner
Grant Thornton Bharat LLP



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Mr.Shaakun Khanna

Vice President
Oracle Corporation



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Mr.Amit Narula

Global IT Senior Director-
Cloud & Infrastructure
Boston Consulting Group (BCG)


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Mr. Keerthi Lal

V.P.- Elcom Innovations
& Senior Advisor Sun Group
Elcon Innovations/ Sun Group



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Mr.Sameer Nigam

CEO
Stratbeans Consulting





 

Title of the Talk: Problems of AI Hardware

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Title of the Talk: Generative AI – status, issues, quo vadis?

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Title of the Talk: Data Intelligence for Decision Making

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Title of the Talk: So you want to be an eCommerce entrepreneur ?

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Title of the Talk: The importance of critical thinking skills in the field of Information Technology

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Abstract : Critical thinking is increasingly recognised as an important 21st century skill for the future world of work. Critical thinkers have a sceptical attitude that requires them to look for evidence behind claims, uncover unstated assumptions, find alternative explanations and identify flaws in reasoning. In the fast-paced and high-stakes world of the Information Technology professional, critical thinking is an essential skill for understanding a client’s needs, and following the best reasoning when developing solutions at a business as well as implementation level. Critical thinking also assists in engaging with ethical challenges associated with new developments in artificial intelligence. This talk will reflect on several years of experience in teaching critical thinking to Information Technology students as well as industry professionals, and research on teaching critical thinking.

Title of the Talk: A Glimpse of Classification Techniques

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Title of the Talk: Artificial Intelligence, Robots, and Automation in Health and Health Informatics: Emerging Technologies, Game Theory Mathematics, Programming, and the Future Opportunities and Concerns

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Abstract : Robots are being created by artificial intelligence to automate many things in the world including the areas of Health/ Health Informatics, Smart Biotechnology, Biotechnology, and Bioinformatics. Many real life environments and the environment of Health informatics, Biotechnology and Bioinformatics are characterized by the existence of intelligent multiagent systems where intelligent agents or robots interact, formulate strategies, cooperate, coordinate, design systems, and plan actions autonomously for achieving their goals. Game theory analyses, formulates strategies, and designs rules or mechanisms for robots in these multiagent systems of Biotechnology and Bioinformatics on the basis of artificial intelligence. For specifying, characterising and modelling and designing these intelligent multiagent systems in Biotechnology and Bioinformatics, mathematical game theory models of different forms can be developed such as static, dynamic, evolutionary, differential, and stochastic game theory models. Different algorithms such as Nash equilibrium, joint optimization, evolutionary algorithms, neural networks, genetic algorithms, and other machine learning algorithms can be applied to different game theory models for analysing, solving, and computing these models. Computer programs such as Python can be used for computing and implementing these models and AI algorithms. Findings from these models are used to formulate strategies, cooperate, coordinate, design systems, and plan actions by different intelligent agents in different disciplines in Health Informatics, Smart Biotechnology: Biotechnology and Bioinformatics. Robotics based on game theory and artificial intelligence has applications in different emerging technologies in Health Informatics, Smart Biotechnology, Biotechnology, and Bioinformatics for automation of many processes, technologies, operations, medical applications, etc. It is an important area for doing highly useful academic and practical activities and for academics and practitioners for building their careers. Therefore, it is necessary to give priority to this area for research and development in emerging technologies in Health Informatics Biotechnology and Bioinformatics.

Title of the Talk: Parallel Algorithms for Community Detection in Large-Scale Networks

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Abstract : A community is a tightly-connected group of nodes in a network. More formally, a community is a group of nodes or vertices that have more edges connecting to each other within the group than to vertices outside the group. Community detection is an important problem in network analysis. Community detection can be used in analyzing real-world complex systems and has applications in many areas such as computer science, network science, physics, and computational biology. Label propagation is a community detection method that is simpler and faster compared to other methods like Louvain, InfoMap, and spectral approaches. At the same time, it preserves the quality of detected communities reasonably well. It is essential to balance both the quality of the solution and the runtime performance. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable to deal with such large graphs. In this talk, I will present our recent work on parallel community detection algorithms based on the label propagation method. The algorithms designed for distributed-memory and hybrid parallel systems. I will discuss a few novel optimizations and communication schemes that lead to very efficient and scalable algorithms. Our parallel algorithms are efficient, scalable to thousands of cores, and can process massive graphs. The hybrid algorithm can detect communities in a large graph with 61 million nodes and 1.9 billion edges in under 125 seconds using 4096 cores.

Title of the Talk: AI for modelling human perceptions - Consensus decision making in complex participatory Systems.

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Abstract : Decision making in participatory systems generally exists in a state of conflict. Human decision making involving natural systems is a critical example of this nature due to participants with conflicting goals attempting to pull the system in opposite and often undesirable directions. We propose an Artificial Intelligence based approach for consensus decision making that is transparent, holistic and realistic in producing effective outcomes. Specifically, it presents an advanced fuzzy cognitive map approach that captures diverse stakeholder views and assembles them into a map of the whole system view. The system map is modelled as a neural network to generate effective solutions that satisfy stakeholder goals in way that they reflect inclusivity and comprehensive coverage of stakeholder views and bring the system into a potentially better state. The approach is applied to decision making in a conflicting and challenging water situation.

Title of the Talk: A Machine Learning Approach for Predicting the Multivariate Performance Phenotype

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Abstract : Genotype-to-phenotype mapping requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause problems for comparative analyses and the assessment of evolutionary trends across species. Measuring the multivariate performance phenotype is especially logistically challenging, and our ability to predict several performance traits from a given morphology is consequently poor. We developed a machine learning model to accurately estimate multivariate performance data from morphology alone by training it on a dataset containing performance and morphology data from 68 lizard species. Our final, stacked model predicts missing performance data accurately at the level of the individual from simple morphological measures. This model performed exceptionally well, even for performance traits that were missing values for >90% of the sampled individuals. Furthermore, incorporating phylogeny did not improve model fit, indicating that the phenotypic data alone preserved sufficient information to predict the performance based on morphological information. This approach can both significantly increase our understanding of performance evolution and act as a bridge to incorporate performance into future work on phenomics.

Title of the Talk: Advances on AI models for Natural Language Understanding and Generative Model-Based Text-to-Speech Synthesis

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Abstract : Recent progress in AI and Machine Learning models for Natural Language processing and understanding as well as models for generative modeling regarding synthesized speech has improved the naturalness of robots and relevant applications responses to humans significantly, advancing the Human Machine Interfacing (HMI) to another higher level. In this speech these AI/Machine Learning and generative model-based approaches for Natural Language understanding and speech synthesis as integral parts of improved HMI will be summarized, outlining, also, speaker's relevant contributions and describing further possible future directions.

Title of the Talk: Adaptive Incremental Learning for Software Reliability Growth Model

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Abstract : Software reliability growth models (SRGM) assist in software release decisions by quantifying metrics based on failure data collected during testing. Complexity of SRGM can increase significantly with increasing complexity of software, thus requiring faster and stable algorithms to identify model parameters. While previous studies have attempted to address this issue through application of machine learning (ML) algorithms, lack of sufficient data to train ML models. Moreover, it is important to assess software in an online manner as data becomes available, which is difficult since limited data is available towards the beginning of testing. Incremental learning provides the possibility of applying machine learning algorithms in an online manner, however lack of large data towards the beginning of testing limits the efficiency. Therefore, this paper proposes an adaptive incremental learning that utilizes a model trained on historical data which can forecast failures for the present data. Historical data is selected based on the Granger causality test. Our results indicate that the adaptive incremental learning approach achieves significantly better accuracy on smaller sample sizes compared to simple application of neural networks.

Title of the Talk: Building Modular Distributed Computer Music Applications with O2

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Abstract : Computer Music presents interesting programming challenges. Rapid prototyping and end-user programming are essential since every composer/performer/developer has different artistic objectives. Musicians and artists also demand high functionality, quality, reliability, and real-time performance. An increasingly practical solution is to combine various applications for sensing, control, sound synthesis, visualization and audio processing. To facilitate coordination and control of multiple applications, a new framework, O2, has been developed, featuring discovery, clock synchronization, and timed message passing. This talk will focus on how O2 is used to facilitate music software development and much more.

Title of the Talk: Neural networks can predict the future - or can they?

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Abstract : Neural networks have gone through several waves of innovation from the McCulloch/Pitts neuron of the 1940s and the Rosenblatt Perceptron in the 1960s to the MLP and related developments of the 1990s and the Deep Learning CNN and LSTM models of the last decade. Most recently, we have the transformer architectures and generative AI models that are at the heart of the ChatGPT phenomenon. At all stages of their development there has been a great deal of hype about neural networks and exaggerated expectations of their capabilities which resulted in two so-called AI winters. In this talk I will explore one such perhaps exaggerated claim - that ‘neural networks can predict the future’ - through the lens of my personal experience of conducting research over 25 years on using neural networks to develop predictive models for applications ranging from predicting final product quality on a manufacturing production line to anticipating human motion and actions for seamless collaborative robotics.

Title of the Talk: Deep Learning and Neural Networks for Medical Imaging.

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Abstract : Brain tumor segmentation is an important task to be performed in analyzing brain Magnetic Resonance Images (MRI). Brain MRI segmentation techniques help in making a biomedical image processing application for supporting neurologists and brain disease patients. There is a need for automatic brain tumor segmentation methods to facilitate neurologists for diagnosis. This research implements a convolutional neural network-based variational autoencoder model, modified with an additional flattening layer, on four different scenarios. In the first scenario, the model was trained on the BraTS-2019 dataset with a batch size of 1 over 10 epochs, and then tested on the BraTS-2018 dataset. In the second scenario, the model was trained on the BraTS-2018 dataset with a batch size of 1 over 10 epochs, and then tested on the BraTS-2019 dataset. In the third scenario, the model was trained on the BraTS-2019 dataset with a batch size of 2 over 20 epochs, and then tested on the BraTS-2018 dataset. In the fourth scenario, the model was trained on the BraTS-2018 dataset with a batch size of 2 over 20 epochs, and then tested on the BraTS-2019 dataset. The average Dice coefficient values of the whole tumor segmentation were 0.89003, 0.88551, 0.88569, and 0.9089, for the four scenarios, respectively. This proposed method’s accuracy on average was 0.893, which was a higher accuracy for whole tumor segmentation than other brain tumor segmentation methods compared in this work.

Title of the Talk: Multidimensional Big Data Analytics: Evolving OLAP and BI towards Next-Generation Big Data Analytics Systems

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Abstract : OLAP and BI have conquered the scene of decision-making systems research for decades, thus defining a wide collection of models, techniques and algorithms whose main goal is that of effectively and efficiently representing, querying and mining massive OLAP data cubes. With the advent of the modern big data era, big OLAP data cubes arise, thus posing novel challenges to be faced-off by the research community. Big OLAP data cubes are mainly processed over Clouds, due to tight high-performance computing requirements, thus opening the door to a large number of big data processing platforms, from both the academic and industrial world. In the so-delineated context, the main research challenges again consist of the following issues: (i) effectively and efficiently representing big OLAP data cubes over Cloud architectures; (ii) effectively and efficiently querying big OLAP data cubes over Cloud architectures; (iii) effectively and efficiently mining big OLAP data cubes over Cloud architectures. This talk will discuss these issues, provide overview on state-of-the-art proposals, and illustrate future research directions in this scientific context.

Title of the Talk: On the Need For Simple But Powerful Optimization Techniques

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Abstract : One of the strengths of metaheuristics such as evolutionary algorithms and tabu search is that they are general ideas that can rather easily be applied to a new optimization problem at hand. In many cases, however, a straightforward implementation of a metaheuristic to an optimization problem does not reach state-of-the-art performance. Therefore, researchers often equip their metaheuristic with problem-specific bits and pieces that might be rather complicated to code. On the other side, the application and implementation of high-performance Operations Research (OR) techniques for optimization are often not achievable for non-experts. In other words, especially in industry, there is a need for high-performance optimization techniques that can be easily implemented by non-experts. In this talk we are going to explain that "Construct, Merge, Solve & Adapt (CMSA)", a new but simple algorithm combining heuristic elements with OR expertise, can fill this gap.

Title of the Talk: Artificial Intelligence for Medical Data Analysis and Decision Support

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Abstract : Knowledge extraction in medical applications requires an intelligent technological support for understanding the characteristics underlying the data, predict the possible evolution and support informed decision making. Complexity of the data are increasingly cumbersome, especially in critical situations in which the behavior and the impact of medical threats, like in new pandemic situations, are not already well-known by doctors and well-disseminated in the medical literature. Artificial intelligence can provide additional flexible techniques for extracting knowledge from data to support informed decisions. Besides, the analysis of time data series based on artificial intelligence may contribute to understand the trends of medical situations. This talk will analyze the opportunities offered by artificial intelligence technologies to support the realization of data analysis and prediction in a flexible way, learning the underlying knowledge embedded in data.

Title of the Talk: Novel Applications in the Area of Computational Ethics for Natural Language Processing

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Abstract : In the last ten years, particularly in the last five, research involving pre-trained language models has expanded rapidly. These language models are expected to assist in fabricating text devoid of human prejudices and biases, and in order to meet such high standards, it is essential that key application areas are clearly delineated and accepted by subject matter experts. This will help determine whether the results obtained by these models are credible and may be implemented in practical applications. In this research presentation, I will introduce the applications and novelties of natural language processing based solutions to better tackle the computational, as well as ethical issues arising from the domains of education, political discourses on social media and news channels, and the emotions expressed in conversation. The key areas of research include semantic similarity of words and sentences; sentiment, stance and emotion analysis of text and recognizing despondency in text. Given that these research projects have direct applications in government agencies, I will further discuss the partnership projects which I have been developing with government agencies.

Title of the Talk: Bringing Order to the Internet of Things: Research Activities and Future Directions

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Abstract : Since the term first coined in 1999 by Kevin Ashton, the Internet of Things (IoT) has gained significant momentum and is widely regarded as an important technology to change the world in the coming decades. Indeed, IoT will play a critical role to improve productivity, operational effectiveness, decision making, and to identify new business service models for social and economic opportunities. While IoT-based digital strategies and innovations provide industries across the spectrum with exciting capabilities to create a competitive edge and build more value into their services, there are still significant technical gaps in making IoT services a reality, especially on effectively managing large volume of IoT devices and the big data generated from them. In this talk, I will first briefly discuss the IoT-related background and then report my research team's 15-year research and implementation activities on IoT search and discovery.

Title of the Talk: Data Mining with Fuzzy Sets

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Abstract : Data mining is commonly used in inducing implicit knowledge from transaction data. Most previous studies focused on mining from binary valued data. Transactions in real-world applications, however, usually consist of quantitative values. Designing a sophisticated data- mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In this speech, I will first introduce some techniques for mining fuzzy association rules from quantitative transactions when the membership functions are known. I will then describe several GA-based fuzzy data-mining methods for automatically extracting membership functions for the association rules. Extension of fuzzy sets to other mining problems is also discussed.

Title of the Talk: Collaborative technologies to accelerate the engineering revolution

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Abstract : Artificial Intelligence revived in the last decade. The need for progress, the growing processing capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. It's about approaching engineering with a lot of knowledge and tact. This involves the use of both connectionist and symbolic systems, and of having a full understanding of the algorithms used. Moreover, to address today’s problems we must work with both historical and real-time data. We must fully comprehend the problem, its time evolution, as well as the relevance and implications of each piece of data, etc. It is also important to consider development time, costs and the ability to create systems that will interact with their environment, will connect with the objects that surround them and will manage the data they obtain in a reliable manner.
In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities.
The use of edge platforms or fog computing helps increase efficiency, reduce network latency, improve security and bring intelligence to the edge of the network; close to the sensors, users and to the medium used. These and other relevant technologies that are transforming our ways to use AI will be discussed along the key note.

Title of the Talk:
O2: Communication middleware for real time distributed music applications.

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Abstract :

Title of the Talk: Next generation process mining

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Abstract : Process mining has been used for two decades to analyze business processes based on available data from transactional information systems. Recent research has substantial expanded the reach of process mining to new types of data and new types of analysis insights. In this talk, I will explain three examples of such recent innovations, and discuss implications for future research.

Title of the Talk: Deep Computer Audition:The Next Level

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Abstract : ChatGPT, Dall-E 2, Lensa, and further generative AI have recently fuelled the AI hype in the grand public. But where’s audio these days when it comes to “next level” AI? Transformers and big(ger) data trained models such as AudioLM also led to substantial change in this landscape, including major leaps forward in performance. This talk aims to briefly summarise the state-of-play when it comes to computers and today’s deep learning-based audio processing. Further to that, it will discuss where the road is likely to take us next. This includes rich state and trait attribution to general audio – both, for analysis of audio or generation of such. Speech processing was the beginning with today’s AI being able to “hear” someone’s age, height, emotion, or heart rate from the voice. It can also increasingly generate these aspects in a voice. Sound is coming up next, such as “produce the sound of smartphone with a broken display vibrating on an oak table on which there is also a half-filled glass”.

Title of the Talk: Small Can Be Powerful

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Abstract : Learning with knowledge distillation is a hot topic in today’s machine learning literature, with the motivation of equipping a student, typically much lighter-weight network with the knowledge from a teacher, typically much larger network. This research has a wide spectrum of real-world applications of developing powerful but light-weight and resource-limited front end devices. In the existing literature, typically the student always underperforms the teacher after knowledge distillation and this is attributed as a result of the typical huge capacity gap between the teacher and the student. In this talk, I will examine the problem of the performance gap between the teacher and the student from a different perspective --- distillation data, and I will show through theoretic analysis and empirical evaluations that even with the existence of the huge capacity gap, as long as the student is large enough, with appropriate distillation data, the student can be made to exhibit the same performance as that of the teacher, and can even outperform the teacher.

Title of the Talk: Framework for Multi Cloud Collaboration

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Abstract : There is a need to develop mechanisms that allow opportunistic collaboration among services without requiring tight standards and also should not do extensive changes to the cloud service delivery model. From a market perspective, it is unlikely that multiple CSPs will agree on an easy and standardized way to access services in collaboration, as this would give clients total freedom to switch to other providers services, it also brings open and direct competition with other providers. CSPs often offer differentiated services with specialized proprietary products and services. We can overcome these restrictions by using network of proxies. A proxy is an edge-node-hosted software instance that a client or a CSP can delegate to carry out operations on its behalf. To facilitate dynamic collaboration between clouds, I would like to discuss a framework that uses proxies to act as mediators between multiple clouds that can share data.

Title of the Talk: A Maturity Model for Hospital Information Systems

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Abstract : In the last five decades, maturity models have been introduced as guides and reference frameworks for information system (IS) management in organizations within different industries and sectors. In the healthcare domain, maturity models have also been used to address a wide variety of challenges, complexities and the high demand for hospital IS (HIS) implementations. The talk describes a research project focused on the development of a comprehensive maturity model applied in the HIS context. The outcome of this research is the HIS maturity model (hereafter referred to as HISMM), which includes six stages of HIS growth and maturity progression. The HISMM has the peculiarity of congregating a set of key maturity-influencing factors and respective characteristics, enabling not only the assessment of the global maturity of a HIS, but also the individual maturity of its different dimensions.

Title of the Talk: People Powered Artificial Intelligence – Challenges and opportunities in navigating the ethical landscape of responsible innovation

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Abstract : The ethical, social, and legal landscape of Artificial Intelligence (AI) driven systems is rapidly changing. Since the General Data Protection Regulation (2018), stakeholders developing AI systems have faced numerous challenges in the interpretation and implementation of Article 22, specifically concerning an individual’s rights in the context of automated decision making, the ability of AI to explain decisions, explanation of the logic involved, and to develop models using only “correct” data. This has and is causing major challenges due to the lack of legal guidance, case law and ethical principles about the use of AI in different contexts. In 2023, the passing of the EU AI Act and other Global “AI and Digital” legalisation promises to also be a big disrupter to all stakeholders working in the field as adopting a risk-based approach to AI becomes the norm. In this talk I will briefly overview ethical issues and relevant legalisation related to AI and data driven technologies that are arising due to the increasing use of AI and whether consumers /audiences/ citizens are aware to these developments. Responsible innovation and ethical tech are essential to build public trust, yet how can businesses and researchers in academia harness People Power through community ideation and involvement and people’s panels? In this talk I will give some examples of bridging the gap between businesses, academia, and people and how the use of ethical toolkits such as consequence scanning can help to build citizen confidence and have impact potential.

Title of the Talk: Federated Learning for Data Privacy

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Abstract : In recent years, mobile devices can be equipped with increasingly advanced computing capabilities, which opens up countless possibilities for meaningful applications. Traditionally, any cloud-based Machine Learning (ML) approach requires that data be centralized on a cloud-based server/data center. However, this can result in critical issues related to unacceptable latency and communication inefficiency as well as major security and privacy concerns. However, conventional ML technologies still require personal data to be shared. Recently, in light of increasing security and privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train a local ML model required by the server. In a large, complex mobile edge networks, FL still faces implementation challenges with regard to communicational costs, resource allocation, security, and privacy. In this talk, we begin with an introduction to the background and fundamentals of FL. We then discuss how FL can work to try and preserve privacy while maintaining security of data. Finally, we discuss some open research areas and specific open problems where attendees may be able to make an impact.

Title of the Talk: Recent Directions and Advances in Robotics and Automation

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Abstract : Recent robotics, automation and sensing endeavors are making it into the mainstream. This talk will highlight several recent research and development projects with in the areas of designing hybrid controllers for robotics and automation applications, service robots, prototyping and synthesis of controllers, simulators, and monitors for manipulators and autonomous mobile platforms, algorithms for uncertainty computation from sense data, and web-based prototyping, control synthesis, and simulation of robotic agents. Several innovations and breakthroughs in Robotics, Intelligent Sensing and Control at Lawrence Technological University will be detailed. Research projects on developing and implementing theoretical and experimental tools to aid performing adaptive goal-directed robotic sensing for modeling, observing and controlling interactive agents and electromechanical systems in unstructured environments will be discussed.

Title of the Talk: On Data Interoperability, Reliability, and Image Processing in IoT

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Abstract : It discusses challenges associated with the Internet of Things(IoT), Edge Computing, data interoperability, and Reliability. It sails on foundation concepts with a direct impact on the data collection process. The perspectives of Data ownership, data sovereignty, and data confidentiality are analyzed in the light of the decision-making process and real-time applications. Thus, the importance of commonalities and building block reuse is addressed. In this context, a few reference implementations and the OpenVino framework are quickly presented for exploding the potentiality of image processing. In this scenario, AI ethics is discussed to put in context its importance for data processing.