Past seminars

In memory of Professor Aditya Ghose – A journey from AI to Business Process Management, Service Science and Software Engineering

Abstract This special event marks the 1000th seminar of the Cafe DSL Series, and honours and celebrates the outstanding contributions to research of DSL’s founding director, Professor Aditya Ghose. The seminar will revisit DSL’s history and its early days, and the impact of Cafe DSL Seminar Series over the past 25 years. It will also feature presentations of Prof Aditya Ghose’s outstanding work in diverse areas of computing, including Artificial Intelligence, Business Process Analytics and Management, Service Science, Conceptual Modelling and Software Engineering. The speakers include: Prof Joseph Davis (University of Sydney), Prof Abdul Sattar (Griffith University), Prof Abhaya Nayak (Macquarie University), Dr Renuka Sindhgatta (IBM Research), Prof Hoa Dam and Geeta Mahala (University of Wollongong).

Date: Friday, March 17, 2023
Time:  11:30am (after Professor Aditya Ghose’s memorial service)
Venue: Building 14, G01 (UOW Wollongong Campus) & Webex

Towards Antifragility in Contested Environments: Using Adversarial Search to Learn, Predict, and Counter Open-Ended Threats.

 Presenter: DrSaad, Research Fellow, School of Computing and IT, University of Wollongong.

Abstract: Resilience and antifragility under duress present significant challenges for autonomic and self-adaptive systems operating in contested environments. In such settings, the system has to continually plan ahead, accounting for either an adversary or an environment that may negate its actions or degrade its capabilities. This will involve projecting future states, as well as assessing recovery options, counter-measures, and progress towards system goals. For antifragile systems to be effective, we envision three self-* properties to be of key importance: self-explorationself-learning and self-training. Systems should be able to efficiently self-explore – using adversarial search – the potential impact of the adversary’s attacks and compute the most resilient responses. The exploration can be assisted by prior knowledge of the adversary’s capabilities and attack strategies, which can be self-learned – using opponent modelling – from previous attacks and interactions. The system can self-train – using reinforcement learning – such that it evolves and improves itself as a result of being attacked. This paper discusses those visions and outlines their realisation in AWaRE, a cyber-resilient and self-adaptive multi-agent system.

Presenter Bio: Dr. Saad received his PhD degree in Computer Science from Macquarie University (Australia, 2020), MSc degree in Computer and Software from Hanyang University (Rep. of Korea, 2015), and BSc degree in Computer Software Engineering from GIK Institute (Pakistan, 2011). He is currently working as a Research Fellow at the School of Computing and IT, University of Wollongong. His current research interests include solving analytical problems in the domain of cyber resilience and human-centric privacy. One of his PhD works received an Outstanding Paper Award at MobiQuitous 2021. 

Date:  Monday, September 5, 2022
Time: 12:30pm onwards  
  Webex & Room 6.209

Environmental Intelligence - Application of AI and Big Data concepts to developing Environmentally Responsible Business Strategies 

Presenter: Professor Bhuvan Unhelkar, Muma College of Business, University of South Florida. 

Abstract: While the environment remains at the forefront of today's business and political domains, the approaches to handling the challenges of reducing the carbon footprint of a business remain multipronged. This presentation has an academic/research focus and it explores the possibilities of applying the concepts of Artificial Intelligence and Big Data to the Environmental responsibilities of businesses. There will also be an update on the current research project happening in Western Sydney University (where the Presenteris an adjunct) in terms of studying the collaborative impact of a group of Small and Medium Enterprises (SMEs) on the environment.

Presenter Bio: Dr Bhuvan Unhelkar is a Professor in Muma College of Business, at the Univ. South Florida; an adjunct Professor at Western Sydney University and an honorary Professor at Amity University, India. He is also Founding Consultant at Method Science and PlatiFi, with Mastery in Business Analysis & Requirements Modeling, Software Engineering, Big Data Strategies, Agile Processes, Mobile Business and Green IT. Bhuvan is a thought-leader and a prolific author of 25 books – including Artificial Intelligence & Business Optimization; and The Art of Agile Practice (CRC Press, USA). Bhuvan is Fellow of the Australian Computer Society, IEEE Senior Member, Life member of the Computer Society of India and Baroda Management Association. He is Past President of Rotary Club of Sarasota Sunrise (Florida) & multiple Paul Harris Fellow, Discovery volunteer at NSW parks and wildlife, and a previous TiE Mentor.

Date: Thursday, June 2, 2022
Time: 12:30pm onwards  
  Webex & Room 6.209

Digital transformation and the future of RPA

Presenter: Partha Banerjee, CEO, Viyat Solutions & Consultancy Pvt. Ltd

Presenter Bio: Partha Banerjee holds a Degree in Electronics & Telecommunications Engineering and a Masters in Business Management with specialization in Operations Research & Management. He has over 30 years of experience in Enterprise Business Planning &Transformation, Operations Management, Digital Transformation, IT Infrastructure Consultancy, and Change Management. As CEO of Viyat, he provides leadership in Digital Transformation, SaaS based Business Process Automation, Cyber Security and Telecom Solutions for the Mid-market Segment.

Date:  Thursday, December 9, 2021. 
Time: 12:30 pm onwards  

What do game playing algorithms teach us about business process execution?

Presenters: Professor Aditya Ghose and Dr. Yingzhi Gou, DSL, UOW

Date: 26 August, 2021

Constraint-based Causal Structure Discovery under Weaker Assumptions

Date: 11 June, 2021
Time 12:30pm onwards
Venue: 6.209 - Smart Building & WebEx

Presenter: Dr. Wolfgang Mayer, Industrial A.I. Research Centre, University of South Australia

Abstract: I will outline the research conducted at the Industrial AI Research Centre at UniSA and discuss the foundations of causal structure discovery from data. Causal inference is of great interest in many scientific areas, and automated discovery of causal structure from data is drawing increasingly more attention in the field of machine learning. Automated causal discovery seeks to infer the possible causal structures from data. I will examine the impact of relaxing some of the underlying assumptions in the context of constraint-based causal structure discovery from data.

Presenter Bio: Dr. Wolfgang Mayer is a Senior Lecturer and a member of the Industrial A.I. Research Centre at the University of South Australia, where he conducts research in the field of Artificial Intelligence. His research interests focus on the intersection of machine learning and knowledge representation and their applications in industry and Defence, including, information fusion, simulation modelling, process optimisation, and related software technologies.

From Startup to Scaleup - The Tangerpay journey

Date: 28 May, 2021
Time: 12:30pm onwards
Venue: 6.209 - Smart Building & WebEx

Presenter: Kishore Aggarwal, Founder of Tangerpay Australia

Presenter Bio: Kishore Aggarwal is the founder of Tangerpay Australia, a fintech startup headquartered in Brisbane. Tangerpay supplies innovative IoT and Cloud based payment solutions for niche unattended environments such as laundry machines, massage chairs, dog washes, pool tables, car park boom gates etc. Tagerpay's products can be found in caravan parks, hotels, universities, high street laundromats, mining camps and boarding houses in Australia, USA and UK and is soon expanding to other global markets in Asia Pacfic, Europe and Americas. Kishore has a Bachelor's degree in Computer Science and Engineering and a Masters of Applied Finance and is passionate about developing affordable payment technology for micro-businesses around the world to help them transition from coin based operations to modern cashless payment solutions. Kishore lives in Brisbane with his wife Lowell and son Jason and says that the best part of being a startup is the liberty of applying creativity to solve everyday pain points for so many customers and businesses out here. He also enjoys travelling and meeting with people from different spheres of life and different cultures as part of his role of turning Tangerpay from a startup to a scaleup to an established global entity in this niche payments market.

Circular Supply Chains for Medical Products

Date: 7 May, 2021
Time: 12:30pm onwards
Venue: 6.209 - Smart Building & WebEx

Presenter: Gyanam Sadananda, Director at PwC, Australia

Abstract: This seminar brings together decades of engineering and supply chain experience, particularly in managing the supply chains for medical products during the COVID-19 response. We will cover how the government and private sector manages elements of their value chain, such as: planning, forecasting, procurement, distribution, storage and disposal or reuse. This will include the challenges discovered through the pandemic as well as the opportunities and future for advanced local manufacturing and medical supply chains.

Digital Twins in Healthcare

Date: 9 April, 2021
Time: 12:30pm onwards
Venue: 6.312 - Smart Building & WebEx

Presenter: Samir Sinha, Founder & CEO of Robonomics AI, Australia

Presenter Bio: Samir is the Founder & CEO of Robonomics AI, an Australian-born platform business aiming to enhance businesses by democratising adoption of exponential technologies. He has pioneered a business model of creating innovation communities and upskilling diverse peoples, who come together to digitalise business challenges. Samir is one of the industry leaders who established offshoring as the prime delivery methodology for transformation projects in Australia and New Zealand. He was the MD for HCL Australia and previously the head of Global operations of the SAP Practice at TCS. He has led teams of 2000 + FTEs several times and has delivered large complex transformation programs for large corporates. Samir started off as a Mechanical Engineer before doing his MBA from Indian Institute of Management, Kolkata. He is specialising in AI for Healthcare from Stanford University. Samir been featured in publications like The Australian, CIO Magazine and Information Age. He is a TEDx Speaker also a keynote speaker in multiple events across the globe.

From Belief Revision to Belief Manipulation -- Exploratory Thoughts

Date: 19 March, 2021
Time: 12:30pm onwards
Venue: 6.209 - Smart Building & WebEx

Presenter: Associate Professor Abhaya Nayak, Dept of Computing, Macquarie University, Australia

Abstract: Belief Dynamics as a field of research is quite mature, with a history of over thirty years. At a very high level, it is akin to science in spirit: to build theories that can be employed to make predictions. Given the belief state of an agent, and some new information that it has accepted (or rejected), Belief Dynamics aims to predict the new belief state of the agent. There is an interesting converse of this problem that has drawn little attention from the community, that we call Belief Manipulation. Given that we know the current state of an agent's knowledge, and some proposition that we want them to believe (or suspend their judgment on), how can we bring about that epistemic change in that agent. In this, the problem of belief manipulation has the flavour of engineering rather than of science. Belief manipulation is not as rare a phenomenon as one would like it to be. Political propaganda, fake news, information warefare, all sorts of scams and market manipulation are all instances of belief manipulation. A good understanding of this process can help us to potentially detect attampts at belief manipulation and take pre-emptive measures. In this talk I will present my preliminary ideas on the logic of belief manipulation.

Intelligent Knowledge Lakes: The Age of Artificial Intelligence and Big Data

Date: 21 December, 2020
Time: 2:30pm onwards
Venue: 6.312 - Smart Building & WebEx

Presenter: Dr. Amin Beheshti, Macquarie University, Australia

Abstract: The continuous improvement in connectivity, storage and data processing capabilities allow access to a data deluge from the big data generated on open, private, social and IoT (Internet of Things) data islands. Data Lakes introduced as a storage repository to organize this raw data in its native format until it is needed. The rationale behind a Data Lake is to store raw data and let the data analyst decide how to curate them later. Previously, we introduced the novel notion of Knowledge Lake, i.e., a contextualized Data Lake, and proposed algorithms to turn the raw data (stored in Data Lakes) into contextualized data and knowledge using extraction, enrichment, annotation, linking and summarization techniques. In this Talk, we introduce Intelligent Knowledge Lakes to facilitate linking Articial Intelligence (AI) and Data Analytics. This will enable AI applications to learn from contextualized data and use them to automate business processes and develop cognitive assistance for facilitating the knowledge intensive processes or generating new rules for future business analytics.

Presenter Bio: Dr. Amin Beheshti is the Director of AI-enabled Processes (AIP) Research Centre and the head of the Data Analytics Research Lab, Department of Computing, Macquarie University. He is also Senior Lecturer (equivalent to Associate Professor in the USA) in Data Science at Macquarie University and Adjunct Academic in Computer Science at UNSW Sydney. Amin completed his Ph.D. and Postdoc in Computer Science and Engineering in UNSW Sydney and holds a Master and Bachelor in Computer Science both with First Class Honours. In addition to his contribution to teaching activities, Amin extensively contributed to research projects; where he was the R&D Team Lead and Key Researcher in the 'Case Walls & Data Curation Foundry' and 'Big Data for Intelligence' projects. Amin has been recognized as a high-quality researcher in Big-Data/Data/Process Analytics and has been invited to serve and served as Keynote Speaker, General-Chair, PC-Chair, Organisation-Chair and program committee member of top international conferences. He is the leading author of the book entitled "Process Analytics", co-authored with other high-profile researchers in UNSW and IBM research, recently published by Springer. Amin was able to secure over $3.9 million research grants for AI-Enabled and Intelligence-Led projects in Banking and Education.

Blockchain as a Solution for Challenges in IoT based Automation

Date: 27 November, 2020
1:30pm onwards

Presenter:  Dr. Sujit Biswas, The University of Dhaka, Bangladesh

Abstract: Internet of Things and Blockchain technologies have been dominating their respective research domains for some time. IoT offers automation at the finest level in different fields, while Blockchain provides secure transaction processing for asset exchanges. The capability of IoT devices to generate transactions prompts their integration with Blockchain as the next logical step. The biggest challenges in this integration are the scalability of the ledger and the rate of transaction execution in Blockchain. On one hand, due to their large numbers, IoT devices will generate transactions at a rate that current blockchain solutions cannot handle. On the other hand, implementing Blockchain peers onto IoT devices is impossible due to resource constraints. This prohibits the direct integration of both technologies in their current state. The proposed solutions to address these challenges by using a local peer (Lpeer) network to bridge the gap, and Proofof Block and Trade (PoBT), a smart lightweight consensus algorithm that enhances the scalability multitimes. Lpeer restricts the number of transactions which enters the global Blockchain by implementing a scalable local ledger, without compromising on the peer validation of transactions at a local and global level. The PoBT consensus algorithm minimizes the validation steps and increases the scalability. The testbed evaluations show significant improvement of scalability, reduction in the block weight, and ledger size on global peers.

Presenter Bio: Sujit Biswas holds PhD in Computer Science and Technology from Beijing Institute of Technology, China, and Master of Engineering degree in Computer Science and Technology from Northwestern Polytechnical University, China. He has received Chinese Government Scholarship for both master and PhD courses. He worked in the Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China. He is an Assistant Professor with the Computer Science and Engineering Department, Faridpur Engineering College, University of Dhaka, Bangladesh. His basic research interest is in the Internet of Things, Blockchain, and Android security. His research has been published in the world's top-level journals including IEEE Internet of Things Journal, IEEE Transactions on BigData, IEEE transactions on Engineering Management, ACM Computing Surveys, Journal of Network and Computer Applications, and so on. He has presented a talk as an invited speaker in different seminars at Khulna University, Bangladesh, and Beijing Institute of Technology, China, etc. Currently, He is serving as principal investigator of the project ‘’Blockchain Based Public Examination Management System”. He is a member of IEEE and a life member of the Bangladesh Computer Society.

Economic Models for Managing Cloud Services

Date: 18 September, 2020
Time: 1:30pm onwards
Venue: WebEx

Presenter:  Dr. Sajib Mistry, Lecture at the School of EECMS, Curtin University, Australia.

Abstract: Cloud computing is inexorably becoming the technology of choice among big and small businesses to deploy and manage their IT infrastructures and applications. An effective cloud service management framework may create a sustainable and efficient cloud service market. As both service providers and consumers are growing quickly in the cloud market, the cloud market requires management frameworks to meet the expected large demand. An economically viable cloud market should maximize the long-term economic expectations of the providers while ensuring the delivery of the promised prices and required QoS to the consumers. To achieve such efficiency in the cloud market, we require a service management framework from a provider’s perspective. It composes service requests according to the provider’s long-term economic expectations, such as revenue and profit. Similarly, consumers should have the confidence to select cloud services for a long-term period (an informed decision) where the service providers offer limited performance information due to their business secrecy. In this talk, I will introduce both the quantitative and qualitative economic models as optimization tools for the selection of long-term cloud service requests. The economic models fit almost intuitively in the way business is usually done and maximize the profit of a cloud provider for a long-term period. Finally, I will discuss the signature-based Selection of IaaS Cloud Services and future research opportunities.

Presenter Bio: Dr. Sajib Mistry is a Lecture at the School of EECMS in Curtin University, Australia. He was a Postdoctoral Research Fellow at the School of Computer Science in the University of Sydney, Australia. He was awarded PhD from the School of Science (Computer Science), RMIT University, Melbourne, Australia. He completed his Masters (MS) and Bachelors (BS) in Computer Science from the University of Dhaka, Bangladesh. He has teaching/academic experience in different roles, e.g., lecturer, head tutor, tutor, the casual lecturer at different institutions including the University of Sydney, RMIT University, Monash University, University of Dhaka, and University of Liberal Arts. His primary research area is Service Computing, Cloud/Edge computing, and the Internet of Things (IoT). He has authored and edited several books and published in several top journals and conferences such as CACM, IEEE TSC, IEEE TKDE, ICSOC, ICWS, etc. He won the Best Research Paper Award in ICSOC 2016 and RMIT Publication Award 2016. One of his journal papers was selected as the spotlight paper for IEEE TSC. He also contributed significantly with his community services as PC Chair in ASSRI 2018, PC member in ICWS 2019-2020, ICSOC 2019, and WISE 2018-2019. He is a regular reviewer of top journals and conferences in the TCSVC field. He is a member of the Sydney IoT Hub.

Exploring Interpretable Predictive Models for Business Processes

Date: 4 September, 2020
Time: 1:30pm onwards
Venue: WebEx

Presenter:  Dr. Renuka Sindhgatta, Lecturer, Queensland University of Technology, Australia.

Abstract: There has been a growing interest in the literature on the application of machine learning and deep learning models for predicting business process behaviour, such as the next event in a case, the time for completion of an event, and the remaining execution trace of a case. Although these models provide high levels of accuracy, their sophisticated internal representations provide little or no understanding about the reason for a particular prediction, resulting in them being used as black-boxes. Model interpretability is necessary to enable transparency and empower users to evaluate when and how much they can rely on the models. This talk will motivate the need for deriving post-hoc explanations to compare and contrast the suitability of multiple predictive models of high accuracy. Additionally, an interpretable and accurate attention-based Long Short Term Memory (LSTM) model for predicting business process behaviour will be presented.

Presenter Bio: Dr. Renuka Sindhgatta is a Lecturer at the School of Information Systems at the Queensland University of Technology. She is an alumna of DSL@UOW and completed her PhD in 2018 while working at IBM Research – India. She has actively worked in the field of data-driven analytics to improve operational efficiencies of software and service delivery teams using machine learning (ML), text mining, and process mining. Her current research focuses on the design of application-oriented and function-oriented explainable systems that can be applied to domains such as business processes.

AI in Radiomics

Date: 28 August, 2020
Time: 1:30pm onwards
Venue: WebEx

Presenter: Professor Andrew Miller, Radiation Oncologist, Wollongong Hospital

Abstract: Artificial Intelligence is in its second summer, but we all know that winter follows summer. Image mining with Radiomics is one area where data mining is purportedly "making great strides" though this is the report from the data mining community, not the medical community. Professor Miller has been working at the interface between medicine and data mining and will discuss the mechanics of Radiomics as well as the difficulties faced in making Artificial Intelligence clinically relevant in the future. This will include a comparison of the usual Radiomics outputs and a novel approach that has more relevance to dOctoberors and will demonstrate the points made within the discussion to try and stave off the coming winter.

Presenter Bio: Professor Andrew Miller is a radiation oncologist working in Wollongong with an Informatics research qualification from UOW in knowledge structure. He has been involved with DSL since the late 2009s.

Engineering Human Values in Software through Value Programming

Date: 5 June, 2020
Time: 1:30pm onwards
Venue: WebEx

Presenter: Dr. Davoud Mougouei, University of Wollongong, Australia

Abstract: Ignoring human values in software development may disadvantage users by breaching their values and introducing biases in software. This can be mitigated by informing developers about the value implications of their choices and taking initiatives to account for human values in software. To this end, we propose the notion of Value Programming with three principles: (P1) annotating source code and related artifacts with respect to values; (P2) inspecting source code to detect conditions that lead to biases and value breaches in software, i.e., Value Smells; and (P3) making recommendations to mitigate biases and value breaches. To facilitate value programming, we propose a framework that allows for automated annotation of software code with respect to human values. The proposed framework lays a solid foundation for inspecting human values in code and making recommendations to overcome biases and value breaches in software.

Presenter Bio: Davoud is a lecturer at the School of Computing and IT, UOW. He has a Ph.D. in Software Engineering with more than 10 years of professional experience as a software engineer, entrepreneur, and researcher. He has developed successful industrial grant applications and participated in architecting and building enterprise systems. His research lies at the intersection of software engineering, artificial intelligence and social sciences. Davoud is particularly interested in building software that adapts to the values and emotions of the users.

Visual Question Answering

Date: 17 April, 2020
Time: 1:30pm onwards
Venue: WebEx

Presenter: Thao Minh Le, Deakin University, Australia

Abstract: Deep learning has recently achieved remarkable successes and become a de facto approach to many computer vision problems. Its superb performance is, however, limited to tasks mostly requiring visual perception. It is still very challenging to solve tasks requiring new knowledge acquired through multi-step inference. In this talk, I present our research on learning to reason visually by asking machines to respond to a natural question based on knowledge presented in a visual scene, either from a static image or a dynamic scene from a video. This visual question answering task is multi-disciplinary by nature, which constitutes the high-level understanding of both vision and language, hence, considered to be a good proxy for visual reasoning.

Presenter Bio: Thao is currently a second-year PhD student at Applied Artificial Intelligence Institute, Deakin University. He works on how machines learn and reason about the world from what they see. His interests are in deep learning and its applications to computer vision and biomedicine. Going back in time, he obtained a Bachelor of Engineering from Hanoi University of Science and Technology in 2014 and a Master of Engineering from Tokyo Institute of Technology under the Japanese Government MEXT Scholarship Program in 2018.

How different are different diff algorithms in Git?

Date:  27 March, 2020
Time: 1:30pm onwards
Venue: WebEx

Presenter: Yusuf Sulistyo Nugroho, Nara Institute of Science and Technology (NAIST), Japan

Abstract: Automatic identification of the differences between two versions of a file is a common and basic task in several applications of mining code repositories. Git, a version control system, has a diff utility and users can select algorithms of diff from the default algorithm Myers to the advanced Histogram algorithm. From our systematic mapping, we identified three popular applications of diff in recent studies. On the impact on code churn metrics in 14 Java projects, we obtained different values in 1.7% to 8.2% commits based on the different diff algorithms. Regarding bug-introducing change identification, we found 6.0% and 13.3% in the identified bug-fix commits had different results of bug-introducing changes from 10 Java projects. For patch application, we found that the Histogram is more suitable than Myers for providing the changes of code, from our manual analysis. Thus, we strongly recommend using the Histogram algorithm when mining Git repositories to consider differences in source code.

SmartShield: Automatic Smart Contract Protection Made Easy

Date: 20 March, 2020
Time: 1:30pm onwards
Venue: 6.312 – Smart Building

Presenter: Dr. Siqi Ma, CSIRO, Data61

Abstract: The immutable feature of blockchain determines that traditional security response mechanisms (e.g., code patching) must change to remedy insecure smart contracts. The only proper way to protect a smart contract is to fix potential risks in its code before it is deployed to the blockchain. However, existing tools for smart contract security analysis focus on the detection of bugs but seldom consider the code fix issues. Meanwhile, it is often time-consuming and error-prone for a developer to understand and fix flawed code manually. In this paper we propose SmartShield, a bytecode rectification system, to fix three typical security-related bugs (i.e., state changes after external calls, missing checks for out-of-bound arithmetic operations, and missing checks for failing external calls) in smart contracts automatically and help developers release secure contracts. Moreover, SmartShield guarantees that the rectified contract is not only immune to certain attacks but also gas-friendly (i.e., a slightly increase of gas cost). To evaluate the effectiveness and efficiency of SmartShield, we applied it to 28,621 real-world buggy contracts on Ethereum blockchain (as of January 2nd 2019). Experiment results demonstrated that among 95,502 insecure cases in those contracts, 87,346 (91.5%) of them were automatically fixed by SmartShield. A following test with both program analysis and real-world exploits further testified that the rectified contracts were secure against common attacks. Moreover, the rectification only introduced a 0.2% gas increment for each contract on average.

Presenter Bio: Dr. Siqi Ma is a postdoctoral research fellow from CSIRO, Data61. She is mentored by Prof. Elisa Bertino and Dr. Surya Nepal. Before joining Data61, she was graduated from Singapore Management University. She has a lot of research interests including IoT security, mobile security, blockchain, etc. Within these areas, she mainly focuses on detecting bugs and vulnerabilities from their implementation codes. She has published lots of top conference and journal papers in Cybersecurity and software engineering areas such as TIFS, ESORICS, SANER.

Engineering Ethics-Aware and Privacy-Respecting Personal Agents

Date: 6 March, 2020
Time: 1:30pm onwards
Venue: 6.209 – Smart Building

Presenter: Nirav Ajmeri

Abstract: Ethics is inherently a multiagent concern. However, research on AI ethics today is dominated by work on individual agents: (1) how an autonomous agent may harm or (differentially) benefit people in hypothetical situations (the so-called trolley problems) and (2) how a machine learning algorithm may produce biased decisions or recommendations. The societal framework is largely omitted. We seek to advance the science of privacy by tackling nuanced notions of privacy, understood as an ethical value. We envision ethics-aware and privacy-respecting socially intelligent agents that facilitate natural interactions among autonomous social entities (people and organizations). To develop foundations for such agents, we adopt a sociotechnical stance in which agents (as technical entities) help autonomous social entities (people and organizations). The challenge in realizing such ethical agent is --- how to understand social reality, i.e., how to understand social norms, social context, values, and ethics. Addressing this challenge, in this talk, I will focus on the research questions of how to (1) model social intelligence, (2) understand social context, and (3) reason about value preferences.

Presenter Bio: Nirav Ajmeri is a Postdoctoral Research Scholar in Computer Science at North Carolina State University. His research interests are in artificial intelligence and software engineering, with a focus on cybersecurity and privacy. Nirav's research seeks to facilitate engineering socially intelligent agents. His research has appeared in prominent artificial intelligence, software engineering, and computing venues including IJCAI, AAAI, AAMAS, TOSEM, RE, KER, JSS, Computer, Intelligent Systems, and Internet Computing. Nirav was awarded the Outstanding Dissertation Award by the Department of Computer Science at North Carolina State University for the year 2019.

Engineering Ethical Multiagent Systems

Date: 25 February, 2020
Time: 1:30pm - 3pm
Venue: 6.105 – Smart Building

Presenter: Professor Munindar P. Singh, North Carolina State University, USA

Abstract: Advances in AI techniques and platforms have triggered a lively discourse on ethical decision making by autonomous agents. Much recent work in AI concentrates on the challenges of moral decision making from a decision-theoretic perspective, and especially the representation of various ethical dilemmas. Such dilemmas in general do not yield productive research questions. In contrast, we consider ethics not from the standpoint of decision making by an agent but of the wider sociotechnical systems (STS) in which the agent operates. We address the problem of designing ethical systems founded on governance based on stakeholder values and social norms, which yield a precise conception is accountability. We turn to the work of Rawls as a basis for criteria to judge ethicality. We address the problem of designing agents that navigate social norms by selecting ethically appropriate actions by understanding their users' preferences among values. Our framework incorporates multicriteria decision making to aggregate value preferences of users and select an ethically appropriate action. We find via a simulation seeded with a survey of user values and attitudes that our agents produce ethical actions that exhibit the Rawlsian property of fairness and yield a satisfactory social experience to their users.

Presenter Bio: Dr. Munindar P. Singh is an Alumni Distinguished Graduate Professor in the Department of Computer Science at North Carolina State University. Munindar's research interests include artificial intelligence and multiagent systems with applications in cybersecurity, privacy, and social computing. He is a codirector of the DoD-sponsored Science of Security Lablet at NCSU, one of six nationwide. Munindar was the editor-in-chief of the ACM Transactions on Internet Technology from 2012 to 2018 and the editor-in-chief of IEEE Internet Computing from 1999 to 2002. His current editorial service includes IEEE Internet Computing, Journal of Autonomous Agents and Multiagent Systems, IEEE Transactions on Services Computing, and ACM Transactions on Intelligent Systems and Technology. His previous editorial service includes the Journal of Artificial Intelligence Research and the Journal of Web Semantics. Munindar served on the founding board of directors of IFAAMAS, the International Foundation for Autonomous Agents and MultiAgent Systems. He also served on the founding steering committee for the IEEE Transactions on Mobile Computing. Munindar was a general cochair for the 2005 International Conference on Autonomous Agents and MultiAgent Systems and a general cochair for the 2016 International Conference on Service-Oriented Computing. Munindar is a Fellow of both the Institute of Electrical and Electronics Engineers (IEEE) and the Association for the Advancement of Artificial Intelligence (AAAI). He won the 2020 ACM/SIGAI Autonomous Agents Research Award as well as the 2016 IFAAMAS Influential Paper Award for his 1998 paper on agent communication. He won NC State University's Outstanding Research Achievement Award in 2015 and 2017, was selected as an Alumni Distinguished Graduate Professor in 2016, and is a member of NCSU's Research Leadership Academy. Munindar's research has been recognized with awards and sponsorship by (alphabetically) Army Research Lab, Army Research Office, Cisco Systems, Consortium for Ocean Leadership, DARPA, Department of Defense, Ericsson, IBM, Intel, National Science Foundation, and Xerox. Twenty-seven students have received PhD degrees and thirty-two students MS degrees under Munindar's direction. Home page, CV

Secure Adaptive Enterprise Architecture for Digital Innovation and Transformation

Date: 30 January, 2020
Time: 4pm onwards
Venue: 6.105 – Smart Building

Presenter: Associate Professor Asif Gill, University of Technology Sydney, Australia

Abstract: Traditional approaches to enterprise architecture focus on heavy documentation and visual static modelling. Recent challenges of digital innovation and transformation enabled by the emerging technologies and trends such as agile, AI/ ML, DevOps, IoT, security and privacy require more adaptive and data driven approach to enterprise architecture documentation and modelling. This talk will discuss once such approach of data-driven secure adaptive enterprise architecture and an industry case study.

Presenter Bio: Asif Gill is a result-oriented academic cum practitioner with extensive 20+ years’ experience in IT in various sectors including banking, consulting, education, finance, government, nonprofit, software and telecommunication. He is Associate Professor & Director of the DigiSAS Lab at the School of Computer Science, UTS. He is also a Director and founder of the Adapt Inn Pty Ltd professional IT advisory company. His earlier professional experience in agile software development, IT business analysis, solution architecture, cyber security and program management provided a strong foundation for later work in strategic enterprise architecture and governance. He specialises in adaptive enterprise architecture & information-centric secure digital ecosystems.

Algorithmic guidance for solving problems - 'software that mimics a consultant'

Date: 9 January, 2020
Time: 4pm onwards
Venue: 6.105 – Smart Building

Presenter: NiranJanuary Deodhar, Open Orbit

Abstract: Enterprises spend more than $100 billion a year globally on improving their business but it takes them longer and costs them more than it should. What if the science and art of solving problems was turned on itself? What if the efficiency expert took their own medicine and drove better, cheaper, faster projects? What if we could remove waste and variation from within the very projects aimed at reducing waste and variation in businesses?


A special workshop by Celonis, the world's largest vendor of process mining software

Date: 27 September, 2019
ime: 4pm - 6pm
Venue: 32.G01 - UOW

Workshop on Robotic Process Automation (RPA) conducted by UIPath, the world's largest vendor of RPA software

Date: 6 September, 2019
Time:1:30pm - 4:30pm
Venue: 32.G01 - UOW

Logistics for data mining

Presenter:  Professor Hong-Cheu Liu,  Decision Systems Lab, UOW, Australia

Date: 8 August, 2019
Time: 4pm onwards
Venue: 6.105 – Smart Building

On Conforming and Conflicting Values 

Presenter:  Dr. Kinzang Chhogyal,  Decision Systems Lab, UOW, Australia

Date: 4 July, 2019
Time: 4pm onwards
Venue: 6.105 – Smart Building

Bayesian inference for large-scale and computationally expensive  landscape evolution models 

Presenter: Dr. Rohitash Chandra, University of Sydney, Australia

Date: 7 February, 2019
Time: 4pm onwards
Venue: 6.105 – Smart Building

Abstract: Badlands (basin and landscape dynamics model) is geoscientific model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that need to be estimated or optimized  with appropriate uncertainty quantification, given the observed ground truth such as surface topography, sediment thickness and stratigraphy through time. This is challenging due to the scarcity of data, sensitivity of the parameters and complexity of the Badlands model. BayesLands is a Bayesian framework for Badlands that fuses information obtained from complex forward models with observational data and prior knowledge. Our previous work showed that BayesLands yields a promising estimation  of free parameters such as precipitation and erodibility for small-scale problems. This presentation gives an overview of BayesLands that features parallel tempering in a high performance computing environment. Furthermore, current progress in surrogate-assisted parallel tempering for is shown that encourages its use for continental scale landscape evolution problems. This motivates the use of evolutionary optimisation methods in synergy with parallel tempering for related geo-scientific models and computationally expensive optimisation problems.

Presenter bio: Dr. Rohitash Chandra is USyd Research Fellow at the School of Geosciences and Centre for Translational Data Science. He holds a Ph.D in Artificial Intelligence (Neural Networks) from Victoria University of Wellington (2012).Dr. Chandra's research interests are in areas of deep learning, neuro-evolution, Bayesian methods, solid Earth Evolution, reef modelling and mineral exploration. Currently, he is involved in projects that employ machine learning and Bayesian inference via parallel tempering for solid Earth evolution, mineral exploration, and reef modelling.

 Logic for Database Systems Implementation

Presenter: Professor David Toman, University of Waterloo, Canada

Date: 7 January, 2019
Time: 3pm onwards
Venue: 6.105 – Smart Building

Abstract: An important part of database technology is the requirement that only a logical appreciation of data is necessary on the part of application developers. This allows the formulating queries (and update requests) without information relating to concrete data sources and their low-level interfaces. A fundamental problem---called query compilation--must therefore be addressed by such systems, the problem of translating user requests over purely conceptual and domain specific ways of understanding of data, commonly called logical designs, to efficient executable programs, called query plans, responsible for evaluating the requests by accessing various concrete data sources through their low-level often iterator-based interfaces. An appreciation of the concrete data sources, their interfaces, and how such capabilities relate to logical design is in turn called a physical design. In the talk we explore how standard KR approaches, such as ODBA-style querying, relate to the above problem and how KR (and Logic at large) techniques can serve as a cornerstone to a comprehensive solution to the query compilation problem. We (briefly) discuss range of topics from adaptations of theorem-proving techniques to low-level query optimizations, commonly considered beyond the reach of logical approaches to query compilation, and conclude with a list of interesting research topics.

Presenter bio: Professor David Toman is a faculty member of the Cheriton School of Computer Science’s Data Systems Group. His research focuses on logic-based foundations of knowledge representation with applications to information systems and databases. He has designed several decidable knowledge representation languages based on description logics and developed efficient algorithms for ontology-based query answering. He is the recipient (with co-authors) of Ray Reiter Prizes in 2010 and 2016. Professor Toman received his Bachelor’s and Master’s degrees from the Masaryk University in the Czech Republic in 1992 and his PhD in computer science from Kansas State University in 1996. After graduation, he was a NATO/NSERC Postdoctoral Fellow at the University of Toronto and then joined the Department of Computer Science at the University of Waterloo in 1998. He was also a Visiting Professor at the Universities of Bolzano and TU Dresden as a part of the EU’s Erasmus Mundus Computational Logic Programme.