Head of Research
Intelligent Voice Ltd
Title: Advances in speech and language technology: an industry 4.0 perspective
Speech and natural language technology have advanced at a rapid pace in recent years. This advance, a facet of the industry 4.0 era, has been driven in part by GPGPU hardware and the deep learning frameworks that use them, and by the adoption of open-source software by the academic and commercial AI community alike. The spirit of cooperation among researchers in the academic and commercial worlds has resulted in claims of human parity in speech recognition models, and the emergence of numerous architectures based on decision trees, DNNs, CNNs, RNNs and Transformers, to mention but a few. These developments have markedly impacted the way in which humans communicate with computers, and are currently driving numerous commercial products that rely on speech, natural language processing and natural language understanding, loosely termed Conversational AI. This talk will present two real world case studies in the medical and insurance domains that exploit speech and language to augment the ability of human operators to do their jobs more efficiently. These use cases, taken from the presenter’s experience working in the speech and natural language processing commercial world, represent an informative snapshot of the possibilities that speech and natural language processing advances are bringing to Industry 4.0 applications.
Cornelius Glackin graduated from the Ulster University, School of Computing & Intelligent Systems with an MSc in Computing & Intelligent Systems in 2004. Cornelius completed a PhD concerning Spiking Neural Network research at Ulster University in 2009. After six years post-doctoral research experience working at the University of Ulster and the University of Hertfordshire, he then moved to industry. Cornelius is an experienced data scientist with over 15 years research and development experience. He published his first paper in neural network research in 2005 and has gone on to publish over 50 papers in the machine and deep learning fields. Cornelius is Head of Research for Intelligent Voice, where he and his team are engaged in research into Acoustic and Language Model Development, Speech Enhancement, Diarization, Natural Language Processing, Neural Machine Translation, GPU Parallelisation, and Privacy Preserving Computation. In addition to this Cornelius works as a consulting data scientist on behalf of the company.
Department of Computer Science
Auckland University of Technology, Auckland, New Zealand
Professor Nikola Kasabov is Fellow of IEEE (Institute for Electrical and Electronic Engineers), Fellow of RSNZ (Royal Society of New Zealand), Fellow of the College of Fellows of INNS (International Neural Network Society), Distinguished Visiting Fellow of the Royal Academy of Engineering UK, Fellow of the NZ IITP (Institute for IT Professionals). He is the Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI) and Professor of Knowledge Engineering in the School of Engineering, Computing and Mathematical Sciences at AUT.
His main interests are in the areas of: computational intelligence; neuro-computing; bioinformatics; neuroinformatics; speech and image processing; data mining; knowledge representation and knowledge discovery.
He has published over 650 works, among them 250 journal papers, 10 text books, edited research books and monographs, conference papers, book chapters, edited conference proceedings, 28 patents and authorship certificates in the area of intelligent systems, connectionist and hybrid connectionist systems, fuzzy systems, expert systems, speech recognition, bioinformatics, neurocomputing and neural networks. Recently he invented the first neuromorphic spatio-temporal data machine called NeuCube currently used in the labs of 25 countries .
He is a Fellow of the Royal Society of New Zealand, Fellow of the Institute of Electrical and Electronic Engineers (IEEE), Fellow of the Institute for IT Professionals NZ, Distinguished Visiting Fellow of the Royal Academy of Engineering, UK.
He has appoints in other universities, such: Honorary and Visiting Professorships at Teesside University UK, Shanghai Jiao Tong University and ETH/UniZurich and George Moore Professorship at Ulster University. He was awarded Doctor Honoris s Causa of Obuda University Budapest.
Professor Kasabov served as the President of the International Neural Network Society (INNS) (2009-2010), Asia-Pacific Neural Network Assembly (APNNA) (2008), Asia Pacific Neural Networks Society (2019) (APNNS).
Professor Kasabov is the General Chairman of a series of biannual international conferences on Neurocomputing and Evolving Intelligence in New Zealand. He received numerous awards, including: EU Marie Curie Fellowship (2011-2012); the INNS Ada Loveloc Meritorious Aaward (2019); the INNS Gabor Award (2012); The Bayer Science Innovator Award (2007); The Royal Society of New Zealand Silver Medal (2001); the AUT Medal (2105).
Professor Kasabov is Associate Editor of numerous international journals, including Neural Networks. He has extensive academic experience at various academic and research organisations: University of Otago, New Zealand; University of Essex, UK; University of Trento, Italy; Technical University of Sofia, Bulgaria; TU Kaiserslautern Germany; ETH Zurich; Shanghai Jiao Tong University; Ulster University UK. Professor Kasabov has Masters degrees in Computer Science and Engineering, and a PhD in Mathematical Sciences from Technical University, Sofia, Bulgaria. He has supervised to completion 50 PhD students.
Professor,Institute for Advanced Co-Creation Studies
Osaka University, Japan
Concurrent affiliation: The Institute of Scientific and Industrial Research (ISIR))
Title: Video-based Gait Analysis and Its Applications
Gait is considered as one of behavioral biometric modalities which is available even at a distance from a camera without subject cooperation. We can perceive a variety of information from gait: identity, age, gender, emotion, situation, health status, aesthetic attributes (e.g., beautiful, graceful, and imposing). Of these, human perception-based aesthetic attributes are important, because people who pay attention to their fashion style and body shape, may also pay attention to their gaits, i.e., whether their gaits look nice or not. In this talk, I’ll give a brief overview of our recent progresses of video-based gait analysis at the beginning. I then introduce three works on the human perception-based gait aesthetic attribute estimation. All of the methods rely on relative attribute frameworks with relative annotation for paired data (e.g., the 1st one is better, neutral, and the 2nd one is better). While training is processed with paired data with Siamese-type (i.e., two identical streams) deep neural networks, a inference is processed with a single data through one of the stream, i.e., we never need to feed paired data in a test stage. Qualitative and quantitative results are shown with our own constructed gait databases and annotations.
Yasushi Makihara received the B.S., M.S., and Ph.D. degrees in Engineering from Osaka University in 2001, 2002, and 2005, respectively. He was appointed as a specially appointed assistant professor (full-time), an assistant professor, and an associate professor at The Institute of Scientific and Industrial Research, Osaka University, in 2005, 2006, and 2014, respectively. He is currently a professor of the Institute for Advanced Co-Creation Studies, Osaka University. His research interests are computer vision, pattern recognition, and image processing including gait recognition, pedestrian detection, morphing, and temporal super resolution. He is a member of IPSJ, IEICE, RSJ, and JSME. He has obtained several honors and awards, including the 2nd Int. Workshop on Biometrics and Forensics (IWBF 2014), IAPR Best Paper Award, the 9th IAPR Int. Conf. on Biometrics (ICB 2016), Honorable Mention Paper Award, the 28th British Machine Vision Conf. (BMVC 2017), Outstanding Reviewers, the 11th IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG 2015), Outstanding Reviewers, the 30th IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2017), Outstanding Reviewers, and the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology, Prizes for Science and Technology, Research Category in 2014. He has served as an associate editor in chief of IEICE Trans. on Information and Systems, an associate editor of IPSJ Transactions on Computer Vision and Applications (CVA), a program co-chair of the 4th Asian Conf. on Pattern Recognition (ACPR 2017), area chairs of ICCV 2019, CVPR 2020, ECCV 2020, and reviewers of journals such as T-PAMI, T-IP, T-CSVT, T-IFS, IJCV, Pattern Recognition, and international conferences such as CVPR, ICCV, ECCV, ACCV, ICPR, FG, etc.
Associate Professor in Computer Engineering
Massey University, New Zealand
Title: Indoor Positioning System: GPS for Smart Homes and Smart Buildings Leveraging Machine Learning and Internet of Things
Location based services and ambient assisted living are key facilities of smart cities and smart homes. Implementation of such services require a functional positioning system. Outdoor positioning systems like GPS do not work reliably inside buildings. Researchers have been working hard for the past two decades to develop robust, affordable Indoor Positioning System (IPS). Traditional research on IPS is fractured with siloed approach, concentrating on single sensing modality. Robust, functioning IPS can only be realised if it is treated as a multidisciplinary, multisensory problem. The rapid adoption of Internet of Things (IoT) is providing opportunity for implementing IPS by repurposing the pre-existing infrastructure of networked devices and ambient sensors in modern buildings. Machine Learning (ML) techniques present an opportunity for data-driven approach. However, ML requires large training corpus incurring substantial cost of human time and labour. This talk introduces the audience to the Indoor Positioning System and covers.
Associate Professor Fakhrul Alam is the Department Leader of Mechanical & Electrical Engineering, Massey University, New Zealand. He also holds the position of Adjunct Professor with the School of Engineering and Technology of Sunway University, Malaysia for 2021-22. He received BSc (Hons) in Electrical & Electronic Engineering from BUET, Bangladesh, and MS and Ph.D. in Electrical Engineering from Virginia Tech, USA. His work involves the development of Intelligent Systems, Smart Sensors and Precision Instrumentation by leveraging his expertise in Wireless and Visible Light Communication, IoT and Signal Processing. His work has been sponsored, among others, by the New Zealand Ministry of Business, Innovation and Employment (MBIE), Auckland Transport and New Zealand Forest Research Institute Limited. A/Professor Alam a Senior Member of the Institute of Electrical & Electronics Engineers (IEEE), a member of the Institution of Engineering & Technology (IET) and the Association for Computing Machinery (ACM). He is an Associate Editor of IEEE Access, Topic Editor of Sensors (MDPI) and sits on the IEEE Conference Technical Program Integrity Committee (TPIC). He is also the only engineering academic at Massey University to have been elected as the “Lecturer of the Year”.
Life Fellow, IEEE
Associate Editor, IEEE-TIM and Regional Editor of Int Journal of Biomedical Engineering and Technology.
National Physical Laboratory, New Delhi India
Title: Current Nano-Sensors and IoT Systems for Ubiquitous Health Care
Newer and newer sensors are being developed with the progress of science, day by day, for various industrial and biomedical applications. However, more advanced sophisticated sensors and systems are still required to be developed for better health care, with reliable quick diagnosis of a particular disease/abnormality in an intelligent manner as well as for therapeutic treatment of say cancer disease, well in time.The recent developments in Nano-sensors and Nano-systems are discussed here for the measurements for better healthcare applications, particularly for old age patients living in isolated areas.The fabrication aspects of new Nano-sensors and smart systems based on different sensing mechanisms are given.. Nano-chip based sensing systems like ultrasound on a chip, are described in detail. Main emphasis is placed on the development of IOT based and cloud-based Nano-sensors and smart systems for new clinical measurements, in the ubiquitous manner.Cancer nanotechnology and therapeutic treatment of deep seated brain tumors with high intensity focused ultrasound, are described, as case studies.Planning of U-health care program is presented with wireless sensor networking (WSN) in different environments, in an effective manner for better health cares.The present study would open a new area of research in the biomedical field.
Chair in Cyber Security
Professor, Department of Computing and Mathematics
Manchester Metropolitan University,United Kingdom
Title: Blockchain as a Trustless Security Architecture for Intelligent Critical National Infrastructure
One of the key enabling technologies of smart cities is the Internet of Things (IoT). In recent years, IoT has developed into many areas of application including critical national infrastructure (CNI) such as transport, hospitals and power distribution grid. CNI systems depend heavily on IoT devices to perform autonomous actions or inform human decision makers. The proliferation of IoT applications raised much serious security and privacy concerns. Recently, blockchain has been advocated as a solution for secure data storage and sharing. In this talk, I will start by giving a sneak preview of blockchain technology. Then, I will outline how to implement blockchain as a fundamental theory for trustless security for connected CNI. The discussion will investigate technologies that can be utilised to achieve a trustless matrix such as blockchain and peer-distributed security systems, for instance, onion-routing, with the wider aim of defining trustless security further. The talk also considers the feasibility of trustless IoT security systems and their application in CNI.