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Keynote Speakers


Prof. Kostas J. Kyriakopoulos
Director
  Postgraduate Program on Automation Systems, National Technical University of Athens, Greece
Professor
  National Technical University of Athens, Greece
Research Interests
Control Systems & Robotics: Applications to Autonomous Systems (ground, underwater, aerial)
Cooperation in Autonomous Systems: Intelligent Control Design to compensate for Lean Communication

Our presentation is centered around the development of provable cooperation schemes for sensor-based motion planning and interaction control of autonomous systems. Our goal is to design sound interfaces of our provable techniques with higher-level machine-intelligence based decision making schemes with the purpose of minimizing explicit communication. We address a decentralized motion planning & control architecture for cooperative loading task using heterogeneous robots operating in a constraint workspace with static obstacles. The optimal loading configuration is selected considering the connectivity of the space and the distance between the robots. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance of the proposed strategy is experimentally verified in a variety of loading scenarios. We continue with a novel distributed leader-follower architecture for Cooperative Manipulation of Multiple Underwater Vehicle Manipulator Systems (UVMS) under Lean Communication. The leading UVMS, which has knowledge of the desired trajectory, tries to achieve tracking behavior via an impedance control law, leading the overall formation towards the goal configuration while avoiding collisions with the obstacles. The following UVMSs estimate the object's desired trajectory via a novel prescribed performance estimation law and implement a similar impedance control law. No explicit data is exchanged online among the robots. Various simulations and experiments clarify the proposed method and verify its efficiency.

Dr. Kostas J. Kyriakopoulos was born in Athens, Greece in 1962 and received a Diploma in Mechanical Eng (Honors) from the National Technical University of Athens (NTUA), in 1985 and the MS & Ph.D. in Computer & Systems Engineering from Rensselaer - ECSE, Troy, NY in 1987 and 1991, respectively. From 1988 to 1991 he did research at the NASA Center for Intelligent Robotic Systems for Space Exploration. Between 1991-93 he was an Assistant Prof. at ECSE - RPI and the NY State CAT in Automation and Robotics. Since 1994 he has been with the Mechanical Engineering Department at NTUA, Greece, where he serves as a Professor, Director of the Post-Graduate Program on "Automation Systems", Director of the Control Systems Lab and Director of the Departmental Computation Lab. His interests are in Nonlinear Control and Embedded Systems applications in Sensor Based Motion Planning & Control of multi-Robotic Systems: Manipulators & Vehicles (Mobile, Marine and Aerial). He was awarded the G.Samaras award of academic excellence (NTUA), the Bodosakis Foundation Fellowship (1986-1989), the Alexander Onassis Foundation Fellowship (1989-1990) and an Alexander Von Humboldt Fellowship (1993). Dr. Kyriakopoulos has published more than 320 papers in journals and refereed conferences; he is Specialty Chief Editor for "Frontiers in Robotics and AI" and he serves in the editorial committees of a number of journals and conferences, while he has served as an administrative member of a number of international conferences. He has acted as a PI in 35 R & D projects, half of which were funded by the European Commission. He is an IEEE Fellow.



Prof. Shou-De Lin
Professor
  National Taiwan University, Taiwan
Research Interests
Machine Learning, Big Data Analytics, Knowledge Discovery and Data Mining, Natural Language Processing, Internet of Things Data Analysis, Social Network Data Analysis
Toward Better Understanding of Encoder-Decoder based Deep Neural Network Models

The Encoder-Decoder models based on Recurrent Neural Network (or Seq2Seq model in brief) or its variant such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) cell has demonstrated decent success in many areas. However, it is still a mystery why a recurrent network with such simple structure can achieve various complex tasks. In this talk, I will first demonstrate the power of a Seq2Seq model in learning hidden connection between inputs and outputs, and then offer an in-depth analysis to the neural level to explain how and why it works. Finally I will describe a general strategy to perform in-depth analysis for deep recurrent neural networks.

Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS degree in EE department from National Taiwan University, an MS-EE degree from the University of Michigan, an MS degree in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, 2015, 2016 merit paper award in TAAI 2010, 2014, 2016, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He is also a freelance writer for Scientific American.




Prof. Minyi Guo
Head
   Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
Director
   Embedded and Pervasive Computing Center, Shanghai Jiao Tong University, China
Professor
   Shanghai Jiao Tong University, China
Research Interests
Parallel and Distributed Processing; Parallelizing Compilers; Cloud Computing; Pervasive Computing; Software Engineering, Embedded Systems; Green Computing; Wireless Sensor Networks.
Towards the new Architecture for Urban Big data processing Systems

Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data implies rich knowledge about a city and can help tackle these challenges when used correctly. That is, holistic urban big data plays the key role in smart city constructions. However, processing urban big data needs the specific computing engine different with traditional one such as Hadoop and Spark, because the sensing and knowledge representation are more complicated than domain-specific big data. In this talk, we will give some properties for processing urban big data and introduce a new platform for processing and analyzing urban big data. Then we discuss how the collaborative computing bridges the data and computation in the cyber space and the environment, systems, people and things in the physical world.

Minyi Guo received the BSc and ME degrees in computer science from Nanjing University, China; and the PhD degree in computer science from the University of Tsukuba, Japan. He is currently Zhiyuan Chair professor of Shanghai Jiao Tong University (SJTU), China. Before joined SJTU, Dr. Guo had been a professor of the school of computer science and engineering, University of Aizu, Japan. Dr. Guo received the national science fund for distinguished young scholars from NSFC in 2007, and was supported by "Recruitment program of Global Experts" in 2010. His present research interests include parallel/distributed computing, compiler optimizations, embedded systems, pervasive computing, big data and cloud computing. He has more than 400 publications in major journals and international conferences in these areas. He received 7 best/highlight paper awards from international conferences including ALSPOS 2017 and ICCD 2018. He is now on the editorial board of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing and Journal of Parallel and Distributed Computing. Dr. Guo is a fellow of IEEE, and a fellow of CCF.



Prof. Seungjin Choi
President
  KIISE AI Society, Korea
Chief Technology Officer
  BARO, Korea
Research Interests
Bayesian Optimization, Deep Meta Learning, Deep Reinforcement Learning, Random Projection, Matrix Factorization
Learning to Learn for Few-shot Problems and Warm-starting

Deep learning has achieved great success in various tasks, when it is trained with large amount of labeled data. However, it is a challenging task to train deep models using only a handful of labeled examples. Few-shot learning, the goal of which is to learn from only a few examples in each class label, has been a popular subject in machine learning and computer vision communities. Learning to learn, which is also known as meta-learning, has emerged again as a promising approach to tackle few-shot problems. In this talk, I introduce recent advances in meta-learning, the underlying idea of which is to leverage past experience to learn a prior over tasks, so that it can quickly adapt to a novel task. I begin with a few metric-based deep learning methods that have been developed to solve a few-shot learning task. I give an information-theoretic unified view of existing metric learning methods and present a method for learning discrete latent representation shared across relevant tasks to enable a model to adapt to new tasks quickly. Experiments show that DIMCO requires less memory (i.e., code length) for performance similar to previous methods and that our method is particularly effective when the training dataset is small. I also illustrate gradient-based meta-learning, such as model-agnostic meta-learning (MAML), emphasizing my recent work on MT nets that learn layer-wise subspace and metric from a set of tasks. Finally I also introduce a method to warm-start Bayesian optimization which is a critical technique for AutoML, experimental design, or neural architecture search.

Seungjin Choi received B.S. and M.S. degrees in electrical engineering from Seoul National University, Korea,n in 1987 and 1989, respectively, and a Ph.D. degree in electrical engineering from the University of Notre Dame, Indiana, in 1996. He was with the Laboratory for Artificial Brain Systems, RIKEN, Japan, in 1997, working with Prof. Andrew Cichocki and Prof. Shunichi Amari on independent component analysis as a Frontier researcher. He was an Assistant Professor in the School of Electrical and Electronics Engineering, Chungbuk National University from 1997 to 2000. From 2001 to 2019, he was a Professor of Computer Science at Pohang University of Science and Technology, Korea. He was the director of Machine Learning Research Center where about 15 professors in top five universities participated. He has held consulting professor position for Shinhan Big Data Center, Samsung Research, Samsung Advanced Institute of Technology. He is currently a president of AI Society in KIISE. He is serving or has served as Area Chairs or Senior Program Committee for top-tier machine learning or AI conferences, including NeurIPS, ICML, AISTATS, AAAI, IJCAI. His primary research interests include statistical machine learning and probabilistic inference. Recently he is working on meta-learning and Bayesian optimization.


2019

Prof. Frank Biocca
Chair
  Department of Informatics, Ying Wu College of Computing
  New Jersey Institute of Technology, USA
Professor
  New Jersey Institute of Technology, USA



Towards Mobile Experience of Virtual and Augmented Reality

Virtual and augmented reality systems are evolving so that phone-based and increasingly immersive experiences are enabled in mobile settings. In this talk we examine hardware and interface design issues in mobile augmented reality and how they affect user experience of larger virtual environments.



Prof. Yasushi Kiyoki
Former President
  Database Society of Japan
Professor
  Keio University, Japan



A SPA-based Semantic Computing System for Natural and Social Environment-Analysis and Visualization with "Multi-Dimensional World-Map"

Humankind, the dominant species on Earth, faces the most essential and indispensable mission; we must endeavor on a global scale to perpetually restore and improve our natural and social environments. One of the essential computations in environmental study is context-dependent semantic-computing to analyze the changes of various situations in a context dependent way with a large amount of environmental information resources.
It is also significant to memorize those situations and compute environment change in various aspects and contexts, in order to discover what are happening in the nature of our planet. We have proposed a multi-dimensional computing model, the Mathematical Model of Meaning (MMM) in 1994. As a global environmental system based on MMM, we have realized "5-Dimensional World Map System" for integrating and analyzing environmental phenomena in ocean and land. We introduce the concept of "SPA (Sensing, Processing and Analytical Actuation Functions" for realizing a global environmental system, to apply it to 5-Dimensional World Map System. This concept is effective and advantageous to design environmental systems with Physical-Cyber integration to detect environmental phenomena as real data resources in a physical-space (real space), map them to cyber-space to make analytical and semantic computing, and actuate the analytically computed results to the real space with visualization for expressing environmental phenomena, causalities and influences. The 5D World Map System is globally utilized as a Global Environmental Semantic Computing System, in SDG14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes ).


Prof. Chidchanok Lursinsa
Professor
  Chulalongkorn University, Thailand



Concept of Discard-after-learn and Multi-stratum Neural Networks with Application

One of the challenging problems in training a neural network is how to train the network so that the number of epochs is approximately a linear time in terms of number of training patterns and the percent of testing accuracy is still in the acceptable range. In this talk, we will discuss the concept of discard-after-learn for training a multi-stratum neural network to achieve this bound under the constraints of data overflow, streaming, time -space complexity preservation, and other issues such as plasticity of the network. An application to the problem in cyber security will also be demonstrated.


Prof. Minh-Triet Tran
Vice Rector
   University of Science, VNU-HCM
Professor
   University of Science, VNU-HCM



Analysing Daily Activity Logs for Smart Ubiquitous Environment

Collecting and analyzing daily activity logs can provide potential insights for better understanding and possible optimization for individual and organizational activities and operations. Lifelog data can be in various media format. It may include audio data recorded during conversations, photos or video clips captured by wearable or regular personal cameras, or even biometric data, such as heart rate or calorie burn. Visual lifelog data is one of the most essential sources for personal diary generation as it has rich potential information and it is easy to be collected. It is an increasing demand to process and analyze daily activity logs, mostly in visual format, to develop useful services and utilities for smart environments. In this talk, we introduce several modalities to analyze and interact with lifelog data to develop potential applications for smart environments. The proposed systems are based on practical social needs and aim to provide people with natural experience with smart services and utilities in a ubiquitous environment.

  • People can access augmented data and services by recognizing the current context and retrieving similar known cases.
  • Lost items can be found or memories can be retrieved or verified by searching daily logs.
  • Reminiscence can help people to positively revive past memories and connections with their relatives.
  • Regular events and anomalies can be detected from surveillance systems for appropriate actions.
  • Image captioning to automatically generate (text) daily diary from visual data.
We also discuss privacy and security issues in collecting and analyzing daily activity logs.

2018


Prof. Angel P. del Pobil
Director
  Robotic Intelligence Laboratory, Jaume I University
Professor
  Engineering and Computer Science Deparment
Jaume I University, Spain



Speech Title: Robots as Cyberphysical Systems: The Challenges Ahead

An intelligent robot is a perfect paradigm of a cyber-physical system (CPS), since its very nature is based on the seamless integration of computational algorithms and physical components, including embedded sensors, processors and actuators in order to sense and interact with the physical world. In my speech I will address some of the challenges for robots considered as CPS, such as adaptability, autonomy, functionality, resiliency, and safety, with emphasis on the physical interaction with the environment. As test cases I will consider robots as personal assistants, along with robots in online shopping warehouses, as an example towards the 4th industrial revolution, the so-called Industry 4.0, with some lessons learned from our recent participation in the Amazon Robotics Challenge 2017 that took place in Nagoya in July 2017. I will also discuss some implications in terms of the interactions of information processing, communication and control of physical processes, with especial emphasis on the difficulties that dealing with open-ended physical entities can bring.



Prof. James Won-Ki Hong
Dean
  Graduate School for Information Technology, POSTECH, Korea
Professor
  Dept. of Computer Science and Engineering
POSTECH, Korea


Speech Title: Towards Carrier Network Virtualization and Applications

Network Virtualization is the technology to enable the creation and management of logical networks on the top of shared underlying physical network. The network virtualization technology has the potentials to significantly reduce both the capital expenditures (CAPEX) and operating expenses (OPEX) of network by supporting multi-tenancy, flexibility, programmability, scalability, and agility. At the same time, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have achieved remarkable advances by changing the networking paradigm during the last decade. The collaboration of SDN, NFV and network virtualization technologies can brings various potential benefits and opportunities for carrier-grade networks, not only data center networks. However, most of network virtualization solutions are deployed in data center networks for cloud platforms to provide connectivity between virtual machines. The way to achieve network virtualization for carrier-grade networks is still far away in terms of reliability, manageability, resiliency, performance, and security. Moreover, network virtualization of carrier-grade networks can be used to realize service slicing, one of the 5G network visions.



Prof. Feng Xia
Assistant Dean
  School of Software
Dalian University of Technology, China
Head
  Department of Cyber Engineering
Dalian University of Technology, China
Professor
  Dalian University of Technology, China


Speech Title: Data Science in Science

As data (especially big data) become the new oil, data science has recently attracted intensive and growing attention from industry, government, and academia. Data science focuses on deriving valuable knowledge from (raw) data in an efficient and intelligent manner, with the purpose of prediction, exploration, understanding, and/or intervention. It encompasses the set of methods and tools that enable data-driven activities in business, government, and scientific research. In particular, data science is playing an increasingly important role in scientific research. One evidence is the so-called fourth paradigm of science, which features data-intensive scientific discovery. Another is the emergence of scholarly big data. Recent years have witnessed the exponential growth of scholarly data in all scientific disciplines. The rapid rise of big scholarly data brings about new issues and challenges with respect to e.g. information retrieval, data management and analysis. Data science in science that exploits scholarly big data enables us to better understand the nature of science, giving rise to a lot of potentials on addressing the challenges. This talk will look into recent advances in this field, and discuss relevant opportunities and challenges.



Prof. Shahrul Azman Mohd Noah
Professor
  Universiti Kebangsaan Malaysia, Malaysia


Speech Title: Ontology and What It Has to Do with Information Retrieval

The discipline of philosophy define ontology as the science of 'what is', the kinds of structures of objects, properties, events, processes and relations in every area of reality. From the perspective of computing, an ontology is an engineering artefact which is constituted by a specific vocabulary used to describe a certain reality, plus a set of explicit assumptions regarding the intended meaning of the vocabulary. Thus, an ontology describes a formal specification of a certain domain, which is a formally shared understanding of a domain of interest that are machine manipulable. Ontologies have been applied in many knowledge-based applications such as decision support systems, expert systems and question-answering systems. Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers). In contrast to ontology-based systems, IR systems rely on the bag-of-word representation approach to retrieve documents. As such, the contextual and semantic meaning of terms as they appear in the documents are lost. Various efforts have been put forward in order to improve the retrieval performance of existing IR systems, such as: feature-based models, term dependence models and entity-based models. In this talk, we describe the use of ontology to enhance the retrieval performance of IR systems. We will first review various applications of ontology to support or enhance semantic IR. We will then show some of the research that we have or currently embarked on ontology-based information retrieval, namely: semantic digital library; crime news retrieval and multimodal ontology retrieval. We then conclude the talk with challenges and future research work in the area.

2017


Prof. Hiroyuki Kitagawa
Former President
  Database Society of Japan
Professor
  Center for Computational Sciences
University of Tsukuba, Japan



Real World Big Data Integration and Analysis: Research Issues and Challenges

Big data technologies have been bringing a huge impact on every aspect of human activities and human society and transforming the world. They are serving as a major driving force which will lead us to the next generation society and industry. In recent several years, many research and development projects have been launched to advance big data technologies and apply them to real societies around the globe. In Japan, the Ministry of Education, Science, Culture, Sports, Science and Technology (MEXT) started "Research and Development on Real World Big Data Integration and Analysis" two years ago, and we, a research team formed by researches from four major Japanese universities, are collaborating and actively working towards its goals. This talk will give an overview of the project including its objectives and goals, main research activities, and the research outcomes obtained in the past two years. Big Data is often characterized by several V's such as Volume, Variety, Velocity, Veracity and Value. This talk will especially highlight our research efforts to address Variety and Velocity issues, since utilization and integration of real-time social streaming data is one of key research issues in the project. I will elaborate on them such as our event-oriented stream processing system, stream OLAP analytics, unified big data processing framework integrating streaming and batch processing, and meta data inference techniques for big streaming data integration.



Prof. Jin Woo Kim
Professor
  School of Business
Yonsei University, Korea


Digital Companions: A combination of HCI and AI for Life-Companionship

In the past, we had been surrounded by many human companions who share activities with us, who involve social relations with others, and who knows us well. Companions include but not limited to husband and wife, sisters and brothers, and good old friends. However, these human companions have become less available recently due to numerous cultural and economic reasons. The lack of life companions leads to serious social problems, such as depression and suicide. In order to cover up the lack of human companions, Digital Companions have been suggested such as Pepper and Jibo. In this talk, I would like to present a conceptual model of digital companion that includes pre-requisites and core components of ideal companions. The conceptual model is composed of seven core technologies, which combine HCI and AI. This talk will explain the seven technologies and present video clips from SF movies that clearly exhibit future direction of digital life companion.



Prof. Dong-Hee Shin
Distinguished Scholar
  Ministry of Education (National Research Foundation), Korea
Professor
  School of Media and Communication
Chung-Ang University, Seoul, Korea


A User-based Model of Quality of Experience for the Internet of Things

The exponential growth of services via the Internet of Things (IoT) is making it increasingly important to cater to the quality expectations of end users. Quality of experience (QoE) can become the guiding paradigm for managing quality provisioning and application design in the IoT. This study examines the relationship between consumer experience and quality perception of IoT and develops a conceptual model for QoE in personal informatics. Using an ethnographic observation, it first characterizes quality of service (QoS) and subjective evaluation to compare QoS with QoE. It then performs a user survey to identify user behavior factors in personal informatics. It finally proposes a user experience model, conceptualizing QoE specific to personal informatics and highlighting its relationships with other factors. The model establishes a foundation for IoT service categories through a heuristic quality assessment tool from a user-centered perspective. The results overall provide the groundwork for developing future IoT services with QoE requirements, as well as for dimensioning the underlying network provisioning infrastructures, particularly with regard to wearable technologies.

2016
Prof. Jim Jansen Professor
  College of Information Sciences and Technology
Director
  Information Searching and Learning Laboratory
  The Pennsylvania State University, USA
Principal Scientist
Qatar Computing Research Institute, Qatar
   
Prof. Hongbin Zha Professor
  Department of Machine Intelligence
Director
  Key Lab of Machine Perception (MOE)
  Peking University, China

The Transformed Role of the Viewer: Second Screens and the Social Soundtrack

The nearly ubiquitous use of mobile devices integrated with easy interface with social media platforms facilitates a unique social interaction about broadcast media and other events that alters the role of the viewer from a passive to an active function, with the visitor engaged in information sharing, consumption, and dissemination often in real time. This technology affordance for online conversation about an event is referred to as the second screen phenomenon, although there may be multiple (i.e., more than two) screens involved. The resulting online conversation from second screen interaction about an event is referred to as the social soundtrack. The social soundtrack is an interesting conversational form of information sharing, information interaction, and information diffusion. This keynote will introduce the theoretical constructs and empirical measures of social soundtrack and second screen research, along with application of these constructs and measures in current investigations involving millions of posts on multiple social media platforms. Research concerning social soundtrack and secondary screens is important in identifying the influence and affordances that technology has on social media conversations from an information sharing. Research findings can also shed light on social communication in relationship to the cultural impact of broadcast media events, the social interaction in cross technology usage for second screens, and the effect of second screen technologies on pop culture and human information processing.

   

3D Reconstruction for Object Modeling and Scene Analysis

3D reconstruction is an important field in computer vision, and results accumulated in the field have found wide applications in virtual reality, creative media design, and robotics. But nevertheless, we still face great challenges when we try to use the techniques in modeling both objects with complex structures or large-scale scenes. The major difficulties come from several constraints in traditional approaches, including ambiguity and uncertainty inherent in the reconstruction algorithms, limitation on viewpoint movements, occlusion of objects, and low-resolutions of available 3D data. In the talk, I will introduce some newly developed methods aiming to solve the problems by making good use of imaging geometry principles and fusion of data from different sensors. Main topics include: reconstruction from silhouettes from a camera system with two planar mirrors; depth image super-resolution based on similarity-aware patchwork assembly; urban scene description by analysis of 3D data collected from car-mounted sensors. I also will report results from an application of such 3D digitization techniques in heritage documentation, mainly for grotto objects and scenes.



2015
Dr. Jihie Kim Vice President
Software R&D Center
Samsung Electronics, Korea
   
Prof. Mary Beth Rosson Professor and Interim Dean
College of Information Sciences and Technology
Pennsylvania State University, USA

Intelligence in Education

Social software such as online forums, Wikis, and social networking sites, plays an important role in various fields, including science, politics, and education. Our goal is to analyze social activities within online communication and collaboration environments, and develop computational tools that support and promote effective interactions and participation. This talk present our work on online discussion modeling and intelligent tools for assisting discussion participants. We first analyze how messages and individual discussants contribute to Q&A discussions. We present a model for capturing information seeking or information providing roles of messages, such as question, answer or acknowledgement. We also identify user intent in the discussion as an information seeker or a provider. We show how the role information can be combined with linguistic and temporal features for developing a predictive model of discussant performance. We also demonstrate how such role information can be used for promoting interactions among potential peer collaborators.
In the latter part of the presentation, we show how such analyses can be a powerful tool for dialogue mediators and participants. In particular, we present a computational workflow (big data) framework that enables efficient and robust integration and analyses of diverse datasets. The analysis results are used for assisting discussion mediators or facilitating just-in-time adaptation to discussants' needs, such as identifying unresolved issues or help seekers who need more assistance.

   

The iSchool Vision for Interdisciplinary University Research and Education

The emergence of iSchools has been much discussed, with respect to an interdisciplinary vision for both research and education of undergraduate and graduate students. The Pennsylvania State University was one the first iSchools, launched in 1998 to meet the needs for workforce development of students who have the skills of information technology but also to research topics in real world interdisciplinary computing. In this talk, I will give a brief history of how and why this new realm of academic pursuits has emerged, illustrated throughout with examples drawn from education and research activities at Penn State and other iSchools. Reflecting on the past 15 years, I will also point to a set of continuing challenges and opportunities for interdisciplinary study that is founded on the integration of the information sciences, an increasingly ubiquitous technological substrate, and the broad and ambiguous implications of human individuals and organizations situated in real world activities.



2014
Prof. Ben Lee Professor
School of Electrical Engineering
   and Computer Science
Oregon State University, USA
   
Prof. Hamid R. Arabnia Professor
Department of Computer Science
University of Georgia, USA

Wireless HD Video Transmission Technology: Challenges and Future Applications

Wireless High Definition Video Transmission (WHDVT) over 802.11-based networks is an important enabling technology for home networks, viewing videos on the move, and N-screen environments. However, significant challenges exist in delivering smooth playback of HD content as WHDVT becomes more pervasive and multiple streams will need to be supported on the same network. These include lossy and delay prone nature of wireless media, unequal importance of video packets, and user mobility. This talk first introduces the basic concepts of WHDVT, which include characteristics of 802.11 networks, H.264 video compression, and video streaming protocols. Then, several solutions at the various layers will be presented, which include application, RTP/UDP and RTP/TCP, MAC, and physical layers. Finally, the talk will conclude with open research issues and future directions.
   

Bio-Inspired Supercomputing and Big Data

In order to convert data to knowledge, it is necessary to search (+process) data sets that are on the order of zettabytes in size (Big Data). Conventional computers (uniprocessor systems) are unable to process Big Data in a timely manner. Inherent limitations on the computational power of sequential uniprocessor systems have lead to the development of parallel multiprocessor systems. The two major issues in the formulation and design of parallel multiprocessor systems are algorithm design and architecture design. The parallel multiprocessor systems should be so designed so as to facilitate the design and implementation of the efficient parallel algorithms that exploit optimally the capabilities of the system. From an architectural point of view, the system should have low hardware complexity, be capable of being built of components that can be easily replicated, should exhibit desirable cost-performance characteristics, be cost effective and exhibit good scalability in terms of hardware complexity and cost with increasing problem size. In distributed memory multiprocessor systems, the processing elements can be considered to be nodes that are connected together via an interconnection network... The design presented in this talk is bio-inspired.

2013
Prof. Hitoshi Aida Professor
Department of Electrical Engineering and
  Information Systems, School of Engineering
The University of Tokyo, Japan
Chairman
Committee for Information, Computer
  and Communications Policy in
  Organisation for Economic Co-operation
  and Development (OECD)
   
Prof. Tei-Wei Kuo Distinguished Professor
Department of Computer Science
  and Information Engineering
National Taiwan University, Taiwan
Executive Director
Intelligent and Ubiquitous Computing
  Thematic Center of the Research Center
  of the IT Innovation, Academia Sinica, Taiwan
Board Director
Genesys Logic, Taiwan
Chairman
Embedded Systems Group of the National
  Networked Communication Program Office
Taiwan

Renewable Energy Powered, Disaster-Resilient Wireless Network Infrastructure

Because of rapid increase of smart phones, mobile phone operators are desperately trying to offload mobile phone traffic to femto cells, WiFi hotspots or WiMAX coverage. On the other hand in Japan, because many base stations stopped operation due to long commercial power failure or broken fiber trunk after Great East Japan Earthquake, people began thinking resilience of network infrastructure seriously. Attaching large batteries or powering mobile phone base station by renewable energy, however, is not usually practical because of the size and weight of the equipments. In this talk, we investigate about the feasibility of WiFi-based wireless network infrastructure powered by renewable energy, which is connected by fiber trunk and is used to offload mobile phone traffic in ordinary times and act as a wireless-relayed mesh network after disaster.
   

The Positioning of Non-Volatile Memory in Embedded System Designs

In recent years, non-volatile memory has shown its great potentials in serving as a layer in the memory hierarchy, such as flash memory for the secondary storage of mobile devices. Their inherent characteristics also point out new directions in system designs and grand challenges. In this talk, we will first have a brief introduction to the non-volatile memory, especially flash memory and phase change memory. We will then present challenges and solutions for flash memory as a storage medium. The talk is concluded by key challenges for system designs of phase change memory.
2012
Prof. Sajal K. Das University Distinguished Scholar Professor
  Department of Computer Science
  and Engineering
Director
  Center for Research in Wireless
  Mobility and Networking (CReWMaN)
The University of Texas at Arlington, USA
   
Prof. Abdullah Mohd Zin Professor
Faculty of Information Science and Technology
Universiti Kebangsaan Malaysia

Cyber-Physical and Networked Sensor Systems: Challenges and Opportunities

Rapid advancements in embedded systems, sensors and wireless communication technologies have led to the development of cyber-physical systems, pervasive computing and smart environments with important applications such as smart grids, sustainability, health care and security. Wireless sensor networks play significant role in building such systems as they can effectively act as the human-physical interface with the digital world through sensing, communication, computing and control or actuation. However,the inherent characteristics of wireless sensor networks, typified by resource constraints, high degree of uncertainty, heterogeneity and distributed control pose significant challenges in ubiquitous information management. After introducing the basic challenges, opportunities and applications, this talk will present a novel framework for multi-modal context recognition from sensor streaming data, context-aware data fusion, and situation-aware decision making with a trade-off between information accuracy (inference quality) and energy consumption. The underlying approach is based on dynamic Bayesian and probabilistic models, machine learning, information theoretic reasoning, and game theory. The talk will be concluded with open research issues and future directions.
   

Beyond Ubiquitos Computing: The HoneyBee Ensemble Computing Environment

Since the 1980s, computing environment has moved from a centralized environment into a distributed computing environment. The distributed computing environment has also moves from one phase to another. In the 1980s, this distributed environment was provided in the form of a client-server computing, followed by the Internet computing in the 1990s. The wide availability of mobile devices together with wireless network has changed the computing environment into mobile computing and later into pervasive or ubiquitos computing environment. In 2008, European Union Interlink WG1 task group has proposed that the next wave of computing environment should be the ensemble computing in order to answer four major research challenges in the current computing environment. These challenges are (i) massive number of nodes in a system, (ii) open environment, (iii) non-deterministic environment, and (iv) adaptation. In an ensemble computing environment, computing devices can communicate and work together to complete a certain task based on peer-to-peer protocol and supporting services. The advantages of this environment can be summarized as follows: ad hoc interaction, fluidity, transience and scalability. There are two models of ensemble computing: a swarm of bats or a bee-hive. In this paper we will describe our proposed model of an ensemble environment known as the HoneyBee environment. The discussion in this paper is be divided into four main issues. The first issue is about the ensemble computing in general followed by a discussion on the formal model of HoneyBee environment. Some possible applications (two issues) within the HoneyBee environment will be described next. The fourth issue is concerning Agent Oriented Programming, which is considered to be the most suitable software development approach for this type of computing environment.
2011
Prof. S. Shyam Sundar Distinguished Professor of Communications
Co-Director
Media Effects Research Lab
The Pennsylvania State University, USA
   
Prof. Ding-Zhu Du Professor
Department of Computer Science
University of Texas at Dallas, USA

Living Interactively and Socializing Ubiquitously

This keynote talk will address the psychology of living in a ubiquitous computing environment, by focusing on how new technological affordances enable individuals to express agency and build community in an ongoing manner. The recent proliferation of location-based information tools and the popularity of communication technologies that encourage social interaction have contributed to a computationally intensive environment, with users constantly managing information for themselves as well as sharing information with others at unprecedented levels. We constantly straddle real and virtual worlds without making the distinction between the real and the virtual. We have come to expect high-fidelity, context-aware systems that serve to blur the boundary between the two. As a result, rules of interaction management are undergoing dramatic changes, with consequences for design of future systems and interfaces.
   

Next Generation Network, Wireless Network and Topology Control with Small Routing Cost

One of important components in the potential next generation network is the wireless network. Topology control is one vital factor to a wireless network efficiency. Since wireless network has no physical infrastructure, it may lead to a severe problem, known as broadcast storm problem caused by flooding inherent in on-demand routing schemes. Inspired by physical backbone in classical wired networks, the virtual backbone has been proposed and studied extensively in the literature for wireless networks to reduce the damage caused by flooding and to maximize resource utilization. However, when we employ the virtual backbone, two problems may be introduced. The first one is the increasing of routing cost. The second one is that the road load on some links may increase, which may cause traffic jam. How do we solve those problems. In this talk, we will introduce recent research work on their solutions.


 
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