8th IFIP International Conference On
Intelligent Information Processing
17-20, October, 2014, Hangzhou, China
International Federation for Information Processing, IFIP TC12
Chinese Association for Artificial Intelligence
Institute of Computing Technology, Chinese Academy of Sciences
T. Dillon (Australia) Z. Wu (China) A. Aamodt (Norway)
Z. Shi (China) D. Leake (USA) U. Sattler (UK)
Challenges of Big Data in Scientific Discovery
Benjamin W. Wah
The Chinese University of Hong Kong
Shatin, Hong Kong
Abstract: Big Data is emerging as one of the hottest multi-disciplinary research fields in recent years. Big data innovations are transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. In this presentation, we examine the key properties of big data (volume, velocity, variety, and veracity) and their relation to some applications in science and engineering. To truly handle big data, new paradigm shifts (as advocated by the late Dr. Jim Gray) will be necessary. Successful applications in big data will require in situ methods to automatically extracting new knowledge from big data, without requiring the data to be centrally collected and maintained. Traditional theory on algorithmic complexity may no longer hold, since the scale of the data may be too large to be stored or accessed. To address the potential of big data in scientific discovery, challenges on data complexity, computational complexity, and system complexity will need to be solved. We illustrate these challenges by drawing on examples in various applications in science and engineering.
Neuromorphic Computing beyond von Neumann
Department of Physics and Astronomy
Abstract: The brain is characterized by extreme power efficiency, fault tolerance, compactness and the ability to develop and to learn. It can make predictions from noisy and unexpected input data. Any artificial system implementing all or some of those features is likely to have a large impact on the way we process information. With the increasingly detailed data from neuroscience and the availability of advanced VLSI process nodes the dream of building physical models of neural circuits on a meaningful scale of complexity is coming closer to realization. Such models deviate strongly from classical processor-memory based numerical machines as the two functions merge into a massively parallel network of almost identical cells. The lecture will introduce current projects worldwide and introduce the approach proposed by the EU Human Brain Project to establish a systematic path from biological data, simulations on supercomputers and systematic reduction of cell complexity to derived neuromorphic hardware implementations with a very high degree of configurability.
Ontology-Based Monitoring of Dynamic Systems
Theoretical Computer Science
TU Dresden, Germany
Abstract: Our understanding of the notion “dynamic system” is a rather broad one: such a system has states, which can change over time. Ontologies are used to describe the states of the system, possibly in an incomplete way. Monitoring is then concerned with deciding whether some run of the system or all of its runs satisfy a certain property, which can be expressed by a formula of an appropriate temporal logic. We consider different instances of this broad framework, which can roughly be classified into two cases. In one instance, the system is assumed to be a black box, whose inner working is not known, but whose states can be (partially) observed during a run of the system. In the second instance, one has (partial) knowledge about the inner working of the system, which provides information on which runs of the system are possible. In this talk, we will review some of our recent research that investigates different instances of this general framework of ontology-based monitoring of dynamic systems.
Cyborg Intelligence: Towards the Convergence
of Machine and Biological Intelligence
Abstract: Recent advances in the multidisciplinary fields of brain-machine interfaces, artificial intelligence, computational neuroscience, microelectronics, and neurophysiology signal a growing convergence between machines and living beings. Brain-machine interfaces (BMIs) enable direct communication pathways between the brain and an external device, making it possible to connect organic and computing parts at the signal level. Cyborg means a biological-machine system consisting of both organic and computing components. Cyborg intelligence aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via BMIs, enhancing strengths and compensating for weaknesses by combining the biological cognition capability with the machine computational capability. This talk will introduce the concept, architectures, and applications of cyborg intelligence. It will also discuss issues and challenges.
EEG-Based Visual Brain-Computer Interfaces
Dept. of Biomedical Engineering
Abstract: Over the past several decades, electroencephalogram (EEG) based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, it remains a challenging problem to build robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual brain responses. This talk will review the current state and future challenges of visual BCI systems. And the taxonomy based on the multiple access methods of telecommunication systems is described. Meanwhile, the challenges will be discussed, i.e., how to translate current technology into real life practices. Specifically, useful guidelines are provided in this talk to help exploring new paradigms and methodologies to improve the current visual BCI technology.