NII Shonan Meeting:

@ Shonan Village Center, September 15-19, 2014

Organizers

• Kazuo Sakiyama, University of Electro‐Communications, Japan
• Patrick Schaumont, Virginia Tech, US
• Ingrid Verbauwhede, K.U. Leuven, Belgium

Overview

This meeting will gather researchers working on secure hardware components that support information
security such as encryption and decryption, electronic signatures, and authentication. Secure hardware
design is characterized by a specialized application domain (cryptography), and it is required for
applications that have a reliability requirement under adversary operating conditions. Similar to other
application domains such as multimedia, there is a need for fast, compact, low power and/or low energy
realizations. In addition however, there is a need for secure realizations ‐ the implementations have to
remain reliable and trustworthy under attacks and adversary operating conditions. This makes the
application domain quite unique.

The meeting will review the state-of-the-art in secure hardware design, and it will create an open
forum for the discussion of important open research questions in this fast‐evolving and important field
of research. The emphasis of the meeting will be on design methods, and the systematic steps that
designers use to construct secure hardware. Several of the questions that we would like to address
include the following.

1. What analysis techniques are applicable to the design descriptions of secure hardware? How
   can risk be quantified and how can we convey the results of this analysis to the designer?
2. When is secure hardware preferable over secure software, and how can we help a designer
   choose between these two options? What aspects of security can be rendered more efficiently
   in hardware than in software?
3. How do implementation attacks (fault analysis and side‐channel leakage) affect classic hardware
   design issues such as reliability, design for testability, and verification? What guidelines can be
   defined for the hardware designer?
4. Can we define correct-by-construction techniques for secure hardware design? What aspects of
   cryptographic engineering are amendable to compiler techniques?
5. How to define design libraries for secure hardware design? Is the problem similar to the classic
   case of Intellectual Property Reuse of hardware components? Do the security properties of
   individual hardware components reflect on the overall design?
6. What runtime techniques are available to evaluate a secure hardware design? How can we
   detect implementation attacks, and what assumptions do we have to make?
7. What techniques can be used to cost-optimize secure hardware? How do we quantify the
   security/cost tradeoff?

NII Shonan Meeting:

@ Shonan Village Center, May 19-22, 2014

Organizers

• Pierre Baldi, University of California in Irvine, USA
• Kenji Fukumizu, Institute of Statistical Mathematics, Japan
• Tomaso Poggio, Massachusetts Institute of Technology, USA

Overview

The ability to learn is essential to the survival and robustness of biological systems. There is also growing evidence that learning is essential to build robust artificial intelligent systems and solve complex problems in most application domains. Indeed, one of the success stories in computer science over the past three decades has been the emergence of machine learning and data mining algorithms as tools for solving large-scale problems in a variety of domains such as text analysis, computer vision, robotics, and bioinformatics. However, we are still far from having a complete understanding of machine learning and its role in AI, and plenty of challenges, both theoretical and practical, remain to be addressed.

Complex problems cannot be solved in one single step and often require multiple processing stages in both natural and artificial systems. For instance, visual recognition in humans is not an instantaneous process and requires activation of a hierarchy of processing stages and pathways. The same is true for all the best performing computer vision systems available today. Thus deep learning architectures, comprising multiple, adaptable, processing layers are important for the understanding and design of both natural and artificial systems and, today, are at the forefront of machine learning research. In the past year alone, deep architectures and deep learning have achieved state-of-the-art performance in many application areas ranging from computer vision, to speech recognition, to bioinformatics.

It is this recent wave of progress that provides the relevant context for the proposed meeting which will focus on all aspects of deep architectures and deep learning, with a particular emphasis on understanding fundamental principles because there is still very little theoretical understanding of deep learning, in spite of the recent progress. Thus a major thrust of the meeting will be to foster theoretical analyses of deep learning. In addition to theory, topics to be covered will include also algorithms and applications. The primary intellectual focus of the meeting will be on deep learning in artificial systems. However, deep learning draws some of its inspiration from, and has close connections to, neuroscience. Thus presentations and discussions bridging learning in natural and artificial learning systems will also be encouraged.

NII Shonan Meeting:

@ Shonan Village Center, November 18-21, 2013

Organizers

• Wolf-Tilo Balke, Technische Universität Braunschweig, Germany
• Seung-won Hwang, POSTECH University, South Korea
• Takahiro Hara, University of Osaka, Japan
• Christoph Lofi, NII Tokyo, Japan

Overview

Currently a variety of platforms like Amazon’s Mechanical Turk, CrowdFlower, or Sama-Source are offering frameworks with different degrees of sophistication where (usually rela-tively simple) cognitive tasks can be dynamically posed to a large and readily available work-force. This ability of cheaply distributing simple jobs via the Web allows for new modes of labor and information processing. In fact, the “knowledge society” has already brought severe changes to business processes in today’s economy. This is especially true for the basic ques-tion of what and where people work.

Here the ubiquity of sophisticated mobile devices and communication services allow for al-most unlimited flexibility and freedom in negotiating and outsourcing short-term work con-tracts and delivering results. Currently, mobile crowdsourcing by smartphone users is a hot research area. In any case, in the industrialized world there is a clear transition from tradition-al production of goods or processing of raw materials towards the provisioning of services and the flexibility with respect to the place where such services are actually physically pro-vided has dramatically increased. Still, although services could in principle be offered flexibly from virtually anywhere in the world, typical constraints like the local cost of labor or easy access to an educated workforce, remain valid. Crowd-Sourcing promises to break with these traditional work models, by offering a dynamic global information-processing workforce which is available 24/7 with close to no overhead. This shift paves the way for approaching large-scale information task which were previously infeasible for both algorithmic and tradi-tional human-based approaches.

The central challenge in the current knowledge society is to efficiently and intelligently deal with an overwhelming amount of information, a daunting task for computer systems and hu-mans alike. To this aims the data management and data mining communities considers a wide variety of operators, algorithms, and workflows.

For some information-heavy areas like for example customer relationship management, where everyday services like ordering procedures, customer data management, complaint handling, etc. have to be performed, out-sourcing the work to specialized workers has become a com-monly accepted solution for increasing efficiency. Although such services do not produce anything in the traditional material sense, they are critical for company goals like efficient sales handling, customer satisfaction and retention, etc. Whereas such tasks used to be done on-site, nowadays ‘call centers’ all over the world centrally provide such services at consider-ably reduced costs for a large number of customers. These services are quite basic and easy to provide in terms of education. On an educationally higher level, business intelligence services can serve as a good example: extracting relevant information from company data and using it to recognize or design value-adding areas like new products, promising customer segments, or better business processes for a company is a profitable business. Indeed ‘infopreneur’ is a term coined for the growing number of persons whose primary business is gathering and sell-ing electronic information. However, this current form of out-sourcing information-centric tasks is still quite static (i.e. a fixed team of specialists is contracted for a larger task). In con-trast, crowdsourcing as understood in in this proposal dynamically assigns small intelligence tasks to workers from a large pool in a demand-driven fashion. The advantages are obvious: if at creation time each process can be effectively broken down to manageable tasks and a viable time plan, it can be fulfilled very efficiently. The main factor is elasticity: peaks and slumps in activity can be dynamically handled and missing expertise or competences can be contracted. Thus, the efficiency of the overall process is hard to beat.

The main purpose of this Shonan meeting is to bring together researchers from the field of data management, information processing, HCI, and mobile computing to discuss the tech-nical challenges, possible societal impact, as well as promising industrial applications for on-demand crowdsourcing techniques in vast information management challenges. The seminar puts a clear focus on operations in data management and data processing work-flows. Indeed there are many open questions to discuss: How can operators/workflows ben-efit from crowdsourcing? Can the resulting quality be controlled? Which workers should be selected? How to determine expected response times? How to deal with privacy risks?

As stated above, a special focus should be paid to crowd-sourceable operators for applica-tions for data and information management, information organization, and information access. In recent years algorithms aimed at these tasks have raised a lot of
attention and indeed, methods have grown quite powerful even over huge and largely unstructured information re-positories like the Web. Applications are almost limitless ranging from basic information ex-traction over knowledge management to complex business intelligence.

However, with more complex information processing, retrieval, or mining capabilities also the algorithms’ complexity, susceptibility for errors and danger of overspecialization increas-es. Since most failings can be traced back to limited cognitive abilities, missing contextual knowledge or heuristics gone wrong, the idea of direct human supervision and intervention at processing time is currently pursued in many domains. But also the quality of the work deliv-ered by workers raises concerns: today’s platforms are facing spam and individual workers’ work quality, skill, and reliability have to be measured for effective quality control. While for spam detection simple methods like gold questions or majority vote may work well, more complex quality assessment need new and more powerful models. Actually, ranking schemes based on reputation mechanisms already play a vital role in Web platforms, where matchings or transactions between anonymous parties are brokered. Hence their applicability for crowd-sourcing scenarios should be discussed.

In fact, the need for human assistance in bridging the final semantic gap for today’s infor-mation processing has already given rise to information systems that rely on hybrid architec-tures. Such hybrid architectures transparently combine the efficiency of current algorithms with the cognitive power and flexibility of humans.

Here, generally two design directions are popular:

• Using human input for improving the steps performed by information processing algo-rithms by providing training samples, answering questions about ambiguous results, or by providing relevance feedback.
• Involving humans directly into the information processing process, explicitly out-sourcing some of the required tasks or operators within the process.

Both general approaches are still very new, and no established research community has yet developed for crowd-assisted information processing algorithms. This Shonan meeting can provide a significant stimulus to the research community in order to advance this still new field of interest.

2.1.Topics of Interest:

The meeting is primarily intended to focus on topics and problems related to information and knowledge processing. In this area, there are many tasks for which basic algorithmic ap-proaches exist, but fall short because they often cannot grasp the semantics of the data they operate on correctly. Here, we envision that crowd-sourcing techniques are running in parallel in a hybrid system, and supplementing the algorithms when necessary. Especially, operators and algorithms of the following areas shall be discussed with their potential synergy with crowd-sourcing in mind:

• Complex databases operators like cognitive comparison and similarity functions, as for example sorting or joining images, ambiguous labels, descriptions, etc.
• Information and knowledge mining tasks, as for example entity and relation detection, enti-ty reconciliation, or improving typical extraction pattern
• Improving data or knowledge representation, as for example schema matching, ontology cleaning, or data cleaning
• Sensor data stream processing (e.g., energy efficient stream join, uncertain stream pro-cessing)
• Obtaining cognitive meta-data from natural-language, as for example sentiment or emo-tion analysis, intention detection, sarcasm detection, etc.
• Semantic querying and retrieval, as for example question answering techniques or seman-tically-aware information retrieval algorithms
• Privacy issues, especially for mobile participants (e.g., location, trajectory, POI)
• Ethics of crowd-computing: discussions and insights on how the large-scale application of crowd-sourcing affects both workers and information management systems from an ethical perspective

NII Shonan Meeting:

@ Shonan Village Center, November 11-14, 2013

Organizers

• Tetsunari Inamura, National Institute of Informatics, Japan
• Tamim Asfour, Karlsruhe Institute of Technology, Germany
• Sethu Vijayakumar, University of Edinburgh, UK

Overview

Understanding mechanisms of intelligence of human beings and animals is one of the most important approaches to develop intelligent robot systems. Since the mechanisms of such real-life intelligent systems are so complex, physical interactions between agents and their environment and the social interactions between agents should be considered. Comprehension and knowledge in many peripheral fields such as cognitive science, developmental psychology, brain science, evolutionary biology, and robotics is also required. Discussions from an interdisciplinary aspect are very important for implementing this approach

In this decade, an academic field named cognitive developmental robotics has been established to aim understanding of human’s intelligence. This approach focuses on embodied intelligence which is one of the deficient points in GOFAI (Good Old Fashioned Artificial Intelligence). Currently, the contribution of the cognitive robotics seems successful; however, the quality and performance of the robots’ intelligence is quite far from humans’ intelligence. One of the difficulties is huge cost of collecting embodied and social experience due to limitation of robots’ operating time and less opportunity of social interaction between humans. Therefore, the next issue will be how to accelerate the development of intelligence from large-scale long-term embodied experience through social interaction. Since the keyword ‘Big-data’ get a lot of attention in informatics recently, intelligence of robots also needs ‘big social experiences’.

This Shonan meeting focuses on synthetic research on cognitive social robots, that intelligence develops based on physical embodied interaction between body and environment, social interaction between agents and human, and so on, towards understanding of intelligence of humans and robots. For such an approach, we set interdisciplinary discussions with wide viewpoint for various research fields such as cognitive science, developmental phycology, brain science, not only robotics. The scope of this meeting will be human-robot interaction, machine learning, cognitive science, simulation technology for long-term large scale interaction, learning by demonstration, model of humans’ body, recognition of visual/audio signal, and so on. One of the target applications as material for the discussion would be acquisition of knowledge and skills from natural interaction between human beings; however, this meeting would like to touch on other future big challenges related to the cognitive social robotics with a broad perspective.

NII Shonan Meeting:

@ Shonan Village Center, November 4-7, 2013

Organizers

• Dr. Akitoshi Kawamura, The University of Tokyo, Japan
• Prof. Jean-Yves Marion, Lorraine University, France
• Dr. David Nowak, CNRS & The University of Tokyo, France (Japan)

Overview

Computational complexity theory aims at classifying computational problems according to their inherent difficulty. The standard way to achieve this classification consists in formalizing a precise execution model (e.g., a Turing machine) and posing explicit bounds on time and memory resources. On the other hand, Implicit Computational Complexity (ICC) aims at studying computational complexity without referring to external measuring conditions or a particular machine model, but only by considering language restrictions or logical/computational principles implying complexity properties. The area of ICC has grown out from several proposals to use logic and formal methods to provide languages for complexity-bounded computation (e.g., polynomial time, logarithmic space computation). ICC methods include, among others, linear logic, typed programming language, second order logic, term ordering. The last decades have seen the development of logical formalisms that characterize functions computable in various complexity classes (polynomial or elementary in time, logarithmic in space).

The goal of the proposed meeting is to explore foundational as well as practical interconnections between formal logic and computational complexity, such as it is done in ICC. The main outcome of this meeting will be to trigger new interactions and enrich the various approaches. In particular, and aside from traditional ICC approaches, we would like to focus on computation involving real numbers and topological spaces, thereby providing a deeper understanding of computational complexity in non-discrete realms of mathematics. By bringing together experts in implicit complexity and in complexity in analysis, we will promote new interaction between the two fields. People in those two fields are currently working separately, but there is enough common ground between them to make it worth having those two communities talking and working together. The meeting would also foster discussions about applications, i.e., the design of methods based on ICC and suitable for static verification of program resource consumption and of security.

Research topics for discussion on the various aspects described above would include, among others, the following topics:

• types for controlling complexity
• logical systems for computational complexity
• linear logic
• semantics of complexity-bounded computation
• rewriting and termination orderings
• interpretation-based methods for implicit complexity
• programming languages for complexity-bounded computation
• application of implicit complexity to the analysis of resource consumption
• application of implicit complexity to security
• complexity over reals and non-discrete spaces
• type-two complexity
• resource bounds in computable analysis and algorithmic randomness
• analog and continuous-time computation
• theory and implementation of efficient validated numerical algorithms

We briefly describe below two topics that we would like to promote. Although they originate from two different communities, they share the same interest in implicit characterization of complexity and they both have applications to formal validation of computer systems.

Very recently ICC methods have been applied to security methods and conversely security methods have been used as a new approach in ICC context. In the context of security proofs, the computational power of adversaries has to be limited so that their potential attacks are feasible. An adversary with unlimited computational power could indeed break most cryptographic schemes (e.g., RSA by efficiently factoring large integers). It is usual to rely on Cobham’s thesis identifying feasibility with computability in polynomial time. Hence the particular interest in the class of functions computable in polynomial time and its implicit characterization with a programming language that can be used to construct adversaries. Conversely, type systems to control the information flow, which are traditionally used for certifying security policies like confidentiality or integrity, are related to the notion of data stratification. As a result, type systems for imperative programming languages have been developed to control resource consumption.

The other proposed focus is on computation over the reals. Computable analysis, the study of abilities and limitations of digital computers applied to problems in mathematical analysis, has originally evolved from computability theory, but there is increasing interest in computational complexity with bounded time and space. The goals here are to analyze the computational costs of algorithms for problems involving real numbers and to explore the principles and structures of computational complexity in this context, providing a foundation of validated numerical methods for
problems arising in physical sciences and engineering. Broader perspectives in computational complexity, including those from implicit complexity theory, have high potential to help here, as can be already seen, for example, in recent studies of computational power of dynamical systems and analog computers, or in the application of type-two complexity theory to time-bounded computable analysis.

NII Shonan Meeting:

@ Shonan Village Center, October 21-25, 2013

Organizers

• Ahmed E. Hassan, Queen’s University/Research In Motion, Canada
• Katsuro Inoue, Osaka University, Japan
• Tao Xie, North Carolina State University, USA
• Dongmei Zhang, Microsoft Research Asia, China

Overview

A wealth of various data (e.g., source change history, test cases, and bug reports) exists in the practice of software development. Further modern software and services in operation produce rich data (e.g., operation logs, field crashes, and support calls). Hidden in these unexplored data is rich and valuable information about the quality of software and services and the dynamics of software development. Companies (Microsoft, Google, Facebook, Cisco, Yahoo, IBM, RIM, etc.) are increasingly adding analytics as an important role in their organizations, leveraging the wealth of various data produced around their software or services.

Software analytics is concerned with the use of data-driven approaches to obtain insightful and actionable information for completing various tasks around software systems, software users, and software development process. Insightful information is information that conveys meaningful and useful understanding or knowledge. Actionable information is information upon which software practitioners can come up with concrete solutions (better than existing solutions if any) towards completing tasks. Typically such information cannot be easily obtained by direct investigation on the raw data without the aid of analytic technologies.

Especially recently the area of Big Data has emerged as a critical and strategic focus by the society. Big data is everywhere now but it is still under-utilized in the area of software engineering. However, leveraging big data is very relevant in software engineering as software and services get larger and more inter-connected, often being developed by a large number of engineers in distributed fashions and being used by a huge number of users around the world. Software analytics needs to be prepared for the upcoming decade’s exciting and yet challenging problem of leveraging big data for software engineering tasks.

The proposed seminar will foster collaboration between industry and academia, bringing academic researchers working on the principles and practice of software analytics together with researchers from industry. The aim is not only to act as a forum for the exchange of ideas, but as a vehicle to stimulate, deepen and widen partnership between academia and industry in software analytics internationally. In the age of Big Data, this seminar also serves as the first step to plan for the next decade of Big Data Analytics in Software Engineering, since it is impossible for individual groups or companies to tackle this challenging problem alone.

Software analytics is an ideal topic for this kind of interaction. It combines challenging research problems with real practical importance for the software industry, and the wider society that it serves. It presents an excellent and wide-ranging set of open research questions to academics concerning, amongst other things, analytic-algorithm design, data analysis, information visualization, scalable computing, software-artifact analysis and mining, social factors, empirical software engineering, measurement, process improvement, and technology transfer and adoption. Software analytics is also of critical practical significance to almost every organization involved in the production and use of software and services. Answers to the currently open research questions in software analytics can have a major impact upon industrial practice, with far-reaching implications for the development of the global economy. This combination of academic challenge and industrial relevance makes software analytics a natural topic for the proposed seminar.

In this seminar, we want to bring together software-analytics researchers in academia and industry. Our main focus is to exploit the synergy of these communities and to provide a platform to forge new collaborations. Participants are invited to present a few plenary talks and demos of new tools, beside which the seminar will provide ample opportunities for small working groups on themes suggested by the participants. We expect the seminar to result in ample cross-fertilization between the different research areas and to show up exciting directions for improving software-engineering practices via practical software analytics.

NII Shonan Meeting:

@ Shonan Village Center, October 7-10, 2013

Organizers

• Ichiro Hasuo, U. of Tokyo, (JP)
• Keiko Nakata, Institute of Cybernetics at TUT, Tallinn (EE)
• Tarmo Uustalu, Institute of Cybernetics at TUT, Tallinn (EE)

Overview

This meeting has as its aim to study the applications of coinduction (coinductive data/predicates,
bisimilarity, corecursion and coinduction) to reasoning about computation and programs, hence
in programming language semantics, program logics. This is motivated by the appropriateness of
coinduction for analyzing infinity, so also infinity in computation structures, the prime example
being infinitely running computations, e.g., of machines or programs, especially in the context of
reactive computation.

Induction and coinduction are, by themselves, dual notions. But in typical non-self-dual settings of
actual interest they come out as quite different. The most important outcome of the asymmetries
is that induction is about finite construction and infinite use, but coinduction is about infinite
construction and finite use of data.

Lately there is a growing interest in coinduction in the areas of programming semantics and
formal verification of software systems, in part thanks to advances in type-theoretical programming
languages and proof assistants. There are many important and practical examples where one needs
to reason about finitely observable infinite computations and replacing this by, e.g., inductive
reasoning about finite initial fragments thereof is unnatural or inadequate. For instance, one may
want to prove that some program transformations do not change the observational behavior of
possibly nonterminating transformational programs or of infinitely running reactive programs.

Although coinduction should be very useful, in reality it is not really well understood. In fact,
coinduction is surrounded by quite some popular confusions and has become a standard tool only
in concurrency theory and coalgebra. The theory of coinduction is on many occasions subtle
and challenging. It is often difficult to translate between the vocabularies developed in different
disciplines (e.g., coalgebra, proof theory, type theory). The applications are sometimes not supported
well by tools. For example, it is by no means clear how to best support coinductive data
and predicates in type-theoretical programming languages and proof assistants. Unfortunately,
the mechanisms offered by the current systems are weak and/or cumbersome. As a result, the
corresponding applications are underdeveloped.

Of interest for the meeting are at least the following topics:

• coinductive computation structures, in particular for possibly infinite behaviors (of machines,
programs etc), interaction, concurrency,
• coinductive program semantics, type systems, program logics,
• computability for coinductive data,
• theory of coinductive types, bisimilarity, corecursion/coinduction in type theory, proof theory,
category theory,
• support for coinductive types in dependently typed programming languages, type-theoretic
proof assistants.

Coinduction is applied by researchers from different communities, with diverse technical backgrounds.
There are multiple approaches to coinduction, it is used in different applications. The
meeting encourages exchanges between researchers representing different areas and communities:
programming languages design, implementation, semantics, functional programming, theory of
concurrency, category theory (coalgebra), proof theory, type theory.

NII Shonan Meeting:

@ Shonan Village Center, September 27-30, 2013

Organizers

• Kunihiko Sadakane (National Institute of Informatics), Japan
• Wing-Kin Sung (National University of Singapore), Singapore

Overview

Big Data are structured and unstructured datasets whose size is in the order of billions
or trillions. Because of their diversity and size, it is difficult to store, search and
analyze them. This meeting therefore focuses on algorithms and data structures for
efficient manipulation of Big Data. Especially, the meeting is devoted to compact data
structures for managing Big Data.

Typical examples of big data are genomic sequences and gene expression data, Web and
SNS data, sensor data in intelligent transport systems, etc. Traditional data structures
do not scale to handle such data, and therefore we should design new data structures to
handle them.

Although the amount of data explodes, the amount of the underlying information inside
the data may not be exploded. It is observed that many big datasets are redundant. In
the Web, many webpages were copies of others. In global positioning system (GPS),
GPS position data change continuously, which can be compressed using differential
encoding. In genomics, although different individuals have different genomes, the
individual genomes have highly similarity. Therefore we can compress such data by
identifying the similar parts. After the data is compressed, other issues are how to
access and search them efficiently. Traditional data structures are not designed to
handle compressed data and they may not manipulate Big Data well because the size of
the data structures exceeds the limit of memory usage, or searching time increases due
to their size. To handle these problems, researchers have worked on developing
compact data structures. Such data structures are also called compressed or succinct
data structures. They are much smaller than standard data structures, while keeping
the same access time to data in theory. However actual performance of such compact
data structures for storing Big Data is unknown or unsatisfactory.

The aim of this workshop is to bring together researchers active in the areas of compact
data structures to exchange ideas for handling Big Data. We will discuss methods for
compressing and storing Big Data. We will also discuss how to design time- and
space-efficient data structures for them Through discussion and sharing knowledge, we
hope to promote collaborations and further improve data structures for Big Data.

NII Shonan Meeting Memorial Symposium ～For Making Future Value From Asia～ will be held.

[Guest speakers from overseas] Prof. Jeffrey D. Ullman (Emeritus, Stanford University), Prof. Robert Anthony Kowalski (Emeritus, Imperial College London) and Prof. Reinhard Wilhelm (Saarland University /Dagstuhl Seminar Director)

[Date] November 1st, 2012

[Time] 13:15～18:00 (*Reception starts at 12:45)

[Place] Gakushi-kaikan Room 202 (3-28 Kandanishikicho, Chiyoda-ku, Tokyo)

[Please refer to our web page for details]

http://www.nii.ac.jp/en/about/international/shonanmtg/sympo2012

[Slides]

Prof. Jeffrey D. Ullman (Emeritus, Stanford University)
Title: “Map-Reduce Algorithms”Slides

Prof. Robert Anthony Kowalski (Emeritus, Imperial College London)
Title: “Towards a Logic-based, Unifying Framework for Computing”Slides

Prof. Reinhard Wilhelm (Saarland University /Scientific Director of Schloss Dagstuhl)
Title: “Dagstuhl Seminar and NII Shonan Meeting”Slides

NII Shonan Meeting:

@ Shonan Village Center, September 26-28, 2013

Organizers

• Atsushi Nakazawa, Osaka University, Japan
• James M Rehg, Georgia Institute of Technology, USA
• Yoichi Sato, University of Tokyo, Japan
• Akihiro Sugimoto, National Institute of Informatics, Japan

Overview

OBJECTIVE
It is known that early diagnosis and treatments are very important for Autism Spectrum Disorders (ASD). However, finding the infant patients who have this disease is quite difficult and takes long-term observations by psychiatrists. In recent years, several projects have been started in U.S to solve this issue. These projects aim to develop a way to quickly diagnose the disorder by introducing digital technologies including image/video analysis, wearable sensing devices and speech recognition.
This workshop is held by Japanese researchers related to this field and U.S and international researchers who are mainly joining the NSF Expedition project “Computational Behavior Science (CBS)”, and includes the following topics:
・Digital visual behavioral analysis techniques for infants, in particular, facial expression, eye gaze and motion analyses.
・Behavior analyses using wearable sensing devices and speech analysis
・Lectures about ASD by psychologists and caseworkers.

BENEFITS
Studies about ASD have been conducted in the psychologists’ community and Autistic
Spectrum Society; however, there are few studies introducing digital analysis techniques. Therefore, this workshop is unique in the sense that it is held by computer scientists and focuses on introducing digital analysis techniques for this task.
Through this workshop, we potentially have the following benefits:
・Develop/share knowledge about the studies related to digital technology for finding an infant’s ASD.
・Create domestic/international interpersonal relationships in this field.
・Create international projects to develop the techniques for this issue, including eye gaze tracking and behavioral analysis based on the RAPID ABC video database developed by NSF CBS group.