Searching for Solutions: Uses of Meta-heuristic Search in Security
Professor John A Clark
14:30, September 12, 2016
1012, New Science & Technology Building, North Campus
Professor Clark holds a Master of Arts in Mathematics and a Master of Science in Applied Statistics. He joined York in 1992 as Lecturer in Safety Critical Systems and has been research active since around 1997. His personal and supervised research work concentrates on aspects of security and software engineering, with a particular interest in applying metaheuristic search and other Artificial Intelligence techniques to these areas. He was awarded a PhD in Computer Science in 2002 (on applications of metaheuristic search to cryptology) and promoted to a Personal Chair in 2005. His research work has been cited over 5580 times. He is part of the EPSRC's Programme Grant on Dynamic Adaptive Automated Software Engineering (DAASE). His collaborative work has been awarded 11 best paper (or similar) prizes. In 2013 he received a personal Royal Society Wolfson Research Merit Award to pursue his work in optimisation based system design and analysis.
He has maintained a very strong interest in teaching throughout his academic career. He has taught on around 30 modules since joining York. From 2005-2007 he was Chair of Examinations within the Department of Computer Science at York. From 2009-2015 he was the Deputy Head of Department, Responsible for Research. From October 2016 he will be the Head of Department.
Meta-heuristic search has sought to use general purpose search and optimisation strategies to navigate complex design and analysis spaces. The problem in question is generally expressed in the following form: given a search space X, find x in X, such that some fitness function f(x) is maximised (or minimised). In this talk Professor Clark will show how optimisation approaches such as simulated annealing and evolutionary computation approaches (genetic algorithms, genetic programming and others) can be used to design secure artefacts such as cryptographic components and security protocols. The presentation will also show how some types of security schemes can be broken by using such techniques. Finally the use of such approaches to discover ideas for analysis strategies will be discussed.
Verifiable Databases with Efficient Updates in Cloud Computing
Professor CHEN Xiaofeng
15:05, September 12, 2016
Xiaofeng Chen received his B.S. and M.S. on Mathematics from Northwest University, China in 1998 and 2000, respectively. He got his Ph.D degree in Cryptography from Xidian University in 2003. Currently, he works at Xidian University as a professor. His research interests include applied cryptography and cloud computing security. He has published over 100 research papers in refereed international conferences and journals. His work has been cited more than 4000 times at Google Scholar. He is in the Editorial Board of IEEE transactions on Depenable and Secure Computing (TDSC)，Security and Communication Networks (SCN), Computing and Informatics (CAI), and International Journal of Embedded Systems (IJES) etc. He has served as the program/general chair or program committee member in over 30 international conferences.
The notion of verifiable database (VDB) enables a resource-constrained client to securely outsource a very large database to an untrusted server so that it could later retrieve a database record and update a record by assigning a new value. Also, any attempt by the server to tamper with the data will be detected by the client. In this talk, we introduce some recent progress on VDB schemes.
Syntax based word embeddings for improved natural language processing (and overview of AI Group research)
Dr. Suresh Manandhar
15:55, September 12, 2016
1012, New Science & Technology Building, North Campus
Dr Manandhar received his MSc in Artificial Intelligence from University of Essex, UK in 1987 and his PhD in Artificial Intelligence from University of Edinburgh, UK in 1994. He has a wide range of interests in Natural Language Processing focusing on unsupervised learning of semantics, morphology, applications of deep learning and on question answering systems. Dr Manandhar has published over 100 peer reviewed papers and successfully supervised 15 PhD students. He currently serves on the editorial boards of the Journal of Natural Language Engineering and the Journal of Applied Intelligence.
I will provide a brief overview of the research being undertaken by the AI Group at York. I will follow this by the current work being undertaken in natural language processing (NLP) focussing on word embeddings. Word embeddings are low dimensional vector space meaning representations for words. There has been a huge interest in the use of word embeddings for many applications in NLP. In this talk, a variation to standard word embeddings will be presented that enable capturing syntactic information and allowing the word embeddings to be modified dynamically based on the context. In particular, dependency syntax is employed. Three common sentence classification tasks: question type classification on the TREC, dataset, binary sentiment classification on Stanford’s Sentiment Treebank and semantic relation classification on the SemEval 201 dataset. Evaluation is done using three different classification methods: a Support Vector Machine, a Convolutional Neural Network and a Long Short Term Memory Network. This works shows that dependency based embeddings outperform standard window based embeddings in most of the settings.
Our embeddings and code are available at https://www.cs.york.ac.uk/nlp/extvec
A New Ranking Based EA for MaOPs
Professor WANG Yuping
16:30, September 12, 2016
Prof. Yuping Wang received his Ph.D. degree in computation mathematics from Xi’an Jiaotong University, Xi’an, China, in 1993. From 1997 to 2010, he was a visiting scholar with the Chinese University of Hong Kong, the Hong Kong Baptist University and the City University of Hong Kong, respectively. He served as a Chair or Co-Chair of several international conferences, Guest Editor of several International Journals and a member of Editorial Board of Journal: ICAE. He is currently a full professor with Xidian University, Xi’an, China and a senior member, IEEE. He has authored and co-authored over 100 research papers in international journals and conferences. His current research interests include evolutionary computation, data mining, optimization algorithms and modeling for engineering.
Many objective optimization problems (MaOPs) are the most challenging problems among multi-objective optimization problems. In this talk, a new ranking method is first proposed, then, a new evolutionary algorithm is proposed based on it. Moreover, the computer simulations are conducted and the results indicate the proposed algorithm is efficient and effective.
Computer assisted safety argument review, a dialectics approach
Dr. Tommy Yuan
17:05, September 12, 2016
Professor Tommy Yuan received his BSc on Railway Transport Engineering from Southwest Jiaotong University, China in 1993, and his MSc on Software Engineering, and PhD on Computer Science from Leeds Metropolitan University, UK. He worked for 4 years in assistant professorship in an Icelandic University and 5 years in Chinese industry as a railway transport engineer and railway station manager. Since 2009, he joined University of York and has been working as the lecturer of Artificial Intelligence Research Group in the Department of Computer Science and Chinese University Collaboration Director. His research concerns argumentation technology and its applications in technology enhance learning (TEL), assistive technologies for autism children and system safety case development. I am also interested in applying artificial intelligent technologies e.g. machine learning and pattern recognition, to industrial applications such as transport systems. He has published over 40 conference and journal papers in the area of artificial intelligence and argumentation.
There has been increasing use of argument-based approaches in the development of safety critical systems. Within this approach, a safety case plays a key role in the system development life cycle. The key components in a safety case are safety arguments, which are designated to demonstrate that the system is acceptably safe. Too often, safety arguments are constructed without proper reasoning. Inappropriate reasoning in safety arguments could undermine a system's safety claims, which in turn contributes to safety-related failures of the system. In this talk, Dr. Yuan will show how the problem can be tackled via an informal logic based approach. A number safety argument schemes and a safety argument review model have been proposed and evaluated. It is anticipated that this work will contribute toward the development of computer system safety arguments, and help to move forward the interplay between research in informal logic and research in computer system safety engineering.
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