20 resultados para Table manipulation (Computer science)
Resumo:
Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.
Resumo:
We present a fully-distributed self-healing algorithm dex that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders—whose expansion properties holddeterministically—that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees on the network expansion whichrapidly degrade in a dynamic setting; in particular, the expansion properties can degrade even more rapidly under adversarial insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only O(logn)O(logn) rounds and O(logn)O(logn) messages are needed with high probability (n is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table on top of dex with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have guaranteed properties (constant bounded degree and expansion) despite dynamic changes.
Gopal Pandurangan has been supported in part by Nanyang Technological University Grant M58110000, Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 2 Grant MOE2010-T2-2-082, MOE AcRF Tier 1 Grant MOE2012-T1-001-094, and the United States-Israel Binational Science Foundation (BSF) Grant 2008348. Peter Robinson has been supported by Grant MOE2011-T2-2-042 “Fault-tolerant Communication Complexity in Wireless Networks” from the Singapore MoE AcRF-2. Work done in part while the author was at the Nanyang Technological University and at the National University of Singapore. Amitabh Trehan has been supported by the Israeli Centers of Research Excellence (I-CORE) program (Center No. 4/11). Work done in part while the author was at Hebrew University of Jerusalem and at the Technion and supported by a Technion fellowship.
Resumo:
Experience obtained in the support of mobile learning using podcast audio is reported. The paper outlines design, storage and distribution via a web site. An initial evaluation of the uptake of the approach in a final year computing module was undertaken. Audio objects were tailored to meet different pedagogical needs resulting in a repository of persistent glossary terms and disposable audio lectures distributed by podcasting. An aim of our approach is to document the interest from the students, and evaluate the potential of mobile learning for supplementing revision
Resumo:
Stealthy attackers move patiently through computer networks - taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a challenge. This paper presents an efficient monitoring technique for stealthy attacks. It investigates the feasibility of proposed method under number of different test cases and examines how design of the network affects the detection. A methodological way for tracing anonymous stealthy activities to their approximate sources is also presented. The Bayesian fusion along with traffic sampling is employed as a data reduction method. The proposed method has the ability to monitor stealthy activities using 10-20% size sampling rates without degrading the quality of detection.
Resumo:
The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.
