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Web Technologies Research and Development - APWeb 2005 1

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丛书名： Lecture Notes in Computer science

1000年

作者： Yanchun Zhang Katsumi Tanaka Jeffrey Xu Yu Shan Wang Minglu Li

ISBN: (数字)9783540318491

ISBN: (纸本)9783540252078

This volume contains papers selected for presentation at the 7th Asia Pacific Conference on Web Technology (APWeb 2005), which was held in Shanghai, China during March 29–April 1, 2005. APWeb is an international conference series on WWW technologies and is the primary forum for researchers and practitioners from both academia and industry to exchange knowledge on WWW-related technologies and new advanced applications. APWeb 2005 received 420 submissions from 21 countries and regions worldwide, including China, Korea, Australia, Japan, Taiwan, France, UK, Canada, USA, India, Hong Kong, Brazil, Germany, Thailand, Singapore, Turkey, Spain, Greece, Belgium, New Zealand, and UAE. After a thorough review process for each submission by the Program Committee members and expert reviewers recommended by PC members, APWeb 2005 accepted 71 regular research papers (acceptance ratio 16.9%) and 22 short papers (acceptance ratio 5.2%). This volume also includes 6 keynote papers and 11 invited demo papers. The keynote lectures were given by six leading experts: Prof. Ah Chung Tsoi (Australia Research Council), Prof. Zhiyong Liu (National Nature science Foundation of China), Prof. John Mylopoulos (University of Toronto), Prof. Ramamohanarao (Rao) Kotagiri (University of Melbourne), Prof. Calton Pu (Georgia Tech), and Prof. Zhiwei Xu (Chinese Academy of sciences).

关键词： Information Storage and Retrieval Information Systems Applications (incl. Internet) Multimedia Information Systems User Interfaces and Human Computer Interaction Computer Communication Networks Computers and Society

Using restart heuristics to improve agent performance in angry birds

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arXiv 2019年

作者： Liu, Tommy Renz, Jochen Zhang, Peng Stephenson, Matthew Research School of Computer Science Australian National University CanberraACT Australia Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However none of these agents attempt one of the key strategies which humans employ to solve Angry Birds levels, which is restarting levels. Restarting is important in Angry Birds because sometimes the level is no longer solvable or some given shot made has little to no benefit towards the ultimate goal of the game. This paper proposes a framework and experimental evaluation for when to restart levels in Angry Birds. We demonstrate that restarting is a viable strategy to improve agent performance in many cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Birds

knowledge science, engineering and Management 1

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丛书名： Lecture Notes in Computer science

1000年

作者： Robert Buchmann Claudiu Vasile Kifor Jian Yu

ISBN: (数字)9783319120966

ISBN: (纸本)9783319120959

This book constitutes the refereed proceedings of the 7th International Conference on knowledge science, engineering and Management, KSEM 2014, held in Sibiu, Romania, in October 2014. The 30 revised full papers presented together with 5 short papers and 3 keynotes were carefully selected and reviewed from 77 submissions. The papers are organized in topical sections on formal semantics; content and document analysis; concept and lexical analysis; clustering and classification; metamodeling and conceptual modeling; enterprise knowledge; knowledge discovery and retrieval; formal knowledge processing; ontology engineering and management; knowledge management; and hybrid knowledge systems.

关键词： Artificial Intelligence data Mining and knowledge Discovery Information Storage and Retrieval Software engineering database Management Information Systems Applications (incl. Internet)

The tradeoff of delta table merging and re-writing algorithms in what-if analysis application

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The tradeoff of delta table merging and re-writing algorithm...

Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Zhang, Yansong Zhang, Yu Xiao, Yanqin Wang, Shan Chen, Hong Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Department of Computer Science Harbin Finance College Harbin 150030 China Computer Center Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9783642006715

What-if analysis can provide more meaningful information than classical OLAP. Multi-scenario hypothesis based on historical data needs efficient what-if data view support. In general, delta table for what-if analysis is more general than other solutions such as query re-writing prototype of Sesame. Delta table is independent of base table and is more suitable to represent complex hypothetical updates and multi-version hypothetical updates. Due to low efficiency of traditional delta table merging algorithm which is based on set operation, there are few researches focus on delta table merging algorithm but on query re-writing algorithm. By analyzing the difference between what-if query and what-if analysis and improving the delta table merging algorithm, we propose novel algorithms without set operation of difference. Considering the feature of aggregate operations in OLAP analysis, pre-merge algorithm is presented without the generation of what if data view before group-bys in the scenario of SUM, AVERAGE and COUNT function OLAP queries. In our experiments, the pre-merge algorithm greatly improved the efficiency of delta table merging procedure which is close to base group-by statement and superior to the re-writing algorithm. A complete comparison between all candidate delta table merging algorithms and re-writing algorithm with different what-if update conditions of update, deletion, insertion and mixed what-if updates is exhibited in our experiments, the policy of what-if analysis among different types is also discussed. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Merging

TREC: transient redundancy elimination-based convolution 22

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TREC: transient redundancy elimination-based convolution

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Guan Feng Zhang Jiesong Liu Hsin-Hsuan Sung Ruofan Wu Xiaoyong Du Xipeng Shen Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Computer Science Department North Carolina State University

ISBN: (纸本)9781713871088

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.

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Mini-batch stochastic three-operator splitting for distributed optimization

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arXiv 2022年

作者： Franci, Barbara Staudigl, Mathias The Department Of Data Science And Knowledge Engineering Maastricht University P.O. Box 616 MaastrichtNL-6200 MD Netherlands

We consider a network of agents, each with its own private cost consisting of a sum of two possibly nonsmooth convex functions, one of which is composed with a linear operator. At every iteration each agent performs local calculations and can only communicate with its neighbors. The challenging aspect of our study is that the smooth part of the private cost function is given as an expected value and agents only have access to this part of the problem formulation via a heavy-tailed stochastic oracle. To tackle such sampling-based optimization problems, we propose a stochastic extension of the triangular pre-conditioned primal-dual algorithm. We demonstrate almost sure convergence of the scheme and validate the performance of the method via numerical experiments. © 2022, CC BY.

关键词： Stochastic systems

Improved reinforcement learning with curriculum

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arXiv 2019年

作者： West, Joseph Maire, Frederic Browne, Cameron Denman, Simon School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD4500 Australia Department of Data Science and Knowledge Engineering Maastricht University Maastricht6211 Netherlands

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions which lead to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state - we call this an end-game-first curriculum. Currently the state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum;instead learning from the entire game at all times. By employing an end-game-first training curriculum to train an AlphaZero inspired player, we empirically show that the rate of learning of an artificial player can be improved when compared to a player not using a training curriculum. Copyright © 2019, The Authors. All rights reserved.

关键词： Curricula

A trust model for capability delegation in federated policy systems

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A trust model for capability delegation in federated policy ...

International Conference on Network and Service Management

作者： Kevin Feeney Rob Brennan Simon N. Foley FAME and Knowledge & Data Engineering Group Trinity College Dublin Ireland FAME and Department of Computer Science University College Cork Ireland

Federated policy systems are required to support the complexity and organizational heterogeneity of the modern marketplace. The Community-based Policy Management System (CBPMS) is such a distributed policy management approach. It utilizes a tree-based capability authority model to partition and delegate federated capabilities. However CBPMS delegation chains have limitations such as: performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and a lack of flexibility with respect to delegation chain reduction or capability authority re-partitioning. In this paper we introduce a trust management model for CBPMS that addresses all of these issues.. A brief security analysis is presented and a telecommunications service management use case described.

关键词： Conferences Authorization Organizations Public key Telecommunications

Hybrid tiled convolutional neural networks (HTCNN) text sentiment classification

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arXiv 2020年

作者： Truşcǎ, Maria Mihaela Spanakis, Gerasimos Department of Informatics and Economic Cybernetics Bucharest University of Economic Studies Bucharest Romania Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

The tiled convolutional neural network (TCNN) has been applied only to computer vision for learning invariances. We adjust its architecture to NLP to improve the extraction of the most salient features for sentiment analysis. Knowing that the major drawback of the TCNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled convolutional neural network (HTCNN) that applies a filter only on the words that appear in similar contexts and on their neighbouring words (a necessary step for preventing the loss of some n-grams). The experiments on the IMDB movie reviews dataset demonstrate the effectiveness of the HTCNN that has a higher level of performance of more than 3% and 1% respectively than both the convolutional neural network (CNN) and the TCNN. These results are confirmed by the SemEval-2017 dataset where the recall of the HTCNN model exceeds by more than six percentage points the recall of its simple variant, CNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks