2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第10页

The Temporal Bin Packing Problem: An Application to Workload Management in Data Centres 时间装箱问题:在数据中心工作负载管理中的应用

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0033

Milan De Cauwer, D. Mehta, B. O’Sullivan

{"title":"The Temporal Bin Packing Problem: An Application to Workload Management in Data Centres","authors":"Milan De Cauwer, D. Mehta, B. O’Sullivan","doi":"10.1109/ICTAI.2016.0033","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0033","url":null,"abstract":"This paper formalises a packing problem that emerges as a core sub-problem for managing workload consolidation in data centres. As a generalisation of the Bin Packing (BP) problem, it considers a set of tasks (items) to be assigned to a set of machines (bins) under capacity constraints (CPU usage) on each machine. Unlike classic BP settings, items have a lifespan. We define the cost of using a bin as the product of the bin's capacity and the time for which it is used. This problem will be referred to as the Temporal Bin Packing problem (TBP). We formalise the problem and present optimisation models using Mixed Integer Programming (MIP) and Constraint Programming (CP) for two contrasting but equivalent perspectives on the problem. The Packing model (PA) extends traditional BP models while the Temporal model (TP) explicitly models time with a sequence of packing problems. In addition, symmetry breaking techniques are developed. Finally, we introduce both a lower bound and an upper bound on the objective function. Our empirical results suggest that the TBP is a rather challenging problem for complete solvers to prove optimality. While breaking symmetry considerably reduces the computational effort for both PA and TP models, the Packing model using CP should be considered for larger instances.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Solving the Multi-Mode Resource-Constrained Project Scheduling Problem with SMT 基于SMT的多模式资源约束项目调度问题求解

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0045

Miquel Bofill, Jordi Coll, Josep Suy, Mateu Villaret

引用次数: 14

WAVE: Wave Agents in a Virtual Environment - A Proof of Concept Applied to Longitudinal Distance Regulation in Platoon Control WAVE:虚拟环境中的波代理——用于排控纵向距离调节的概念验证

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0017

Baudouin Dafflon, Maxime Guériau, Franck Gechter

引用次数: 1

A Comparison between Two Optimisation Alternatives for Mapping in Wireless Network on Chip 片上无线网络中两种映射优化方案的比较

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0145

Maribell Sacanamboy Franco, L. Quesada, F. Bolanos, Álvaro Bernal Noreña, B. O’Sullivan

引用次数: 6

Deploying Agents for Monitoring and Notification of Wireless Sensor Networks 部署用于监测和通知无线传感器网络的代理

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0118

A. Khalifeh, S. Al-Agtash, Rabi Tanash, Mahmoud AlQudah

引用次数: 13

Biterm Pseudo Document Topic Model for Short Text 短文本的双词伪文档主题模型

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0134

Lan Jiang, Heng-yang Lu, Ming Xu, Chong-Jun Wang

{"title":"Biterm Pseudo Document Topic Model for Short Text","authors":"Lan Jiang, Heng-yang Lu, Ming Xu, Chong-Jun Wang","doi":"10.1109/ICTAI.2016.0134","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0134","url":null,"abstract":"In the past few years, we have witnessed a rapid development of online social media, from which we can access various short texts. Understanding the topic patterns of these short text is significant. Traditional topic models, like LDA, are not suitable when applied to short text topic analysis due to data sparsity. A lot of efforts have been made to solve this problem. However, there is still significant space to improve the effectiveness of these short text specific methods. In this paper, we proposed a novel word co-occurrence network based method, referred to as biterm pseudo document topic model (BPDTM), which extended the previous biterm topic model(BTM) for short text. We utilized the word co-occurrence network to construct biterm pseudo documents. The proposed model is promising since it represents words with their semantic adjacent biterms and is able to model the corpus-level semantic relation between two words. Besides, BPDTM naturally lengthens the documents, which alleviate the influence for performance exerted by data sparsity. Experiments demonstrated that our model outperformed two baselines, i.e. LDA and BTM, which proved its effectiveness on short text topic modeling task.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Rewriting Minimisations for Efficient Ontology-Based Query Answering 改写最小化以实现高效的基于本体的查询应答

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0168

Tassos Venetis, G. Stoilos, V. Vassalos

{"title":"Rewriting Minimisations for Efficient Ontology-Based Query Answering","authors":"Tassos Venetis, G. Stoilos, V. Vassalos","doi":"10.1109/ICTAI.2016.0168","DOIUrl":"https://doi.org/10.1109/ICTAI.2016.0168","url":null,"abstract":"Computing a (Union of Conjunctive Queries - UCQ) rewriting R for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. However, R can be large and complex in structure hence additional techniques, like query subsumption and data constraints, need to be employed in order to minimise Rew and lead to an efficient evaluation. Although sound in theory, how to efficiently and effectively implement many of these techniques in practice could be challenging. For example, many systems do not implement query subsumption. In the current paper we present several practical techniques for UCQ rewriting minimisation. First, we present an optimised algorithm for eliminating redundant (w.r.t. subsumption) queries as well as a novel framework for rewriting minimisation using data constraints. Second, we show how these techniques can also be used to speed up the computation of R in the first place. Third, we integrated all our techniques in our query rewriting system IQAROS and conducted an extensive experimental evaluation using many artificial as well as challenging real-world ontologies obtaining encouraging results as, in the vast majority of cases, our system is more efficient compared to the two most popular state-of-the-art systems.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114286692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Incremental Learning from Scratch Using Analogical Reasoning 使用类比推理从零开始增量学习

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0040

Vincent Letard, S. Rosset, Gabriel Illouz

引用次数: 1

Clustering with Quantitative User Preferences on Attributes 基于属性的定量用户偏好聚类

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0065

Adnan El Moussawi, A. Cheriat, A. Giacometti, Nicolas Labroche, Arnaud Soulet

引用次数: 3

A Virtual Character Learns to Defend Himself in Sword Fighting Based on Q-Network 基于Q-Network的虚拟人物剑术自卫学习

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2016-11-01 DOI: 10.1109/ICTAI.2016.0052

Yumeng Wang, Er Li, Feng Wang, Bo Xu

引用次数: 2