2014 IEEE 26th International Conference on Tools with Artificial Intelligence最新文献_第9页

Collaborative Ranking via Learning Social Experts 通过学习社会专家的协作排名

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.41

Zhi Yin, Xin Wang, Xiao-Jun Wu, Chenxi Liang, Congfu Xu

{"title":"Collaborative Ranking via Learning Social Experts","authors":"Zhi Yin, Xin Wang, Xiao-Jun Wu, Chenxi Liang, Congfu Xu","doi":"10.1109/ICTAI.2014.41","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.41","url":null,"abstract":"Recommendation as a universal service has driven much research works, among which explicit feedback estimation (e.g., Rating prediction in the Netflix competition) is probably the most well-known and well-studied problem. However, in various online and mobile applications, data resources of implicit feedbacks from users' interaction behaviors and linked connections from pervasive social media sites are more abundant. In this paper, we aim to integrate the users' implicit feedbacks and social connections in order to improve the ranking-oriented recommendation performance. One fundamental challenge is the noise of the social connections, which may cause incorrect social influences during learning of users' preferences. As a response, we propose to learn social experts (rather than to rely on connected individual users) as the major influence source for a certain user, which is likely to generate more accurate social influences. Specifically, we design a novel user preference generation function so as to seamlessly incorporate influences from the learned social experts. We then develop a general learning algorithm correspondingly, i.e., Collaborative ranking via learning social experts (CRSE). To verify our idea of learning social experts, we study the ranking performance of CRSE on two real-world datasets, and find that it can produce more accurate recommendations than the state-of-the-art methods.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693550","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: 1

Intelligent Models for Predicting the Thrust Force and Perpendicular Vibrations in Microdrilling Processes 微钻过程中推力和垂直振动的智能预测模型

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.82

Gerardo Beruvides, F. Castaño, R. Haber, Ramón Quiza Sardiñas, M. R. Santana

引用次数: 2

Multi-label Classification: Dealing with Imbalance by Combining Labels 多标签分类:通过组合标签处理不平衡

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.42

Ming Fang, Yuqi Xiao, Chong-Jun Wang, Junyuan Xie

引用次数: 6

Ramification Algorithm for Transporting Routes in R2 R2中传输路径的分枝算法

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.104

Jaroslaw Piersa

引用次数: 3

Two-Stage Multiclassifier System with Correction of Competence of Base Classifiers Applied to the Control of Bioprosthetic Hand 基于基分类器能力校正的两阶段多分类器系统在生物假手控制中的应用

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.98

M. Kurzynski, Maciej Krysmann, Pawel Trajdos, A. Wolczowski

引用次数: 7

Full Body Person Identification Using the Kinect Sensor 使用Kinect传感器的全身人识别

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.99

Virginia O. Andersson, R. M. Araújo

引用次数: 16

On Diagnosis of Violations of Constraints in Petri Net Models of Discrete Event Systems 离散事件系统Petri网模型中违反约束的诊断

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.106

B. Bordbar, Ahmed Al-Ajeli, M. Alodib

{"title":"On Diagnosis of Violations of Constraints in Petri Net Models of Discrete Event Systems","authors":"B. Bordbar, Ahmed Al-Ajeli, M. Alodib","doi":"10.1109/ICTAI.2014.106","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.106","url":null,"abstract":"Failure detection in partially observable model based Discrete Event Systems requires modelling failures as unobservable events within the system. Representing failures as events is not always realistic. For example, some classes of failure are in form of violations of constraints such as Service Level Agreement (SLA) and Quality of Service (QoS). These forms of failures do not represent events by themselves. They have to be modelled as additional events. Modifying the plant model is not always acceptable. Firstly, this may make the models large, causing extra computational complexity. Secondly, adding extra transitions is not always acceptable from engineers' perspective, because these constraints may change over the time leading to alternations of models every time these constraints are changed. To address this issue, this paper presents a new definition of diagnosability which extends the existing definition. In the new definition, a formalism has been introduced which captures failures as logical constraints instead of events. We show that starting from a Petri net, if the failure is expressed in Yen's logic, we can create a new Petri net with additional transitions, including transitions modelling failure, such that detection of violation of the constraint in the first Petri net is converted to diagnosis of failure in the second.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134546677","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: 3

DNVA: A Tool for Visualizing and Analyzing Multi-agent Learning in Networks DNVA:网络中多智能体学习可视化分析工具

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.67

Sherief Abdallah, Sima Sadleh, Iyad Rahwan, Aamena Al Shamsi, V. Lesser

{"title":"DNVA: A Tool for Visualizing and Analyzing Multi-agent Learning in Networks","authors":"Sherief Abdallah, Sima Sadleh, Iyad Rahwan, Aamena Al Shamsi, V. Lesser","doi":"10.1109/ICTAI.2014.67","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.67","url":null,"abstract":"Networks are seen everywhere in our modern life, including the Internet, the Grid, P2P file sharing, and sensor networks. Consequently, researchers in Artificial Intelligence (and Multi-Agent Systems in particular) have been actively seeking methods for optimizing the performance of these networks. A promising yet challenging optimization direction is multi-agent learning: allowing agents to adapt their behavior through interaction with one another. However, understanding the dynamics of an adaptive agent network is complicated due to the large number of system parameters, the concurrency by which the system parameters change, and the delay in the effect/consequence of parameter changes. All these factors make it hard to understand why an adaptive network of agents performed well at some time and poorly at another. In this paper we present a software tool that enables researchers in the multi-agent systems field to visualize and analyze the evolution of adaptive networks. The proposed software customizes and implements techniques from data mining and social network analysis research and augment these techniques in order to analyze local agent behaviors. We use our tool to analyze two domains. In both domains we are able to report and explain interesting observations using our tool.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130270256","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: 1

Conversation Emotional Modeling in Social Networks 社交网络中的会话情感建模

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.78

Andreas Kanavos, I. Perikos, Pantelis Vikatos, I. Hatzilygeroudis, C. Makris, A. Tsakalidis

引用次数: 27

Utilitarian and Egalitarian Solutions for Multi-objective Constraint Optimization 多目标约束优化的功利和平等解

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.34

Nicolas Schwind, Tenda Okimoto, S. Konieczny, M. Wack, Katsumi Inoue

引用次数: 5