2014 IEEE 26th International Conference on Tools with Artificial Intelligence最新文献

Trolls Identification within an Uncertain Framework 不确定框架中的巨魔识别

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.153

I. Dlala, Dorra Attiaoui, Arnaud Martin, B. B. Yaghlane

{"title":"Trolls Identification within an Uncertain Framework","authors":"I. Dlala, Dorra Attiaoui, Arnaud Martin, B. B. Yaghlane","doi":"10.1109/ICTAI.2014.153","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.153","url":null,"abstract":"The web plays an important role in people's social lives since the emergence of Web 2.0. It facilitates the interaction between users, gives them the possibility to freely interact, share and collaborate through social networks, online community forums, blogs, wikis and other online collaborative media. However, an other side of the web is negatively taken such as posting inflammatory messages. Thus, when dealing with the online community forums, the managers seek to always enhance the performance of such platforms. In fact, to keep the serenity and prohibit the disturbance of the normal atmosphere, managers always try to novice users against these malicious persons by posting such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to reduce this phenomenon. In this context we propose a new approach for detecting malicious people also called 'Trolls' in order to allow community managers to take their ability to post online. To be more realistic, our proposal is defined within an uncertain framework. Based on the assumption consisting on the trolls' integration in the successful discussion threads, we try to detect the presence of such malicious users. Indeed, this method is based on a conflict measure of the belief function theory applied between the different messages of the thread. In order to show the feasibility and the result of our approach, we test it in different simulated data.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"34 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":"121363089","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: 14

TCMF: Trust-Based Context-Aware Matrix Factorization for Collaborative Filtering 基于信任的协同过滤上下文感知矩阵分解

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.126

Jiyun Li, Caiqi Sun, Juntao Lv

引用次数: 10

Categorical Data Clustering: A Correlation-Based Approach for Unsupervised Attribute Weighting 分类数据聚类:一种基于关联的无监督属性加权方法

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.46

J. Carbonera, Mara Abel

引用次数: 10

Publication Venue Recommendation Based on Paper Abstract 基于论文摘要的出版地点推荐

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.152

Eric Medvet, Alberto Bartoli, Giulio Piccinin

引用次数: 60

Model-Checking an Ecosystem Model for Decision-Aid 模型——检验决策辅助的生态系统模型

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.87

M. Cordier, C. Largouët, Yulong Zhao

引用次数: 8

Local Max-Resolution in Branch and Bound Solvers for Max-SAT Max-SAT的分支和界解的局部最大分辨率

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.58

André Abramé, Djamal Habet

{"title":"Local Max-Resolution in Branch and Bound Solvers for Max-SAT","authors":"André Abramé, Djamal Habet","doi":"10.1109/ICTAI.2014.58","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.58","url":null,"abstract":"One of the most critical components of Branch & Bound (BnB) solvers for Max-SAT is the estimation of the lower bound. At each node of the search tree, they detect inconsistent subsets (IS) of the formula by unit propagation based methods and apply a treatment on them. Depending on the structure of the IS, current best performing BnB solvers transform them by several max-resolution steps and keep the changes in the sub-part of the sub tree or simply remove the clauses of these subsets from the formula and restore them before the next decision. The formula obtained after this last treatment is not equivalent to the original one and the number of detectable remaining inconsistencies may be reduced. In this paper, instead of applying such a removal, we propose to fully exploit all the inconsistent subsets by applying the well-known max-resolution inference rule to transform them locally in the current node of the search tree. The expected benefits of this transformation are an accurate lower bound estimation and the reduction of the number of decisions needed to solve an instance. We show experimentally the interest of our approach on weighted and unweighted Max-SAT instances and discuss the obtained results.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"34 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":"115196173","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: 9

An SVM Plait for Improving Affect Recognition in Intelligent Tutoring Systems 一种改进智能辅导系统情感识别的支持向量机方案

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.38

R. Janning, Carlotta Schatten, L. Schmidt-Thieme, G. Backfried, N. Pfannerer

{"title":"An SVM Plait for Improving Affect Recognition in Intelligent Tutoring Systems","authors":"R. Janning, Carlotta Schatten, L. Schmidt-Thieme, G. Backfried, N. Pfannerer","doi":"10.1109/ICTAI.2014.38","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.38","url":null,"abstract":"Usually, in intelligent tutoring systems the task sequencing is done by means of expert and domain knowledge. In a former work we presented a new efficient task sequencer without using the expensive expert and domain knowledge. This task sequencer only uses former performances and decides about the next task according to Vygotsky's Zone of Proximal Development, that is to neither bore nor frustrate the student. We aim to support this task sequencer by a further automatically to gain information, namely students affect recognized from his speech input. However, the collection of the data from children needed for training an affect recognizer in this field is challenging as it is costly and complex and one has to consider privacy issues carefully. These problems lead to small data sets and limited performances of classification methods. Hence, in this work we propose an approach for improving the affect recognition in intelligent tutoring systems, which uses a special structure of several support vector machines with different input feature vectors. Furthermore, we propose a new kind of features for this problem. Different experiments with two real data sets show, that our approach is able to improve the classification performance on average by 49% in comparison to using a single classifier.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"291 2 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":"114384074","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: 8

Using a SMT Solver for Risk Analysis: Detecting Logical Mistakes in Texts 使用SMT求解器进行风险分析:检测文本中的逻辑错误

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.133

Florence Dupin de Saint-Cyr -- Bannay, M. Lagasquie-Schiex, W. Raynaut, P. Saint-Dizier

引用次数: 1

Identifying Significant Places Using Multi-day Call Detail Records 使用多日通话记录识别重要地点

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.61

Peiyu Yang, T. Zhu, Xuejin Wan, Xuejiao Wang

{"title":"Identifying Significant Places Using Multi-day Call Detail Records","authors":"Peiyu Yang, T. Zhu, Xuejin Wan, Xuejiao Wang","doi":"10.1109/ICTAI.2014.61","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.61","url":null,"abstract":"Call detail records (CDRs) containing mass position information allow us to reveal characteristics about the city dynamics and human behaviors, which are crucial for policy decisions such as urban planning and transportation engineering. Being able to identify the trajectory and significant places is of prime importance. In this paper, we aim to extract trajectory from anonymized call detail records and adopt two-step clustering to obtain significant places from multi-day data. We propose a new method for mining trajectory by identifying users' stop and move state based on location gradient, which can be applied to users with low communication frequency. We analyze the feature of real CDR data and propose novel methods for noise handling. Home Time and Work Time are extracted from statistics of users' mobility pattern to recognize their significant places including home and work of a single day. Utilizing the characteristic of cyclical mobility, we conduct a cluster analysis to identify users' significant places which are not limited to one home or one work based on multi-day data. We run four experiments to show the robustness and stability of our method. During both typical stop and move period, our method performs better than state-of-art method.","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":"130639311","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: 19

A Sequence-Based Approach to Analysing and Representing Engineering Project Normality 基于序列的工程项目常态性分析与表征方法

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.146

Lei Shi, J. Gopsill, L. Newnes, S. Culley

{"title":"A Sequence-Based Approach to Analysing and Representing Engineering Project Normality","authors":"Lei Shi, J. Gopsill, L. Newnes, S. Culley","doi":"10.1109/ICTAI.2014.146","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.146","url":null,"abstract":"Engineering projects are often highly complex, unique and safety critical, which can lead to the complex engineering processes and activity. To ensure the success of engineering projects, the projects often have to comply with stringent regulations and company processes. In addition, the increasing in-service lifespan of products has led to an increase in the number of re-design and maintenance projects. These are often run concurrently in a highly time-constrained and high-pressured environment, which has led to the monitoring of the sequence of engineering activity becoming difficult. This is because, the sequence of engineering activity is typically achieved through the ability of the project managers to use their knowledge, experience and constant contact with the engineers. However, the viability of the current method to manually generate and evaluate the activity plan is becoming an issue due to the increasing number and distributed nature of these projects. As regulatory and/or company process demands, the data relating to the project is often archived and thus, provides a wealth of potentially useful information that could be utilised in the management of current projects. Therefore, this research investigates the potential value provided by the automatic construction of past project activity sequences, and proposes analytical methods to represent the normality of project activity based on the extracted patterns from their sequences. The evaluation applies industrial data, and shows that the results generated by the proposed approach can accurately reflect the similarity and normality of the projects.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"10 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":"116492401","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: 10