2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第3页

Electricity Theft Detection Using Generative Models 基于生成模型的窃电检测

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00050

Qianru Zhang, Meng Zhang, Tinghuan Chen, Jinan Fan, Zhou Yang, Guoqing Li

引用次数: 11

Possibilistic Networks: MAP Query and Computational Analysis 可能性网络:MAP查询与计算分析

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00142

S. Benferhat, Amélie Levray, Karim Tabia

引用次数: 1

Historical Best Q-Networks for Deep Reinforcement Learning 历史上最好的深度强化学习q网络

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00012

Wenwu Yu, Rui Wang, Ruiying Li, Jing Gao, Xiaohui Hu

引用次数: 5

Dynamic Ensemble Selection by K-Nearest Local Oracles with Discrimination Index 基于判别指数的k -最近局部预言机动态集成选择

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00120

Marcelo Pereira, A. Britto, Luiz Oliveira, R. Sabourin

引用次数: 7

Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems 树的分支跟踪——解决部分有序HTN规划问题的命题编码

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00022

G. Behnke, D. Höller, Susanne Biundo-Stephan

引用次数: 21

Hybrid CODBA-II Algorithm Coupling a Co-Evolutionary Decomposition-Based Algorithm with Local Search Method to Solve Bi-Level Combinatorial Optimization 基于协同进化分解和局部搜索的混合CODBA-II算法求解双层次组合优化问题

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00084

Abir Chaabani, L. B. Said

{"title":"Hybrid CODBA-II Algorithm Coupling a Co-Evolutionary Decomposition-Based Algorithm with Local Search Method to Solve Bi-Level Combinatorial Optimization","authors":"Abir Chaabani, L. B. Said","doi":"10.1109/ICTAI.2018.00084","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00084","url":null,"abstract":"Bi-level optimization problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The usefulness of bi-level optimization in designing hierarchical decision processes prompted several researchers, in particular the evolutionary computation community, to pay more attention to such kind of problems. Several solution approaches have been proposed to solve these problems; however, most of them are restricted to the continuous case. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-based Algorithm (CODBA-II) to solve combinatorial bi-level problems. CODBA-II scheme has been able to improve the bi-level performance and to bring down the computational expense significantly as compared to other competitive approaches within this research area. In this paper, we present an extension of the recently proposed CODBA-II algorithm. The improved version, called CODBA-IILS, further improves the algorithm by incorporating a local search process to both upper and lower levels in order to help in faster convergence of the algorithm. The improved results have been demonstrated on two different sets of test problems based on the bi-level production-distribution problems in supply chain management, and comparison results against the contemporary approaches are also provided.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132736916","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

Predicting Stances in Twitter Conversations for Detecting Veracity of Rumors: A Neural Approach 预测Twitter对话中的立场以检测谣言的真实性:一种神经方法

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00021

Lahari Poddar, W. Hsu, M. Lee, Shruti Subramaniyam

{"title":"Predicting Stances in Twitter Conversations for Detecting Veracity of Rumors: A Neural Approach","authors":"Lahari Poddar, W. Hsu, M. Lee, Shruti Subramaniyam","doi":"10.1109/ICTAI.2018.00021","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00021","url":null,"abstract":"Detecting rumors is a crucial task requiring significant time and manual effort in forms of investigative journalism. In social media such as Twitter, unverified information can get disseminated rapidly making early detection of potentially false rumors critical. We observe that the early reactions of people towards an emerging claim can be predictive of its veracity. We propose a novel neural network architecture using the stances of people engaging in a conversation on Twitter about a rumor for detecting its veracity. Our proposed solution comprises two key steps. We first detect the stance of each individual tweet, by considering the textual content of the tweet, its timestamp, as well as the sequential conversation structure leading up to the target tweet. Then we use the predicted stances of all tweets in a conversation tree to determine the veracity of the original rumor. We evaluate our model on the SemEval2017 rumor detection dataset and demonstrate that our solution outperforms the state-of-the-art approaches for both stance prediction and rumor veracity prediction tasks.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134321990","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: 38

Obstacle-Avoiding Euclidean Steiner Trees by n-Star Bundles 基于n星束的避障欧几里得斯坦纳树

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00057

V. Parque, T. Miyashita

引用次数: 7

Performance Comparison of Machine Learning Models Trained on Manual vs ASR Transcriptions for Dialogue Act Annotation 对话行为注释中手动与ASR转录训练的机器学习模型的性能比较

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00156

Usman Malik, Mukesh Barange, Julien Saunier, A. Pauchet

引用次数: 4

Investigating the Efficiency of Machine Learning Algorithms on MapReduce Clusters with SSDs 基于ssd的MapReduce集群机器学习算法的效率研究

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00157

Leonidas Akritidis, Athanasios Fevgas, P. Tsompanopoulou, Panayiotis Bozanis

{"title":"Investigating the Efficiency of Machine Learning Algorithms on MapReduce Clusters with SSDs","authors":"Leonidas Akritidis, Athanasios Fevgas, P. Tsompanopoulou, Panayiotis Bozanis","doi":"10.1109/ICTAI.2018.00157","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00157","url":null,"abstract":"In the big data era, the efficient processing of large volumes of data has became a standard requirement for both organizations and enterprises. Since single workstations cannot sustain such tremendous workloads, MapReduce was introduced with the aim of providing a robust, easy, and fault-tolerant parallelization framework for the execution of applications on large clusters. One of the most representative examples of such applications is the machine learning algorithms which dominate the broad research area of data mining. Simultaneously, the recent advances in hardware technology led to the introduction of high-performing alternative devices for secondary storage, known as Solid State Drives (SSDs). In this paper we examine the perfor-mance of several parallel data mining algorithms on MapReduce clusters equipped with such modern hardware. More specifically, we investigate standard dataset preprocessing methods including vectorization and dimensionality reduction, and two supervised classifiers, Naive Bayes and Linear Regression. We compare the execution times of these algorithms on an experimental cluster equipped with both standard magnetic disks and SSDs, by employing two different datasets and by applying several different cluster configurations. Our experiments demonstrate that the usage of SSDs can accelerate the execution of machine learning methods by a margin which depends on the cluster setup and the nature of the applied algorithms.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988639","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: 0