2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)最新文献

Reasoning About Future Cyber-Attacks Through Socio-Technical Hacking Information 通过社会技术黑客信息推断未来的网络攻击

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00030

E. Marin, Mohammed Almukaynizi, P. Shakarian

引用次数: 3

Adaptive Phone Orientation Method for Continuous Authentication Based on Mobile Motion Sensors 基于移动运动传感器的自适应手机方向连续认证方法

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00236

Shixuan Wang, Jiabin Yuan, Jing Wen

{"title":"Adaptive Phone Orientation Method for Continuous Authentication Based on Mobile Motion Sensors","authors":"Shixuan Wang, Jiabin Yuan, Jing Wen","doi":"10.1109/ICTAI.2019.00236","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00236","url":null,"abstract":"With the popularity of mobile terminals and the convenience of mobile operating, more and more private data is stored in mobile phones, which makes users pay more attention to mobile security. The data of mobile motion sensors are used to construct the user's behavioral characteristics and biometric characteristics. The principle is to capture the subtle changes in the smartphones caused by the user holding smartphones and touching screens. These changes are unique to different users. This paper studies the effects of mobile phone orientations on continuous authentication based on mobile motion sensors. Additionally, this paper constructs an adaptive phone orientation method for continuous authentication. An orientation detection model is constructed using K-means and Random Forest. The authentication model is constructed by using one-class SVM. Meanwhile, our experiments show that the data of mobile motion sensors are great difference in different phone orientations. We believe considering the situation of multi-orientations data affecting authentication accuracy. The adaptive phone orientation method can better fit for the scenarios where users use mobile devices in different phone orientations. Considering the orientation of the data will improve the robustness of the system and the accuracy of authentication.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117117701","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

Real-Coded Genetic Algorithm Realizing Fast Convergence by Reducing Its Population Size 实数编码遗传算法通过减小种群大小实现快速收敛

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00264

Kazuki Nishisaka, H. Iima

引用次数: 1

Optimizing Value of Information Over an Infinite Time Horizon 在无限时间范围内优化信息价值

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00101

Sarthak Ghosh, C. Ramakrishnan

{"title":"Optimizing Value of Information Over an Infinite Time Horizon","authors":"Sarthak Ghosh, C. Ramakrishnan","doi":"10.1109/ICTAI.2019.00101","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00101","url":null,"abstract":"Decision-making based on probabilistic reasoning often involves selecting a subset of expensive observations that best predict the system state. In an earlier work, adopting the general notion of value of information (VoI) first introduced by Krause and Guestrin, Ghosh and Ramakrishnan considered the problem of determining optimal conditional observation plans in temporal graphical models, based on non-myopic (non-greedy) VoI, over a finite time horizon. They cast the problem as determining optimal policies in finite-horizon, non-discounted Markov Decision Processes (MDPs). However, there are many practical scenarios where a time horizon is undefinable. In this paper, we consider the VoI optimization problem over an infinite (or equivalently, undefined) time horizon. Adopting an approach similar to Ghosh and Ramakrishnan's, we cast this problem as determining optimal policies in infinite-horizon, finite-state, discounted MDPs. Although our MDP-based framework addresses Dynamic Bayesian Networks (DBNs) that are more restricted than those addressed by Ghosh and Ramakrishnan, we incorporate Krause and Guestrin's general idea of VoI even though it was fundamentally envisioned for finite-horizon settings. We establish the utility of our approach on two graphical models based on real-world datasets.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122123740","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

Distant-Supervised Relation Extraction with Hierarchical Attention Based on Knowledge Graph 基于知识图的层次关注的远程监督关系提取

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00040

Hong Yao, Lijun Dong, Shiqi Zhen, Xiaojun Kang, Xinchuan Li, Qingzhong Liang

{"title":"Distant-Supervised Relation Extraction with Hierarchical Attention Based on Knowledge Graph","authors":"Hong Yao, Lijun Dong, Shiqi Zhen, Xiaojun Kang, Xinchuan Li, Qingzhong Liang","doi":"10.1109/ICTAI.2019.00040","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00040","url":null,"abstract":"Relation Extraction is concentrated on finding the unknown relational facts automatically from the unstructured texts. Most current methods, especially the distant supervision relation extraction (DSRE), have been successfully applied to achieve this goal. DSRE combines knowledge graph and text corpus to corporately generate plenty of labeled data without human efforts. However, the existing methods of DSRE ignore the noisy words within sentences and suffer from the noisy labelling problem; the additional knowledge is represented in a common semantic space and ignores the semantic-space difference between relations and entities. To address these problems, this study proposes a novel hierarchical attention model, named the Bi-GRU-based Knowledge Graph Attention Model (BG2KGA) for DSRE using the Bidirectional Gated Recurrent Unit (Bi-GRU) network. BG2KGA contains the word-level and sentence-level attentions with the guidance of additional knowledge graph, to highlight the key words and sentences respectively which can contribute more to the final relation representations. Further-more, the additional knowledge graph are embedded in the multi-semantic vector space to capture the relations in 1-N, N-1 and N-N entity pairs. Experiments are conducted on a widely used dataset for distant supervision. The experimental results have shown that the proposed model outperforms the current methods and can improve the Precision/Recall (PR) curve area by 8% to 16% compared to the state-of-the-art models; the AUC of BG2KGA can reach 0.468 in the best case.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128440596","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

General Chair’s Foreword 主席的前言

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ictai.2019.00005

Mohammed Atiquzzaman

引用次数: 0

TPOT-SH: A Faster Optimization Algorithm to Solve the AutoML Problem on Large Datasets TPOT-SH:一种解决大型数据集上AutoML问题的快速优化算法

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00072

Laurent Parmentier, Olivier Nicol, Laetitia Vermeulen-Jourdan, Marie-Éléonore Kessaci

{"title":"TPOT-SH: A Faster Optimization Algorithm to Solve the AutoML Problem on Large Datasets","authors":"Laurent Parmentier, Olivier Nicol, Laetitia Vermeulen-Jourdan, Marie-Éléonore Kessaci","doi":"10.1109/ICTAI.2019.00072","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00072","url":null,"abstract":"Data are omnipresent nowadays and contain knowledge and patterns that machine learning (ML) algorithms can extract so as to take decisions or perform a task without explicit instructions. To achieve that, these algorithms learn a mathematical model using sample data. However, there are numerous ML algorithms, all learning different models of reality. Furthermore, the behavior of these algorithms can be altered by modifying some of their plethora of hyperparameters. Cleverly tuning these algorithms is costly but essential to reach decent performance. Yet it requires a lot of expertise and remains hard even for experts who tend to resort to exploration-only approaches like random search and grid search. The field of AutoML has consequently emerged in the quest for automatized machine learning processes that would be less expensive than brute force searches. In this paper we continue the research initiated on the Tree-based Pipeline Optimization Tool (TPOT), an AutoML based on Evolutionary Algorithms (EA). EAs are typically slow to converge which makes TPOT incapable of scaling to large datasets. As a consequence, we introduce TPOT-SH inspired from the concept of Successive Halving used in Multi-Armed Bandit problems. This solution allows TPOT to explore the search space faster and have much better performance on larger datasets.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128729019","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

Texture Recognition on Metal Surface using Order-Less Scale Invariant GLAC 基于无阶尺度不变性GLAC的金属表面纹理识别

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00093

Shangbo Mao, V. Natarajan, L. Chia, G. Huang

{"title":"Texture Recognition on Metal Surface using Order-Less Scale Invariant GLAC","authors":"Shangbo Mao, V. Natarajan, L. Chia, G. Huang","doi":"10.1109/ICTAI.2019.00093","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00093","url":null,"abstract":"Inspection of metal surface textures using computer vision and machine learning techniques plays an important role in Automated Visual Inspection (AVI) systems. Texture recognition on metal surface is challenging because the characteristics of each texture type are dependent on the properties of the metal surface when captured under different lighting conditions. Since these textures have no obvious repetitive patterns like general textures, this results in high intra-class diversities. Prior knowledge has shown that surface properties such as surface curvature and depth are discriminant to different texture types on metal surface. Since scale, shapes and location of textures within the same type are not fixed, scale property and spatial ordering information are less important for differentiating between texture types. There-fore, surface property, scale invariance and order-less property should be considered when exploring a suitable image feature for metal surface texture recognition. This paper proposes Order-less Scale Invariant Gradient Local Auto-Correlation (OS-GLAC) which meets all three requirements for robust texture recognition. The experiment results show that OS-GLAC is robust to separate different metal surface texture types. In addition, we observed that OS-GLAC is not only useful for texture recognition on metal surface but also for general texture recognition when combined with pre-trained deep learning features as these two features capture complimentary information. The experiment results show that such a combination of OS-GLAC achieves competitive results on three well-established general texture datasets i.e., KTH-TIP-2a, KTH-TIPS-2b and FMD.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127024862","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

A Divide and Conquer Algorithm for Dominance Testing in Acyclic CP-Nets 一种非循环cp -网络优势测试的分治算法

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00062

Sultan Ahmed, Malek Mouhoub

引用次数: 2

Accurate and Robust RGB-D Dense Mapping with Inertial Fusion and Deformation-Graph Optimization 基于惯性融合和变形图优化的精确鲁棒RGB-D密集映射

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00249

Yong Liu, Liming Bao, Chaofan Zhang, Wen Zhang, Yingwei Xia

{"title":"Accurate and Robust RGB-D Dense Mapping with Inertial Fusion and Deformation-Graph Optimization","authors":"Yong Liu, Liming Bao, Chaofan Zhang, Wen Zhang, Yingwei Xia","doi":"10.1109/ICTAI.2019.00249","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00249","url":null,"abstract":"RGB-D dense mapping has become more and more popular, however, when encountering rapid movement or shake, the robustness and accuracy of most RGB-D dense mapping methods are degraded and the generated maps are overlapped or distorted, due to the drift of pose estimation. In this paper, we present a novel RGB-D dense mapping method, which can obtain accurate, robust and global consistency map even in the above complex conditions. Firstly, the improved ORBSLAM method, which tightly-couples RGB-D information and inertial information to estimate the current pose of robot, is firstly introduced for accurate pose estimation rather than traditional frame-to-frame method in most RGB-D dense mapping methods. Besides, the TSDF (Truncated Signed Distance Function) method is used to effectively fuse depth frame into a global model, and to keep the global consistency of the generated map. Furthermore, since the drift error is inevitable, a deformation graph is constructed to minimize the consistent error in global model, to further improve the mapping performance. The performance of the proposed RGB-D dense mapping method was validated by extensive localization and mapping experiments on public datasets and real scene datasets, and it showed strongly accuracy and robustness over other state-of-the-art methods. What's more, the proposed method can achieve real-time performance implemented on GPU.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127932370","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