2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第8页

Sleep Activity Recognition Using Binary Motion Sensors 利用二元运动传感器识别睡眠活动

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00049

Yassine El-Khadiri, G. Corona, C. Rose, F. Charpillet

引用次数: 5

Efficient Instance Selection Based on Spatial Abstraction 基于空间抽象的高效实例选择

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00053

J. Carbonera, Mara Abel

引用次数: 14

IPC-Net: 3D Point-Cloud Segmentation Using Deep Inter-Point Convolutional Layers 使用深度点间卷积层的三维点云分割

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00054

F. Marulanda, P. Libin, T. Verstraeten, A. Nowé

{"title":"IPC-Net: 3D Point-Cloud Segmentation Using Deep Inter-Point Convolutional Layers","authors":"F. Marulanda, P. Libin, T. Verstraeten, A. Nowé","doi":"10.1109/ICTAI.2018.00054","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00054","url":null,"abstract":"Over the last decade, the demand for better segmentation and classification algorithms in 3D spaces has significantly grown due to the popularity of new 3D sensor technologies and advancements in the field of robotics. Point-clouds are one of the most popular representations to store a digital description of 3D shapes. However, point-clouds are stored in irregular and unordered structures, which limits the direct use of segmentation algorithms such as Convolutional Neural Networks. The objective of our work is twofold: First, we aim to provide a full analysis of the PointNet architecture to illustrate which features are being extracted from the point-clouds. Second, to propose a new network architecture called IPC-Net to improve the state-of-the-art point cloud architectures. We show that IPC-Net extracts a larger set of unique features allowing the model to produce more accurate segmentations compared to the PointNet architecture. In general, our approach outperforms PointNet on every family of 3D geometries on which the models were tested. A high generalisation improvement was observed on every 3D shape, especially on the rockets dataset. Our experiments demonstrate that our main contribution, inter-point activation on the network's layers, is essential to accurately segment 3D point-clouds.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"8 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":"128923729","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

A Syntax-Guided Neural Model for Natural Language Interfaces to Databases 数据库自然语言接口的语法引导神经模型

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00043

Florin Brad, R. Iacob, Ionel-Alexandru Hosu, Stefan Ruseti, Traian Rebedea

引用次数: 1

A Linked Data Browser with Recommendations 带推荐的关联数据浏览器

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00038

F. Durão, D. Bridge

引用次数: 0

Multi-Algorithmic Techniques and a Hybrid Model for Increasing the Efficiency of Recommender Systems 提高推荐系统效率的多算法技术和混合模型

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00037

C. Troussas, Akrivi Krouska, M. Virvou

{"title":"Multi-Algorithmic Techniques and a Hybrid Model for Increasing the Efficiency of Recommender Systems","authors":"C. Troussas, Akrivi Krouska, M. Virvou","doi":"10.1109/ICTAI.2018.00037","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00037","url":null,"abstract":"The explosive growth in the amount of available digital information has increased the demand for recommender systems. Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user's preferences or interests. Recommender systems have the ability to predict whether a particular user would prefer an item or not based on his/her personal profile. To this direction, this paper presents multi-algorithmic techniques, such as content-based filtering and collaborative filtering, which increase the efficiency of recommender systems. Moreover, a hybrid model for recommendation, employing content-based and collaborative filtering, is introduced. The presented recommender system takes as input information about users from their profile in Facebook, one of the most well-known social networking services. Examples of operation are given and they hold promising results for the described techniques. Finally, the paper attests that the aforementioned techniques can be used for different kind of software, such as e-learning, e-commerce, etc.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"57 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":"124227937","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

Multi-LCNN: A Hybrid Neural Network Based on Integrated Time-Frequency Characteristics for Acoustic Scene Classification Multi-LCNN:一种基于时频综合特征的混合神经网络用于声场景分类

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00019

Jin Lei, Changjian Wang, Boqing Zhu, Q. Lv, Zhen Huang, Yuxing Peng

{"title":"Multi-LCNN: A Hybrid Neural Network Based on Integrated Time-Frequency Characteristics for Acoustic Scene Classification","authors":"Jin Lei, Changjian Wang, Boqing Zhu, Q. Lv, Zhen Huang, Yuxing Peng","doi":"10.1109/ICTAI.2018.00019","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00019","url":null,"abstract":"Acoustic scene classification (ASC) is an important task in audio signal processing and can be useful in many real-world applications. Recently, several deep neural network models have been proposed for ASC, such as LSTMs based on temporal analysis and CNNs based on frequency spectrum, as well as hybrid models of LSTM and CNN to further improve classification performance. However, existing hybrid models fail to properly preserve the temporal information when transferring data between different models. In this work, we first analyze the cause of such temporal information loss. We then propose Multi-LCNN, a new hybrid model with two important mechanisms: (1) a LCNN architecture to effectively preserve temporal information; and (2) a multi-channel feature fusion mechanism (MCFF) that combines enhanced temporal information and frequency spectrogram information to learn highly integrated and discriminative features for ASC. Evaluations on the TUT ASC 2016 dataset show that our model can achieve an improvement of 10.23% over the baseline method, and is currently the best-performing end-to-end model on this dataset.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"38 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":"115555669","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

Semi-Supervised Learning Techniques for Automated Fault Detection and Diagnosis of HVAC Systems 暖通空调系统故障自动检测与诊断的半监督学习技术

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00136

M. Dey, S. P. Rana, S. Dudley

引用次数: 9

In-Network Decision Making Intelligence for Task Allocation in Edge Computing 边缘计算中任务分配的网络决策智能

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00104

Konstantinos Kolomvatsos, C. Anagnostopoulos

引用次数: 6

On the Relevance of Optimal Tree Decompositions for Constraint Networks 约束网络最优树分解的相关性研究

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00116

Philippe Jégou, Hélène Kanso, C. Terrioux

{"title":"On the Relevance of Optimal Tree Decompositions for Constraint Networks","authors":"Philippe Jégou, Hélène Kanso, C. Terrioux","doi":"10.1109/ICTAI.2018.00116","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00116","url":null,"abstract":"For the study and the solving of NP-hard problems, the concept of tree decomposition is nowadays a major topic in Computer Science, in Artificial Intelligence and particularly in Constraint Programming. It appears as a promising field for the theoretical study of numerous graphical models like Bayesian Networks or (Weighted) Constraint Networks, since it can ensure, under some hypothesis, the existence of polynomial time algorithms. This concept is also used in a wide range of applications. Recently, a real improvement in the practical computation of optimal tree decompositions has been observed, allowing new promising applications of this concept in real applications. In this paper, we first aim to analyze the real relevance of such optimal decompositions. We first set that a larger set of instances are now optimally decomposable in practice but using these algorithms on a practical level still constitutes a real difficulty. In a second time, we assess the impact of such optimal decompositions for solving these instances and note a discrepancy between the empirical results and what is expected from the complexity analysis. Finally, we discuss of the next investigations which are needed on this topic.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"55 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":"123761126","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