2017 Artificial Intelligence and Signal Processing Conference (AISP)最新文献_第6页

The effect of online opponent modeling on utilities of agents in bilateral negotiation 在线对手建模对双边谈判中代理效用的影响

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8515122

Sahar Mirzayi, F. Taghiyareh, Faria Nassiri-Mofakham

引用次数: 2

A novel framework to generate clustering algorithms based on a particular classification structure 一种基于特定分类结构生成聚类算法的新框架

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324081

Hossein Karami, M. Taheri

引用次数: 4

Leveraging deep learning representation for search-based image annotation 利用深度学习表示进行基于搜索的图像标注

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324073

Mahya Mohammadi Kashani, S. H. Amiri

{"title":"Leveraging deep learning representation for search-based image annotation","authors":"Mahya Mohammadi Kashani, S. H. Amiri","doi":"10.1109/AISP.2017.8324073","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324073","url":null,"abstract":"Image annotation aims to assign some tags to an image such that these tags provide a textual description for the content of image. Search-based methods extract relevant tags for an image based on the tags of nearest neighbor images in the training set. In these methods, similarity of two images is determined based on the distance between feature vectors of the images. Thus, it is essential to extract informative feature vectors from images. In this paper, we propose a framework that utilize deep learning to obtain visual representation of images. We apply different architectures of convolutional neural networks (CNN) to the input image and obtain a single feature vector that is a rich representation for visual content of the image. In this way, we eliminate the usage of multiple feature vectors used in the state-of-the-art annotation methods. We also integrate our feature extractors with a nearest neighbors approach to obtain relevant tags of an image. Our experiments on the standard datasets of image annotation (including Corel5k, ESP Game, IAPR) demonstrate that our approach reaches higher precision, recall and F1 than the state-of-the-art methods such as 2PKNN, TagProp, NMF-KNN and etc.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122203868","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 survey of distributed resource allocation for device-to-device communication in cellular networks 蜂窝网络中设备对设备通信的分布式资源分配研究

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324088

Omid Yazdani, G. Mirjalili

引用次数: 10

An online fuzzy model for classification of data streams with drift 带有漂移的数据流在线模糊分类模型

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324115

H. Shahparast, E. Mansoori

引用次数: 4

A new brain-robot interface system based on SVM-PSO classifier 基于SVM-PSO分类器的脑-机器人接口系统

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/aisp.2017.8324122

V. Azimirad, Mahdiyeh Hajibabzadeh, P. Shahabi

{"title":"A new brain-robot interface system based on SVM-PSO classifier","authors":"V. Azimirad, Mahdiyeh Hajibabzadeh, P. Shahabi","doi":"10.1109/aisp.2017.8324122","DOIUrl":"https://doi.org/10.1109/aisp.2017.8324122","url":null,"abstract":"This paper presents a new noninvasive brain-robot interface system for control of two degrees of freedom robot through motor imagery EEG signals. Signal classification is based on optimized Support Vector Machine (SVM) by Particle Swarm Optimization (PSO) algorithm. EEG signals of FC3, C3, CP3, FC4, C4 and CP4 Channels that are related to hands movement as well as Cz and FCz channels that are related to feet movement are considered. Radial basis function (RBF) and penalty functions of SVM are optimized through PSO algorithm. For validation of SVM-PSO classifier, the EEG signals are collected from two databases: PhysioNet and BCI Competition III, then features including Power Spectral Density (PSD) and wavelet parameters are used as the input of the classifier. By comparing the results of the SVM and SVM-PSO classifiers, is concluded that performance of classifier in terms of accuracy is increased through PSO algorithm. SVM-PSO classification accuracy for wavelet and PSD features are obtained 81% and 92%, respectively. The best algorithm is used to control a two degrees of freedom (one for left and right hand movements and the other for left and right foot movements) industrial robot experimentally. It shows the applicability and effectiveness of proposed method for high accuracy brain-robot interface systems.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132250109","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: 6

A new compression based method for android malware detection using opcodes 基于操作码的基于压缩的android恶意软件检测方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324092

Nazanin Bakhshinejad, A. Hamzeh

{"title":"A new compression based method for android malware detection using opcodes","authors":"Nazanin Bakhshinejad, A. Hamzeh","doi":"10.1109/AISP.2017.8324092","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324092","url":null,"abstract":"Nowadays, the functionality of mobile devices improved substantially which in some cases they were as capable as personal computers. We perform a wide range of our daily tasks with mobile devices like browsing the internet, checking mail, social networking and transforming money. As these smart devices become more popular and usable, they attracted more attackers. Recently, mobile malwares increased sharply and their caused detriments menace the usability and privacy due to the sensitive data which are stored in these devices. According to the intense increase in the number of these attacks yearly, malware detection becomes a prominent topic in mobile security. Since traditional signature based techniques which are used by commercial anti-virus have failed to detect new and obfuscated malwares, machine learning approaches have been employed to find and detect behavior patterns of malwares from extracted features. In this paper, a new heuristic malware detection technique was proposed based on compression methods. The momentous superiority of this approach is using opcode as an input for compression models which causes accuracy to be increased. To assess the potency of the proposed methods, several experiments are conducted. The experimental results of method show promising improvement of accuracy to support the main idea.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127606585","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: 2

A weakly-supervised factorization method with dynamic graph embedding 动态图嵌入的弱监督分解方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324084

Seyed Amjad Seyedi, P. Moradi, F. Tab

引用次数: 3

Design expanded BCI with improved efficiency for VR-embedded neurorehabilitation systems 设计扩展脑机接口，提高vr嵌入式神经康复系统的效率

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324087

Farhad Parivash, Leila Amuzadeh, A. Fallahi

引用次数: 2

An incremental fuzzy controller for large dec-POMDPs 大型deco - pomdp的增量模糊控制器

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324075

S. Hamzeloo, M. Z. Jahromi

引用次数: 3