2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第10页

An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments 一种改进的基于人工势场的无人机动态环境路径规划算法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304346

Shoufeng Chen, Zhihua Yang, Zhentao Liu, Haojie Jin

引用次数: 15

An effective single image depth estimating algorithm 一个有效的单幅图像深度估计算法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304283

Baijiang Fan, Yunbo Rao, W. Liu, Jiali Song

引用次数: 0

EEG-based emotion classification using convolutional neural network 基于脑电图的卷积神经网络情绪分类

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304263

Han Mei, Xiangmin Xu

引用次数: 24

An effective SVM-based active feedback framework for image retrieval 一种有效的基于svm的图像检索主动反馈框架

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304281

Yunbo Rao, W. Liu, Shiqi Wang, Jianping Gou, Wu He

引用次数: 0

Multiple feature fused for visual tracking via correlation filters 通过相关滤波器融合多个特征进行视觉跟踪

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304256

Di Yuan, Xiaohuan Lu, Donghao Li, Zhenyu He, Nan Luo

引用次数: 5

Cancer classification with multi-task deep learning 基于多任务深度学习的癌症分类

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304254

Qing Liao, Z. L. Jiang, Xuan Wang, Chunkai Zhang, Ye Ding

{"title":"Cancer classification with multi-task deep learning","authors":"Qing Liao, Z. L. Jiang, Xuan Wang, Chunkai Zhang, Ye Ding","doi":"10.1109/SPAC.2017.8304254","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304254","url":null,"abstract":"Microarray technique can generate a large amount of gene expression profiles for thousands of genes simultaneously. The gene expression data has been widely used in disease diagnosis and deep learning approach has achieved great successes in this task. However, the deep learning approach may fail when the expression data for a particular tumor is insufficient for training an effective model. In this paper, we propose a novel multi-task deep learning (MTDL) to overcome the aforementioned deficiency by leveraging the knowledge among multiple expression data of related cancers. MTDL learns local features from each task with some private neurons, and learns shared features for all tasks simultaneously with some shared neurons, and learns to inference for each task separately in the end layer. Since MTDL leverages the expression data of multiple cancers, it can learn more stable representation for each cancer even its expression profiles are inadequate. The experimental results show that MTDL significantly improves the performance of diagnosing each type of cancer when it jointly learns from the expression data of twelve cancer datasets.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787436","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

Face recognition using adaptive local directional pattern 基于自适应局部方向模式的人脸识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304304

Guangchao Yang, Bin Fang

引用次数: 0

Modeling anomalous attention over an online social network through read/post analytics 通过阅读/发布分析对在线社交网络上的异常注意力进行建模

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304266

Zijian Zhang, J. Liu

{"title":"Modeling anomalous attention over an online social network through read/post analytics","authors":"Zijian Zhang, J. Liu","doi":"10.1109/SPAC.2017.8304266","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304266","url":null,"abstract":"Online social platforms revolutionarize the way in which people communicate, shattering physical boundaries and bringing people together in the virtual environment. While users are able to access information and share knowledge with unprecedented ease and openness, danger also lurks in the dark. Social networks have the potential to draw unwanted and anomalous attention to their users. Through online social networks, the daily routines of an individual may be under constant surveillance of others. Such risks are closely associated with information leakage, and have posed serious privacy and safety concerns. This paper investigates such risks, which are typically captured by excessive, unprecedented and persistent gathering of personal information through the cyberspace. We focus on ways to mitigate such risks through formalizing the concepts of anomalous attention. This is a challenging question, as such behaviors are usually victim-defined and often occurs without visible trace. Viewing a network as interconnected nodes who exchange information through posting and reading messages, we provide an abstract model of attention, and quantify the level of attention a user pays towards another. Analyzing the sequence of attention between pairs of users in the network allow one to capture anomalous activities.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133949065","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

Improved fuzzy clustering for image segmentation based on local and non-local information 基于局部和非局部信息的图像分割改进模糊聚类

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304249

Xiaofeng Zhang, Yujuan Sun

引用次数: 2

Research on slipping prediction algorithm based on terrain slope in complex terrain environment 复杂地形环境下基于地形坡度的滑动预测算法研究

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304252

Lanfeng Zhou, Weijie Qian, W. Xu

引用次数: 0