International Conference on Signal Processing and Machine Learning最新文献_第4页

Detecting Blind Cross-Site Scripting Attacks Using Machine Learning 利用机器学习检测盲跨站脚本攻击

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297096

Gurpreet Kaur, Yasir Malik, Hamman W. Samuel, Fehmi Jaafar

引用次数: 9

Brain Function Networks Reveal Movement-related EEG Potentials Associated with Exercise-induced Fatigue 脑功能网络揭示与运动诱发疲劳相关的运动相关脑电图电位

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297074

Jiahui Wang, Kun Yang, Jianhai Zhang, N. Zhang, Bin Chen

{"title":"Brain Function Networks Reveal Movement-related EEG Potentials Associated with Exercise-induced Fatigue","authors":"Jiahui Wang, Kun Yang, Jianhai Zhang, N. Zhang, Bin Chen","doi":"10.1145/3297067.3297074","DOIUrl":"https://doi.org/10.1145/3297067.3297074","url":null,"abstract":"The present research was aimed to find out EEG potentials related to movement in exercise-induced fatigue task using brain function network analysis, so that future researchers can find more accurate mutual informations between these potentials to detect fatigue to make healthy people exercise better and especially improve the effectiveness of rehabilitation in patients with motor dysfunction. EEG signals from 32 electrode sites of 20 subjects(10 adults (5 females and 5 males) and 10 children (6 females and 4 males) were recorded. We applied network topologies extracted from brain function networks constructed by phase synchronization to identify movement-related electrode sites.\u0000 We first found that there were significant differences on the global network topologies of subjects of different ages and genders, and the difference between subjects of different ages was greater, so adults and children in the subjects were separated to discuss potential selection related to movement. The following finding illustrated that local network topologies of some electrode sites correlated significantly with the degree of fatigue, we thought and selected such electrode sites to be movement-related. Results showed that 17 potentials in adults, 6 most relevant potentials as important potentials(CP5,C3,AF4,CZ,PZ,C4), and 4 potentials (F4,F8,F3,FC5) in children were selected as movement-related EEG potentials associated with exercise-induced fatigue in rotating the forearm repetitively task. We demonstrated that the credibility of our selections by observing the classification accuracy of local network topologies of non-fatigue state and fatigue state in our selected electrode sites was higher than that of local network topologies of non-fatigue state and fatigue state in our unselected electrode sites, which suggested that our selected movement-related electrode sites were more able to detect non-fatigue state and fatigue state.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131202418","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

Pedestrian Detection in Fish-eye Images using Deep Learning: Combine Faster R-CNN with an effective Cutting Method 使用深度学习的鱼眼图像行人检测:将更快的R-CNN与有效的切割方法相结合

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297069

Hongli Lin, Zhenzhen Kong, Weisheng Wang, K. Liang, Jun Chen

引用次数: 4

Feature Selection by Maximizing Part Mutual Information 最大化零件互信息的特征选择

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297068

Wanfu Gao, Liang Hu, Ping Zhang

引用次数: 2

Target-depth Estimation for Active Towed Array Sonar in Shallow Sea base on Matched Field Processing 基于匹配场处理的浅海有源拖曳阵声呐目标深度估计

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297087

Jun Wang, Fuchen Liu

{"title":"Target-depth Estimation for Active Towed Array Sonar in Shallow Sea base on Matched Field Processing","authors":"Jun Wang, Fuchen Liu","doi":"10.1145/3297067.3297087","DOIUrl":"https://doi.org/10.1145/3297067.3297087","url":null,"abstract":"Target depth estimation can facilitate classification of surface ships or water-column targets thus reducing the false rates in active surveillance systems. Active sonar mainly determines the distance of the target by measuring the roundtrip time of the transmitted signal to the received echo, but it can't determine the depth of the target. For the long distance sound field, the echo is regarded as a point source sound field emitted from the reflector, the distance-depth space is divided into grids, and the sound field at each grid point is calculated according to the parameters of the ocean environment, and then matched with the received echoes, the best match point is the distance and depth of the target. In the active matched field depth-estimation algorithm, the pulse signal generated by the active sonar is sent to the transmitter to generate sound wave, at the same time, it is sent to the emission model to calculate the copy field of the hypothetical target point, and then the reflected sound field of the hypothetical target is calculated through the reflection model, finally, calculate the total copy vector at the receiving hydrophone array. The active matched field processor matches the received echo signal with the calculated total copy vector and outputs an ambiguity surface, it can be seen that the active matched field processing makes full use of the ocean environment. Since the active sonar has estimated the distance of the target according to the arrival time of the echo, the matched field depth estimation is to search for the target depth in a small range so as to determine the depth of the target. Sea trial data show that under good hydrological conditions, when the SNR of target echo is relatively high, the low-frequency active towed array sonar has good depth estimation capability.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129305764","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

Unsupervised Depth Estimation from Monocular Video based on Relative Motion 基于相对运动的单目视频无监督深度估计

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297094

H. Cao, Chao Wang, Ping Wang, Qingquan Zou, Xiao Xiao

引用次数: 0

Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method 基于边界不确定性的分类器模型参数状态选择方法的最优性分析

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297076

David R Ha, Hideyuki Watanabe, Yuya Tomotoshi, Emilie Delattre, S. Katagiri

引用次数: 2

Speech Emotion Classification using Raw Audio Input and Transcriptions 使用原始音频输入和转录的语音情感分类

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297089

Gabriel Lima, Jinyeong Bak

引用次数: 2

Object Detection Based on Binocular Vision with Convolutional Neural Network 基于卷积神经网络的双目视觉目标检测

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297081

Zekun Luo, Xia Wu, Qingquan Zou, Xiao Xiao

{"title":"Object Detection Based on Binocular Vision with Convolutional Neural Network","authors":"Zekun Luo, Xia Wu, Qingquan Zou, Xiao Xiao","doi":"10.1145/3297067.3297081","DOIUrl":"https://doi.org/10.1145/3297067.3297081","url":null,"abstract":"Autonomous vehicles are widely accepted as one of the most potential technologies in alleviating traffic problems. In most existing autonomous vehicles for object detection and distance measurement, compared with radar or LIDAR which obviously increases the cost, camera combined with Convolutional Neural Network (CNN) has advantage in accuracy and low cost. However, most object detection methods applied on camera cannot perform distance measurement. In this paper, we simultaneously carry out real-time object detection and distance measurement (DDM) in one system by utilizing CNN on a binocular camera. Firstly, a binocular camera is used to acquire disparity maps. Secondly, a set of high-quality region proposals is generated by those disparity maps and the number of region proposals is reduced. Thirdly, CNN is utilized to classify those region proposals and get the bounding box of detected objects. Consequently, those reduced region proposals generated by disparity maps lead to improved computational efficiency. Finally, the object distance is measured by the disparity map and the bounding box. The experiment results show that the proposed method can achieve an accuracy of 87.2% on KITTI dataset and an accuracy of 68% in the real environment for object detection. The average relative error of the distance measurement is 0.85% within 10 meters in real environment. The operation time of the whole DDM system is less than 80 ms.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124931248","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

Expressway Crash Prediction based on Traffic Big Data 基于交通大数据的高速公路碰撞预测

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297093

Hailang Meng, Xinhong Wang, X. Wang

{"title":"Expressway Crash Prediction based on Traffic Big Data","authors":"Hailang Meng, Xinhong Wang, X. Wang","doi":"10.1145/3297067.3297093","DOIUrl":"https://doi.org/10.1145/3297067.3297093","url":null,"abstract":"With the development of society, the number of vehicles increases rapidly. The vehicle plays an important role in people's life, however the problem of traffic safety caused by vehicles has also become increasingly prominent. In China, the high crash rate and casualty rate on expressways have always troubled traffic management department. So crash prediction on expressway becomes vital. Conventionally, crash prediction is based on traffic flow data. These data do not contain all the necessary factors. In this paper, we propose a method of prediction using real-world data, including historical accident data, road geometry data, vehicle speed data, and weather data. We treat the crash prediction problem as a binary classification problem. For classification, sample imbalanced is a great challenge in practice. Modifying sample weights is applied to handle this challenge. Three machine learning classification techniques, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Xgboost, are considered to carry out the crash prediction task respectively. The best recall and precision rate of these models are respectively 0.764253 and 0.01062. The proposed method can be integrated into urban traffic control systems toward police dispatch and crash prevention.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114248185","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: 12