2010 2nd International Workshop on Cognitive Information Processing最新文献

User-driven Call Admission Control for VoIP over WLAN with a Neural Network based cognitive engine 基于神经网络认知引擎的WLAN VoIP用户驱动呼叫准入控制

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-10-14 DOI: 10.1109/CIP.2010.5604128

N. Baldo, P. Dini, Jaume Nin-Guerrero

引用次数: 9

3D multi-car tracking based on monocular video 基于单目视频的3D多车跟踪

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604140

Paris Kaimakis, S. I. Hill, W. Fitzgerald, J. Bacon

引用次数: 1

Evaluation of network effects on the Kalman filter and accumulated state density filter 卡尔曼滤波和累计状态密度滤波的网络效应评价

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604110

F. Govaers, Christoph Fuchs, N. Aschenbruck

引用次数: 7

Predicting relations in news-media content among EU countries 预测欧盟国家间新闻媒体内容的关系

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604092

I. Flaounas, Nick Fyson, N. Cristianini

引用次数: 5

Millimeterwave radar network for foreign object detection 毫米波雷达网络异物检测

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604116

H. Essen, G. Luedtke, P. Warok, W. Koch, M. Schikora, K. Wild

引用次数: 9

Real-world particle filtering-based speech enhancement 现实世界基于粒子滤波的语音增强

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604235

F. Mustière, M. Bolic, M. Bouchard

引用次数: 2

Bayesian and pairwise local similarity discriminant analysis 贝叶斯和两两局部相似判别分析

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604118

Peter Sadowski, L. Cazzanti, M. Gupta

引用次数: 5

On the spectrum occupancy perception of cognitive radio terminals in realistic scenarios 现实场景下认知无线电终端频谱占用感知研究

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604131

M. López-Benítez, F. Casadevall

引用次数: 39

Tracking the flu pandemic by monitoring the social web 通过监测社交网络追踪流感大流行

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604088

Vasileios Lampos, N. Cristianini

{"title":"Tracking the flu pandemic by monitoring the social web","authors":"Vasileios Lampos, N. Cristianini","doi":"10.1109/CIP.2010.5604088","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604088","url":null,"abstract":"Tracking the spread of an epidemic disease like seasonal or pandemic influenza is an important task that can reduce its impact and help authorities plan their response. In particular, early detection and geolocation of an outbreak are important aspects of this monitoring activity. Various methods are routinely employed for this monitoring, such as counting the consultation rates of general practitioners. We report on a monitoring tool to measure the prevalence of disease in a population by analysing the contents of social networking tools, such as Twitter. Our method is based on the analysis of hundreds of thousands of tweets per day, searching for symptom-related statements, and turning statistical information into a flu-score. We have tested it in the United Kingdom for 24 weeks during the H1N1 flu pandemic. We compare our flu-score with data from the Health Protection Agency, obtaining on average a statistically significant linear correlation which is greater than 95%. This method uses completely independent data to that commonly used for these purposes, and can be used at close time intervals, hence providing inexpensive and timely information about the state of an epidemic.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123158912","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: 361

Speaker-independent negative emotion recognition 不依赖说话人的负面情绪识别

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604091

M. Kotti, F. Paternò, Constantine Kotropoulos

{"title":"Speaker-independent negative emotion recognition","authors":"M. Kotti, F. Paternò, Constantine Kotropoulos","doi":"10.1109/CIP.2010.5604091","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604091","url":null,"abstract":"This work aims to provide a method able to distinguish between negative and non-negative emotions in vocal interaction. A large pool of 1418 features is extracted for that purpose. Several of those features are tested in emotion recognition for the first time. Next, feature selection is applied separately to male and female utterances. In particular, a bidirectional Best First search with backtracking is applied. The first contribution is the demonstration that a significant number of features, first tested here, are retained after feature selection. The selected features are then fed as input to support vector machines with various kernel functions as well as to the K nearest neighbors classifier. The second contribution is in the speaker-independent experiments conducted in order to cope with the limited number of speakers present in the commonly used emotion speech corpora. Speaker-independent systems are known to be more robust and present a better generalization ability than the speaker-dependent ones. Experimental results are reported for the Berlin emotional speech database. The best performing classifier is found to be the support vector machine with the Gaussian radial basis function kernel. Correctly classified utterances are 86.73%±3.95% for male subjects and 91.73%±4.18% for female subjects. The last contribution is in the statistical analysis of the performance of the support vector machine classifier against the K nearest neighbors classifier as well as the statistical analysis of the various support vector machine kernels impact","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126686016","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