2019 IEEE International Conference on Cognitive Computing (ICCC)最新文献_第2页

Fusing Acoustic and Electroencephalographic Modalities for User-Independent Emotion Prediction 融合声学和脑电图模式用于用户独立情绪预测

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-08 DOI: 10.1109/ICCC.2019.00018

S. Ntalampiras, F. Avanzini, L. A. Ludovico

引用次数: 3

IEEE ICCC 2019 Program Committee IEEE ICCC 2019项目委员会

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/iccc.2019.00012

引用次数: 0

Message from the IEEE ICCC 2019 Chairs IEEE ICCC 2019主席致辞

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/iccc.2019.00011

引用次数: 0

Using EEG to Predict and Analyze Password Memorability 利用脑电图预测和分析密码记忆性

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/ICCC.2019.00019

Ruba AlOmari, Miguel Vargas Martin, Shane MacDonald, Christopher Bellman

{"title":"Using EEG to Predict and Analyze Password Memorability","authors":"Ruba AlOmari, Miguel Vargas Martin, Shane MacDonald, Christopher Bellman","doi":"10.1109/ICCC.2019.00019","DOIUrl":"https://doi.org/10.1109/ICCC.2019.00019","url":null,"abstract":"Brain-Computer Interfaces (BCIs) have given us insight into the human brain, and as sensors grow cheaper and smaller, and devices get more connected, more researchers from new domains are motivated to use BCIs. In this 19-participant lab study, we use off-the-shelf BCIs to investigate password memorability and recall. We record electroencephalogram (EEG) potentials collected by BCIs upon presenting passwords of different characteristics to participants while asking them to memorize these passwords, and then recall them. Features from the EEG signals are extracted in three domains: power spectrum from the frequency domain, statistics from the time domain, and wavelet coefficients from the time-frequency domain. Lasso feature selection method is used, and the selected parameters and feature subsets are submitted for classification with two classes, recalled and not recalled, based on the user's subsequent recall of the passwords. Results show discriminating features of EEG signals in the two different classes, achieving a classification accuracy of 88%. Our results indicate that it may be possible to predict subsequent password recall based on EEG activity during password presentation.","PeriodicalId":262923,"journal":{"name":"2019 IEEE International Conference on Cognitive Computing (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130627749","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

IEEE ICCC 2019 Reviewers IEEE ICCC 2019审稿人

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/iccc.2019.00013

Abel, Marie-Helene, Ab. Jabal, Amani Anisetti, Marco Araújo, Rui Ardagna, Claudio Agostino

引用次数: 0

An Opinion Diversity Enhanced Social Connection Recommendation Re-Ranking Method Based on Opinion Distance in Cyber Argumentation with Social Networking 基于意见距离的意见多样性增强社交网络论证社会联系推荐重排序方法

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/ICCC.2019.00029

J. Sirrianni, X. Liu, Md Mahfuzer Rahman, Douglas Adams

{"title":"An Opinion Diversity Enhanced Social Connection Recommendation Re-Ranking Method Based on Opinion Distance in Cyber Argumentation with Social Networking","authors":"J. Sirrianni, X. Liu, Md Mahfuzer Rahman, Douglas Adams","doi":"10.1109/ICCC.2019.00029","DOIUrl":"https://doi.org/10.1109/ICCC.2019.00029","url":null,"abstract":"The quality of crowd wisdom extracted from online communities decreases as the community becomes less ideologically diverse, which is an issue in many online spaces. One cause of this decline is that users tend not to engage with diverse, idea-challenging content that contrasts their prior opinions. However, they do tend to engage with content endorsed by their social connections, even if it goes against their personal opinion. Thus, by increasing the diversity of opinion in a user's social network, they will likely engage with more diverse content. We are developing a cyber argumentation system with social networking and present a social connection recommendation re-ranking method that promotes opinion diversity. We use artificial intelligence and data mining techniques to mine and analyze user opinions from argumentation data on important issues, then use furthest opinion distance to re-rank the recommendations. Our method is designed to easily integrate with existing social connection recommenders, which preserves platform specific criteria. We compare the opinion diversity of recommendations from five types of social connection recommendation methods, with and without our re-ranking method, on a large empirical dataset. Our results show that our method improves the recommended diversity by around 15% for five existing social connection recommendation methods, while only reordering around 50% of the initial social connection recommendations.","PeriodicalId":262923,"journal":{"name":"2019 IEEE International Conference on Cognitive Computing (ICCC)","volume":"696 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132707167","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

Criteria for Learning without Forgetting in Artificial Neural Networks 人工神经网络中无遗忘学习的标准

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/ICCC.2019.00027

R. Karn, P. Kudva, I. Elfadel

{"title":"Criteria for Learning without Forgetting in Artificial Neural Networks","authors":"R. Karn, P. Kudva, I. Elfadel","doi":"10.1109/ICCC.2019.00027","DOIUrl":"https://doi.org/10.1109/ICCC.2019.00027","url":null,"abstract":"Task progressive learning without catastrophic forgetting using artificial neural networks (ANNs) has demonstrated viability and promise. Due to the large number of ANN hyper-parameters, a model already trained over a group of tasks can further learn a new task without forgetting the previous tasks. Several algorithms have been proposed for progressive learning, including synaptic weight consolidation, ensemble, rehearsal, and sparse coding. One major problem with such methods is that they fail to detect the congestion in the ANN shared parameter space to indicate the saturation of the existing network and its inability to add new tasks using progressive learning. The detection of such saturation is especially needed to avoid the catastrophic forgetting of old trained task and the concurrent loss in their generalization quality. In this paper, we address such problem and propose a methodology for ANN congestion detection. The methodology is based on computing the Hessian of the ANN loss function at the optimal weights for a group of previously learned tasks. Since the Hessian calculation is compute-intensive, we provide a set of approximation heuristics that are computationally efficient. The algorithms are implemented and analyzed in the context of two cloud network security datasets, namely, UNSW-NB15 and AWID, as well as the MNIST image recognition dataset. Results show that the proposed congestion metrics give an accurate assessment of the ANN progressive learning capacity for these various datasets. Furthermore, the results show that models that have more features exhibit higher congestion thresholds and are therefore more amenable to progressive learning.","PeriodicalId":262923,"journal":{"name":"2019 IEEE International Conference on Cognitive Computing (ICCC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131796915","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

An AI Enabled System for Distributed System Characterization 分布式系统表征的人工智能支持系统

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/ICCC.2019.00015

S. Calo, D. Verma, Maroun Touma, Franck Le, Douglas M. Freimuth, E. Nahum

引用次数: 0

Bargaining Compatible Explanations 讨价还价相容的解释

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/ICCC.2019.00028

B. Apolloni, Aamna Al Shehhi, E. Damiani

引用次数: 1

IoT Data Management System for Rapid Development of Machine Learning Models 快速发展机器学习模型的物联网数据管理系统

2019 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2019-07-01 DOI: 10.1109/ICCC.2019.00021

Keith Grueneberg, Bongjun Ko, D. Wood, Xiping Wang, Dean Steuer, Yeonsup Lim

引用次数: 3