2018 IEEE International Conference on Cognitive Computing (ICCC)最新文献

Long-Term Monitoring of NIRS and EEG Signals for Assessment of Daily Changes in Emotional Valence 长期监测近红外光谱和脑电图信号评估情绪效价的日常变化

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00026

Labiblais Rahman, K. Oyama

引用次数: 4

A Neural Network-Powered Cognitive Method of Identifying Semantic Entities in Earth Science Papers 基于神经网络的地球科学论文语义实体识别认知方法

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00009

Xiaoyi Duan, Jia Zhang, R. Ramachandran, P. Gatlin, M. Maskey, Jeffrey J. Miller, K. Bugbee, Tsengdar J. Lee

引用次数: 4

Title Page iii 第三页标题

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/iccc.2018.00002

引用次数: 0

All-Implicants Neural Networks for Efficient Boolean Function Representation 高效布尔函数表示的全隐含神经网络

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00019

Federico Buffoni, G. Gianini, E. Damiani, M. Granitzer

引用次数: 1

Quantitative Modeling of Polarization in Online Intelligent Argumentation and Deliberation for Capturing Collective Intelligence 获取集体智慧的在线智能辩论与审议极化定量建模

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00015

J. Sirrianni, X. Liu, Douglas Adams

{"title":"Quantitative Modeling of Polarization in Online Intelligent Argumentation and Deliberation for Capturing Collective Intelligence","authors":"J. Sirrianni, X. Liu, Douglas Adams","doi":"10.1109/ICCC.2018.00015","DOIUrl":"https://doi.org/10.1109/ICCC.2018.00015","url":null,"abstract":"Massive online argumentation deliberation has the potential to capture collective intelligence and crowd wisdom. However, certain observable phenomena, such as polarization, often emerge in online argumentation and deliberation, preventing constructive discourse. Accurately detecting the presence and intensity of polarization is important to determining if collective intelligence and crowd wisdom can be captured from online deliberation. In this paper, an innovative method of measuring polarization quantitatively in online argumentation is presented. Important polarization attributes such as homogeneity in groups, heterogeneity across groups, the number of poles, and the size of poles are identified to measure polarization in online argumentation. This new method uses our argumentation tool, Intelligent Cyber Argumentation System's (ICAS) cognitive computing component, a fuzzy logic engine, to derive the participant's agreement distribution. Then we apply an income polarization measurement from the field of economics [7] that we have modified and expanded for argumentation, on the agreement distribution to produce a polarization index value. We discuss why our method to measure argumentation polarization is a significant improvement over existing measurements for online argumentation polarization in terms of these identified attributes. We conducted empirical studies using ICAS that demonstrate that our method outperforms others that exist on our empirical data.","PeriodicalId":306012,"journal":{"name":"2018 IEEE International Conference on Cognitive Computing (ICCC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122734198","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: 11

Publisher's Information 出版商的信息

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/iccc.2018.00030

引用次数: 0

Incremental Learning through Graceful Degradations in Autonomous Systems 自主系统中基于优雅退化的增量学习

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00011

G. Mani, B. Bhargava, B. Shivakumar

{"title":"Incremental Learning through Graceful Degradations in Autonomous Systems","authors":"G. Mani, B. Bhargava, B. Shivakumar","doi":"10.1109/ICCC.2018.00011","DOIUrl":"https://doi.org/10.1109/ICCC.2018.00011","url":null,"abstract":"Intelligent Autonomous Systems (IAS) are highly cognitive, reflexive, multitasking, trustworthy (secure and ethical), and rich in knowledge discovery. IAS are deployed in dynamic environments and connected with numerous devices of different types, and receive large sets of diverse data. They often receive new types of raw data that was not present in either training or testing data sets thus they are unknown to the learning models. In a dynamic environment, these unknown data objects cannot be ignored as anomalies. Hence the learning models should provide incremental guarantees to IAS for learning and adapting in the presence of unknown data. The model should support progressive enhancements when the environment behaves as expected or graceful degradations when it does not. In the case of graceful degradations, there are two alternatives for IAS: (1) weaken the acceptance test of data object (operating at a lower capacity) or (2) replace primary system with a replica or an alternate system that can pass the acceptance test. In this paper, we provide a combinatorial design—MACROF configuration—built with balanced incomplete block design to support graceful degradations in IAS and aid them to adapt in dynamic environments. The architecture provides stable and robust degradations in unpredictable operating environments with limited number of replicas. Since the replicas receive frequent updates from primary systems, they can take over primary system's functionality immediately after an adverse event. We also propose a Bayesian learning model to dynamically change the frequency of updates. Our experimental results show that MACROF configuration provides an efficient replication scheme to support graceful degradations in autonomous systems.","PeriodicalId":306012,"journal":{"name":"2018 IEEE International Conference on Cognitive Computing (ICCC)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133747402","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: 7

Automatic Hyperparameter Tuning in Deep Convolutional Neural Networks Using Asynchronous Reinforcement Learning 基于异步强化学习的深度卷积神经网络自动超参数整定

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00017

P. Neary

引用次数: 37

Classification of Taxonomical Relationship by Word Embedding 基于词嵌入的分类关系分类

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00027

Kazuki Omine, Incheon Paik

引用次数: 3

Analysis of Shapelet Transform Usage in Traffic Event Detection Shapelet变换在交通事件检测中的应用分析

2018 IEEE International Conference on Cognitive Computing (ICCC) Pub Date : 2018-07-01 DOI: 10.1109/ICCC.2018.00013

Ahmed Al Dhanhani, E. Damiani, R. Mizouni, Di Wang

引用次数: 9