2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)最新文献_第3页

Acute stress causes over confidence in Situation Awareness 急性压力会导致对形势感知的过度自信

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497778

Tom F. Price, M. Tenan, James Head, W. Maslin, M. LaFiandra

引用次数: 9

Elements of team effectiveness: A qualitative study with pilots 团队效能的要素:对领航员的定性研究

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497781

Ulrika Ohlander, J. Alfredson, M. Riveiro, G. Falkman

引用次数: 7

Applying measurement to complementary situation awareness 将测量应用于互补态势感知

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497798

Ashley A. Cain, David Schuster

{"title":"Applying measurement to complementary situation awareness","authors":"Ashley A. Cain, David Schuster","doi":"10.1109/COGSIMA.2016.7497798","DOIUrl":"https://doi.org/10.1109/COGSIMA.2016.7497798","url":null,"abstract":"As networks in complex domains such as cyber security increasingly become distributed, with multiple human and automated agents working together to complete team goals, capturing situation awareness (SA) becomes more difficult. Often, SA is defined and measured as individual SA (the knowledge held by an individual, such as a system administrator) or as shared SA (the knowledge held in common by multiple individuals). For these two types, ideal and actual SA have been measured using goal-oriented task analysis and knowledge-specific queries, respectively. We argue that measurements of SA could fill a gap by additionally measuring complementary SA (the knowledge elements held separately by individuals). In the current paper, we suggest how measures for individual SA can be applied to the measurement of the complementary component of SA. We adapt a technique that involves completing a goal-oriented task analysis for a given context and then querying human operators about specific knowledge elements. This adaption allows for the quantification of goal-oriented knowledge elements that are held by team members but are not shared. This technique for quantifying team SA that is complementary as well as shared can be applied to assess trainees and to inform future training programs. Understanding and measuring multiple facets of SA will help improve efficiency and security in distributed teams in cyber security. First, we review the literature on existing measurement techniques for SA, then we outline how measurement can be applied to complementary SA. Lastly, we discuss some applications of measuring complementary SA.","PeriodicalId":194697,"journal":{"name":"2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124627747","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

Mining the disaster hotspots - situation-adaptive crowd knowledge extraction for crisis management 灾害热点挖掘——面向危机管理的情境自适应人群知识提取

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497812

Andrea Salfinger, W. Schwinger, W. Retschitzegger, B. Proll

{"title":"Mining the disaster hotspots - situation-adaptive crowd knowledge extraction for crisis management","authors":"Andrea Salfinger, W. Schwinger, W. Retschitzegger, B. Proll","doi":"10.1109/COGSIMA.2016.7497812","DOIUrl":"https://doi.org/10.1109/COGSIMA.2016.7497812","url":null,"abstract":"When disaster strikes, emergency professionals rapidly need to gain Situation Awareness (SAW) on the unfolding crisis situation, thus need to determine what has happened and where help and resources are needed. Nowadays, platforms like Twitter are used as real-time communication hub for sharing such information, like humans' on-site observations, advice and requests, and thus can serve as a network of “human sensors” for retrieving information on crisis situations. Recently, so-called crowd-sensing systems for crisis management have started to utilize these networks for harvesting crisis-related social media content. However, up to now these mainly support their human operators in the visual analysis of retrieved messages only and do not aim at the automated extraction and fusion of semantically-grounded descriptions of the underlying real-world crisis events from these textual contents, such as providing structured descriptions of the types and locations of reported damage. This hampers further computational situation assessment, such as providing overall description of the on-going crisis situation, its associated consequences and required response actions. Consequently, this lack of semantically-grounded situational context does not allow to fully implement situation-adaptive crowd knowledge extraction, meaning the system can utilize already established (crowd) knowledge to correspondingly adapt its crowd-sensing and knowledge extraction process alongside the monitored situation, to keep pace with the underlying real-world incidents. In the light of this, in the present paper, we illustrate the realization of a situation-adaptive crowd-sensing and knowledge extraction system by introducing our crowdSA prototype, and examine its potential in a case study on a real-world Twitter crisis data set.","PeriodicalId":194697,"journal":{"name":"2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182824","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: 18

Comparing methods for assessing operator functional state 评估算子功能状态的方法比较

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497792

Olivier Gagnon, M. Parizeau, D. Lafond, J. Gagnon

{"title":"Comparing methods for assessing operator functional state","authors":"Olivier Gagnon, M. Parizeau, D. Lafond, J. Gagnon","doi":"10.1109/COGSIMA.2016.7497792","DOIUrl":"https://doi.org/10.1109/COGSIMA.2016.7497792","url":null,"abstract":"The assessment of an operator's functional state (i.e., the multidimensional pattern of human psychophysiological conditions that mediates performance) has great potential for increasing safety and reliability of critical systems. However, live monitoring of functional state using physiological and behavioral data still faces several challenges before achieving the level of precision required in many operational contexts. One open question is the level of granularity of the models. Is a general model sufficient or should subject-specific models be trained to ensure high accuracy? Another challenge concerns the formalization of a valid ground truth for training classifiers. This is critical in order to train models that are operationally relevant. This paper introduces the Decontextualized Dynamic Performance (DDP) metric which allows models to be trained simultaneously on different tasks using machine learning algorithms. This paper reports the performance of various classification algorithms at different levels of granularity. We compare a general model, task-specific models, and subject-specific models. Results show that the classification methods do not lead to statistically different performance, and that the predictive accuracy of subject-specific and task-specific models was actually comparable to a general model. We also compared various time-window sizes for the new DDP metric and found that results were degrading with a larger time window size.","PeriodicalId":194697,"journal":{"name":"2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735408","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

Tool-supported comparative visualizations to reveal the difference between ‘what has been designed’ and ‘how it is perceived’ for monitoring interface design 工具支持的比较可视化，以揭示监控界面设计的“设计内容”和“感知方式”之间的差异

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497809

Bertram Wortelen, S. Feuerstack

引用次数: 1

Enhancing hazard awareness with a mobile application for high risk, high consequence avalanche terrain decisions 提高危险意识与移动应用程序的高风险，高后果雪崩地形决策

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497814

Michael P. Jenkins, D. Young, Keith Gale

{"title":"Enhancing hazard awareness with a mobile application for high risk, high consequence avalanche terrain decisions","authors":"Michael P. Jenkins, D. Young, Keith Gale","doi":"10.1109/COGSIMA.2016.7497814","DOIUrl":"https://doi.org/10.1109/COGSIMA.2016.7497814","url":null,"abstract":"Design of decision support tools for high risk, high consequence decision making has been an area of focus for many high profile domains (e.g., aviation, military operations, first response, trauma surgery); however, one domain that has seen an increase in individuals undertaking the risks, without proper training or tools to properly assess their situation when making decisions to accept and proceed with those risks is the traversing of avalanche terrain. This paper provides a brief overview of the history of avalanche science that has evolved as the number of recreational and professional individuals journeying into avalanche territory, and the number of fatal accidents has increased. It then provides an overview of several analog decision support tools that exist but have not been widely adopted or effective for a variety of reasons. Finally, it presents an overview of a work in progress smartphone application that is being designed to address this gap, which we hope will provide a more robust and reliable risk assessment and decision support tool to aid users in making the most appropriate decision when considering entering into avalanche territory.","PeriodicalId":194697,"journal":{"name":"2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127532765","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

Feature selection for situation recognition in Fuzzy SOM-based Case-based Reasoning 基于模糊som的案例推理情境识别特征选择

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497802

Arezoo Sarkheyli, D. Söffker

{"title":"Feature selection for situation recognition in Fuzzy SOM-based Case-based Reasoning","authors":"Arezoo Sarkheyli, D. Söffker","doi":"10.1109/COGSIMA.2016.7497802","DOIUrl":"https://doi.org/10.1109/COGSIMA.2016.7497802","url":null,"abstract":"Case-based Reasoning (CBR) is a problem solving approach applied to different cognitive systems for planning, decision making, etc. This approach benefits/utilizes the solutions of previous similar problems for solving a new problem. Situation recognition as an important process in CBR provides knowledge about actual problem including the situation of system. The system's situation defined with a set of characteristics/features, models the scene and illustrates an internal structure of the system. The situations are learned as experiences by the system for further usage. Dealing with a large amount of experiences as well as imprecise, uncertain, and redundant data (characteristics) is a challenge for situation recognition. Investigation of all characteristics of a situation for defining the actual problem may decrease the system performance in terms of recognition accuracy and computational complexity. Therefore, using an appropriate method to discard irrelevant characteristics may improve situation recognition approaches. Here, an improved CBR based on Situation-Operator Modeling (SOM) and Fuzzy Logic (FL) is applied as the base CBR. The fuzzy SOM-based CBR benefits an effective knowledge representation approach to support different situation recognition levels and handles uncertainties. This contribution aims to address the effects of feature selection in dealing with data redundancy in fuzzy SOM-based CBR. A feature selection approach based on Rough Set Theory is then applied to the CBR to find an optimal set of relevant characteristics for the situations. Finally, the proposed CBR approach is realized using an experimental application (driving maneuvers) to show the effectiveness of the feature selection on situation recognition.","PeriodicalId":194697,"journal":{"name":"2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125858415","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: 6

Spatio-temporal situation recognition for groups in caregiving services 照护服务群体的时空态势识别

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497790

K. Sato, K. Hiraishi, Koichi Kobayashi

引用次数: 1

The effect of a virtual agent's emotional facial expressions on the Mind's Eye test 虚拟代理人的情绪面部表情对心灵之眼测试的影响

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) Pub Date : 2016-03-21 DOI: 10.1109/COGSIMA.2016.7497791

Yesenia Garcia, P. Khooshabeh, Brett Ouimette

引用次数: 1