2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)最新文献

{"title":"Towards an Optimal Design of a Data Fusion System for Maritime Domain Awareness (Poster)","authors":"M. Hadzagic, E. Shahbazian","doi":"10.1109/COGSIMA.2018.8423993","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423993","url":null,"abstract":"This paper discusses various design challenges to optimize fusion in the contexts of system capabilities and requirements constrained by application specific data characteristics, user expectations as well as programmatic constraints like cost and time. It provides a use-case in which a data driven and scenario-based design (SBD) methodology has been used to design a surveillance system to improve situation understanding and aid decision making in the maritime domain. It is shown for a real data fusion application that the incremental SBD methodology can be optimal with respect to all types of requirements in the presence of all design constraints.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130831548","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}

{"title":"Towards an Ontology of Scenes and Situations","authors":"J. P. Almeida, P. D. Costa, G. Guizzardi","doi":"10.1109/COGSIMA.2018.8423994","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423994","url":null,"abstract":"It is no surprise that the notion of situation is key to situation awareness. The development of the discipline can thus benefit from careful analysis of the notion. In this paper, we approach this by proposing an ontology of situations and scenes. The main contribution of this ontology is that it accounts for how situations progress in time changing qualitatively, constituting what we call scenes. The ontology is built by reusing basic elements from the Unified Foundational Ontology (UFO). It addresses objects, occurrences, and their formal relations to situations and scenes. We use the theory of embodiment proposed by the philosopher Kit Fine to explicate how scenes and situations form wholes constituted of parts.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117248663","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}

{"title":"Interactive Decision Support: A Framework to Improve Diagnostic Processes of Cancerous Diseases Using Bayesian Networks","authors":"Patrick Philipp, J. Beyerer, Sebastian Robert, D. Hempel","doi":"10.1109/COGSIMA.2018.8423989","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423989","url":null,"abstract":"Recommendations for actions included in a Clin- ical Practice Guideline (CPG) provide a reference framework for medical experts during diagnostic processes. To support the implementation of these recommendations, we propose an interactive decision support. In order to realize this, the diagnostic processes in the CPGs of Mantle Cell Lymphoma (MCL) and Multiple Myeloma (MM) are formalized using activities of the Unified Modeling Language (UML). Based on UML activities, a Bayesian Network is generated. The resulting models enable an assistance function allowing for patient specific CPG recommendations and subsequently for a suitable as well as personalized diagnosis embedded in an interactive Decision Support System (DSS).","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127743509","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}

{"title":"High Clutter, Close Range, Wi-Fi Imaging and Probabilistic, Learning Classifier (Poster)","authors":"Paul C. Proffitt, Honggang Wang","doi":"10.1109/COGSIMA.2018.8423976","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423976","url":null,"abstract":"While robotics have been used in manufacturing for decades, we are now seeing robots whether animal-like or human-like open doors and maneuver indoors. To maneuver indoors these robots need to be aware of their situation within each room and the objects within. It’s no longer acceptable for a robot to use just pulses for collision avoidance; it must interact with static (non-moving) objects, too. Today we have a large array of imaging methods being visible light imaging, thermal imaging, night-vision imaging, but we need imaging in other cases such as a smoke-filled area containing same temperature objects. To see static objects in this arena, this research introduces Wi-Fi static object imaging and classification. It will allow a robot to maneuver a room with static objects when these other imaging methods won’t work well. This involves many challenges. The images created will be barely identifiable due to the low resolution of Wi-Fi, but the images need to be classified (identified). There are two major portions to this research. The first is the signal processing portion, where images are created, and the second is the image classification portion. In the signal processing portion, images will be created by using Wi-Fi signals transmitted and received on Ettus Universal Software Radio Peripheral (USRP) hardware and directional antennas. These USRP’s are interfaced to GNU Radio for which the researchers have developed specialized routines to implement this portion of the research. In the image classification phase, the images created will be very blob-like with different reflective intensities. These images need some form of intelligence to classify them, and the system needs to improve its classification over time. AI neural networks will be employed and developed to work on the images. This research is a work-in-progress where the signal processing portion is complete except for further tuning. The images created so far have very distinct characteristics. This means these Wi-Fi images will be good candidates for the classification phase.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"35 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126803467","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}

{"title":"Automatic Identification of Maritime Incidents from Unstructured Articles","authors":"A. Teske, R. Falcon, R. Abielmona, E. Petriu","doi":"10.1109/COGSIMA.2018.8423975","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423975","url":null,"abstract":"In this paper, we present two Natural Language Processing (NLP) techniques for identifying maritime incidents described in unstructured articles from multiple sources. The first technique is a document classification scheme that determines if an article describes a maritime incident. Two variations of each article are created: the first only contains the article’s title, the other contains the title and content. These are converted to both binary and frequency bags-of-words. Furthermore, two feature selection methods are tested: Weka’s CfsSubsetEval and retaining the 300 most frequent words. Each dataset is tested with 41 classifiers from the Weka suite, with the most accurate classifiers including Logistic Regression (98.5%), AdaBoostM1(BayesNet) (98.33%), and RandomForest (97.56%). The second technique performs information extraction on an article to determine the location of the maritime incident. In addition to using regular expressions and Named Entity Recognition (NER), the approach focuses its attention on sentences that contain piracy keywords as well as sentences which occur earlier in the article. In our testing, this approach achieved 87.9% accuracy. Together the two techniques form a pipeline where the positive examples from the document classification algorithm are fed into the information extraction algorithm.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128951931","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}

{"title":"Virtual Leadership in Complex Multiorganizational Research and Development Programs","authors":"G. M. Gelston, Carol Wells, Angela C. Dalton","doi":"10.1109/COGSIMA.2018.8423974","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423974","url":null,"abstract":"A 2002 congressional mandate initiated the U.S. Department of Homeland Security’s Centers of Excellence programs with a requirement to conduct cross-organizational research and development. The resulting complex multiorganizational programs required more effective virtual leadership and management strategies. Despite the growing government investment in these programs over the past decade, evidence indicates a persistent lack of complex virtual management strategies that account for the intended research outcomes and interdisciplinary expectations of these multiorganizational programs. As top academic researchers are drawn upon to organize and manage multiorganizational programs, the management challenge in a virtual collaborative context engenders interesting research questions. Using complex-systems and leader-member exchange theories, we report findings from a case study of Centers of Excellence program participants’ perspectives regarding virtual leadership. The findings inform the development of an engagement framework and suggest that targeted training might be conducive to positive social change for future leaders of similar complex multiorganizational virtual programs","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538007","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}

{"title":"The Complex Dynamics of Team Situation Awareness in Human-Autonomy Teaming","authors":"David A. Grimm, Mustafa Demir, Jamie C. Gorman, Nancy J. Cooke","doi":"10.1109/COGSIMA.2018.8423990","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423990","url":null,"abstract":"This study explores how Human-Autonomy Teams work together in a Remotely Piloted Aircraft System (RPAS) to overcome three types of degraded conditions, including automation and autonomy failures, and malicious attack. The two human participants were informed that the pilot was a “synthetic” agent that has limited communication capacity. For in-depth exploratory analysis, we identified one high- and one low- performing team in terms of overcoming failures and malicious attack, and then we used nonlinear dynamical methods to understand how human-autonomy interactions might affect overall Team Situation Awareness (TSA) in terms of level of complexity. We first produced Joint Recurrence Plots (JRP) to demonstrate predictability of team communication behavior during the TSA. After that, in order to identify how flexible the team was during degraded conditions, we examined entropy across four layers to represent RPAS:communication - chat-based interactions; vehicle - the RPA itself; control - user interface; and system - total activity of all layers. Results from the JRP showed that the high performing team communicated more effectively than the low performing team during the all three types of failures, while the entropy analysis showed that the high performing team appeared to be more flexible in their communication and overall system patterns.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124657194","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}

{"title":"The Observer Effect","authors":"K. Baclawski","doi":"10.1109/COGSIMA.2018.8423983","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423983","url":null,"abstract":"The observer effect is the fact that observing a situation or phenomenon necessarily changes it. Observer effects are especially prominent in physics where observation and uncertainty are fundamental aspects of modern quantum mechanics. Observer effects are well known in fields other than physics, such as sociology, psychology, linguistics and computer science, but none of these other fields have experienced the same level of publicity and controversy as physics. This may be responsible for the widely held implicit assumption that “real” observer effects are exhibited only by quantum objects and not by classical objects. This misunderstanding may be due, to some extent, to confusing the observer effect with the Heisenberg uncertainty principle and with other quantum uncertainty principles. In fact, observer effects occur in both classical and quantum systems. This article presents a number of examples of observer effects in purely classical processes. It also introduces a framework for understanding and analyzing many of such effects for classical systems. Ignoring observer effects can cause errors in experiments at a macroscopic level where no quantum effects would be discernible. Consequently, there are practical reasons for being careful to address observer effects.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121126923","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}

{"title":"Automating Behaviour Tree Generation for Simulating Troop Movements (Poster)","authors":"Gabriel Berthling-Hansen, Eivind Morch, R. A. Løvlid, Odd Erik Gundersen","doi":"10.1109/COGSIMA.2018.8423978","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423978","url":null,"abstract":"Computer generated forces are simulated units that are used in simulation based training and decision support in the military. These simulations are used to help trainees build a mental model of how different scenarios could play out, and thus give them a better situation awareness when conducting operations in real life. The behaviour of these simulated units should be as realistic as possible, so that the lessons learned while simulating are applicable in real situations. However, it is time consuming and difficult to build behaviour models manually. Instead, we explore the possibility of applying machine learning to generate behaviour models from a set of examples. In this paper we present the results of our preliminary experiments on using machine learning for behaviour modelling. We implement a follow behaviour by using behaviour trees that are evolved using genetic algorithms. The fitness of the evolved behaviour trees have been evaluated by comparing them with a manually generated behaviour tree that implements the behaviour properly. The genetic algorithm converges to a tree that is very similar to the manually generated behaviour tree, suggesting that the method works. Further work is necessary to test whether this approach will work on more complex behaviours.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124273567","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}

{"title":"Situations in Simulations: An Initial Appraisal","authors":"A. Baldi, P. D. Costa, E. Zambon, J. P. Almeida","doi":"10.1109/COGSIMA.2018.8423971","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423971","url":null,"abstract":"Approaches to simulation in the literature range from conventional object-orientation programming, which dates back to the seminal Simula programming language, to the more recent graphical modeling environments in the scope of agent-based modeling and simulation. Despite their clear impact and benefits, we have observed that these approaches have not yet provided support for situation lifecycle management, and do not address a notion of “situation” explicitly. In this paper, we perform a case study in order to examine how the various approaches support the design and execution of simulations. We are particularly interested in contrasting existing approaches to simulation with a declarative rule-based approach that supports situations explicitly. We take into account design time concerns as well as execution time performance. The study focuses on the simulation of populations of Aedes aegypti mosquitoes in urban environments.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124380213","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}