Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-167
Pruthuvi Maheshakya Wijewardena, Thimal Kempitiya, T. Rathnayake, Kevin Rathnasekara, Thushan Ganegedara, A. Perera, D. Alahakoon
{"title":"Heterogeneous data fusion with multiple kernel growing self organizing maps","authors":"Pruthuvi Maheshakya Wijewardena, Thimal Kempitiya, T. Rathnayake, Kevin Rathnasekara, Thushan Ganegedara, A. Perera, D. Alahakoon","doi":"10.3233/978-1-61499-589-0-167","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-167","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114421816","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-255
H. Steinhauer, Alexander Karlsson
In this paper we apply our method for traceable uncertainty to the application scenario of threat evaluation. The paper shows how the uncertainty within a decision support process can be traced and ...
{"title":"Traceable Uncertainty for Threat Evaluation in Air to Ground Scenarios","authors":"H. Steinhauer, Alexander Karlsson","doi":"10.3233/978-1-61499-330-8-255","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-255","url":null,"abstract":"In this paper we apply our method for traceable uncertainty to the application scenario of threat evaluation. The paper shows how the uncertainty within a decision support process can be traced and ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404839","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-145
S. K. Jensen, Christoffer Moesgaard, Christoffer Samuel Nielsen, Sine Lyhne Viesmose
. Human gesture recognition is an area, which has been studied thoroughly in recent years, and close to 100% recognition rates in restricted environments have been achieved, often either with single separated gestures in the input stream, or with computationally intensive systems. The results are unfortunately not as strik- ing, when it comes to a continuous stream of gestures. In this paper we introduce a hierarchical system for gesture recognition for use in a gaming setting, with a continuous stream of data. Layer 1 is based on Nearest Neighbor Search and layer 2 uses Hidden Markov Models. The system uses features that are computed from Microsoft Kinect skeletons. We propose a new set of features, the relative angles of the limbs from Kinect’s axes to use in NNS. The new features show a 10 percent point increase in precision when compared with features from previously published results. We also propose a way of attributing recognised gestures with a force at- tribute, for use in gaming. The recognition rate in layer 1 is 68.2%, with an even higher rate for simple gestures. Layer 2 reduces the noise and has a average recog- nition rate of 85.1%. When some simple constraints are added we reach a precision of 90.5% with a recall of 91.4%.
{"title":"A Hierarchical Model for Continuous Gesture Recognition Using Kinect","authors":"S. K. Jensen, Christoffer Moesgaard, Christoffer Samuel Nielsen, Sine Lyhne Viesmose","doi":"10.3233/978-1-61499-330-8-145","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-145","url":null,"abstract":". Human gesture recognition is an area, which has been studied thoroughly in recent years, and close to 100% recognition rates in restricted environments have been achieved, often either with single separated gestures in the input stream, or with computationally intensive systems. The results are unfortunately not as strik- ing, when it comes to a continuous stream of gestures. In this paper we introduce a hierarchical system for gesture recognition for use in a gaming setting, with a continuous stream of data. Layer 1 is based on Nearest Neighbor Search and layer 2 uses Hidden Markov Models. The system uses features that are computed from Microsoft Kinect skeletons. We propose a new set of features, the relative angles of the limbs from Kinect’s axes to use in NNS. The new features show a 10 percent point increase in precision when compared with features from previously published results. We also propose a way of attributing recognised gestures with a force at- tribute, for use in gaming. The recognition rate in layer 1 is 68.2%, with an even higher rate for simple gestures. Layer 2 reduces the noise and has a average recog- nition rate of 85.1%. When some simple constraints are added we reach a precision of 90.5% with a recall of 91.4%.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133751035","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-3
M. Wooldridge
{"title":"Playing Games with Games","authors":"M. Wooldridge","doi":"10.3233/978-1-60750-754-3-3","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-3","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131155130","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-128
Sharmin Sultana Sheuly, Sudhangathan Bankarusamy, S. Begum, M. Behnam
Cloud computing has recently drawn much attention due to the benefits that it can provide in terms of high performance and parallel computing. However, many industrial applications require certain quality of services that need efficient resource management of the cloud infrastructure to be suitable for industrial applications. In this paper, we focus mainly on the services, usually executed within virtual machines, allocation problem in the cloud network. To meet the quality of service requirements we investigate different algorithms that can achieve load balancing which may require migrating virtual machines from one node/server to another during runtime and considering both CPU and communication resources. Three different allocation algorithms based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Best-fit heuristic algorithm are applied in this paper. We evaluate the three algorithms in terms of cost/objective function and calculation time. In addition, we explore how tuning different parameters (including population size, probability of mutation and probability of crossover) can affect the cost/objective function in GA. Depending on the evaluation, it is concluded that algorithm performance is dependent on the circumstances i.e. available resource, number of VMs etc.
{"title":"Resource Allocation in Industrial Cloud Computing Using Artificial Intelligence Algorithms","authors":"Sharmin Sultana Sheuly, Sudhangathan Bankarusamy, S. Begum, M. Behnam","doi":"10.3233/978-1-61499-589-0-128","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-128","url":null,"abstract":"Cloud computing has recently drawn much attention due to the benefits that it can provide in terms of high performance and parallel computing. However, many industrial applications require certain quality of services that need efficient resource management of the cloud infrastructure to be suitable for industrial applications. In this paper, we focus mainly on the services, usually executed within virtual machines, allocation problem in the cloud network. To meet the quality of service requirements we investigate different algorithms that can achieve load balancing which may require migrating virtual machines from one node/server to another during runtime and considering both CPU and communication resources. Three different allocation algorithms based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Best-fit heuristic algorithm are applied in this paper. We evaluate the three algorithms in terms of cost/objective function and calculation time. In addition, we explore how tuning different parameters (including population size, probability of mutation and probability of crossover) can affect the cost/objective function in GA. Depending on the evaluation, it is concluded that algorithm performance is dependent on the circumstances i.e. available resource, number of VMs etc.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131401132","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-137
Maj Stenmark
In the English-speaking world, the idea of human-robot interaction in natural language has been well established. The tools for other languages are lacking, more specifically, Scandinavian languages are not supported by robot programming environments. The RobotLab at Lund University has a programming environment with English natural language programming. In this paper a module for Swedish natural language programming is presented. Program statements for force-based assembly tasks for an industrial robot are extracted from unstructured Swedish text. The goal is to create action sequences with motion and force constraints for the robot. The method produces tuples with actions and objects and uses the dependency relations to find nested temporal conditions. (Less)
{"title":"Bilingual Robots: Extracting Robot Program Statements from Swedish Natural Language Instructions","authors":"Maj Stenmark","doi":"10.3233/978-1-61499-589-0-137","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-137","url":null,"abstract":"In the English-speaking world, the idea of human-robot interaction in natural language has been well established. The tools for other languages are lacking, more specifically, Scandinavian languages are not supported by robot programming environments. The RobotLab at Lund University has a programming environment with English natural language programming. In this paper a module for Swedish natural language programming is presented. Program statements for force-based assembly tasks for an industrial robot are extracted from unstructured Swedish text. The goal is to create action sequences with motion and force constraints for the robot. The method produces tuples with actions and objects and uses the dependency relations to find nested temporal conditions. (Less)","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115809351","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-4
A. Ram
{"title":"User-Generated AI for Interactive Digital Entertainment","authors":"A. Ram","doi":"10.3233/978-1-60750-754-3-4","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-4","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506086","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}
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