Due to limited power onboard, a significant factor for success of distributed teams of robots is energy-awareness. The ability to predict when power will be depleted beyond a certain point is necessary for recharging or returning to a base station. This paper presents a framework for forecasting state of charge (SOC) of a robot's battery for a given mission. A generalized and customizable mission description is formulated as a sequence of parametrized tasks defined for the robot; the missions are then mapped to expected change in SOC by training neural networks on experimental data. We present results from experiments on the Turtlebot 2 to establish the efficacy of this framework. The performance of the proposed framework is demonstrated for three distinct mission representations and compared to an existing method in the literature. Finally, we discuss the strengths and weaknesses of feedforward and recurrent neural network models in the context of this work.
{"title":"Forecasting battery state of charge for robot missions","authors":"Ameer Hamza, Nora Ayanian","doi":"10.1145/3019612.3019705","DOIUrl":"https://doi.org/10.1145/3019612.3019705","url":null,"abstract":"Due to limited power onboard, a significant factor for success of distributed teams of robots is energy-awareness. The ability to predict when power will be depleted beyond a certain point is necessary for recharging or returning to a base station. This paper presents a framework for forecasting state of charge (SOC) of a robot's battery for a given mission. A generalized and customizable mission description is formulated as a sequence of parametrized tasks defined for the robot; the missions are then mapped to expected change in SOC by training neural networks on experimental data. We present results from experiments on the Turtlebot 2 to establish the efficacy of this framework. The performance of the proposed framework is demonstrated for three distinct mission representations and compared to an existing method in the literature. Finally, we discuss the strengths and weaknesses of feedforward and recurrent neural network models in the context of this work.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86271973","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}
In this paper, we explore the suitability of employing Convolutional Neural Networks (ConvNets) for multi-label movie trailer genre classification. Assigning genres to movies is a particularly challenging task because genre is an immaterial feature that is not physically present in a movie frame, so off-the-shelf image detection models cannot be easily adapted to this context. Moreover, multi-label classification is more challenging than single-label classification considering that one instance can be assigned to multiple classes at once. We propose a novel classification method that encapsulates an ultra-deep ConvNet with residual connections. Our approach extracts temporal information from image-based features prior to performing the mapping of trailers to genres. We compare our novel approach with the current state-of-the-art techniques for movie classification, which make use of well-known image descriptors and low-level handcrafted features. Results show that our method significantly outperforms the state-of-the-art in this task, improving the classification accuracy for all genres.
{"title":"Convolutions through time for multi-label movie genre classification","authors":"Jonatas Wehrmann, Rodrigo C. Barros","doi":"10.1145/3019612.3019641","DOIUrl":"https://doi.org/10.1145/3019612.3019641","url":null,"abstract":"In this paper, we explore the suitability of employing Convolutional Neural Networks (ConvNets) for multi-label movie trailer genre classification. Assigning genres to movies is a particularly challenging task because genre is an immaterial feature that is not physically present in a movie frame, so off-the-shelf image detection models cannot be easily adapted to this context. Moreover, multi-label classification is more challenging than single-label classification considering that one instance can be assigned to multiple classes at once. We propose a novel classification method that encapsulates an ultra-deep ConvNet with residual connections. Our approach extracts temporal information from image-based features prior to performing the mapping of trailers to genres. We compare our novel approach with the current state-of-the-art techniques for movie classification, which make use of well-known image descriptors and low-level handcrafted features. Results show that our method significantly outperforms the state-of-the-art in this task, improving the classification accuracy for all genres.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88515772","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}
Networks using WiFi continue to proliferate which has contributed to increased levels of crowding in the unlicensed bands in which these networks operate. Given the limited number of channels available in these bands, a key problem that arises is how to avoid / minimise conflicting channel assignment among neighbouring networks. This is particularly challenging in uncoordinated, unplanned deployments wherein Access Points (APs) belonging to different administrative entities may be operating in isolation in the vicinity of each other. Given the difficulty of planning conflict free channel allocation in such deployments, it is highly desirable to devise mechanisms such that the APs themselves become radio neighbourhood aware and adapt their operation (channel switching on the fly) so as to mitigate interference issues. In this paper, we propose two distributed channel selection algorithms for accomplishing the above mentioned objective. In particular, we show that these algorithms are practical from an implementation perspective, accomplish their objective without requiring any changes to the hardware, the existing infrastructure and the WLAN standard, and highlight findings from a simulation based study. Findings indicate that the proposed algorithms significantly outperform the typical random selection approach commonly employed by low cost commercial off-the-shelf hardware.
{"title":"Moving away from the crowd: channel selection in uncoordinated unplanned dense wireless LANs","authors":"P. Kulkarni, Zhenzhe Zhong, Fengming Cao","doi":"10.1145/3019612.3019771","DOIUrl":"https://doi.org/10.1145/3019612.3019771","url":null,"abstract":"Networks using WiFi continue to proliferate which has contributed to increased levels of crowding in the unlicensed bands in which these networks operate. Given the limited number of channels available in these bands, a key problem that arises is how to avoid / minimise conflicting channel assignment among neighbouring networks. This is particularly challenging in uncoordinated, unplanned deployments wherein Access Points (APs) belonging to different administrative entities may be operating in isolation in the vicinity of each other. Given the difficulty of planning conflict free channel allocation in such deployments, it is highly desirable to devise mechanisms such that the APs themselves become radio neighbourhood aware and adapt their operation (channel switching on the fly) so as to mitigate interference issues. In this paper, we propose two distributed channel selection algorithms for accomplishing the above mentioned objective. In particular, we show that these algorithms are practical from an implementation perspective, accomplish their objective without requiring any changes to the hardware, the existing infrastructure and the WLAN standard, and highlight findings from a simulation based study. Findings indicate that the proposed algorithms significantly outperform the typical random selection approach commonly employed by low cost commercial off-the-shelf hardware.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88738283","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}
At present, recommendations become an acceptable choice to replace annoying and widespread advertisements. Recommender systems (RS) are mainly used by large scale e-businesses, such as Amazon [1] and Netflix [4], because implementing and deploying RS can involve substantial investments. Many e-commerce businesses prefer to outsource the recommendation services. Therefore, Recommendation as a Service (RaaS) becomes a newly emerging trend for providing a feasible RS alternative. The providers of these services, the RS providers, need to pay the fee of cloud computing services, which is proportional to the amount of time, memory requirement, and computation resources. In addition, the RS providers must support rapidly recommendation services to meet the requests of clients. In this paper, we propose a numerical similarity-aware data partitioning (NSDP) scheme that effectively uses the numeric and similarity of datasets to exactly estimate the memory and the computation requirements for distributing the workloads. The simulation results demonstrate that NSDP significantly improves the speedup performance and achieves high scalability in the RaaS distributed-memory environment.
{"title":"Numerical similarity-aware data partitioning for recommendations as a service","authors":"Ting-Ting Yang, Hsueh-Wen Tseng","doi":"10.1145/3019612.3019676","DOIUrl":"https://doi.org/10.1145/3019612.3019676","url":null,"abstract":"At present, recommendations become an acceptable choice to replace annoying and widespread advertisements. Recommender systems (RS) are mainly used by large scale e-businesses, such as Amazon [1] and Netflix [4], because implementing and deploying RS can involve substantial investments. Many e-commerce businesses prefer to outsource the recommendation services. Therefore, Recommendation as a Service (RaaS) becomes a newly emerging trend for providing a feasible RS alternative. The providers of these services, the RS providers, need to pay the fee of cloud computing services, which is proportional to the amount of time, memory requirement, and computation resources. In addition, the RS providers must support rapidly recommendation services to meet the requests of clients. In this paper, we propose a numerical similarity-aware data partitioning (NSDP) scheme that effectively uses the numeric and similarity of datasets to exactly estimate the memory and the computation requirements for distributing the workloads. The simulation results demonstrate that NSDP significantly improves the speedup performance and achieves high scalability in the RaaS distributed-memory environment.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"376 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77119840","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}
Several studies in the model-driven engineering (MDE) literature report on companies adopting MDE technologies as a result of long collaborations with academics. However, there are more companies than reported that are already using MDE, even without active MDE researchers partners. Therefore we do not have a clear view of how these industries are using MDE and how it is benefiting them. Presche Legacy is a company that already started using MDE even before collaborating with us. To better understand how MDE is used in such industries and to what extent MDE can solve challenges in this business, we conducted a series of interviews with the employees working with MDE. The findings presented in this paper report a successful integration of MDE tools with their in-house software used in production, challenges encountered, and how they surmounted them. We also discuss how this success story can help other companies benefit from MDE.
{"title":"Feedback on how MDE tools are used prior to academic collaboration","authors":"V. Sousa, Eugene Syriani, Martin Paquin","doi":"10.1145/3019612.3019775","DOIUrl":"https://doi.org/10.1145/3019612.3019775","url":null,"abstract":"Several studies in the model-driven engineering (MDE) literature report on companies adopting MDE technologies as a result of long collaborations with academics. However, there are more companies than reported that are already using MDE, even without active MDE researchers partners. Therefore we do not have a clear view of how these industries are using MDE and how it is benefiting them. Presche Legacy is a company that already started using MDE even before collaborating with us. To better understand how MDE is used in such industries and to what extent MDE can solve challenges in this business, we conducted a series of interviews with the employees working with MDE. The findings presented in this paper report a successful integration of MDE tools with their in-house software used in production, challenges encountered, and how they surmounted them. We also discuss how this success story can help other companies benefit from MDE.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86971688","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}
Baptiste de La Robertie, Y. Pitarch, A. Takasu, O. Teste
Numereous digital library projects mine heterogeneous data from different sources to provide expert finding services. However, a variety of models seek experts as simple sources of information and neglect authority signals. In this paper we address the issue of modelling the authority of researchers in academic networks. A model, RAC, is proposed that merges several graph representations and incorporate external knowledge about the authority of some major scientific conferences to improve the identification of authoritative researchers. Based on the provided structural model a biased label propagation algorithm aimed to strenghten the scores calculation of the labelled entities and their neighbors is developped. Both quantitative and qualitative analyses validate the effectiveness of the proposal. Indeed, RAC outperforms state-of-the-art models on a real-world graph containing more than 5 million nodes constructed using Microsoft Academic Search, AMiner and Core.edu databases.
{"title":"Identifying authoritative researchers in digital libraries using external a priori knowledge","authors":"Baptiste de La Robertie, Y. Pitarch, A. Takasu, O. Teste","doi":"10.1145/3019612.3019809","DOIUrl":"https://doi.org/10.1145/3019612.3019809","url":null,"abstract":"Numereous digital library projects mine heterogeneous data from different sources to provide expert finding services. However, a variety of models seek experts as simple sources of information and neglect authority signals. In this paper we address the issue of modelling the authority of researchers in academic networks. A model, RAC, is proposed that merges several graph representations and incorporate external knowledge about the authority of some major scientific conferences to improve the identification of authoritative researchers. Based on the provided structural model a biased label propagation algorithm aimed to strenghten the scores calculation of the labelled entities and their neighbors is developped. Both quantitative and qualitative analyses validate the effectiveness of the proposal. Indeed, RAC outperforms state-of-the-art models on a real-world graph containing more than 5 million nodes constructed using Microsoft Academic Search, AMiner and Core.edu databases.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88925525","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}
Login webpages are the entry points into sensitive parts of web applications, dividing between public access to a website and private, user-specific, access to the website resources. As such, these entry points must be guarded with great care. A vast majority of today's websites relies on text-based user-name/password pairs for user authentication. While much prior research has focused on the strengths and weaknesses of textual passwords, this paper puts a spotlight on the security of the login webpages themselves. We conduct an empirical study of the Alexa top 100,000 pages to identify login pages and scrutinize their security. Our findings show several widely spread vulnerabilities, such as possibilities for password leaks to third parties and password eavesdropping on the network. They also show that only a scarce number of login pages deploy advanced security measures. Our findings on open-source web frameworks and content management systems confirm the lack of support against the login attacker. To ameliorate the problematic state of the art, we discuss measures to improve the security of login pages.
{"title":"Measuring login webpage security","authors":"S. Acker, Daniel Hausknecht, A. Sabelfeld","doi":"10.1145/3019612.3019798","DOIUrl":"https://doi.org/10.1145/3019612.3019798","url":null,"abstract":"Login webpages are the entry points into sensitive parts of web applications, dividing between public access to a website and private, user-specific, access to the website resources. As such, these entry points must be guarded with great care. A vast majority of today's websites relies on text-based user-name/password pairs for user authentication. While much prior research has focused on the strengths and weaknesses of textual passwords, this paper puts a spotlight on the security of the login webpages themselves. We conduct an empirical study of the Alexa top 100,000 pages to identify login pages and scrutinize their security. Our findings show several widely spread vulnerabilities, such as possibilities for password leaks to third parties and password eavesdropping on the network. They also show that only a scarce number of login pages deploy advanced security measures. Our findings on open-source web frameworks and content management systems confirm the lack of support against the login attacker. To ameliorate the problematic state of the art, we discuss measures to improve the security of login pages.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88709118","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}
Jarkko Hyysalo, Gavin Harper, J. Sauvola, A. Keskinarkaus, I. Juuso, Miikka Salminen, Juha Partala
The architecture of a system specifies how the system should be designed and built. However, shortcomings are identified in current architecture process frameworks concerning evolving domains like healthcare. We claim that an iterative architecture process is required, where the technical concerns are separated from the non-technical ones. Furthermore, a strong guiding vision is required. Based on our experiences from a biobank IT infrastructure process, we present an architecture process that is modular, interoperable, controlled and abstracted, thus being capable of handling complex systems with large uncertainties.
{"title":"Defining an architecture for evolving environments","authors":"Jarkko Hyysalo, Gavin Harper, J. Sauvola, A. Keskinarkaus, I. Juuso, Miikka Salminen, Juha Partala","doi":"10.1145/3019612.3019902","DOIUrl":"https://doi.org/10.1145/3019612.3019902","url":null,"abstract":"The architecture of a system specifies how the system should be designed and built. However, shortcomings are identified in current architecture process frameworks concerning evolving domains like healthcare. We claim that an iterative architecture process is required, where the technical concerns are separated from the non-technical ones. Furthermore, a strong guiding vision is required. Based on our experiences from a biobank IT infrastructure process, we present an architecture process that is modular, interoperable, controlled and abstracted, thus being capable of handling complex systems with large uncertainties.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"09 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85864027","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}
Modeling correct software-intensive Systems of Systems architectures is a challenging research direction that can be mastered by providing modeling abstractions. For this purpose, we provide an iterative modeling solution for a multi-scale description of software architectures. We provide a visual notation extending the graphical UML notations to represent structural as well as behavioral features of software architectures. We define a step-wise iterative process from a coarse-grain to a fine-grain description. The intermediate iterations provide a description with a given abstraction that allow the validation to be conducted significantly while remaining tractable w.r.t. complexity. The iterative process involves both system-independent structural features ensuring the model correctness, and system-specific features related to the expected behavior of the modeled domain. We apply our approach for a methodological design of an Emergency Response and Crisis Management System (ERCMS).
{"title":"Iterative multi-scale modeling of software-intensive systems of systems architectures","authors":"Ilhem Khlif, M. Kacem, A. Kacem","doi":"10.1145/3019612.3019801","DOIUrl":"https://doi.org/10.1145/3019612.3019801","url":null,"abstract":"Modeling correct software-intensive Systems of Systems architectures is a challenging research direction that can be mastered by providing modeling abstractions. For this purpose, we provide an iterative modeling solution for a multi-scale description of software architectures. We provide a visual notation extending the graphical UML notations to represent structural as well as behavioral features of software architectures. We define a step-wise iterative process from a coarse-grain to a fine-grain description. The intermediate iterations provide a description with a given abstraction that allow the validation to be conducted significantly while remaining tractable w.r.t. complexity. The iterative process involves both system-independent structural features ensuring the model correctness, and system-specific features related to the expected behavior of the modeled domain. We apply our approach for a methodological design of an Emergency Response and Crisis Management System (ERCMS).","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85920226","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}
Claudio D. G. Linhares, B. Travençolo, J. G. Paiva, Luis E C Rocha
The concept of networks has been important in the study of complex systems. In networks, links connect pairs of nodes forming complex structures. Studies have shown that networks not only contain structure but may also evolve in time. The addition of the temporal dimension adds complexity on the analysis and requests the development of innovative methods for the visualization of real-life networks. In this paper we introduce the Dynamic Network Visualization System (DyNetVis), a software tool for visualization of dynamic networks. The system provides several tools for user interaction and offers two coordinated visual layouts, named structural and temporal. Structural refers to standard network drawing techniques, in which a single snapshot of nodes and links are placed in a plane, whereas the temporal layout allows for simultaneously visualization of several temporal snapshots of the dynamic network. In addition, we also investigate two approaches for temporal layout visualization: (i) Recurrent Neighbors, a node ordering strategy that highlights frequent connections in time, and (ii) Temporal Activity Map (TAM), a layout technique with focus on nodes activity. We illustrate the applicability of the layouts and interaction functionalities provided by the system in two visual analysis case studies, demonstrating their advantages to improve the overall user experience on visualization and exploratory data analysis on dynamic networks.
{"title":"DyNetVis: a system for visualization of dynamic networks","authors":"Claudio D. G. Linhares, B. Travençolo, J. G. Paiva, Luis E C Rocha","doi":"10.1145/3019612.3019686","DOIUrl":"https://doi.org/10.1145/3019612.3019686","url":null,"abstract":"The concept of networks has been important in the study of complex systems. In networks, links connect pairs of nodes forming complex structures. Studies have shown that networks not only contain structure but may also evolve in time. The addition of the temporal dimension adds complexity on the analysis and requests the development of innovative methods for the visualization of real-life networks. In this paper we introduce the Dynamic Network Visualization System (DyNetVis), a software tool for visualization of dynamic networks. The system provides several tools for user interaction and offers two coordinated visual layouts, named structural and temporal. Structural refers to standard network drawing techniques, in which a single snapshot of nodes and links are placed in a plane, whereas the temporal layout allows for simultaneously visualization of several temporal snapshots of the dynamic network. In addition, we also investigate two approaches for temporal layout visualization: (i) Recurrent Neighbors, a node ordering strategy that highlights frequent connections in time, and (ii) Temporal Activity Map (TAM), a layout technique with focus on nodes activity. We illustrate the applicability of the layouts and interaction functionalities provided by the system in two visual analysis case studies, demonstrating their advantages to improve the overall user experience on visualization and exploratory data analysis on dynamic networks.","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84703502","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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