Pub Date : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118465
Nabil Almashfi, Lunjin Lu
JavaScript is a client-side scripting language that is widely used in web applications. It is dynamic, loosely-typed and prototype-based with first-class functions. The dynamic nature of JavaScript makes it powerful and highly flexible in almost every way. However, this flexibility may result in what is known as code smells. Code smells are characteristics in the source code of a program that usually correspond to a deeper problem. They can lead to a variety of comprehension and maintenance issues and they may impact fault- and change-proneness of the application in the future. We present TAJSlint, an automated code smell detection tool for JavaScript programs that is based on static analysis. TAJSlint includes a set of 14 code smells, 9 of which are collected from various sources and 5 new smells we propose. We conduct an empirical evaluation of TAJSlint on a number of JavaScript projects and show that TAJSlint achieves an overall precision of 98% with a small number of false positives. We also study the prevalence of code smells in these projects.
{"title":"Code Smell Detection Tool for Java Script Programs","authors":"Nabil Almashfi, Lunjin Lu","doi":"10.1109/ICCCS49078.2020.9118465","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118465","url":null,"abstract":"JavaScript is a client-side scripting language that is widely used in web applications. It is dynamic, loosely-typed and prototype-based with first-class functions. The dynamic nature of JavaScript makes it powerful and highly flexible in almost every way. However, this flexibility may result in what is known as code smells. Code smells are characteristics in the source code of a program that usually correspond to a deeper problem. They can lead to a variety of comprehension and maintenance issues and they may impact fault- and change-proneness of the application in the future. We present TAJSlint, an automated code smell detection tool for JavaScript programs that is based on static analysis. TAJSlint includes a set of 14 code smells, 9 of which are collected from various sources and 5 new smells we propose. We conduct an empirical evaluation of TAJSlint on a number of JavaScript projects and show that TAJSlint achieves an overall precision of 98% with a small number of false positives. We also study the prevalence of code smells in these projects.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126367949","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118431
Si Thu Aung, T. Thein
Categorized Internet protocol, and demand prediction based on usage behavior can offer substantial benefits to dynamic Quality of Service (QoS) for internet service providers (ISPs). The critical requirement for dynamic QoS is to classify and predict network traffic in the next control time interval. Traffic prediction, with the aid of traffic categories can utilize the network resources more efficiently and support Dynamic QoS to function appropriately. This paper proposes an internet traffic categories demand prediction framework using machine learning techniques to support dynamic QoS. In this framework, three algorithms are implemented at transformation stage to feed into machine learning algorithms and develops efficient prediction model to predict internet network traffic demand. Experimental results show that prediction accuracy of the model is 98.97% and is efficient and suitable to support real-world network traffic prediction.
{"title":"Internet Traffic Categories Demand Prediction to Support Dynamic QoS","authors":"Si Thu Aung, T. Thein","doi":"10.1109/ICCCS49078.2020.9118431","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118431","url":null,"abstract":"Categorized Internet protocol, and demand prediction based on usage behavior can offer substantial benefits to dynamic Quality of Service (QoS) for internet service providers (ISPs). The critical requirement for dynamic QoS is to classify and predict network traffic in the next control time interval. Traffic prediction, with the aid of traffic categories can utilize the network resources more efficiently and support Dynamic QoS to function appropriately. This paper proposes an internet traffic categories demand prediction framework using machine learning techniques to support dynamic QoS. In this framework, three algorithms are implemented at transformation stage to feed into machine learning algorithms and develops efficient prediction model to predict internet network traffic demand. Experimental results show that prediction accuracy of the model is 98.97% and is efficient and suitable to support real-world network traffic prediction.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"657 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113996421","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 order to solve the problem that the power terminal cross-domain authentication system is not compatible with the existing authentication system, this paper designs a cross-domain authentication model for power terminals based on master-slave Blockchain, including ubiquitous sensing module, cross-domain authentication module and grid application system module, original authentication system module. Secondly, based on the authentication model, the Blockchain-based registration process of the power terminal, the intra-domain authentication process based on the Blockchain, and the cross-domain authentication process based on the Blockchain are designed. Finally, from the two aspects of the registration process of the power terminal and the authentication process of the power terminal, it is verified that the authentication mechanism proposed in this paper can not only achieve cross-domain authentication of the power terminal, but also be compatible with the original identity authentication system.
{"title":"Master-slave Blockchain Based Cross-domain Trust Access Mechanism for UPIOT","authors":"Chenguang Wu, Jizhao Lu, Wencui Li, Huiping Meng, Yinlin Ren","doi":"10.1109/ICCCS49078.2020.9118552","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118552","url":null,"abstract":"In order to solve the problem that the power terminal cross-domain authentication system is not compatible with the existing authentication system, this paper designs a cross-domain authentication model for power terminals based on master-slave Blockchain, including ubiquitous sensing module, cross-domain authentication module and grid application system module, original authentication system module. Secondly, based on the authentication model, the Blockchain-based registration process of the power terminal, the intra-domain authentication process based on the Blockchain, and the cross-domain authentication process based on the Blockchain are designed. Finally, from the two aspects of the registration process of the power terminal and the authentication process of the power terminal, it is verified that the authentication mechanism proposed in this paper can not only achieve cross-domain authentication of the power terminal, but also be compatible with the original identity authentication system.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123923346","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118437
T. Matsuo, Masafumi Kosugi, O. Uchida
To minimize damage during disasters, the rapid collection and delivery of accurate information are essential. Then, we proposed an automatic disaster situation visualization system and implemented a prototype of it in the previous study. The system collects images taken by the car-mounted camera of a truck running in areas where a disaster occurs and displays them on a web-based map. By using the system, we can grasp the situation in the target area quickly and comprehensively. In this study, we improve the system from two perspectives. The first one is to add a function to post images to Twitter, and the second one is the improvement of the image mapping function.
{"title":"Improvement of Disaster Situation Visualization System Using Truck-mounted Camera","authors":"T. Matsuo, Masafumi Kosugi, O. Uchida","doi":"10.1109/ICCCS49078.2020.9118437","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118437","url":null,"abstract":"To minimize damage during disasters, the rapid collection and delivery of accurate information are essential. Then, we proposed an automatic disaster situation visualization system and implemented a prototype of it in the previous study. The system collects images taken by the car-mounted camera of a truck running in areas where a disaster occurs and displays them on a web-based map. By using the system, we can grasp the situation in the target area quickly and comprehensively. In this study, we improve the system from two perspectives. The first one is to add a function to post images to Twitter, and the second one is the improvement of the image mapping function.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124364020","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118460
Kechang Qian, Youchen Fan, Dapeng Xiong, Jie Qiang
For the acquired multivariate measurement data, a reasonable multivariate measurement data fusion algorithm needs to be designed to improve the outer ballistic measurement accuracy. Based on the theory of the classic EMBET method, this paper proposes a method of multivariate measurement data fusion based on principal component estimation. By optimizing the characteristic root screening method, the ill-conditioned phenomenon of the Jacobian matrix of the classic EMBET method is weakened, and the accuracy of measurement is effectively improved. Simulation experiments and measured data have confirmed the effectiveness of this method.
{"title":"A Fusion Method of Multivariate Measurement Data Based on Principal Component Estimation","authors":"Kechang Qian, Youchen Fan, Dapeng Xiong, Jie Qiang","doi":"10.1109/ICCCS49078.2020.9118460","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118460","url":null,"abstract":"For the acquired multivariate measurement data, a reasonable multivariate measurement data fusion algorithm needs to be designed to improve the outer ballistic measurement accuracy. Based on the theory of the classic EMBET method, this paper proposes a method of multivariate measurement data fusion based on principal component estimation. By optimizing the characteristic root screening method, the ill-conditioned phenomenon of the Jacobian matrix of the classic EMBET method is weakened, and the accuracy of measurement is effectively improved. Simulation experiments and measured data have confirmed the effectiveness of this method.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122071675","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118463
Sisamouth Hongvanthong
Software defined 5G network (SD-5G) is an evolving networking technology. The integration of SDN and 5G brings scalability, and efficiency. However, Quality of Service (QoS) provision is still challenging in SD-5G due to improper load balancing, traffic unawareness and so on. To overwhelm these issues this paper designs a novel load balancing scheme using Artificial Intelligence (AI) techniques. Firstly, novel four-layered SD-5G network is designed with user plane, smart data plane, load balancing plane, and distributed control plane. In the context to 5G, the data transmission rate must satisfy the QoS constraints based on the traffic type such as text, audio, video etc. Thus, the data from the user plane is classified by Smart Traffic Analyzer in the data plane. For traffic analysis, Enriched Neuro-Fuzzy (ENF) classifier is proposed. In the load balancing plane, Primary Load balancer and Secondary Load Balancer are deployed. This plane is responsible for balancing the load among controllers. For controller load balancing, switch migration is presented. Overloaded controller is predicted by Entropy function. Then decision for migration is made by Fitness-based Reinforcement Learning (F-RL) algorithm. Finally, the four-layered SD-5G network is modeled in the NS-3.26. The observations shows that the proposed work improves the SD-5G network in terms of Loss Rate, Packet Delivery Rate, Delay, and round trip time.
{"title":"Novel Four-Layered Software Defined 5G Architecture for AI-based Load Balancing and QoS Provisioning","authors":"Sisamouth Hongvanthong","doi":"10.1109/ICCCS49078.2020.9118463","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118463","url":null,"abstract":"Software defined 5G network (SD-5G) is an evolving networking technology. The integration of SDN and 5G brings scalability, and efficiency. However, Quality of Service (QoS) provision is still challenging in SD-5G due to improper load balancing, traffic unawareness and so on. To overwhelm these issues this paper designs a novel load balancing scheme using Artificial Intelligence (AI) techniques. Firstly, novel four-layered SD-5G network is designed with user plane, smart data plane, load balancing plane, and distributed control plane. In the context to 5G, the data transmission rate must satisfy the QoS constraints based on the traffic type such as text, audio, video etc. Thus, the data from the user plane is classified by Smart Traffic Analyzer in the data plane. For traffic analysis, Enriched Neuro-Fuzzy (ENF) classifier is proposed. In the load balancing plane, Primary Load balancer and Secondary Load Balancer are deployed. This plane is responsible for balancing the load among controllers. For controller load balancing, switch migration is presented. Overloaded controller is predicted by Entropy function. Then decision for migration is made by Fitness-based Reinforcement Learning (F-RL) algorithm. Finally, the four-layered SD-5G network is modeled in the NS-3.26. The observations shows that the proposed work improves the SD-5G network in terms of Loss Rate, Packet Delivery Rate, Delay, and round trip time.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123625932","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118567
Atchara Mahaweerawa, Chatree Nilnumpetch, P. Kraipeerapun
In this paper, the difference of truth and falsity data is applied to stacked generalization. The truth and falsity data are created in level 0 of stacked generalization. In level 1, the truth data produced from level 0 together with the difference of truth and falsity data are trained. This proposed approach is used to predict performance of undergraduate students in the Department of Computer Science, Ramkhamhaeng University, Thailand. It is found that the proposed technique provides better accuracy result than the existing stacked generalization techniques for the prediction of student’s performance.
{"title":"Applying Stacked Generalization with the Difference of Truth and Falsity Data to Predict Student’s Performance","authors":"Atchara Mahaweerawa, Chatree Nilnumpetch, P. Kraipeerapun","doi":"10.1109/ICCCS49078.2020.9118567","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118567","url":null,"abstract":"In this paper, the difference of truth and falsity data is applied to stacked generalization. The truth and falsity data are created in level 0 of stacked generalization. In level 1, the truth data produced from level 0 together with the difference of truth and falsity data are trained. This proposed approach is used to predict performance of undergraduate students in the Department of Computer Science, Ramkhamhaeng University, Thailand. It is found that the proposed technique provides better accuracy result than the existing stacked generalization techniques for the prediction of student’s performance.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129191878","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118450
Alok Yadav, C. Nuthong
Traffic congestions are a recurring problem that results in significant losses both financially and environmentally. optimizing traffic signal timings is one of the most cost-effective ways to mitigate such effects. optimization of traffic signal timings capable of minimizing congestion is, however, computationally expensive. Research needs to be conducted to develop algorithms capable of better optimization using fewer computational resources. This paper presents a novel approach to traffic signal optimization that combines genetic algorithms and a gradient descent like algorithm to obtain optimized traffic signal timings. The genetic algorithm is used to arrive at a starting point for gradient descent; gradient descent is then used to obtain further improvement.
{"title":"Traffic Signal Timings optimization Based on Genetic Algorithm and Gradient Descent","authors":"Alok Yadav, C. Nuthong","doi":"10.1109/ICCCS49078.2020.9118450","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118450","url":null,"abstract":"Traffic congestions are a recurring problem that results in significant losses both financially and environmentally. optimizing traffic signal timings is one of the most cost-effective ways to mitigate such effects. optimization of traffic signal timings capable of minimizing congestion is, however, computationally expensive. Research needs to be conducted to develop algorithms capable of better optimization using fewer computational resources. This paper presents a novel approach to traffic signal optimization that combines genetic algorithms and a gradient descent like algorithm to obtain optimized traffic signal timings. The genetic algorithm is used to arrive at a starting point for gradient descent; gradient descent is then used to obtain further improvement.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050314","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118438
Bernardo Santos, Bruno Dzogovic, Boning Feng, Niels Jacot, V. T. Do, T. V. Do
As we notice the increasing adoption of Cellular IoT solutions (smart-home, e-health, among others), there are still some security aspects that can be improved as these devices can suffer various types of attacks that can have a high-impact over our daily lives. In order to avoid this, we present a multi-front security solution that consists on a federated cross-layered authentication mechanism, as well as a machine learning platform with anomaly detection techniques for data traffic analysis as a way to study devices’ behavior so it can preemptively detect attacks and minimize their impact. In this paper, we also present a proof-of-concept to illustrate the proposed solution and showcase its feasibility, as well as the discussion of future iterations that will occur for this work.
{"title":"Improving Cellular IoT Security with Identity Federation and Anomaly Detection","authors":"Bernardo Santos, Bruno Dzogovic, Boning Feng, Niels Jacot, V. T. Do, T. V. Do","doi":"10.1109/ICCCS49078.2020.9118438","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118438","url":null,"abstract":"As we notice the increasing adoption of Cellular IoT solutions (smart-home, e-health, among others), there are still some security aspects that can be improved as these devices can suffer various types of attacks that can have a high-impact over our daily lives. In order to avoid this, we present a multi-front security solution that consists on a federated cross-layered authentication mechanism, as well as a machine learning platform with anomaly detection techniques for data traffic analysis as a way to study devices’ behavior so it can preemptively detect attacks and minimize their impact. In this paper, we also present a proof-of-concept to illustrate the proposed solution and showcase its feasibility, as well as the discussion of future iterations that will occur for this work.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116336851","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 : 2020-05-01DOI: 10.1109/icccs49078.2020.9118503
{"title":"[ICCCS 2020 Copyright notice]","authors":"","doi":"10.1109/icccs49078.2020.9118503","DOIUrl":"https://doi.org/10.1109/icccs49078.2020.9118503","url":null,"abstract":"","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122424848","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：