Pub Date : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-10
Yosuke Seki
Dialogue systems, which give users quick and easy access to required information interactively, have been widely used in various fields. To increase user satisfaction, it is important to analyze usage status in detail after introduction of dialogue systems. However, since usage status is analyzed from a large number of logs and other collected information, it can take much time and effort to find beneficial information. This study proposes a method that utilizes the dialogue system introduced for public relations in universities to support the analysis of usage status by visualization, aiming discovery by intuitive awareness. In the results of evaluation using real data of the dialogue system, a variety of information that matches different purposes and unexpected information were discovered by intuitive awareness and supporting functions.
{"title":"Visualization for Analyzing Usage Status from Dialogue Systems","authors":"Yosuke Seki","doi":"10.1109/COMPSAC48688.2020.00-10","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-10","url":null,"abstract":"Dialogue systems, which give users quick and easy access to required information interactively, have been widely used in various fields. To increase user satisfaction, it is important to analyze usage status in detail after introduction of dialogue systems. However, since usage status is analyzed from a large number of logs and other collected information, it can take much time and effort to find beneficial information. This study proposes a method that utilizes the dialogue system introduced for public relations in universities to support the analysis of usage status by visualization, aiming discovery by intuitive awareness. In the results of evaluation using real data of the dialogue system, a variety of information that matches different purposes and unexpected information were discovered by intuitive awareness and supporting functions.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133398416","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-55
Higor Pereira Delfino, R. M. Costa, J. P. Félix, João Gabriel Junqueira da Silva, Hedenir Monteiro Pinheiro, V. Siqueira, E. Camilo, D. Fernandes, Fabrízzio Soares
This work aims to investigate, by means of a Systematic Literature Review, to evaluate the current state of the use of artificial intelligence in automated pupillometric technology and its application in helping to diagnose diseases, to identify the methods and equipment used and propose case new equipment based on computer vision is feasible. We also investigated the accuracy of methodologies and equipment that use computerized pupilometry to identify pathologies or disorders, as well as the viability and usability of existing pupilometers. In this sense, creating a pupilometer capable of stimulating and varying wavelengths, providing an interface to preview the exam, and embedding the classification algorithms is a great challenge. In this systematic review of the literature, we consider publications from the last ten years (2010 - 2020) indexed by seven solid scientific databases. The review identified a vast amount of work on pupillometry; however, a small amount related to the construction and viability of a pupilometer with an embedded system, easy to use and with a preview interface. Having identified this, we propose a new methodology for the construction of the pupilometer as well as the algorithm for extracting the characteristics through pupilometry.
{"title":"Techniques and Equipment for Automated Pupillometry and its Application to Aid in the Diagnosis of Diseases: A Literature Review","authors":"Higor Pereira Delfino, R. M. Costa, J. P. Félix, João Gabriel Junqueira da Silva, Hedenir Monteiro Pinheiro, V. Siqueira, E. Camilo, D. Fernandes, Fabrízzio Soares","doi":"10.1109/COMPSAC48688.2020.00-55","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-55","url":null,"abstract":"This work aims to investigate, by means of a Systematic Literature Review, to evaluate the current state of the use of artificial intelligence in automated pupillometric technology and its application in helping to diagnose diseases, to identify the methods and equipment used and propose case new equipment based on computer vision is feasible. We also investigated the accuracy of methodologies and equipment that use computerized pupilometry to identify pathologies or disorders, as well as the viability and usability of existing pupilometers. In this sense, creating a pupilometer capable of stimulating and varying wavelengths, providing an interface to preview the exam, and embedding the classification algorithms is a great challenge. In this systematic review of the literature, we consider publications from the last ten years (2010 - 2020) indexed by seven solid scientific databases. The review identified a vast amount of work on pupillometry; however, a small amount related to the construction and viability of a pupilometer with an embedded system, easy to use and with a preview interface. Having identified this, we propose a new methodology for the construction of the pupilometer as well as the algorithm for extracting the characteristics through pupilometry.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133866268","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00017
T. Takayama, Kenichi Kourai
As cloud computing is widely used, even parallel applications run in virtual machines (VMs) of clouds. When CPU overcommitment is performed in clouds, physical CPU cores (pCPUs) can become less than virtual CPUs (vCPUs). In such a situation, it is reported that application performance degrades more largely than expected by the decrease of pCPUs available to each VM. To address this issue, several researchers have proposed optimization techniques of reducing the number of vCPUs assigned to each VM. However, their effectiveness is confirmed only in a limited VM configuration. In this paper, we have first investigated application performance under three configurations and revealed that the previous work cannot always achieve optimal performance. Then we propose pCPU-Est for improving application performance under CPU overcommitment. pCPU-Est dynamically optimizes the number of vCPUs on the basis of correlation between CPU utilization and execution time (dynamic vCPU optimization). In addition, it dynamically optimizes the number of application threads when possible (thread optimization). According to our experiments, dynamic vCPU optimization improved application performance by up to 42%, while thread optimization did by up to 72x.
{"title":"Optimization of Parallel Applications Under CPU Overcommitment","authors":"T. Takayama, Kenichi Kourai","doi":"10.1109/COMPSAC48688.2020.00017","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00017","url":null,"abstract":"As cloud computing is widely used, even parallel applications run in virtual machines (VMs) of clouds. When CPU overcommitment is performed in clouds, physical CPU cores (pCPUs) can become less than virtual CPUs (vCPUs). In such a situation, it is reported that application performance degrades more largely than expected by the decrease of pCPUs available to each VM. To address this issue, several researchers have proposed optimization techniques of reducing the number of vCPUs assigned to each VM. However, their effectiveness is confirmed only in a limited VM configuration. In this paper, we have first investigated application performance under three configurations and revealed that the previous work cannot always achieve optimal performance. Then we propose pCPU-Est for improving application performance under CPU overcommitment. pCPU-Est dynamically optimizes the number of vCPUs on the basis of correlation between CPU utilization and execution time (dynamic vCPU optimization). In addition, it dynamically optimizes the number of application threads when possible (thread optimization). According to our experiments, dynamic vCPU optimization improved application performance by up to 42%, while thread optimization did by up to 72x.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114430817","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-192
Fumiya Toyoda, Yusuke Sakumoto, H. Ohsaki
A blind search in a network is used to discover a target node without detailed knowledge on the network. Because of its simplicity and the robust against network uncertainty, the blind search has been widely utilized by diverse applications in different types of networks (e.g., unstructured P2P (Peer-to-Peer) networks, ICNs (Information Centric Networks), mobile ad-hoc networks, and social networks). One of the major drawbacks of the blind search is its inefficiency; i.e., a large number of message exchanges is unavoidable for shortening the search time. In this paper, we propose an efficient blind search method utilizing the rendezvous of multiple random walkers, whose transition probabilities are adjusted based on our analysis results. Through simulation experiments, we show that the performance of the proposed search method is comparable with the flooding, which is the fastest but the least efficient method among blind search methods, and that it requires much smaller message exchanges than the flooding. We also show that the proposed search method works more effectively in scale-free networks than in non-scale-free networks.
{"title":"Proposal of an Efficient Blind Search Utilizing the Rendezvous of Random Walk Agents","authors":"Fumiya Toyoda, Yusuke Sakumoto, H. Ohsaki","doi":"10.1109/COMPSAC48688.2020.0-192","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-192","url":null,"abstract":"A blind search in a network is used to discover a target node without detailed knowledge on the network. Because of its simplicity and the robust against network uncertainty, the blind search has been widely utilized by diverse applications in different types of networks (e.g., unstructured P2P (Peer-to-Peer) networks, ICNs (Information Centric Networks), mobile ad-hoc networks, and social networks). One of the major drawbacks of the blind search is its inefficiency; i.e., a large number of message exchanges is unavoidable for shortening the search time. In this paper, we propose an efficient blind search method utilizing the rendezvous of multiple random walkers, whose transition probabilities are adjusted based on our analysis results. Through simulation experiments, we show that the performance of the proposed search method is comparable with the flooding, which is the fastest but the least efficient method among blind search methods, and that it requires much smaller message exchanges than the flooding. We also show that the proposed search method works more effectively in scale-free networks than in non-scale-free networks.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116735036","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-90
Jingyu Sun, Susumu Takeuchi, I. Yamasaki
Nowadays, explosive growth of ontologies are used for managing data in various domains. They usually own different vocabularies and structures following different fashions. Ontology alignment finding semantic correspondences between elements of these ontologies can effectively facilitate the data communication and novel application creation in many practical scenarios. However, we noticed that, the traditional parametric ontology mapping methods still depend on individualistic abilities for setting proper parameters for mapping. When trying to utilize artificial neural networks for the automatic ontology mapping, the training data are found insufficient in most of the cases. This paper analyzes these problems, and proposes a few-shot ontology alignment model, which can automatically learn how to map two ontologies from only a few training links between their element pairs. The proposed model applies the Siamese neural network in computer vision on ontology alignment and designs an attention detection network learning the attention weights for different ontology attributes. A few experiments that conducted on the anatomy ontology alignment show that our model achieves good performance (94.3% of F-measure) with 200 training alignments without traditional parametric setting.
{"title":"Few-Shot Ontology Alignment Model with Attribute Attentions","authors":"Jingyu Sun, Susumu Takeuchi, I. Yamasaki","doi":"10.1109/COMPSAC48688.2020.00-90","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-90","url":null,"abstract":"Nowadays, explosive growth of ontologies are used for managing data in various domains. They usually own different vocabularies and structures following different fashions. Ontology alignment finding semantic correspondences between elements of these ontologies can effectively facilitate the data communication and novel application creation in many practical scenarios. However, we noticed that, the traditional parametric ontology mapping methods still depend on individualistic abilities for setting proper parameters for mapping. When trying to utilize artificial neural networks for the automatic ontology mapping, the training data are found insufficient in most of the cases. This paper analyzes these problems, and proposes a few-shot ontology alignment model, which can automatically learn how to map two ontologies from only a few training links between their element pairs. The proposed model applies the Siamese neural network in computer vision on ontology alignment and designs an attention detection network learning the attention weights for different ontology attributes. A few experiments that conducted on the anatomy ontology alignment show that our model achieves good performance (94.3% of F-measure) with 200 training alignments without traditional parametric setting.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116736427","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-223
Weidong Liu, Wenbo Qiao, Xin Liu
With the growing importance of intellectual property, the amount of patent increases every year. The patents realize their values by the patent conversion. However, many patents do not realize their values since the paths to realize the patent value have not been found. To predict the paths, we explore a Bayesian neural network based model. In the model, the patents are represented by the function-effects, from which some technical features are extracted. We use Bayesian neural network to predict the paths toward the realization of patent valuation. The model is evaluated by the evaluation measurements. The results show our method performs well in the evaluation measurements. Such model can be applied to further patent recommendation and automated trading.
{"title":"Bayesian Neural Network Based Path Prediction Model Toward the Realization of Patent Valuation","authors":"Weidong Liu, Wenbo Qiao, Xin Liu","doi":"10.1109/COMPSAC48688.2020.0-223","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-223","url":null,"abstract":"With the growing importance of intellectual property, the amount of patent increases every year. The patents realize their values by the patent conversion. However, many patents do not realize their values since the paths to realize the patent value have not been found. To predict the paths, we explore a Bayesian neural network based model. In the model, the patents are represented by the function-effects, from which some technical features are extracted. We use Bayesian neural network to predict the paths toward the realization of patent valuation. The model is evaluated by the evaluation measurements. The results show our method performs well in the evaluation measurements. Such model can be applied to further patent recommendation and automated trading.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123080639","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00013
B. K. Sreedhar, Nagarajan Shunmugam
The field of self-driving cars is a fast-growing one, and numerous companies and organizations are working at the forefront of this technology. One of the major requirements for self-driving cars is the necessity of expensive hardware to run complex models. This project aims to identify a suitable deep learning model under hardware constraints. We obtain the results of a supervised model trained with data from a human driver and compare it to a reinforcement learning-based approach. Both models will be trained and tested on devices with low-end hardware, and their results visualized with the help of a driving simulator. The objective is to demonstrate that even a simple model with enough data augmentation can perform specific tasks and does not require much investment in time and money. We also aim to introduce portability to deep learning models by trying to deploy the model in a mobile device and show that it can work as a standalone module.
{"title":"Deep Learning for Hardware-Constrained Driverless Cars","authors":"B. K. Sreedhar, Nagarajan Shunmugam","doi":"10.1109/COMPSAC48688.2020.00013","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00013","url":null,"abstract":"The field of self-driving cars is a fast-growing one, and numerous companies and organizations are working at the forefront of this technology. One of the major requirements for self-driving cars is the necessity of expensive hardware to run complex models. This project aims to identify a suitable deep learning model under hardware constraints. We obtain the results of a supervised model trained with data from a human driver and compare it to a reinforcement learning-based approach. Both models will be trained and tested on devices with low-end hardware, and their results visualized with the help of a driving simulator. The objective is to demonstrate that even a simple model with enough data augmentation can perform specific tasks and does not require much investment in time and money. We also aim to introduce portability to deep learning models by trying to deploy the model in a mobile device and show that it can work as a standalone module.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123363338","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00039
Yingchao Wu, Bo Dong, Q. Zheng, Rongzhe Wei, Zhiwen Wang, Xuanya Li
Tax evasion usually refers to the false declaration of taxpayers to reduce their tax obligations; this type of behavior leads to the loss of taxes and damage to the fair principle of taxation. Tax evasion detection plays a crucial role in reducing tax revenue loss. Currently, efficient auditing methods mainly include traditional data-mining-oriented methods, which cannot be well adapted to the increasingly complicated transaction relationships between taxpayers. Driven by this requirement, recent studies have been conducted by establishing a transaction network and applying the graphical pattern matching algorithm for tax evasion identification. However, such methods rely on expert experience to extract the tax evasion chart pattern, which is time-consuming and labor-intensive. More importantly, taxpayers' basic attributes are not considered and the dual identity of the taxpayer in the transaction network is not well retained. To address this issue, we have proposed a novel tax evasion detection framework via fused transaction network representation (TED-TNR), to detecting tax evasion based on fused transaction network representation, which jointly embeds transaction network topological information and basic taxpayer attributes into low-dimensional vector space, and considers the dual identity of the taxpayer in the transaction network. Finally, we conducted experimental tests on real-world tax data, revealing the superiority of our method, compared with state-of-the-art models.
{"title":"A Novel Tax Evasion Detection Framework via Fused Transaction Network Representation","authors":"Yingchao Wu, Bo Dong, Q. Zheng, Rongzhe Wei, Zhiwen Wang, Xuanya Li","doi":"10.1109/COMPSAC48688.2020.00039","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00039","url":null,"abstract":"Tax evasion usually refers to the false declaration of taxpayers to reduce their tax obligations; this type of behavior leads to the loss of taxes and damage to the fair principle of taxation. Tax evasion detection plays a crucial role in reducing tax revenue loss. Currently, efficient auditing methods mainly include traditional data-mining-oriented methods, which cannot be well adapted to the increasingly complicated transaction relationships between taxpayers. Driven by this requirement, recent studies have been conducted by establishing a transaction network and applying the graphical pattern matching algorithm for tax evasion identification. However, such methods rely on expert experience to extract the tax evasion chart pattern, which is time-consuming and labor-intensive. More importantly, taxpayers' basic attributes are not considered and the dual identity of the taxpayer in the transaction network is not well retained. To address this issue, we have proposed a novel tax evasion detection framework via fused transaction network representation (TED-TNR), to detecting tax evasion based on fused transaction network representation, which jointly embeds transaction network topological information and basic taxpayer attributes into low-dimensional vector space, and considers the dual identity of the taxpayer in the transaction network. Finally, we conducted experimental tests on real-world tax data, revealing the superiority of our method, compared with state-of-the-art models.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123572707","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-151
Layan Etaiwi, Sylvie Hamel, Yann-Gaël Guéhéneuc, William Flageol, Rodrigo Morales
The continuous growth of the mobile apps industry creates a competition among apps developers. To succeed, app developers must attract and retain users. User reviews provide a wealth of information about bugs to fix and features to add and can help app developers offer high-quality apps. However, apps may receive hundreds of unstructured reviews, which makes transforming them into change requests a difficult task. Approaches exist for analyzing and extracting topics from mobile app reviews, however, prioritizing these reviews has not gained much attention. In this study, we introduce the use of a consensus algorithm to help developers prioritize user reviews for the purpose of app evolution. We evaluate the usefulness of our approach and meaningfulness of its consensus rankings on four Android apps. We compare the rankings against reviews ranked by app developers manually and show that there is a strong correlation between the two (average Kendall rank correlation coefficient = 0.516). Thus, our approach can prioritize user reviews and help developers focus their time/effort on improving their apps instead of on identifying reviews to address in the next release.
{"title":"Order in Chaos: Prioritizing Mobile App Reviews using Consensus Algorithms","authors":"Layan Etaiwi, Sylvie Hamel, Yann-Gaël Guéhéneuc, William Flageol, Rodrigo Morales","doi":"10.1109/COMPSAC48688.2020.0-151","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-151","url":null,"abstract":"The continuous growth of the mobile apps industry creates a competition among apps developers. To succeed, app developers must attract and retain users. User reviews provide a wealth of information about bugs to fix and features to add and can help app developers offer high-quality apps. However, apps may receive hundreds of unstructured reviews, which makes transforming them into change requests a difficult task. Approaches exist for analyzing and extracting topics from mobile app reviews, however, prioritizing these reviews has not gained much attention. In this study, we introduce the use of a consensus algorithm to help developers prioritize user reviews for the purpose of app evolution. We evaluate the usefulness of our approach and meaningfulness of its consensus rankings on four Android apps. We compare the rankings against reviews ranked by app developers manually and show that there is a strong correlation between the two (average Kendall rank correlation coefficient = 0.516). Thus, our approach can prioritize user reviews and help developers focus their time/effort on improving their apps instead of on identifying reviews to address in the next release.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124693802","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-193
Ryohei Banno, Kazuyuki Shudo
Publish/subscribe is a communication model for exchanging messages via a broker while providing loose coupling. So far, several studies have been conducted to address load concentration on the broker by forming distributed brokers. However, although they achieve higher throughput by load distribution among multiple brokers, these existing studies require an increased latency for message delivery. In this paper, we propose a novel method to construct and maintain an adaptive topology that features both scalability and immediacy in distributed publish/subscribe messaging. The proposed method is for topic-based publish/subscribe systems and uses a number of brokers to form an overlay network. Its topology changes dynamically to compose a subgraph for each topic in a single-hop or multi-hop manner according to the topic load (i.e., the number of clients). The experimental results show that compared to existing studies, the proposed method reduces the delivery path length, which is a principal factor that affects latency. Especially for low load topics, the reduction rate of the proposed method reaches values greater than 60%.
{"title":"Adaptive Topology for Scalability and Immediacy in Distributed Publish/Subscribe Messaging","authors":"Ryohei Banno, Kazuyuki Shudo","doi":"10.1109/COMPSAC48688.2020.0-193","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-193","url":null,"abstract":"Publish/subscribe is a communication model for exchanging messages via a broker while providing loose coupling. So far, several studies have been conducted to address load concentration on the broker by forming distributed brokers. However, although they achieve higher throughput by load distribution among multiple brokers, these existing studies require an increased latency for message delivery. In this paper, we propose a novel method to construct and maintain an adaptive topology that features both scalability and immediacy in distributed publish/subscribe messaging. The proposed method is for topic-based publish/subscribe systems and uses a number of brokers to form an overlay network. Its topology changes dynamically to compose a subgraph for each topic in a single-hop or multi-hop manner according to the topic load (i.e., the number of clients). The experimental results show that compared to existing studies, the proposed method reduces the delivery path length, which is a principal factor that affects latency. Especially for low load topics, the reduction rate of the proposed method reaches values greater than 60%.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124752797","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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