Mohammed Al-habib, Dong-jun Huang, Majjed Al-Qatf, Kamal Al-Sabahi
Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. However, there are still several challenges that are related to multiple groups with different activities within a scene. The strong correlation that exists among individual motion, groups and activities can be utilized to detect groups and recognize their concurrent activities. Motivated by these observations, we propose a unified deep learning framework for detecting multiple groups and recognizing their corresponding collective activity based on Long Short-Term Memory (LSTM) network. In this framework, we use a pre-trained convolutional neural network (CNN) to extract features from the frames and appearances of persons. An objective function has been proposed to learn the amount of pairwise interaction between persons. The obtained individual features are passed to a clustering algorithm to detect groups in the scene. Then, an LSTM based model is used to recognize group activities. Together with this, a scene level CNN followed by LSTM is used to extract and learn scene level feature. Finally, the activities from the group level and the scene context level are integrated to infer the collective activity. The proposed method is evaluated on the benchmark collective activity dataset and compared with several baselines. The experimental results show its competitive performance for the collective activity recognition task.
{"title":"Cooperative Hierarchical Framework for Group Activity Recognition: From Group Detection to Multi-activity Recognition","authors":"Mohammed Al-habib, Dong-jun Huang, Majjed Al-Qatf, Kamal Al-Sabahi","doi":"10.1145/3316615.3316722","DOIUrl":"https://doi.org/10.1145/3316615.3316722","url":null,"abstract":"Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. However, there are still several challenges that are related to multiple groups with different activities within a scene. The strong correlation that exists among individual motion, groups and activities can be utilized to detect groups and recognize their concurrent activities. Motivated by these observations, we propose a unified deep learning framework for detecting multiple groups and recognizing their corresponding collective activity based on Long Short-Term Memory (LSTM) network. In this framework, we use a pre-trained convolutional neural network (CNN) to extract features from the frames and appearances of persons. An objective function has been proposed to learn the amount of pairwise interaction between persons. The obtained individual features are passed to a clustering algorithm to detect groups in the scene. Then, an LSTM based model is used to recognize group activities. Together with this, a scene level CNN followed by LSTM is used to extract and learn scene level feature. Finally, the activities from the group level and the scene context level are integrated to infer the collective activity. The proposed method is evaluated on the benchmark collective activity dataset and compared with several baselines. The experimental results show its competitive performance for the collective activity recognition task.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133905923","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}
Nowadays, with the improvement of social network check-in and positioning technology, the positioning information is more accurate, and a large amount of network check-in data is generated. The recommendation research of interest points based on social networks is also increasing. Most of the points of interest refer to rely on geography, time, space, and textual information. In spatial-temporal, most studies consider the check-in rules from the geographical distance and time series. This paper introduces a geographic spatial-temporal distance measurement model to map temporal space information into a three-dimensional elliptical spherical coordinate system. The spatial-temporal distance is measured under the same reference standard. Helps alleviate the problems caused by cold start and data sparseness for location recommendation accuracy. Based on the Bayesian personalized ranking, this paper measures the temporal and spatial distance by using a Gaussian kernel function to weight the spatial-temporal distance, and proposes a personalized ranking recommendation algorithm based on the spatial-temporal distance metric. And it performs well on both datasets and is superior to the benchmark method.
{"title":"Personalized Ranking Point of Interest Recommendation Based on Spatial-Temporal Distance Metric in LBSNs","authors":"Chang Su, Hao Li, Xianzhong Xie","doi":"10.1145/3316615.3316715","DOIUrl":"https://doi.org/10.1145/3316615.3316715","url":null,"abstract":"Nowadays, with the improvement of social network check-in and positioning technology, the positioning information is more accurate, and a large amount of network check-in data is generated. The recommendation research of interest points based on social networks is also increasing. Most of the points of interest refer to rely on geography, time, space, and textual information. In spatial-temporal, most studies consider the check-in rules from the geographical distance and time series. This paper introduces a geographic spatial-temporal distance measurement model to map temporal space information into a three-dimensional elliptical spherical coordinate system. The spatial-temporal distance is measured under the same reference standard. Helps alleviate the problems caused by cold start and data sparseness for location recommendation accuracy. Based on the Bayesian personalized ranking, this paper measures the temporal and spatial distance by using a Gaussian kernel function to weight the spatial-temporal distance, and proposes a personalized ranking recommendation algorithm based on the spatial-temporal distance metric. And it performs well on both datasets and is superior to the benchmark method.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"933 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418435","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}
The recent development of 3D sensing technology enables a number of consumer facing 3D cameras, such as Kinect, TrueDepth camera on IPhoneX etc., emerge. These cameras are much cheaper than conventional and professional 3D scanning devices, and thus they can be acquired by consumers easily. However, consumer 3D scanning applications bring a new set of challenges. One of the challenges is that it is difficult for consumers to obtain the full head model by self-scanning. The proposed algorithm in this paper aims at reconstructing 3D human back head model based on gradient filling method. Due to the lack of related researches, to be more specific, repairing large holes without extra information in the 3D scale, the problem is migrated to 2D scale by projecting 3D model to a spherical space. Then the depth value at each position in back head is calculated via gradient interpolation. The algorithm is simple and effective and it can reconstruct a model within seconds.
{"title":"Reconstruct the Back of 3D Face Model Using 2D Gradient Based Interpolation","authors":"W. Luo","doi":"10.1145/3316615.3316660","DOIUrl":"https://doi.org/10.1145/3316615.3316660","url":null,"abstract":"The recent development of 3D sensing technology enables a number of consumer facing 3D cameras, such as Kinect, TrueDepth camera on IPhoneX etc., emerge. These cameras are much cheaper than conventional and professional 3D scanning devices, and thus they can be acquired by consumers easily. However, consumer 3D scanning applications bring a new set of challenges. One of the challenges is that it is difficult for consumers to obtain the full head model by self-scanning. The proposed algorithm in this paper aims at reconstructing 3D human back head model based on gradient filling method. Due to the lack of related researches, to be more specific, repairing large holes without extra information in the 3D scale, the problem is migrated to 2D scale by projecting 3D model to a spherical space. Then the depth value at each position in back head is calculated via gradient interpolation. The algorithm is simple and effective and it can reconstruct a model within seconds.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134515223","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}
The rapid development of smartphones has greatly facilitated our lives. At the same time, securing the data stored and accessed from smartphones makes it important to authenticate the user. However, current smartphones perform one-time authentication at the entrance while they don't authenticate users continuously when in use, which brings serious privacy and security issues, such as collisions and social engineering to bypass the authentication. This paper introduces CDAS (Continuous Dynamic Authentication System), which uses the Support Vector Machine (SVM) to construct user's behavior model by collecting his touch data to judge him authorized whether or not. CDAS works independently in the background without interacting with users most time. Therefore, CDAS is featured with security, efficiency and continuity. We conducted a two-week experiment involving more than 20 users which shows that the system we design achieves a high accuracy, a low False Accept Rate (FAR) and a low False Reject Rate (FRR), which indicates that CDAS ensures the security and enjoys a promising prospect.
{"title":"CDAS","authors":"Qi Li, Hao Chen","doi":"10.1145/3316615.3316691","DOIUrl":"https://doi.org/10.1145/3316615.3316691","url":null,"abstract":"The rapid development of smartphones has greatly facilitated our lives. At the same time, securing the data stored and accessed from smartphones makes it important to authenticate the user. However, current smartphones perform one-time authentication at the entrance while they don't authenticate users continuously when in use, which brings serious privacy and security issues, such as collisions and social engineering to bypass the authentication. This paper introduces CDAS (Continuous Dynamic Authentication System), which uses the Support Vector Machine (SVM) to construct user's behavior model by collecting his touch data to judge him authorized whether or not. CDAS works independently in the background without interacting with users most time. Therefore, CDAS is featured with security, efficiency and continuity. We conducted a two-week experiment involving more than 20 users which shows that the system we design achieves a high accuracy, a low False Accept Rate (FAR) and a low False Reject Rate (FRR), which indicates that CDAS ensures the security and enjoys a promising prospect.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115610683","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}
Sharifah Mashita Syed-Mohamad, M. Husin, W. Zainon
Software testing is an essential activity in all software projects. The key issue in testing is determining the sufficiency of tests and, traditionally this has been done by using the Software Reliability Growth Models (SRGMs). However, SRGMs are not applicable when there are no stabilization phases as required by most software reliability models. Test-Defect Coverage Analytics Model (TDCAM) has been proposed to address this problem. This paper proposes the application of visual analytic techniques as an approach for supporting informed decision making in deciding the sufficiency of tests. Visual analytics research considers interactive visualization as the common platform for combining various computational data analysis techniques to support the analytical reasoning process. We presented four visual representations of TDCAM to demonstrate how analytical models have been applied to indicate the adequacy of tests in relation to sufficient and efficient test coverage. The techniques provide an effective and generally applicable test estimation on the basis of a general trend that higher test coverage correlates with higher probability of detecting more defects.
{"title":"Visualizing Test-Defect Coverage Information to Support Analytical Reasoning and Testing","authors":"Sharifah Mashita Syed-Mohamad, M. Husin, W. Zainon","doi":"10.1145/3316615.3316666","DOIUrl":"https://doi.org/10.1145/3316615.3316666","url":null,"abstract":"Software testing is an essential activity in all software projects. The key issue in testing is determining the sufficiency of tests and, traditionally this has been done by using the Software Reliability Growth Models (SRGMs). However, SRGMs are not applicable when there are no stabilization phases as required by most software reliability models. Test-Defect Coverage Analytics Model (TDCAM) has been proposed to address this problem. This paper proposes the application of visual analytic techniques as an approach for supporting informed decision making in deciding the sufficiency of tests. Visual analytics research considers interactive visualization as the common platform for combining various computational data analysis techniques to support the analytical reasoning process. We presented four visual representations of TDCAM to demonstrate how analytical models have been applied to indicate the adequacy of tests in relation to sufficient and efficient test coverage. The techniques provide an effective and generally applicable test estimation on the basis of a general trend that higher test coverage correlates with higher probability of detecting more defects.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117155740","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}
Internet of Things (IoT) attacks have rapidly risen in frequency in recent years as IoT devices become more commonplace in industry, businesses, and homes. Since these devices have very basic functionality and are not designed with security in mind, they are easy targets for attacks that can steal data or gain access to the network the devices are connected to. Here we propose a tiered system of Hidden Markov Models (HMMs) for identifying these attacks and classifying them by type of attack. This system has a tree-based structure, with the main HMM being applied to the raw network data to identify attacks. This main HMM branches off into separate HMMs for each type of attack to classify the attacks according to how important the consequences of the attack are and how likely each attack is to happen.
{"title":"Internet of Things Attacks Detection and Classification Using Tiered Hidden Markov Model","authors":"Ahmad Alshammari, M. Zohdy","doi":"10.1145/3316615.3316729","DOIUrl":"https://doi.org/10.1145/3316615.3316729","url":null,"abstract":"Internet of Things (IoT) attacks have rapidly risen in frequency in recent years as IoT devices become more commonplace in industry, businesses, and homes. Since these devices have very basic functionality and are not designed with security in mind, they are easy targets for attacks that can steal data or gain access to the network the devices are connected to. Here we propose a tiered system of Hidden Markov Models (HMMs) for identifying these attacks and classifying them by type of attack. This system has a tree-based structure, with the main HMM being applied to the raw network data to identify attacks. This main HMM branches off into separate HMMs for each type of attack to classify the attacks according to how important the consequences of the attack are and how likely each attack is to happen.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115992152","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}
Fatima Samea, F. Azam, Muhammad Waseem Anwar, Mehreen Khan, M. Rashid
The rapid evolution in cloud computing leads to a rising elegance of serverless cloud-based software architectures which primarily focuses on providing the software developers a great potential for executing different arbitrary functions having minor overhead in server management as FaaS (Function-as-a-service). These FaaS applications are a set of stateless functions that are triggered by events defined by the cloud provider. However, the service configuration of such event-driven serverless applications is a complex process. Particularly, the changing configuration requirements on multiple clouds create low-level implementation challenges. Therefore, this article introduces UMLPMSC (UML Profile for Multi-Cloud Service Configuration) for event driven serverless applications to model the service configuration design requirements at high abstraction level. This leads to transform the high-level source UMLPMSC models into low level serverless framework implementations for the service configuration. The applicability of the profile has been validated through two case studies for AWS and Azure serverless function providers. It has been concluded that UMLPMSC significantly simplifies the multi-cloud service configuration process for event-driven serverless applications.
{"title":"A UML Profile for Multi-Cloud Service Configuration (UMLPMSC) in Event-driven Serverless Applications","authors":"Fatima Samea, F. Azam, Muhammad Waseem Anwar, Mehreen Khan, M. Rashid","doi":"10.1145/3316615.3316636","DOIUrl":"https://doi.org/10.1145/3316615.3316636","url":null,"abstract":"The rapid evolution in cloud computing leads to a rising elegance of serverless cloud-based software architectures which primarily focuses on providing the software developers a great potential for executing different arbitrary functions having minor overhead in server management as FaaS (Function-as-a-service). These FaaS applications are a set of stateless functions that are triggered by events defined by the cloud provider. However, the service configuration of such event-driven serverless applications is a complex process. Particularly, the changing configuration requirements on multiple clouds create low-level implementation challenges. Therefore, this article introduces UMLPMSC (UML Profile for Multi-Cloud Service Configuration) for event driven serverless applications to model the service configuration design requirements at high abstraction level. This leads to transform the high-level source UMLPMSC models into low level serverless framework implementations for the service configuration. The applicability of the profile has been validated through two case studies for AWS and Azure serverless function providers. It has been concluded that UMLPMSC significantly simplifies the multi-cloud service configuration process for event-driven serverless applications.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358919","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}
The need for savings in ship fuel consumption has led to the proliferation of various cloud-based service-oriented approach towards predicting and optimizing ship operation. However, majority of the cloud-based services are generally designed for general purpose prediction where ship owners do not have the liberty to select and customize machine learning algorithms and parameters that they desire to experiment with for their specific datasets. In this paper, the feasibility of a novel Do-It-Yourself (DIY) approach towards performing predictive modeling and analytics of ship fuel consumption based on out-of-the-box cloud-based Azure Machine Learning (ML) Studio tool sets is demonstrated. The POC system implementing multiple regression model (MLR) model may provide insight into ship operational fuel consumption based on historical operational IoT data collected from ships operated under various operational parameters. The derived predictive model is validated with coefficient of determination, R2 for goodness of fit. The coefficient of determination, R2 result at 0.9707 indicates the good fitness of regression.
{"title":"Cloud-Based IoT Solution for Predictive Modeling of Ship Fuel Consumption","authors":"K. Kee, Simon Boung-Yew Lau","doi":"10.1145/3316615.3316710","DOIUrl":"https://doi.org/10.1145/3316615.3316710","url":null,"abstract":"The need for savings in ship fuel consumption has led to the proliferation of various cloud-based service-oriented approach towards predicting and optimizing ship operation. However, majority of the cloud-based services are generally designed for general purpose prediction where ship owners do not have the liberty to select and customize machine learning algorithms and parameters that they desire to experiment with for their specific datasets. In this paper, the feasibility of a novel Do-It-Yourself (DIY) approach towards performing predictive modeling and analytics of ship fuel consumption based on out-of-the-box cloud-based Azure Machine Learning (ML) Studio tool sets is demonstrated. The POC system implementing multiple regression model (MLR) model may provide insight into ship operational fuel consumption based on historical operational IoT data collected from ships operated under various operational parameters. The derived predictive model is validated with coefficient of determination, R2 for goodness of fit. The coefficient of determination, R2 result at 0.9707 indicates the good fitness of regression.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132229240","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}
The paper is devoted to analysis of legal issues concerned to development of AI technologies. The main question here: should governments develop rules regulating use of artificial intelligence and a system of licensing like with automobile transport or ban some types of AI? Comprehension of the current and future legal framework is very important. First of all, law is used to govern a society. It implies that examining AI from legal point of view allows to realize what challenges to social security are caused by expansive introduction of autonomous systems. Secondly, for developer of high technology products it is easier to decide what products should not be invested to since they may lead to negative legal consequences.
{"title":"Artificial Intelligence Legal Policy: Limits of Use of Some Kinds of AI","authors":"Roman Dremliuga, N. Prisekina","doi":"10.1145/3316615.3316627","DOIUrl":"https://doi.org/10.1145/3316615.3316627","url":null,"abstract":"The paper is devoted to analysis of legal issues concerned to development of AI technologies. The main question here: should governments develop rules regulating use of artificial intelligence and a system of licensing like with automobile transport or ban some types of AI? Comprehension of the current and future legal framework is very important. First of all, law is used to govern a society. It implies that examining AI from legal point of view allows to realize what challenges to social security are caused by expansive introduction of autonomous systems. Secondly, for developer of high technology products it is easier to decide what products should not be invested to since they may lead to negative legal consequences.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"10 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132576058","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}
UML model matching and retrieval is widely known as optimization problem. This is because of the inconsistencies between software properties. Matching is a fundamental operation for UML model reuse, as such accurate matching between models' elements results in better reuse of such models. UML models consist of number of properties such as functional properties, structural properties, and behavioral properties. Such properties are source of numerous errors during software matching, because each property represents software system from different views. In this paper we empirically investigate the use of different weight values when computing the similarity of software system from multiple views. The paper investigates the improvement of similarity values through the calibration of aggregated metrics. The result reported shows the superiority of structural properties if assign higher metric value compared to other properties.
{"title":"Empirical Investigation of UML Models Matching through Different Weight Calibration","authors":"Alhassan Adamu, W. Zainon, S. Abdulrahman","doi":"10.1145/3316615.3316618","DOIUrl":"https://doi.org/10.1145/3316615.3316618","url":null,"abstract":"UML model matching and retrieval is widely known as optimization problem. This is because of the inconsistencies between software properties. Matching is a fundamental operation for UML model reuse, as such accurate matching between models' elements results in better reuse of such models. UML models consist of number of properties such as functional properties, structural properties, and behavioral properties. Such properties are source of numerous errors during software matching, because each property represents software system from different views. In this paper we empirically investigate the use of different weight values when computing the similarity of software system from multiple views. The paper investigates the improvement of similarity values through the calibration of aggregated metrics. The result reported shows the superiority of structural properties if assign higher metric value compared to other properties.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127728676","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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