The automatic verification of power meters is of great significance, and the key point is the classification of the power meter types. In this paper, we propose a power meter type recognition method based on machine vision. We construct a Bag-of-Words model(BOW), and extract the image features of the instrument, and construct a visual dictionary, based on which to train a support vector machine classifier to realize the automatic identification of the instrument type. The experimental results show that the proposed method achieves a classifiaction rate of 100% for several specific power meters, and is of great significance for applications.
{"title":"Electric Power Meter Classification Based on BOW","authors":"W. Mo, Liqiang Pei, Qingdan Huang, Weijie Liao","doi":"10.1145/3316615.3316654","DOIUrl":"https://doi.org/10.1145/3316615.3316654","url":null,"abstract":"The automatic verification of power meters is of great significance, and the key point is the classification of the power meter types. In this paper, we propose a power meter type recognition method based on machine vision. We construct a Bag-of-Words model(BOW), and extract the image features of the instrument, and construct a visual dictionary, based on which to train a support vector machine classifier to realize the automatic identification of the instrument type. The experimental results show that the proposed method achieves a classifiaction rate of 100% for several specific power meters, and is of great significance for applications.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"153 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":"133300719","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}
Mehreen Khan, F. Azam, Muhammad Waseem Anwar, Fatima Samea, Mudassar Adeel Ahmed
With the paradigm shift from desktop to mobile applications and the growing demand for mobile devices has awakened the interest of the IT industry on how to tackle the development of mobile applications. Managing application state is hard in building modern mobile applications. As application complexity increases, it becomes increasingly difficult to keep the track of changing state and mapping those changes back to user interface. State management is challenging due to its low-level implementation complexity. To overcome above mentioned issue, there is a strong need to introduce model driven approach for state management in mobile applications. This paper proposes a Unified Modeling Language profile for Mobile Application State Management (UMASM) to simplify mobile application state management requirements. Particularly, UMASM is capable of representing mobile state management requirements at higher abstraction level. This provides the strong basis to automatically generate low level implementations in target platform like Redux from high level UMASM models. The applicability of UMASM is validated through e-banking application case study.
{"title":"A Model Driven Approach for State Management in Mobile Applications","authors":"Mehreen Khan, F. Azam, Muhammad Waseem Anwar, Fatima Samea, Mudassar Adeel Ahmed","doi":"10.1145/3316615.3316637","DOIUrl":"https://doi.org/10.1145/3316615.3316637","url":null,"abstract":"With the paradigm shift from desktop to mobile applications and the growing demand for mobile devices has awakened the interest of the IT industry on how to tackle the development of mobile applications. Managing application state is hard in building modern mobile applications. As application complexity increases, it becomes increasingly difficult to keep the track of changing state and mapping those changes back to user interface. State management is challenging due to its low-level implementation complexity. To overcome above mentioned issue, there is a strong need to introduce model driven approach for state management in mobile applications. This paper proposes a Unified Modeling Language profile for Mobile Application State Management (UMASM) to simplify mobile application state management requirements. Particularly, UMASM is capable of representing mobile state management requirements at higher abstraction level. This provides the strong basis to automatically generate low level implementations in target platform like Redux from high level UMASM models. The applicability of UMASM is validated through e-banking application case study.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"140 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":"133446886","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}
There is an increase in the number of studies being carried out on the acceptance of social media applications. Nonetheless, the identification of the factors affecting its acceptance in educational purposes is still neglected. Hence, the objective of this study is to develop a conceptual model through the extension of the Technology Acceptance Model (TAM) with perceived playfulness to measure the students' acceptance of social networks in education. A total of 320 valid questionnaire surveys were collected from the students enrolled at University of Fujairah in the United Arab of Emirates (UAE). The partial least squares-structural equation modeling (PLS-SEM) approach was used to analyze the collected data. The empirical results indicated that perceived playfulness, perceived usefulness, and perceived ease of use are significant indicators of students' intention to use social networks in education.
{"title":"Factors affecting the Social Networks Acceptance: An Empirical Study using PLS-SEM Approach","authors":"M. Alshurideh, S. Salloum, B. Kurdi, M. Al-Emran","doi":"10.1145/3316615.3316720","DOIUrl":"https://doi.org/10.1145/3316615.3316720","url":null,"abstract":"There is an increase in the number of studies being carried out on the acceptance of social media applications. Nonetheless, the identification of the factors affecting its acceptance in educational purposes is still neglected. Hence, the objective of this study is to develop a conceptual model through the extension of the Technology Acceptance Model (TAM) with perceived playfulness to measure the students' acceptance of social networks in education. A total of 320 valid questionnaire surveys were collected from the students enrolled at University of Fujairah in the United Arab of Emirates (UAE). The partial least squares-structural equation modeling (PLS-SEM) approach was used to analyze the collected data. The empirical results indicated that perceived playfulness, perceived usefulness, and perceived ease of use are significant indicators of students' intention to use social networks in education.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"17 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":"132143240","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 the field of oil and gas exploration, reservoir parameter prediction is often affected by multi-solution of the seismic attribute combination, which leads to low prediction accuracy. In this paper, a feature selection method based on Gamma test is proposed to optimize the attribute combination and then combine it with deep neural network to accomplish reservoir parameter prediction. By computing the value of the statistics, it not only provides the best combination of the corresponding attributes to predict the target but also provides the proper training mean square error of neural network and the proper size of the training set. With this guide, over-fitting can be effectively avoided and the prediction accuracy is improved. The selected seismic attributes combination is used as the optimized network input, then use extreme learning machine to accomplish the regression problem. Through the analysis of the real seismic data experimental results, it is proved that the Gamma test is an effective nonparametric tool for feature selection.
{"title":"Reservoir Parameter Prediction Using Optimized Seismic Attributes Based on Gamma Test","authors":"Ying Li, Guohe Li, Yifeng Zheng","doi":"10.1145/3316615.3316652","DOIUrl":"https://doi.org/10.1145/3316615.3316652","url":null,"abstract":"In the field of oil and gas exploration, reservoir parameter prediction is often affected by multi-solution of the seismic attribute combination, which leads to low prediction accuracy. In this paper, a feature selection method based on Gamma test is proposed to optimize the attribute combination and then combine it with deep neural network to accomplish reservoir parameter prediction. By computing the value of the statistics, it not only provides the best combination of the corresponding attributes to predict the target but also provides the proper training mean square error of neural network and the proper size of the training set. With this guide, over-fitting can be effectively avoided and the prediction accuracy is improved. The selected seismic attributes combination is used as the optimized network input, then use extreme learning machine to accomplish the regression problem. Through the analysis of the real seismic data experimental results, it is proved that the Gamma test is an effective nonparametric tool for feature selection.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"5 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":"128089666","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}
This paper proposes a collaborative filtering recommendation algorithm based on user characteristics and time weight which focuses on the data sparseness and cold start problems of collaborative filtering algorithms. First, digitize user's characteristics in the dataset and calculate the similarity degree of the user's feature, then weight the similarity calculation formula with the integration time function to obtain the comprehensive similarity so that a more accurate prediction score is obtained. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is extremely sparse, and to some extent, it alleviates the cold start problem and improves the prediction accuracy of the recommendation algorithm.
{"title":"Collaborative Filtering Algorithm Based on User Characteristic and Time Weight","authors":"Panpan Wang, Hong Hou, Xiaoqun Guo","doi":"10.1145/3316615.3316681","DOIUrl":"https://doi.org/10.1145/3316615.3316681","url":null,"abstract":"This paper proposes a collaborative filtering recommendation algorithm based on user characteristics and time weight which focuses on the data sparseness and cold start problems of collaborative filtering algorithms. First, digitize user's characteristics in the dataset and calculate the similarity degree of the user's feature, then weight the similarity calculation formula with the integration time function to obtain the comprehensive similarity so that a more accurate prediction score is obtained. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is extremely sparse, and to some extent, it alleviates the cold start problem and improves the prediction accuracy of the recommendation algorithm.","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":"132769464","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}
An important method of detecting zero-day attacks is to identify the shellcode which is usually taken as part of the attacks. It is vital to detect programs that have the characteristics of shellcode behavior in the network traffic detection. In this paper, a shellcode detection method named Shellfier based on Dynamic Binary Instrumentation and Convolutional Neural Network (CNN) is proposed. The method of program instrumentation can obtain the behavior characteristics of shellcode in fine-grained manner. The CNN algorithm trains and classifies the sample data, and compares the classification effect of Support Vector Machine (SVM) algorithm based on n-grams model to extract feature vectors. The experimental results show that CNN has strong representation ability for behavioral characteristics, which is more accurate than SVM classification, and the false positive rate and vulnerability rate are lower.
{"title":"Shellfier","authors":"Yue Pan, Jing An, Wenqing Fan, Wei Huang","doi":"10.1145/3316615.3316731","DOIUrl":"https://doi.org/10.1145/3316615.3316731","url":null,"abstract":"An important method of detecting zero-day attacks is to identify the shellcode which is usually taken as part of the attacks. It is vital to detect programs that have the characteristics of shellcode behavior in the network traffic detection. In this paper, a shellcode detection method named Shellfier based on Dynamic Binary Instrumentation and Convolutional Neural Network (CNN) is proposed. The method of program instrumentation can obtain the behavior characteristics of shellcode in fine-grained manner. The CNN algorithm trains and classifies the sample data, and compares the classification effect of Support Vector Machine (SVM) algorithm based on n-grams model to extract feature vectors. The experimental results show that CNN has strong representation ability for behavioral characteristics, which is more accurate than SVM classification, and the false positive rate and vulnerability rate are lower.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"200 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":"126068985","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}
Researches have pointed out that the colors have the function of conveying messages and is even easier than words to be memorized. The color image of a city, which means people connect with color through knowledge acquired and their life experiences. It includes landscapes, buildings, food, the city culture, local specialties and so on. Thus, building up the distinctive style of a city is an important part of the city image and its related applications. Nowadays, people mostly use three different applications in order to set up and correct the city color tickets. First, field research; second, residents participate in the comprehensive community development; third, particular projects. In this paper, we use data mining to analyze the connecting between people and the city color image. At the beginning, we select ten cities of Taiwan, which were elected by Taiwan Tourism Bureau as our research object. Secondly, we use Word to vector and Google searching engine to find the relevance between the city and adjectives. For the third step, the highest connection of adjective will be the keyword of Google searching engine to collect the pictures by doing cross-comparison of the results. Last but not least, we capture ten colors from city pictures and result in city color combinations. According to the result of this paper, although it needs to adjust in accordance with local culture, we still can find the regional pictures that correspond with the characteristics and also can obtain the city color combinations, which can be used as a reference of harmonious colors.
{"title":"Analyzing the Color Image of Taiwan Town by Using Data Mining","authors":"Yushan Su, Tzren-Ru Chou","doi":"10.1145/3316615.3316624","DOIUrl":"https://doi.org/10.1145/3316615.3316624","url":null,"abstract":"Researches have pointed out that the colors have the function of conveying messages and is even easier than words to be memorized. The color image of a city, which means people connect with color through knowledge acquired and their life experiences. It includes landscapes, buildings, food, the city culture, local specialties and so on. Thus, building up the distinctive style of a city is an important part of the city image and its related applications. Nowadays, people mostly use three different applications in order to set up and correct the city color tickets. First, field research; second, residents participate in the comprehensive community development; third, particular projects. In this paper, we use data mining to analyze the connecting between people and the city color image. At the beginning, we select ten cities of Taiwan, which were elected by Taiwan Tourism Bureau as our research object. Secondly, we use Word to vector and Google searching engine to find the relevance between the city and adjectives. For the third step, the highest connection of adjective will be the keyword of Google searching engine to collect the pictures by doing cross-comparison of the results. Last but not least, we capture ten colors from city pictures and result in city color combinations. According to the result of this paper, although it needs to adjust in accordance with local culture, we still can find the regional pictures that correspond with the characteristics and also can obtain the city color combinations, which can be used as a reference of harmonious colors.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"35 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":"124912501","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}
S. Abdulrahman, P. Brazdil, W. Zainon, Alhassan Adamu
The average ranking method (AR) is one of the simplest and effective algorithms selection methods. This method uses metadata in the form of test results of a given set of algorithms on a given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. In this paper we investigate the problem of how the rankings can be reduced by removing non-competitive and redundant algorithms, thereby reducing the number of tests a user needs to conduct on a new dataset to identify the most suitable algorithm. The method proposed involves two phases. In the first one, the aim is to identify the most competitive algorithms for each dataset used in the past. This is done with the recourse to a statistical test. The second phase involves a covering method whose aim is to reduce the algorithms by eliminating redundant variants. The proposed method differs from one earlier proposal in various aspects. One important one is that it takes both accuracy and time into consideration. The proposed method was compared to the baseline strategy which consists of executing all algorithms from the ranking. It is shown that the proposed method leads to much better performance than the baseline.
{"title":"Simplifying the Algorithm Selection Using Reduction of Rankings of Classification Algorithms","authors":"S. Abdulrahman, P. Brazdil, W. Zainon, Alhassan Adamu","doi":"10.1145/3316615.3316674","DOIUrl":"https://doi.org/10.1145/3316615.3316674","url":null,"abstract":"The average ranking method (AR) is one of the simplest and effective algorithms selection methods. This method uses metadata in the form of test results of a given set of algorithms on a given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. In this paper we investigate the problem of how the rankings can be reduced by removing non-competitive and redundant algorithms, thereby reducing the number of tests a user needs to conduct on a new dataset to identify the most suitable algorithm. The method proposed involves two phases. In the first one, the aim is to identify the most competitive algorithms for each dataset used in the past. This is done with the recourse to a statistical test. The second phase involves a covering method whose aim is to reduce the algorithms by eliminating redundant variants. The proposed method differs from one earlier proposal in various aspects. One important one is that it takes both accuracy and time into consideration. The proposed method was compared to the baseline strategy which consists of executing all algorithms from the ranking. It is shown that the proposed method leads to much better performance than the baseline.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"79 3-4 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":"124070383","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}
Nabil Almashfi, Lunjin Lu, Koby Picker, Christian Maldonado
Existing static analyzers for JavaScript use constant propagation domains to analyze strings. The simplicity of these domains results in a huge loss of precision when dealing with features such as dynamic property access. This paper presents a string analysis for the full JavaScript language based on abstract interpretation. The analysis uses finite state automata to track all possible strings a variable might hold during execution. We present an empirical performance and precision evaluation on some JavaScript benchmarks and show that the analysis achieves a higher level of precision especially when handling dynamic property access.
{"title":"Precise String Analysis for JavaScript Programs Using Automata","authors":"Nabil Almashfi, Lunjin Lu, Koby Picker, Christian Maldonado","doi":"10.1145/3316615.3316662","DOIUrl":"https://doi.org/10.1145/3316615.3316662","url":null,"abstract":"Existing static analyzers for JavaScript use constant propagation domains to analyze strings. The simplicity of these domains results in a huge loss of precision when dealing with features such as dynamic property access. This paper presents a string analysis for the full JavaScript language based on abstract interpretation. The analysis uses finite state automata to track all possible strings a variable might hold during execution. We present an empirical performance and precision evaluation on some JavaScript benchmarks and show that the analysis achieves a higher level of precision especially when handling dynamic property access.","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":"130103301","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}
Sinan Q. Salih, Abdulrahman A. Alsewari, Z. Yaseen
The new era knowledge of optimization algorithm is massively boosted recently. Among several optimization models, multi-swarm approach has been proposed most recently for balancing the exploration and exploitation capability through the Particle Swarm Optimization (PSO) algorithm. The proposed multi-swarm model which is called Meeting Room Approach (MRA), is tested and evaluated based on solving normal and large-scale problems. In the current research, the feasibility of the proposed Multi-Swarm Particle Swarm Optimization (MPSO) is adopted to simulate mechanical engineering problem namely pressure vessel design (PVD). The results indicated the potential of the proposed MPSO model on simulating the PVD problem with optimum solution over the standalone PSO. Further, the current study results authenticated against other famous meta-heuristics models. Overall, MPSO reported an excellent optimization solution with fast convergence learning process.
{"title":"Pressure Vessel Design Simulation: Implementing of Multi-Swarm Particle Swarm Optimization","authors":"Sinan Q. Salih, Abdulrahman A. Alsewari, Z. Yaseen","doi":"10.1145/3316615.3316643","DOIUrl":"https://doi.org/10.1145/3316615.3316643","url":null,"abstract":"The new era knowledge of optimization algorithm is massively boosted recently. Among several optimization models, multi-swarm approach has been proposed most recently for balancing the exploration and exploitation capability through the Particle Swarm Optimization (PSO) algorithm. The proposed multi-swarm model which is called Meeting Room Approach (MRA), is tested and evaluated based on solving normal and large-scale problems. In the current research, the feasibility of the proposed Multi-Swarm Particle Swarm Optimization (MPSO) is adopted to simulate mechanical engineering problem namely pressure vessel design (PVD). The results indicated the potential of the proposed MPSO model on simulating the PVD problem with optimum solution over the standalone PSO. Further, the current study results authenticated against other famous meta-heuristics models. Overall, MPSO reported an excellent optimization solution with fast convergence learning process.","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":"130316194","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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