In recent years, as network information technology has increasingly advanced, there is a growing trend of people using electronic devices. With the rise of social network services (SNSs), people are using SNSs more frequently, SNSs have gradually replaced many traditional methods of contacting, such as sending e-mails, typing text messages, or chatting on the phone. However, current commercially-available SNSs pair strangers randomly and they are unable to conduct further understandings on particular subjects. In order to solve this problem, this study introduces the small-world phenomenon and the concept of network density to implement "Want You" SNS, this system can calculate on how many people it takes to get to know the stranger one would like to know, and presents the calculated results to the user. Moreover, our questionnaire administered to participants of the study found that Want You is a fairly useful SNS for the public, as it allows users to see the correlations between strangers in social networks and increases the success rate of meeting strangers through correlations to expand interpersonal relationships.
{"title":"Want You: A Novel Social Network Service","authors":"Chen-Yi Lin, W. Lai, Wan-Tian Fu, Yun-Sheng Chen, Yuan-Zhen Wang, Kuan-Cheng Jian","doi":"10.1109/ICS.2016.0074","DOIUrl":"https://doi.org/10.1109/ICS.2016.0074","url":null,"abstract":"In recent years, as network information technology has increasingly advanced, there is a growing trend of people using electronic devices. With the rise of social network services (SNSs), people are using SNSs more frequently, SNSs have gradually replaced many traditional methods of contacting, such as sending e-mails, typing text messages, or chatting on the phone. However, current commercially-available SNSs pair strangers randomly and they are unable to conduct further understandings on particular subjects. In order to solve this problem, this study introduces the small-world phenomenon and the concept of network density to implement \"Want You\" SNS, this system can calculate on how many people it takes to get to know the stranger one would like to know, and presents the calculated results to the user. Moreover, our questionnaire administered to participants of the study found that Want You is a fairly useful SNS for the public, as it allows users to see the correlations between strangers in social networks and increases the success rate of meeting strangers through correlations to expand interpersonal relationships.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127764922","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 diameter of a graph is the maximum distance among all pairs of nodes. Determining the diameter of a graph in the tradition way costs O(mn) time, where n is the number of nodes and m is the number of edges. A social network can be modelled as a graph. With the rapid expansion of social networks, the number of nodes in a social network could be hundreds of millions. In this paper, we propose a new approach for computing the diameters of large undirected unweighted graphs. The worst case time complexity is still O(mn). In practice, especially for social network graphs, the running time is O(m). Our approach is based on BFS to select a proper node as the starting node of a BFS process is the most important issue when computing the diameter. We show how to choose the good nodes with small cost.
{"title":"Computing the Diameters of Huge Social Networks","authors":"Ting-Chun Lin, Mei-Jin Wu, Wei-Jie Chen, B. Wu","doi":"10.1109/ICS.2016.0011","DOIUrl":"https://doi.org/10.1109/ICS.2016.0011","url":null,"abstract":"The diameter of a graph is the maximum distance among all pairs of nodes. Determining the diameter of a graph in the tradition way costs O(mn) time, where n is the number of nodes and m is the number of edges. A social network can be modelled as a graph. With the rapid expansion of social networks, the number of nodes in a social network could be hundreds of millions. In this paper, we propose a new approach for computing the diameters of large undirected unweighted graphs. The worst case time complexity is still O(mn). In practice, especially for social network graphs, the running time is O(m). Our approach is based on BFS to select a proper node as the starting node of a BFS process is the most important issue when computing the diameter. We show how to choose the good nodes with small cost.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443182","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}
Shang-En Yang, Hung-Yuan Chen, Vorakit Vorakitphan, Yao-Chung Fan
In this paper, we present a heuristic for labeling a given term a taxonomy label. Specifically, for a given term, our goal is to construct a model for determining an "is-a" relationship between the given term and an inferred concept. Such term-labelling problem is not new, but the existing solutions require semi-supervised training processing, e.g., supervised LDA, or rely on lexicographers, e.g., wordnet. The model construction cost becomes burdens for employing such semantic understanding capability in various emerging applications. Aiming at these issues, in this study, we present a lightweight approach with the following features. First, the proposed approach is unsupervised and take only pain text as inputs. Second, the proposed approach allows incremental model construction. Third, the proposed approach is simple but effective and computationally efficient in comparison with the existing solutions. We demonstrate these results through experiments by comparing our approach with DBpedia and employ the popular search terms as test set. From experiment results, we see that 30 percent improvement in accuracy can be achieved by the proposed approach.
{"title":"Learning Term Taxonomy Relationship from a Large Collection of Plain Text","authors":"Shang-En Yang, Hung-Yuan Chen, Vorakit Vorakitphan, Yao-Chung Fan","doi":"10.1109/ICS.2016.0061","DOIUrl":"https://doi.org/10.1109/ICS.2016.0061","url":null,"abstract":"In this paper, we present a heuristic for labeling a given term a taxonomy label. Specifically, for a given term, our goal is to construct a model for determining an \"is-a\" relationship between the given term and an inferred concept. Such term-labelling problem is not new, but the existing solutions require semi-supervised training processing, e.g., supervised LDA, or rely on lexicographers, e.g., wordnet. The model construction cost becomes burdens for employing such semantic understanding capability in various emerging applications. Aiming at these issues, in this study, we present a lightweight approach with the following features. First, the proposed approach is unsupervised and take only pain text as inputs. Second, the proposed approach allows incremental model construction. Third, the proposed approach is simple but effective and computationally efficient in comparison with the existing solutions. We demonstrate these results through experiments by comparing our approach with DBpedia and employ the popular search terms as test set. From experiment results, we see that 30 percent improvement in accuracy can be achieved by the proposed approach.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129210785","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 real-time hand finger motion capturing method using Kinect. It consists of three modules: hand region segmentation, feature points extraction, and joint angle estimation. The first module extracts the hand region from the depth image. The second module applies a pixel classifier to segment the hand region into eight characteristic sub-regions and the residual sub-region. The centroid of each characteristic sub-region is extracted as the feature point. The third module converts these feature points to the feature vector for finger joint angle estimation by using the regression forest. The estimation process has both the speed and precision advantages and it can also deal with the hand finger motion parameter of novel hand gesture. The experimental results show that our method can capture the hand finger motion parameters of global in-plane hand rotation with sufficient estimation accuracy.
{"title":"Real-Time Hand Finger Motion Capturing Using Regression Forest","authors":"Pei-Chi Hsieh, Shih-Chung Hsu, Chung-Lin Huang","doi":"10.1109/ICS.2016.0091","DOIUrl":"https://doi.org/10.1109/ICS.2016.0091","url":null,"abstract":"This paper proposes a real-time hand finger motion capturing method using Kinect. It consists of three modules: hand region segmentation, feature points extraction, and joint angle estimation. The first module extracts the hand region from the depth image. The second module applies a pixel classifier to segment the hand region into eight characteristic sub-regions and the residual sub-region. The centroid of each characteristic sub-region is extracted as the feature point. The third module converts these feature points to the feature vector for finger joint angle estimation by using the regression forest. The estimation process has both the speed and precision advantages and it can also deal with the hand finger motion parameter of novel hand gesture. The experimental results show that our method can capture the hand finger motion parameters of global in-plane hand rotation with sufficient estimation accuracy.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122595650","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}
Cloud computing has achieved great development both in academic and industry communities due to the fact that it relieves the burden of data storage and data management. However, data stored in the cloud may suffer from malicious use by cloud service providers. Therefore, exploring an efficient search technique for encrypted data is extremely urgent. This paper focuses on semantic search and preferred search schemes in cloud. We survey the existed schemes' advantage or disadvantage. At last, we give some open issues and research challenges in the future.
{"title":"Semantic Search and Preferred Search Survey in Cloud Computing Environment","authors":"Liu Yang, Lili Xia","doi":"10.1109/ICS.2016.0137","DOIUrl":"https://doi.org/10.1109/ICS.2016.0137","url":null,"abstract":"Cloud computing has achieved great development both in academic and industry communities due to the fact that it relieves the burden of data storage and data management. However, data stored in the cloud may suffer from malicious use by cloud service providers. Therefore, exploring an efficient search technique for encrypted data is extremely urgent. This paper focuses on semantic search and preferred search schemes in cloud. We survey the existed schemes' advantage or disadvantage. At last, we give some open issues and research challenges in the future.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117303675","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}
B. Wong, J. Hsieh, Chia-Jen Hsiao, S. Chien, Feng-Chia Chang
This paper proposes a novel Shift with Importance Sampling (SIS) scheme to improve the efficiency in DPM-based object detection but maintain its high accuracy. For fast and efficient object detection, the cascade-boosting structure is the commonly-used approach in the literature. However, its detection performance is quite lower due to non-robust features and a fully-scanning on image especially when deformable part models are adopted. Another powerful method "deformable part model" is commonly adopted to deal with the above problem. However, its full combinations of parts to represent an object make its inefficiency in the scanning process which needs to check all possible object positions. The proposed SIS scheme can avoid many redundant positions and thus significantly improve the efficiency of the DPM scheme up to a time order. Firstly, various interest points are first detected and then clustered via the ISO-data clustering scheme to produce potential candidates. Since each candidate will not exactly locate in the center of the detected target, it will be shifted according to the weights of its eight neighborhoods. The importance of each neighbor is scored by the DPM-classifier. Once it is shifted, the size of search window to find its positions will be narrowed to only quarter. Thus, the proposed SIS scanning scheme can quickly find the correct location of each pedestrian with minimum tries and tests. After analysis, the time complexity of scanning is reduced from O(n2) to O(logn), where the frame dimension is n×n. After that, the particle filter is adopted to track targets if they are missed. Experimental results show the superiority of our SIS method in pedestrian detection (evaluated on different famous datasets).
{"title":"Efficient DPM-Based Object Detection Using Shift with Importance Sampling","authors":"B. Wong, J. Hsieh, Chia-Jen Hsiao, S. Chien, Feng-Chia Chang","doi":"10.1109/ICS.2016.0075","DOIUrl":"https://doi.org/10.1109/ICS.2016.0075","url":null,"abstract":"This paper proposes a novel Shift with Importance Sampling (SIS) scheme to improve the efficiency in DPM-based object detection but maintain its high accuracy. For fast and efficient object detection, the cascade-boosting structure is the commonly-used approach in the literature. However, its detection performance is quite lower due to non-robust features and a fully-scanning on image especially when deformable part models are adopted. Another powerful method \"deformable part model\" is commonly adopted to deal with the above problem. However, its full combinations of parts to represent an object make its inefficiency in the scanning process which needs to check all possible object positions. The proposed SIS scheme can avoid many redundant positions and thus significantly improve the efficiency of the DPM scheme up to a time order. Firstly, various interest points are first detected and then clustered via the ISO-data clustering scheme to produce potential candidates. Since each candidate will not exactly locate in the center of the detected target, it will be shifted according to the weights of its eight neighborhoods. The importance of each neighbor is scored by the DPM-classifier. Once it is shifted, the size of search window to find its positions will be narrowed to only quarter. Thus, the proposed SIS scanning scheme can quickly find the correct location of each pedestrian with minimum tries and tests. After analysis, the time complexity of scanning is reduced from O(n2) to O(logn), where the frame dimension is n×n. After that, the particle filter is adopted to track targets if they are missed. Experimental results show the superiority of our SIS method in pedestrian detection (evaluated on different famous datasets).","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"105 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763944","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}
Traffic prediction systems are currently the most important techniques, as they can be wildly applied in different aspects. Given a set of past traffic data, a traffic prediction system is able to predict the future traffic conditions. However, the existing traffic prediction systems are hard to implement and are quite expensive. Hence, this work proposed a Matlab-based traffic prediction system, which can be easily implemented by Matlab and operated on the internet. That is, the problem of the existing traffic prediction systems can be solved.
{"title":"A Matlab-Based Traffic Prediction System","authors":"Yi-Chung Chen, Yin-Wei Lin, Ming Lu, Yuanhai Wang","doi":"10.1109/ICS.2016.0065","DOIUrl":"https://doi.org/10.1109/ICS.2016.0065","url":null,"abstract":"Traffic prediction systems are currently the most important techniques, as they can be wildly applied in different aspects. Given a set of past traffic data, a traffic prediction system is able to predict the future traffic conditions. However, the existing traffic prediction systems are hard to implement and are quite expensive. Hence, this work proposed a Matlab-based traffic prediction system, which can be easily implemented by Matlab and operated on the internet. That is, the problem of the existing traffic prediction systems can be solved.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128373789","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 number of vehicles increasing rapidly, road traffic congestion in urban areas becomes a significant issue. In this paper, a dynamically restricted road segments protocol is proposed for quickly evacuating vehicles from the area and prohibiting vehicles from entering into the area where traffic congestion happens. The trajectory information maintained in the server assists determining the size of restricted area. The server determines the restricted area where the event occurs and then checks the position of all cars to adjust the size of restricted area. If vehicles are in the restricted area, the sever uses car-flow scheme to let the vehicles leave the restricted area. The other vehicles which are out of the restricted area obtain a path to avoid the area. The simulation results demonstrate that the work, on average, could reduce 10% travel time to evacuate vehicles, compared to the shortest path algorithm.
{"title":"A Dynamically Adjusted Vehicles Navigation Scheme with Real-Time Traffic Information to Relieve Regional Traffic Congestion in Vehicular Ad-Hoc Networks","authors":"Hung-Jen Pan, K. Ssu, En-Wei Chang, Yu-Yuan Lin","doi":"10.1109/ICS.2016.0103","DOIUrl":"https://doi.org/10.1109/ICS.2016.0103","url":null,"abstract":"Nowadays, with the number of vehicles increasing rapidly, road traffic congestion in urban areas becomes a significant issue. In this paper, a dynamically restricted road segments protocol is proposed for quickly evacuating vehicles from the area and prohibiting vehicles from entering into the area where traffic congestion happens. The trajectory information maintained in the server assists determining the size of restricted area. The server determines the restricted area where the event occurs and then checks the position of all cars to adjust the size of restricted area. If vehicles are in the restricted area, the sever uses car-flow scheme to let the vehicles leave the restricted area. The other vehicles which are out of the restricted area obtain a path to avoid the area. The simulation results demonstrate that the work, on average, could reduce 10% travel time to evacuate vehicles, compared to the shortest path algorithm.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114224908","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 popularity of mobile devices and the opportunity to learn regardless of time and place make mobile learning an important method in lifelong learning. However, acceptance of mobile learning by learners is crucial to the success of mobile learning. The objective of this study is to investigate main determinants of mobile learning acceptance in higher education in China. Based on the unified theory of acceptance and use of technology, this study proposed a hypothesized model of m-learning acceptance. Employing a stepwise multiple regression analysis, the model was assessed based on the data collected from 186 participants at Beijing Normal University using a survey questionnaire. Results indicate that achievement value, effort expectancy, social influence and performance expectancy have significant influence on the behavioral intention to use mobile learning. Both theoretical and practical implications are discussed.
{"title":"Investigating the Determinants of Mobile Learning Acceptance in Higher Education Based on UTAUT","authors":"Aofan Lu, Qian Chen, Yan Zhang, T. Chang","doi":"10.1109/ICS.2016.0133","DOIUrl":"https://doi.org/10.1109/ICS.2016.0133","url":null,"abstract":"The popularity of mobile devices and the opportunity to learn regardless of time and place make mobile learning an important method in lifelong learning. However, acceptance of mobile learning by learners is crucial to the success of mobile learning. The objective of this study is to investigate main determinants of mobile learning acceptance in higher education in China. Based on the unified theory of acceptance and use of technology, this study proposed a hypothesized model of m-learning acceptance. Employing a stepwise multiple regression analysis, the model was assessed based on the data collected from 186 participants at Beijing Normal University using a survey questionnaire. Results indicate that achievement value, effort expectancy, social influence and performance expectancy have significant influence on the behavioral intention to use mobile learning. Both theoretical and practical implications are discussed.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114473530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a performance study of a five-axis iterative learning control program. Several approaches are proposed to enhance the performance. Eliminating redundant computation, parallelizing programs using OpenMP, and loop-invariant code motion are used to enhance the performance. For a single learning iteration with 5021 positions and using compiler option "-O2", the experimental results show that the execution time of the optimized program has a 4.84 times speedup compared to the non-optimized version.
{"title":"Improving the Performance of an Iterative Learning Control Program","authors":"Kai-Lun Huang, Peng Chen","doi":"10.1109/ICS.2016.0129","DOIUrl":"https://doi.org/10.1109/ICS.2016.0129","url":null,"abstract":"In this paper, we present a performance study of a five-axis iterative learning control program. Several approaches are proposed to enhance the performance. Eliminating redundant computation, parallelizing programs using OpenMP, and loop-invariant code motion are used to enhance the performance. For a single learning iteration with 5021 positions and using compiler option \"-O2\", the experimental results show that the execution time of the optimized program has a 4.84 times speedup compared to the non-optimized version.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124391092","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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