Pub Date : 2017-06-09DOI: 10.1109/AICCSA.2016.7945613
C. Guegan, S. Hariri
AICCSA 2016 Conference, in collaboration with IEEE, continues its mission of covering all contemporary areas in computer systems and applications, and providing the latest results on theory, design, analysis, implementation, and application in these important and rapidly changing disciplines. Indeed, this edition addresses eleven areas and the technical program presents high-quality papers in: (1) IR, Big Data, Business Intelligence, & Knowledge Management; (2) Cloud, Parallel, Distributed, and High Performance Computing; (3) Multimedia, Computer Vision, and Image Processing; (4) Advances in Software Engineering; (5) Natural Language Processing and Applications; (6) Advances in AI, Agent Systems, and Ambient Intelligence; (7) Advances Networking and Sensor Networks; (8) Systems and Software Security; (9) Social and Mobile Computing; (10) Advances in Web Technologies, Semantics, and Future Internet; (11) Digital Living, including E-Learning, E-Government, E-Health. The breadth of the prominence of approaches in these areas is underlined by the topics of the four distinguished keynote speakers: Ahmed Elmagarmid and Jim Jansen, the Executive Director and senior research of Qatar Computing Research Institute (QCRI) respectively; Athman Bouguettaya, professor and the head of School of Computer Science and Information Technology at RMIT University; and Abdella Battou, the Division Chief of the Advanced Network Technologies Division, within The Information Technology Lab at National Institute of Standards and Technology (NIST).
{"title":"Foreword — Message from the general chairs","authors":"C. Guegan, S. Hariri","doi":"10.1109/AICCSA.2016.7945613","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945613","url":null,"abstract":"AICCSA 2016 Conference, in collaboration with IEEE, continues its mission of covering all contemporary areas in computer systems and applications, and providing the latest results on theory, design, analysis, implementation, and application in these important and rapidly changing disciplines. Indeed, this edition addresses eleven areas and the technical program presents high-quality papers in: (1) IR, Big Data, Business Intelligence, & Knowledge Management; (2) Cloud, Parallel, Distributed, and High Performance Computing; (3) Multimedia, Computer Vision, and Image Processing; (4) Advances in Software Engineering; (5) Natural Language Processing and Applications; (6) Advances in AI, Agent Systems, and Ambient Intelligence; (7) Advances Networking and Sensor Networks; (8) Systems and Software Security; (9) Social and Mobile Computing; (10) Advances in Web Technologies, Semantics, and Future Internet; (11) Digital Living, including E-Learning, E-Government, E-Health. The breadth of the prominence of approaches in these areas is underlined by the topics of the four distinguished keynote speakers: Ahmed Elmagarmid and Jim Jansen, the Executive Director and senior research of Qatar Computing Research Institute (QCRI) respectively; Athman Bouguettaya, professor and the head of School of Computer Science and Information Technology at RMIT University; and Abdella Battou, the Division Chief of the Advanced Network Technologies Division, within The Information Technology Lab at National Institute of Standards and Technology (NIST).","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"53 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114033729","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945650
Safaa Azzakhnini, Lahoucine Ballihi, D. Aboutajdine
Automatic gender recognition, from face images, plays an important role in various biometric applications. This task has attracted the interest of not only computer vision researchers, but also of many psychologists. Inspired by the psychological results for human gender perception. There are two main purposes for this work. First; it aims at finding out which facial parts are most effective at making the difference between men and women. Second; it tries to combine the decisions of these parts using a voting system to improve the recognition quality. Recently, with the appearance of depth sensing technology; especially the low cost devices such as the Microsoft kinect; high quality images containing color and depth information can easily be acquired. This gives us the opportunity to combine depth information with standard vision systems in order to offer a better recognition quality. In this paper, we propose an approach for classifying gender using RGB-D data based on the separation of facial parts. The experimental results show that the proposed approach improves the recognition accuracy for gender classification.
{"title":"Machine perception in gender recognition using RGB-D sensors","authors":"Safaa Azzakhnini, Lahoucine Ballihi, D. Aboutajdine","doi":"10.1109/AICCSA.2016.7945650","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945650","url":null,"abstract":"Automatic gender recognition, from face images, plays an important role in various biometric applications. This task has attracted the interest of not only computer vision researchers, but also of many psychologists. Inspired by the psychological results for human gender perception. There are two main purposes for this work. First; it aims at finding out which facial parts are most effective at making the difference between men and women. Second; it tries to combine the decisions of these parts using a voting system to improve the recognition quality. Recently, with the appearance of depth sensing technology; especially the low cost devices such as the Microsoft kinect; high quality images containing color and depth information can easily be acquired. This gives us the opportunity to combine depth information with standard vision systems in order to offer a better recognition quality. In this paper, we propose an approach for classifying gender using RGB-D data based on the separation of facial parts. The experimental results show that the proposed approach improves the recognition accuracy for gender classification.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115509229","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945682
Wiem Housseyni, O. Mosbahi, M. Khalgui, M. Chetto
This paper explores the energy-aware strategy for adaptive task allocation of sporadic task model in distributed reconfigurable embedded systems powered by a renewable energy source. It is challenging to ensure hard real-time deadlines of sporadic tasks in distributed reconfigurable embedded systems that entirely rely on energy harvesting with limited capacity storage because their release times and periods are unknown beforehand. In this regard, we propose a new scheduling algorithm for sporadic task model in energy harvesting reconfigurable distributed embedded system. The proposed approach generates an energy efficient offline task assignment heuristic. Then, the offline stage is dynamically extended online by applying a dynamic adaption algorithm. The latter performs three solutions: i) Migration, ii) Degradation of the execution mode, and iii) Removal of tasks in order to maintain schedulability and sustainability of the application software. Simulations show the efficiency of the proposed approach in term of deadline success ratio.
{"title":"Real-time scheduling of sporadic tasks in energy harvesting distributed reconfigurable embedded systems","authors":"Wiem Housseyni, O. Mosbahi, M. Khalgui, M. Chetto","doi":"10.1109/AICCSA.2016.7945682","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945682","url":null,"abstract":"This paper explores the energy-aware strategy for adaptive task allocation of sporadic task model in distributed reconfigurable embedded systems powered by a renewable energy source. It is challenging to ensure hard real-time deadlines of sporadic tasks in distributed reconfigurable embedded systems that entirely rely on energy harvesting with limited capacity storage because their release times and periods are unknown beforehand. In this regard, we propose a new scheduling algorithm for sporadic task model in energy harvesting reconfigurable distributed embedded system. The proposed approach generates an energy efficient offline task assignment heuristic. Then, the offline stage is dynamically extended online by applying a dynamic adaption algorithm. The latter performs three solutions: i) Migration, ii) Degradation of the execution mode, and iii) Removal of tasks in order to maintain schedulability and sustainability of the application software. Simulations show the efficiency of the proposed approach in term of deadline success ratio.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122171104","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945631
Amr Elmasry, Ayman Khalafallah, Moustafa Meshry
In this paper, we propose a scalable algorithm for finding the exact solution to the maximum-clique problem. At the heart of our approach lies a multi-phase partitioning strategy, which enables iterative, in-memory processing of graphs. The multi-phase partitioning is tuned for the resources of the machine/cluster to get the best performance. To promote parallelization and scalability on both a cluster-level (distributing the problem on a number of machines) and on a machine-level (using all available cores on each machine), we use Apache Spark. We explore the untraditional usage of distributed frameworks, such as Apache Spark, to distribute computational load, as opposed to distributing big data. We focus on dense graphs, typically with thousands of vertices and a few millions edges; this is in contrast to sparse real-world graphs that don't initially fit into the memory of a single driver machine. Our experiments show that, for large dense graphs, we get up to 100% performance speedup compared to the state-of-the-art parallel approaches. Moreover, our algorithm is highly scalable and fault-tolerant.
{"title":"A scalable maximum-clique algorithm using Apache Spark","authors":"Amr Elmasry, Ayman Khalafallah, Moustafa Meshry","doi":"10.1109/AICCSA.2016.7945631","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945631","url":null,"abstract":"In this paper, we propose a scalable algorithm for finding the exact solution to the maximum-clique problem. At the heart of our approach lies a multi-phase partitioning strategy, which enables iterative, in-memory processing of graphs. The multi-phase partitioning is tuned for the resources of the machine/cluster to get the best performance. To promote parallelization and scalability on both a cluster-level (distributing the problem on a number of machines) and on a machine-level (using all available cores on each machine), we use Apache Spark. We explore the untraditional usage of distributed frameworks, such as Apache Spark, to distribute computational load, as opposed to distributing big data. We focus on dense graphs, typically with thousands of vertices and a few millions edges; this is in contrast to sparse real-world graphs that don't initially fit into the memory of a single driver machine. Our experiments show that, for large dense graphs, we get up to 100% performance speedup compared to the state-of-the-art parallel approaches. Moreover, our algorithm is highly scalable and fault-tolerant.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129947890","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945819
Ali Salem, H. Ben-Abdallah
Similar to any software system, the success of the development of a decisional information system depends on the quality of its specification, often modeled as star schemes. In this paper, we present a new approach assisted by Prolog in order to verify the conformity of multidimensional star schemas with its source schema and to check summarizability constraint. In addidition, we propose a tool to assist decision makers to design valid schemas. Our tool presents a new idea to check summarizability constraints and to generate a warning messages to inform the decision makers about quality of results of the future decisional system.
{"title":"Data warehousing design: Automatic verification of conformity and summarizability constraints","authors":"Ali Salem, H. Ben-Abdallah","doi":"10.1109/AICCSA.2016.7945819","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945819","url":null,"abstract":"Similar to any software system, the success of the development of a decisional information system depends on the quality of its specification, often modeled as star schemes. In this paper, we present a new approach assisted by Prolog in order to verify the conformity of multidimensional star schemas with its source schema and to check summarizability constraint. In addidition, we propose a tool to assist decision makers to design valid schemas. Our tool presents a new idea to check summarizability constraints and to generate a warning messages to inform the decision makers about quality of results of the future decisional system.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128284280","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945752
Molka Dhieb, M. Frikha
Thresholding is a popular image segmentation method that converts gray-level image into binary image. The problem of thresholding has been quite extensively studied for many years in order to get an optimum threshold value. The multi-level thresholding becomes very computationally challenges. In this paper, a novel multilevel thresholding method based on particle swarm optimization (PSO) algorithm is proposed, or it seems to be the best tool, to maximize the Kapur and Otsu objective functions. We employed the properties of discriminate analysis using Kapur and Otsu methods to render the optimal thresholding technics more applicable and effective. The obtained result and the comparative study illustrate the algorithm's outstanding performances in segmenting both the grey level image and the MRI scans.
{"title":"A multilevel thresholding algorithm for image segmentation based on particle swarm optimization","authors":"Molka Dhieb, M. Frikha","doi":"10.1109/AICCSA.2016.7945752","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945752","url":null,"abstract":"Thresholding is a popular image segmentation method that converts gray-level image into binary image. The problem of thresholding has been quite extensively studied for many years in order to get an optimum threshold value. The multi-level thresholding becomes very computationally challenges. In this paper, a novel multilevel thresholding method based on particle swarm optimization (PSO) algorithm is proposed, or it seems to be the best tool, to maximize the Kapur and Otsu objective functions. We employed the properties of discriminate analysis using Kapur and Otsu methods to render the optimal thresholding technics more applicable and effective. The obtained result and the comparative study illustrate the algorithm's outstanding performances in segmenting both the grey level image and the MRI scans.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128209898","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945825
Fadoua Nejjari, Laila Benhlima, Slimane Bah
Deaths due to traffic accidents is a major problem in several countries principally because of the fact that emergency actions are not provided at the place of accident at required time, Basically, in the event of a road accident, a real time traffic accident detection system can intelligently inform the site of accident, reporting it to the proper authorities, informing about the number of injured people so that the type and the amount of emergency services needed can be provided. This paper aims to introduce an approach to the traffic event detection, especially in highways, by the means of heterogeneous WSNs (using unmanned aerial vehicles called UAVs, vehicular sensors, smartphones and ground WSNs). The work includes a system overview and the proposed event detection approach.
{"title":"Event traffic detection using heterogenous wireless sensors network","authors":"Fadoua Nejjari, Laila Benhlima, Slimane Bah","doi":"10.1109/AICCSA.2016.7945825","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945825","url":null,"abstract":"Deaths due to traffic accidents is a major problem in several countries principally because of the fact that emergency actions are not provided at the place of accident at required time, Basically, in the event of a road accident, a real time traffic accident detection system can intelligently inform the site of accident, reporting it to the proper authorities, informing about the number of injured people so that the type and the amount of emergency services needed can be provided. This paper aims to introduce an approach to the traffic event detection, especially in highways, by the means of heterogeneous WSNs (using unmanned aerial vehicles called UAVs, vehicular sensors, smartphones and ground WSNs). The work includes a system overview and the proposed event detection approach.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128953937","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945715
Hui Li, Weihao Xie, Xingang Wang, Shousheng Liu, Y. Gai, Lei Yang
Multi-scale Retinex algorithm is an image enhancement algorithm that aims at image reconstruction. The algorithm maintains the high fidelity and the dynamic range compression of the image, so the enhancement effect is obvious. The algorithm exploits a large number of convolution operations to achieve dynamic range compression and color/brightness rendition, and the calculation time increased significantly with the increase of the image resolution. In order to improve the real-time performance of the algorithm, a multi-scale Retinex image enhancement algorithm based on GPU CUDA is proposed in this paper. Through the data mining and parallel analysis of the algorithm, time-consuming modules of the calculation, such as Gauss filter, convolution, logarithm difference, are implemented in GPU by exploiting the massively parallel threading and heterogeneous memory hierarchy of GPGPU to improve efficiency. The experimental results show that the algorithm can improve the computing speed significantly in NVIDIA Tesla K20 and CUDA7.5, and with the increase of image resolution, the maximum speedup can reach 202 times.
多尺度Retinex算法是一种以图像重建为目标的图像增强算法。该算法保持了图像的高保真度和动态范围压缩，增强效果明显。该算法利用大量的卷积运算来实现动态范围压缩和色彩/亮度还原，计算时间随着图像分辨率的增加而显著增加。为了提高算法的实时性，本文提出了一种基于GPU CUDA的多尺度Retinex图像增强算法。通过对该算法的数据挖掘和并行分析，利用GPGPU的大规模并行线程和异构内存层次，在GPU上实现高斯滤波、卷积、对数差分等耗时的计算模块，提高计算效率。实验结果表明，该算法在NVIDIA Tesla K20和CUDA7.5上可以显著提高计算速度，随着图像分辨率的提高，最大加速可达到202倍。
{"title":"GPU implementation of multi-scale Retinex image enhancement algorithm","authors":"Hui Li, Weihao Xie, Xingang Wang, Shousheng Liu, Y. Gai, Lei Yang","doi":"10.1109/AICCSA.2016.7945715","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945715","url":null,"abstract":"Multi-scale Retinex algorithm is an image enhancement algorithm that aims at image reconstruction. The algorithm maintains the high fidelity and the dynamic range compression of the image, so the enhancement effect is obvious. The algorithm exploits a large number of convolution operations to achieve dynamic range compression and color/brightness rendition, and the calculation time increased significantly with the increase of the image resolution. In order to improve the real-time performance of the algorithm, a multi-scale Retinex image enhancement algorithm based on GPU CUDA is proposed in this paper. Through the data mining and parallel analysis of the algorithm, time-consuming modules of the calculation, such as Gauss filter, convolution, logarithm difference, are implemented in GPU by exploiting the massively parallel threading and heterogeneous memory hierarchy of GPGPU to improve efficiency. The experimental results show that the algorithm can improve the computing speed significantly in NVIDIA Tesla K20 and CUDA7.5, and with the increase of image resolution, the maximum speedup can reach 202 times.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129863997","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945743
El Moatez Billah Nagoudi, A. Khorsi, H. Cherroun
Text search is the basis of countless applications and techniques. It is constrained by space and time resource limitations inherent in different contexts and scenarios. A common approach to minimize the cost of the general search task is to start by the characteristics which are particular to the targeted entity. In this paper, we propose an approximative index-based text searching algorithm that performances can be customized respect to both time/memory user constraints. The main idea is to exploit the uneven distribution of frequencies of letters and n-grams in natural language text, to reduce the index size and the search time, where we store only the less frequent letters and n-grams. Moreover, our technique can also provide to the user the flexibility to choose the tradeoff between index size and query performance. Search time and the index size can be balanced by varying three parameters in our approach. This makes our approach flexible to different settings. The tests described in this paper are driven on an Arabic collection of more than 450 documents and more than 20 million words. Experimental results show that the size of our n-gram inverted index is reduced by up to 40%–85% (with tolerable performance penalties) compared with those of the full n-gram inverted index. Generalization to other languages should be straightforward as long as the underlying statistical property applies.
{"title":"Efficient inverted index with n-gram sampling for string matching in Arabic documents","authors":"El Moatez Billah Nagoudi, A. Khorsi, H. Cherroun","doi":"10.1109/AICCSA.2016.7945743","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945743","url":null,"abstract":"Text search is the basis of countless applications and techniques. It is constrained by space and time resource limitations inherent in different contexts and scenarios. A common approach to minimize the cost of the general search task is to start by the characteristics which are particular to the targeted entity. In this paper, we propose an approximative index-based text searching algorithm that performances can be customized respect to both time/memory user constraints. The main idea is to exploit the uneven distribution of frequencies of letters and n-grams in natural language text, to reduce the index size and the search time, where we store only the less frequent letters and n-grams. Moreover, our technique can also provide to the user the flexibility to choose the tradeoff between index size and query performance. Search time and the index size can be balanced by varying three parameters in our approach. This makes our approach flexible to different settings. The tests described in this paper are driven on an Arabic collection of more than 450 documents and more than 20 million words. Experimental results show that the size of our n-gram inverted index is reduced by up to 40%–85% (with tolerable performance penalties) compared with those of the full n-gram inverted index. Generalization to other languages should be straightforward as long as the underlying statistical property applies.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130532700","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945717
Houssem Lahiani, M. Kherallah, M. Neji
This paper focuses on hand pose estimation by proposing a system that solves real-time static hand gesture detection and recognition issues for interacting with smartphones. The first step of our work consists in detecting and tracking the hand in a complex background. The second step consists in recognizing hand gestures using SVM “Support Vector Machine”. Finally, we developed a grammar to generate gesture commands for mobile applications control. This work presents a system based on a real-time hand gesture recognition algorithm for Android devices. The idea here is to allow the user interacting with the mobile device without the need to touch the screen. In this system, the Smartphone is able to perform all necessary steps to recognize gestures without the need to connect to a distant device.
{"title":"Hand pose estimation system based on Viola-Jones algorithm for Android devices","authors":"Houssem Lahiani, M. Kherallah, M. Neji","doi":"10.1109/AICCSA.2016.7945717","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945717","url":null,"abstract":"This paper focuses on hand pose estimation by proposing a system that solves real-time static hand gesture detection and recognition issues for interacting with smartphones. The first step of our work consists in detecting and tracking the hand in a complex background. The second step consists in recognizing hand gestures using SVM “Support Vector Machine”. Finally, we developed a grammar to generate gesture commands for mobile applications control. This work presents a system based on a real-time hand gesture recognition algorithm for Android devices. The idea here is to allow the user interacting with the mobile device without the need to touch the screen. In this system, the Smartphone is able to perform all necessary steps to recognize gestures without the need to connect to a distant device.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126894443","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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