Simulations of molecular dynamics play an important role in computational chemistry and physics. Such simulations require accurate information about the state and properties of interacting systems. The computation of water cluster potential energy surface is a complex and computationally expensive operation. Therefore, machine learning methods such as Artificial Neural Networks have been recently employed to machine-learn and further approximate clusters potential energy surfaces. This works presents the application of another highly successful machine learning method, the Support Vector Regression, for the modeling and approximation of the potential energy of water clusters as representatives of more general molecular clusters.
{"title":"Towards the Modeling of Atomic and Molecular Clusters Energy by Support Vector Regression","authors":"A. Vítek, Martin Stachon, P. Krömer, V. Snás̃el","doi":"10.1109/INCOS.2013.26","DOIUrl":"https://doi.org/10.1109/INCOS.2013.26","url":null,"abstract":"Simulations of molecular dynamics play an important role in computational chemistry and physics. Such simulations require accurate information about the state and properties of interacting systems. The computation of water cluster potential energy surface is a complex and computationally expensive operation. Therefore, machine learning methods such as Artificial Neural Networks have been recently employed to machine-learn and further approximate clusters potential energy surfaces. This works presents the application of another highly successful machine learning method, the Support Vector Regression, for the modeling and approximation of the potential energy of water clusters as representatives of more general molecular clusters.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127490898","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 propose a fast approach to detecting human facial emotions, using a hierarchical multiple stage scheme and only the PHOG feature descriptors basing on frontal images of human faces. In this model, the facial expression is the composition of a set of expressive facial regions which can be evaluated with the trained emotional templates. Within this framework, the proposed algorithm is able to achieve acceptable detection accuracy for Cohn-Kanade dataset, with less time and space complexities compared with the approaches in other research literature, making it applicable to low cost hardware such as mobile device. In addition, the experiments illustrated that the approach presented in this paper has good robustness and extendibility.
{"title":"Facial Emotion Recognition Using PHOG and a Hierarchical Expression Model","authors":"Zhao Zhong, Gang Shen, Wenhu Chen","doi":"10.1109/INCoS.2013.143","DOIUrl":"https://doi.org/10.1109/INCoS.2013.143","url":null,"abstract":"In this paper, we propose a fast approach to detecting human facial emotions, using a hierarchical multiple stage scheme and only the PHOG feature descriptors basing on frontal images of human faces. In this model, the facial expression is the composition of a set of expressive facial regions which can be evaluated with the trained emotional templates. Within this framework, the proposed algorithm is able to achieve acceptable detection accuracy for Cohn-Kanade dataset, with less time and space complexities compared with the approaches in other research literature, making it applicable to low cost hardware such as mobile device. In addition, the experiments illustrated that the approach presented in this paper has good robustness and extendibility.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121682976","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}
We introduce the technique of searchable encryption into the problem of secure two-party computation, and obtain a novel approach to accomplish Private Set Intersection (PSI), which uses the Public Key Encryption with Multiple Keywords Search (MPEKS) as the basic tool. We aim to achieve PSI in computationally asymmetric settings which can be instantiated by Cloud Computing. Our protocol satisfies the privacy with respect to semi-honest behaviors and the client only needs to compute m multiplications, m MapToPoint operations and one modular exponentiation to obtain the intersection, where m denotes the cardinality of the client's set.
{"title":"Private Set Intersection via Public Key Encryption with Multiple Keywords Search","authors":"Zhiyi Shao, Bo Yang, Yong Yu","doi":"10.1109/INCoS.2013.60","DOIUrl":"https://doi.org/10.1109/INCoS.2013.60","url":null,"abstract":"We introduce the technique of searchable encryption into the problem of secure two-party computation, and obtain a novel approach to accomplish Private Set Intersection (PSI), which uses the Public Key Encryption with Multiple Keywords Search (MPEKS) as the basic tool. We aim to achieve PSI in computationally asymmetric settings which can be instantiated by Cloud Computing. Our protocol satisfies the privacy with respect to semi-honest behaviors and the client only needs to compute m multiplications, m MapToPoint operations and one modular exponentiation to obtain the intersection, where m denotes the cardinality of the client's set.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"437 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124250899","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}
Ming-Kai Wu, Jing Li, J. Ge, Chensi Zhang, Florin Pop
This study deals with the performance analysis of time division broadcast (TDBC) protocol with channel estimation errors. The tight lower bounds for individual and system outage probabilities are first presented in closed-form. It is shown that the presence of channel estimation error causes outage probability to maintain a fixed level even when a noiseless channel is adopted. Simulation results validate the accuracy of our analytical results. Furthermore, comparison of signal-to-noise ratio (SNR) gap ratio shows that TDBC protocol is less sensitive to the effect of channel estimate error than analog network coding (ANC) protocol.
{"title":"Impact of Channel Estimation Error on Time Division Broadcast Protocol in Bidirectional Relaying Systems","authors":"Ming-Kai Wu, Jing Li, J. Ge, Chensi Zhang, Florin Pop","doi":"10.1109/INCoS.2013.65","DOIUrl":"https://doi.org/10.1109/INCoS.2013.65","url":null,"abstract":"This study deals with the performance analysis of time division broadcast (TDBC) protocol with channel estimation errors. The tight lower bounds for individual and system outage probabilities are first presented in closed-form. It is shown that the presence of channel estimation error causes outage probability to maintain a fixed level even when a noiseless channel is adopted. Simulation results validate the accuracy of our analytical results. Furthermore, comparison of signal-to-noise ratio (SNR) gap ratio shows that TDBC protocol is less sensitive to the effect of channel estimate error than analog network coding (ANC) protocol.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114156285","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}
Junxiu Zhou, Shigang Liu, Guoyong Qiu, Fengmin Zhang, Jiancheng Sun
In this paper, a tensor missing value recovery method on the tensor Tucker decomposition is presented. The contribution of this paper is to extend matrix shrinkage operator to the tensor Tucker higher-order singular value decomposition operator to obtain the best low-n-rank automatic. To obtain the optimal approximation tensor which is the key factor in recovery missing value of tensors, a tensor Tucker higher-order orthogonal iteration decomposition is presented which can solve the tensor trace norm objective function directly. In order to avoid relaxing the tensor trace norm function, the augment Lagrange multiplier method is adapted to the solving process. Without turning it into the average of the trace norms of all matrices unfolded along each mode, our method has more recovery accuracy and robust than the off-the-shelf method.
{"title":"Tensor Missing Value Recovery with Tucker Thresholding Method","authors":"Junxiu Zhou, Shigang Liu, Guoyong Qiu, Fengmin Zhang, Jiancheng Sun","doi":"10.1109/INCoS.2013.138","DOIUrl":"https://doi.org/10.1109/INCoS.2013.138","url":null,"abstract":"In this paper, a tensor missing value recovery method on the tensor Tucker decomposition is presented. The contribution of this paper is to extend matrix shrinkage operator to the tensor Tucker higher-order singular value decomposition operator to obtain the best low-n-rank automatic. To obtain the optimal approximation tensor which is the key factor in recovery missing value of tensors, a tensor Tucker higher-order orthogonal iteration decomposition is presented which can solve the tensor trace norm objective function directly. In order to avoid relaxing the tensor trace norm function, the augment Lagrange multiplier method is adapted to the solving process. Without turning it into the average of the trace norms of all matrices unfolded along each mode, our method has more recovery accuracy and robust than the off-the-shelf method.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"36 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114408255","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}
Hua Deng, Bo Qin, Ruiying Du, Huanguo Zhang, Lina Wang, Jianwei Liu, Jian Mao
In distribution of data encrypted to multiple users, there is a problem that the system will become low efficient if a single key center has to generate keys for a large number of users. Besides, it is a risk that some users could deliberately disclose their keys for some benefits. We in this paper give a key leakage discovering scheme where users are partitioned into groups and groups are hierarchically organized. In our scheme, users in upper-level groups can delegate keys for users in lower-level groups, which alleviates the key generation burden of the trusted third party. As an interesting feature, our scheme provide a key leakage discovering measure that if some users in groups leaked their decryption keys then at least one of them can be found out. This enables the data owners to accuse the illegal users when they infringed the copyright. At last, we analyze the performance of our system.
{"title":"Finding Key Leakage in Hierarchical Distribution of Encrypted Data","authors":"Hua Deng, Bo Qin, Ruiying Du, Huanguo Zhang, Lina Wang, Jianwei Liu, Jian Mao","doi":"10.1109/INCoS.2013.149","DOIUrl":"https://doi.org/10.1109/INCoS.2013.149","url":null,"abstract":"In distribution of data encrypted to multiple users, there is a problem that the system will become low efficient if a single key center has to generate keys for a large number of users. Besides, it is a risk that some users could deliberately disclose their keys for some benefits. We in this paper give a key leakage discovering scheme where users are partitioned into groups and groups are hierarchically organized. In our scheme, users in upper-level groups can delegate keys for users in lower-level groups, which alleviates the key generation burden of the trusted third party. As an interesting feature, our scheme provide a key leakage discovering measure that if some users in groups leaked their decryption keys then at least one of them can be found out. This enables the data owners to accuse the illegal users when they infringed the copyright. At last, we analyze the performance of our system.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114778973","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}
Network traffic prediction is hot spot in recent years' research, which is of great significance in area such as congestion control, network management and diagnostic. Network traffic is non-linear, non-stationary, and uncertain, and its uncertainty increases rapidly when making short-term traffic flow prediction. After reviewing current network traffic prediction algorithms' merits and drawbacks based on Time-Series analysis, Artificial Neural Network here, a new network traffic prediction algorithms in short-term is proposed. The time interval when detecting that network data packet pass on certain section is treated as a stochastic process. In the ARCH (autoregressive conditional heteroskedasticity) framework, stochastic process is described by a marked point process that different point processes may generate different ACD (autoregressive conditional duration) model, then ACD model can be used to complete the description of time interval when network data packet passing. Based on this model, a particle filter is applied to non-stationary motion system for short-term network traffic prediction. At last, this algorithm is applied to real data for real-evidence analysis.
{"title":"Short-Term Network Traffic Prediction with ACD and Particle Filter","authors":"Gaoyu Zhang, Duying Huang","doi":"10.1109/INCoS.2013.57","DOIUrl":"https://doi.org/10.1109/INCoS.2013.57","url":null,"abstract":"Network traffic prediction is hot spot in recent years' research, which is of great significance in area such as congestion control, network management and diagnostic. Network traffic is non-linear, non-stationary, and uncertain, and its uncertainty increases rapidly when making short-term traffic flow prediction. After reviewing current network traffic prediction algorithms' merits and drawbacks based on Time-Series analysis, Artificial Neural Network here, a new network traffic prediction algorithms in short-term is proposed. The time interval when detecting that network data packet pass on certain section is treated as a stochastic process. In the ARCH (autoregressive conditional heteroskedasticity) framework, stochastic process is described by a marked point process that different point processes may generate different ACD (autoregressive conditional duration) model, then ACD model can be used to complete the description of time interval when network data packet passing. Based on this model, a particle filter is applied to non-stationary motion system for short-term network traffic prediction. At last, this algorithm is applied to real data for real-evidence analysis.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604283","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}
Tetsuya Oda, Shinji Sakamoto, Evjola Spaho, Makoto Ikeda, F. Xhafa, L. Barolli
Wireless Mesh Networks (WMNs) are important networking infrastructure for providing cost-efficient broadband wireless connectivity. Mesh router node placement is important to achieve network connectivity and coverage in such networks. In this paper, we implement and evaluate the performance of Genetic Algorithm (GA) for mesh router node placement in WMNs. We consider Exponential and Weibull distributions of mobile mesh clients. As evaluation metrics we used size of giant component and number of covered mesh clients. Simulation results show that WMN-GA have a good performance when mesh clients are mobile.
{"title":"Performance Evaluation of WMN-GA for Wireless Mesh Networks Considering Mobile Mesh Clients","authors":"Tetsuya Oda, Shinji Sakamoto, Evjola Spaho, Makoto Ikeda, F. Xhafa, L. Barolli","doi":"10.1109/INCOS.2013.50","DOIUrl":"https://doi.org/10.1109/INCOS.2013.50","url":null,"abstract":"Wireless Mesh Networks (WMNs) are important networking infrastructure for providing cost-efficient broadband wireless connectivity. Mesh router node placement is important to achieve network connectivity and coverage in such networks. In this paper, we implement and evaluate the performance of Genetic Algorithm (GA) for mesh router node placement in WMNs. We consider Exponential and Weibull distributions of mobile mesh clients. As evaluation metrics we used size of giant component and number of covered mesh clients. Simulation results show that WMN-GA have a good performance when mesh clients are mobile.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115460991","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 introduce a real time process modeling framework for supply chains. The superior feature of this framework is that it draws a dynamically packaged process utilizing the existing available resources and supporting activities for supply chain management. The paper presents a top level rule based design that can handle heterogeneous networks and allows integration with key enabling technologies like RFID tracking and leverages the power of an inference engine to manage, monitor and optimize flow networks and supply chain entities. An extension of previous work by the authors, some details of the runtime examples are given and impacts for industries.
{"title":"Framework for Real-Time Process Modeling of Supply Chains","authors":"P. Moynihan, W. Dai","doi":"10.1109/INCoS.2013.146","DOIUrl":"https://doi.org/10.1109/INCoS.2013.146","url":null,"abstract":"In this paper, we introduce a real time process modeling framework for supply chains. The superior feature of this framework is that it draws a dynamically packaged process utilizing the existing available resources and supporting activities for supply chain management. The paper presents a top level rule based design that can handle heterogeneous networks and allows integration with key enabling technologies like RFID tracking and leverages the power of an inference engine to manage, monitor and optimize flow networks and supply chain entities. An extension of previous work by the authors, some details of the runtime examples are given and impacts for industries.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054061","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 new numerical method which proves by computers a class of geometric inequalities in a real plane. The method use interval arithmetic and related techniques to constitute the computing procedures to verify the geometric inequalities. Some numerical examples of verification are illustrated and demonstrate the effectiveness of our method.
{"title":"Numerical Approach for Automatic Theorem Proving in Plane Geometry","authors":"Siwen Guo","doi":"10.1109/INCoS.2013.38","DOIUrl":"https://doi.org/10.1109/INCoS.2013.38","url":null,"abstract":"In this paper, we present a new numerical method which proves by computers a class of geometric inequalities in a real plane. The method use interval arithmetic and related techniques to constitute the computing procedures to verify the geometric inequalities. Some numerical examples of verification are illustrated and demonstrate the effectiveness of our method.","PeriodicalId":353706,"journal":{"name":"2013 5th International Conference on Intelligent Networking and Collaborative Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130066696","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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