Pub Date : 2009-12-15DOI: 10.1109/IIT.2009.5413781
C. Leite, Bruno G. de Araujo, R. Valentim, G. B. Brandao, Ana Gueirreiro
The development of middleware has emerged as an area of expanding research, focused on the integration of services available for distributed applications. In this context, many challenges have also arisen with the use of middleware, such as communication, flexibility, performance, as well as integration with the Web and the computer itself. The development of middleware for mobile computing poses new challenges to developers because of the limitations of mobile devices. Thus, these developers must understand the new mobile computing and middleware technologies in order to integrate them. The objective of this work is to develop a service-oriented context-aware middleware for real-time data management in real-time using mobile devices.
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Pub Date : 2009-12-15DOI: 10.1109/IIT.2009.5413766
Siwar Rekik, S. Selouani, H. Hamam
The aim of this paper is to present a cooperative approach to improve the Human-System spoken dialogues. The main advantage of the proposed approach is its ability to reach both of the user and the system goals more efficiently. The strategy that underlines our system is well adapted to the mobile applications since it involves effective spoken exchanges to reach the users' and system mutual goals. The proposed framework is built in order to use the Hidden Markov Models (HMMs) based CMUS-phinx speech recognition engine for mobile communications. To evaluate our approach a case-study in the M-trading field is considered. The analysis of the case study shows the efficiency of our strategy in comparison with the usual ones.
{"title":"A cooperative and conversational virtual agent for M-commerce applications","authors":"Siwar Rekik, S. Selouani, H. Hamam","doi":"10.1109/IIT.2009.5413766","DOIUrl":"https://doi.org/10.1109/IIT.2009.5413766","url":null,"abstract":"The aim of this paper is to present a cooperative approach to improve the Human-System spoken dialogues. The main advantage of the proposed approach is its ability to reach both of the user and the system goals more efficiently. The strategy that underlines our system is well adapted to the mobile applications since it involves effective spoken exchanges to reach the users' and system mutual goals. The proposed framework is built in order to use the Hidden Markov Models (HMMs) based CMUS-phinx speech recognition engine for mobile communications. To evaluate our approach a case-study in the M-trading field is considered. The analysis of the case study shows the efficiency of our strategy in comparison with the usual ones.","PeriodicalId":239829,"journal":{"name":"2009 International Conference on Innovations in Information Technology (IIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125109494","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 : 2009-12-01DOI: 10.1109/IIT.2009.5413787
S. Kiranyaz, T. Ince, M. Gabbouj
Particle Swarm Optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. In this paper, we draw the focus on a major drawback of the PSO algorithm: the poor gbest update. This can be a severe problem, which causes pre-mature convergence to local optima since gbest as the common term in the update equation of all particles, is the primary guide of the swarm. Therefore, we basically seek a solution for the social problem in PSO, i.e. “Who will guide the guide?” which resembles the rhetoric question posed by Plato in his famous work on government: “Who will guard the guards?” (Quis custodiet ipsos custodes?). Stochastic approximation (SA) is purposefully adapted into two approaches to guide (or drive) the gbest particle (with simultaneous perturbation) towards the right direction with the gradient estimate of the underlying surface (or function) whilst avoiding local traps due to its stochastic nature. We purposefully used simultaneous perturbation SA (SPSA) for its low cost and since SPSA is applied only to the gbest (not the entire swarm), both approaches have thus a negligible overhead cost over the entire PSO process. Yet we have shown over a wide range of non-linear functions that both approaches significantly improve the performance of PSO especially if the parameters of SPSA suits to the problem in hand.
{"title":"Stochastic approximation driven Particle Swarm Optimization","authors":"S. Kiranyaz, T. Ince, M. Gabbouj","doi":"10.1109/IIT.2009.5413787","DOIUrl":"https://doi.org/10.1109/IIT.2009.5413787","url":null,"abstract":"Particle Swarm Optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. In this paper, we draw the focus on a major drawback of the PSO algorithm: the poor gbest update. This can be a severe problem, which causes pre-mature convergence to local optima since gbest as the common term in the update equation of all particles, is the primary guide of the swarm. Therefore, we basically seek a solution for the social problem in PSO, i.e. “Who will guide the guide?” which resembles the rhetoric question posed by Plato in his famous work on government: “Who will guard the guards?” (Quis custodiet ipsos custodes?). Stochastic approximation (SA) is purposefully adapted into two approaches to guide (or drive) the gbest particle (with simultaneous perturbation) towards the right direction with the gradient estimate of the underlying surface (or function) whilst avoiding local traps due to its stochastic nature. We purposefully used simultaneous perturbation SA (SPSA) for its low cost and since SPSA is applied only to the gbest (not the entire swarm), both approaches have thus a negligible overhead cost over the entire PSO process. Yet we have shown over a wide range of non-linear functions that both approaches significantly improve the performance of PSO especially if the parameters of SPSA suits to the problem in hand.","PeriodicalId":239829,"journal":{"name":"2009 International Conference on Innovations in Information Technology (IIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115630769","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 : 2009-11-01DOI: 10.1109/IIT.2009.5413775
A. Rahimi, S. Mohammadi, Aidin Foroughi
As CMOS technology scales down into the deep-submicron domain, the cost of design, complexity and customization for Systems-On-Chip (SoCs) is rapidly increasing due to the inefficiency of traditional CAD tools. In this paper we present a new interactive refinement algorithm in high-level synthesis, based on dynamic programming, which maximizes resource optimization in data path. We start by quantifying the properties of the given application C code in terms of control data flow graph (CDFG), available parallelism and other metrics. We then apply designer guided constraints to a data path refinement algorithm for an initial data path. It attempts to reduce the number of the most expensive components while meeting the constraints. The experimental results show that not only the refined data path outperforms data paths refined by other heuristic methods, but also presents lower cost, less overhead and can be generated in less time.
{"title":"Data path refinement algorithm in high-level synthesis based on dynamic programming","authors":"A. Rahimi, S. Mohammadi, Aidin Foroughi","doi":"10.1109/IIT.2009.5413775","DOIUrl":"https://doi.org/10.1109/IIT.2009.5413775","url":null,"abstract":"As CMOS technology scales down into the deep-submicron domain, the cost of design, complexity and customization for Systems-On-Chip (SoCs) is rapidly increasing due to the inefficiency of traditional CAD tools. In this paper we present a new interactive refinement algorithm in high-level synthesis, based on dynamic programming, which maximizes resource optimization in data path. We start by quantifying the properties of the given application C code in terms of control data flow graph (CDFG), available parallelism and other metrics. We then apply designer guided constraints to a data path refinement algorithm for an initial data path. It attempts to reduce the number of the most expensive components while meeting the constraints. The experimental results show that not only the refined data path outperforms data paths refined by other heuristic methods, but also presents lower cost, less overhead and can be generated in less time.","PeriodicalId":239829,"journal":{"name":"2009 International Conference on Innovations in Information Technology (IIT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121149679","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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