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Paper Title Authors Conference Abstract
An Electronic Voting Scheme through Blind Signature Toktam Taghavi
Abbas Ghaemi Bafghi
Mohsen Kahani
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Many e-voting schemes have been proposed in the literature. However, none of them is both secure and practical. In this paper, a practical and secure electronic voting protocol for large-scale voting ... more
Many e-voting schemes have been proposed in the literature. However, none of them is both secure and practical. In this paper, a practical and secure electronic voting protocol for large-scale voting over the Internet is investigated. Blind signature is applied to a voter's ballot making it impossible for anyone to trace the ballot back to the voter. Unlike previous blind signature based schemes, in which the authority directly signs its blind signature on voters' ballots, the authority in the proposed scheme signs blind signature on the voter marks that are generated by voters from ballot serial numbers. Moreover, threshold cryptosystem has been used to guarantee the fairness of the voting process. Using blind signature, this scheme can support all types of election easily and flexibly. Since we haven’t use complex cryptographic techniques the proposed scheme is suitable for large scale elections. less
Many e-voting schemes have been proposed in the literature. However, none of them is both secure and practical. In this paper, a practical and secure electronic voting protocol for large-scale voting ... more
خرید مقاله
Online Coloring Co-interval Graphs Hamid Zarrabi-Zadeh
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Abstract. We study the problem of online coloring co-interval graphs. In this problem, a set of intervals on the real line is presented to the online algorithm in some arbitrary order, ... more
Abstract. We study the problem of online coloring co-interval graphs. In this problem, a set of intervals on the real line is presented to the online algorithm in some arbitrary order, and the algorithm must assign each interval a color that is di®erent from the colors of all previously presented intervals not intersecting the current interval. It is known that the competitive ratio of the simple First-Fit algorithm on the class of co-interval graphs is at most 2.We show that for the class of unit co-interval graphs, where all intervals have equal length, the 2-bound on the competitive ratio of First-Fit is tight. On the other hand, we show that no deterministic online algorithm for coloring unit co-interval graphs can be better than 3/2-competitive. We then study the e®ect of randomization in our problem, and prove a lower bound of 4/3 on the competitive ratio of any randomized algorithm for the unit co-interval coloring problem. We also prove that for the class of general co-interval graphs no randomized algorithm has competitive ratio better than 3/2. less
Abstract. We study the problem of online coloring co-interval graphs. In this problem, a set of intervals on the real line is presented to the online algorithm in some arbitrary order, ... more
خرید مقاله
Evaluation and review of several color indices used for segmenting plant from non-plant regions in color images Mahmood R. Golzarian
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
The main part of a machine vision system is to distinguish the object of interest (in the case of this project, a plant) from non-important regions (we refer it as background). ... more
The main part of a machine vision system is to distinguish the object of interest (in the case of this project, a plant) from non-important regions (we refer it as background). Distinguishing the objects of interest is simplified if the high contrast between the objects of interest and background is created. The objective of this study is to find the best color index by which the algorithm is able to create the highest contrast between plant and non-plant regions. For this study, images were taken of varying numbers of wheat plants under several growth stages in a loamy sand soil and in diffused light condition. Three regions were predefined on the images; plant, pebble, and soil regions. Regions for plants, soil and pebbles were separately cropped within each image, aiming to provide a pooled representation for each object in each image. For each image, 13 mean color index were computed for each the three regions of interest (plant, soil, and pebble). The results of applying Analysis of variance (ANOVA) and consequently t-tests indicated that modified Excessive Green Index (MEGI) can potentially make the highest contrast between plant and non-plant regions rather than other color indices. less
The main part of a machine vision system is to distinguish the object of interest (in the case of this project, a plant) from non-important regions (we refer it as background). ... more
خرید مقاله
Bayesian Continuous-State Reinforcement Learning Saeed Amizadeh
Majid Nili Ahmadabadi
Caro Lucas
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Continuous-State Reinforcement Learning (RL) has been recently favored because of the continuous nature of the real world RL problems and many theoretical approaches have been devised to handle the case. However, most ... more
Continuous-State Reinforcement Learning (RL) has been recently favored because of the continuous nature of the real world RL problems and many theoretical approaches have been devised to handle the case. However, most of these methods presume that the structure of the agent's perceptual environment is fed to it. But this is not the case in many real situations. Inspired from the subjective view existing in the Cognitive Constructivist learning theory, in this paper, a new method is presented to discover and construct the structure of the environment in parallel with learning the optimal policy. To achieve these goals, the proposed approach incorporates the Bayesian formalism to organize the perceptual space while it tries to learn the optimal behavior using a Q-learning-like learning algorithm. These characteristics as a whole define a Reinforcement Learning algorithm which is developed based on a mixture of Cognitive Constructivism and traditional Behaviorism ideas. Simulation results demonstrate the viability and efficiency of the proposed algorithm on continuous state RL problems. less
Continuous-State Reinforcement Learning (RL) has been recently favored because of the continuous nature of the real world RL problems and many theoretical approaches have been devised to handle the case. However, most ... more
خرید مقاله
Bayesian Approach to Learning Temporally Extended Concepts Saied Haidarian Shahri
Farzad Rastegar
Majid Nili Ahmadabadi
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
In several previous studies it has been shown that the generalization capabilities of humans through concept learning is reminiscent of Bayesian modeling. When discriminating concepts from one another, human subjects tend to ... more
In several previous studies it has been shown that the generalization capabilities of humans through concept learning is reminiscent of Bayesian modeling. When discriminating concepts from one another, human subjects tend to focus on the relevant features of the subspace and ignore the irrelevant ones. In this paper we propose a Bayesian concept learning paradigm that utilizes unrestricted Bayesian networks to learn the required concepts for optimal decision making. This approach has several beneficial characteristics that a concept learning algorithm should hold. At first it can both learn form observing an expert performing the desired task and from its own experience while carrying it out. Secondly, it is a close and computationally feasible approximation to the Bayesian modeling capabilities of humans. Thirdly, the Markov blanket surrounding the decision variable can render the irrelevant features independent and therefore this approach can ignore them seamlessly from the feature subspace. The simulation and experimental results are promising and show that our approach can successfully extract the required temporally extended concepts for a mobile robot task. less
In several previous studies it has been shown that the generalization capabilities of humans through concept learning is reminiscent of Bayesian modeling. When discriminating concepts from one another, human subjects tend to ... more
خرید مقاله
Human Surveillance: A New Non Linear Tracking Technique Hadi Sadoghi Yazdi
Seyed Ebrahim Hosseini
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
This paper presents the theoretical development of nonlinear adaptive filter based on a concept of filtering in high dimensional space (HDS). The most common procedures for nonlinear estimation are the extended Kalman ... more
This paper presents the theoretical development of nonlinear adaptive filter based on a concept of filtering in high dimensional space (HDS). The most common procedures for nonlinear estimation are the extended Kalman filter. The basic idea of the extended Kalman filter (EKF) is to linearize the state-space model at each time instant around the most recent state estimate. Once a linear model is obtained, the standard Kalman filter equations are applied. Main innovation in this paper is new linearization technique in EKF. The Linearization is performed by converting existing space to high dimensional space. HDS helps having linear space from nonlinear space. In this linear space, the standard Kalman filter gives rise to better results in estimation and prediction purposes. It is proven that MSE and error variance in this space is less than the input space. The proposed EKF is implemented in pedestrian tracking and results show that our method is superior to the standard extended Kalman filter. less
This paper presents the theoretical development of nonlinear adaptive filter based on a concept of filtering in high dimensional space (HDS). The most common procedures for nonlinear estimation are the extended Kalman ... more
خرید مقاله
Learning Concepts from a Sequence of Experiences by Reinforcement Learning Agents Farzad Rastegar
Majid Nili Ahmadabadi
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
In this paper, we propose a novel approach whereby a reinforcement learning agent attempts to understand its environment via meaningful temporally extended concepts in an unsupervised way. Our approach is inspired ... more
In this paper, we propose a novel approach whereby a reinforcement learning agent attempts to understand its environment via meaningful temporally extended concepts in an unsupervised way. Our approach is inspired by findings in neuroscience on the role of mirror neurons in action-based abstraction. Since there are so many cases in which the best decision cannot be made just by using instant sensory data, in this study we seek to achieve a framework for learning temporally extended concepts from sequences of sensory-action data. To direct the agent to gather fertile information for concept learning, a reinforcement learning mechanism utilizing experience of the agent is proposed. Experimental results demonstrate the capability of the proposed approach in retrieving meaningful concepts from the environment. The concepts and the way of defining them are thought such that they not only can be applied to ease decision making but also can be utilized in other applications as elaborated in the paper. less
In this paper, we propose a novel approach whereby a reinforcement learning agent attempts to understand its environment via meaningful temporally extended concepts in an unsupervised way. Our approach is inspired ... more
خرید مقاله
On Static Node Failure Disconnection in Interconnect Networks F. Safaei
A. Khonsari
M. Fathy
H. Jalali
S. Khosravipour
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Massively parallel systems are often composed of hundreds or thousands of components (such as routers, channels and connectors) that collectively possess failure rates higher than what arise in the ordinary systems. ... more
Massively parallel systems are often composed of hundreds or thousands of components (such as routers, channels and connectors) that collectively possess failure rates higher than what arise in the ordinary systems. For these systems, new measures have been introduced that can evaluate the capability of a system for gracefully degradation. In the design of such systems, one of the most fundamental considerations is the reliability of their interconnected networks, which can be usually characterized by connectivity of the network topological structure. Resilience of graphs and various types of deterministic networks have attracted significant attention in the research literature. A classical problem in this line of study is to understand failure conditions which the network disconnects and/or starts to offer noticeably lower performance (such as increased routing distance) to its users. In this paper, we investigate the problem of network disconnection by means of simulation in the context of large-scale interconnect networks and understand how static patterns of node failure affect the resilience of such networks. less
Massively parallel systems are often composed of hundreds or thousands of components (such as routers, channels and connectors) that collectively possess failure rates higher than what arise in the ordinary systems. ... more
خرید مقاله
Application of GA in Feature Optimization of Nearest Neighbor Classifiers M. Analoui
M. Fadavi Amiri
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This ... more
The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This is usually a difficult task in practice, normally requiring the application of heuristic knowledge about the specific problem domain. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector. In this paper a new approach is presented to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. less
The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This ... more
خرید مقاله
A Maze Solver based on a New Architecture of XCS Ali Hamzeh
Adel Rahmani
دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Learning capabilities of an agent relies on the way that agent perceives the environment. When the agent’s sensations convey only partial information about the environment, there may be different situations that ... more
Learning capabilities of an agent relies on the way that agent perceives the environment. When the agent’s sensations convey only partial information about the environment, there may be different situations that appear identical to the agent but require different actions to behave optimally. In this paper, we propose a new approach to improve XCS’s performance in Partially Observable Markov Decision Process (POMDP) using a newly introduced method to detect aliased states in the current environment. In our approach, at the initial state, there exists only a single main XCS which handles all of the environmental states. When an existing aliased state is detected using a simple mechanism, the system creates a new XCS, in addition to the main XCS which we call Cooperative XCS. The new XCS is responsible for handling this detected state. This mechanism allows the main XCS to handle non-aliased states and the other XCS’s cooperate with it by handling existing aliased states independently. Thus, the system is called Cooperative Specialized XCS and its performance is compared with some other classifier systems in some benchmark problems. The presented results demonstrate the effectiveness of our proposed approach. less
Learning capabilities of an agent relies on the way that agent perceives the environment. When the agent’s sensations convey only partial information about the environment, there may be different situations that ... more
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