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عنوان مقاله |
نویسنده (ها) |
مربوط به کنفرانس |
چکیده |
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An Electronic Voting Scheme through Blind Signature |
Toktam Taghavi
Abbas Ghaemi Bafghi
Mohsen Kahani
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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Online Coloring Co-interval Graphs |
Hamid Zarrabi-Zadeh
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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, ... مشاهده کامل
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. عدم مشاهده کامل
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, ... مشاهده کامل |
خرید مقاله
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Evaluation and review of several color indices used for segmenting plant from non-plant regions in color images |
Mahmood R. Golzarian
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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). ... مشاهده کامل
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. عدم مشاهده کامل
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). ... مشاهده کامل |
خرید مقاله
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Bayesian Continuous-State Reinforcement Learning |
Saeed Amizadeh
Majid Nili Ahmadabadi
Caro Lucas
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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Bayesian Approach to Learning Temporally Extended Concepts |
Saied Haidarian Shahri
Farzad Rastegar
Majid Nili Ahmadabadi
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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Human Surveillance: A New Non Linear Tracking Technique |
Hadi Sadoghi Yazdi
Seyed Ebrahim Hosseini
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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Learning Concepts from a Sequence of Experiences by Reinforcement Learning Agents |
Farzad Rastegar
Majid Nili Ahmadabadi
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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On Static Node Failure Disconnection in Interconnect Networks |
F. Safaei
A. Khonsari
M. Fathy
H. Jalali
S. Khosravipour
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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. ... مشاهده کامل
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. عدم مشاهده کامل
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. ... مشاهده کامل |
خرید مقاله
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Application of GA in Feature Optimization of Nearest Neighbor Classifiers |
M. Analoui
M. Fadavi Amiri
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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A Maze Solver based on a New Architecture of XCS |
Ali Hamzeh
Adel Rahmani
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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 ... مشاهده کامل
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. عدم مشاهده کامل
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 ... مشاهده کامل |
خرید مقاله
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