Abstract: A human verification system is presented in this
paper. The system consists of several steps: background subtraction,
thresholding, line connection, region growing, morphlogy, star
skelatonization, feature extraction, feature matching, and decision
making. The proposed system combines an advantage of star
skeletonization and simple statistic features. A correlation matching
and probability voting have been used for verification, followed by a
logical operation in a decision making stage. The proposed system
uses small number of features and the system reliability is
convincing.
Abstract: Internet today has a huge impact on all aspects of life,
and also in the area of the broader context of democracy, politics and
politicians. If democracy is freedom of choice, there are a number of
conditions that can ensure in practice the freedom to be achieved and
realized. These preconditions must be achieved regardless of the
manner of voting. The key contribution of ICT to achieve freedom of
choice is that technology enables the correlation of the citizens and
elected representatives on the better way than it was possible without
the Internet. In this sense, we can say that the Internet and ICT are
changing significantly, and potentially improving the environment in
which democratic processes are taking place. This paper aims to
describe trends in use of ICT in democratic processes, and analyzes
the challenges for implementation of e-Democracy in Montenegro
Abstract: This article presents a method for elections between the members of a group that is founded by fuzzy logic. Linguistic variables are objects for decision on election cards and deduction is based on t-norms and s-norms. In this election-s method election cards are questionnaire. The questionnaires are comprised of some questions with some choices. The choices are words from natural language. Presented method is accompanied by center of gravity (COG) defuzzification added up to a computer program by MATLAB. Finally the method is illustrated by solving two examples; choose a head for a research group-s members and a representative for students.
Abstract: Extracting and elaborating software requirements and
transforming them into viable software architecture are still an
intricate task. This paper defines a solution architecture which is
based on the blurred amalgamation of problem space and solution
space. The dependencies between domain constraints, requirements
and architecture and their importance are described that are to be
considered collectively while evolving from problem space to
solution space. This paper proposes a revised version of Twin Peaks
Model named Win Peaks Model that reconciles software
requirements and architecture in more consistent and adaptable
manner. Further the conflict between stakeholders- win-requirements
is resolved by proposed Voting methodology that is simple
adaptation of win-win requirements negotiation model and QARCC.
Abstract: Diabetes is one of the high prevalence diseases
worldwide with increased number of complications, with retinopathy
as one of the most common one. This paper describes how data
mining and case-based reasoning were integrated to predict
retinopathy prevalence among diabetes patients in Malaysia. The
knowledge base required was built after literature reviews and
interviews with medical experts. A total of 140 diabetes patients- data
were used to train the prediction system. A voting mechanism selects
the best prediction results from the two techniques used. It has been
successfully proven that both data mining and case-based reasoning
can be used for retinopathy prediction with an improved accuracy of
85%.
Abstract: Resistance of denial of service attacks is a key security requirement in voting protocols. Acquisti protocol plays an important role in development of internet voting protocols and claims its security without strong physical assumptions. In this study firstly Acquisti protocol is modeled in extended applied pi calculus, and then resistance of denial of service attacks is proved with ProVerif. The result is that it is not resistance of denial of service attacks because two denial of service attacks are found. Finally we give the method against the denial of service attacks.
Abstract: Due to the legacy of apartheid segregation South Africa remains a divided society where most voters live in politically homogenous social environments. This paper argues that political discussion within one’s social context plays a primary role in shaping political attitudes and vote choice. Using data from the Comparative National Elections Project 2004 and 2009 South African post-election surveys, the paper explores the extent of social context partisan homogeneity in South Africa and finds that voters are not overly embedded in homogenous social contexts. It then demonstrates the consequences of partisan homogeneity on voting behavior. Homogenous social contexts tend to encourage stronger partisan loyalties and fewer defections in vote choice while voters in more heterogeneous contexts show less consistency in their attitudes and behaviour. Finally, the analysis shows how momentous sociopolitical events at the time of a particular election can change the social context, with important consequences for electoral outcomes.
Abstract: The clustering ensembles combine multiple partitions
generated by different clustering algorithms into a single clustering
solution. Clustering ensembles have emerged as a prominent method
for improving robustness, stability and accuracy of unsupervised
classification solutions. So far, many contributions have been done to
find consensus clustering. One of the major problems in clustering
ensembles is the consensus function. In this paper, firstly, we
introduce clustering ensembles, representation of multiple partitions,
its challenges and present taxonomy of combination algorithms.
Secondly, we describe consensus functions in clustering ensembles
including Hypergraph partitioning, Voting approach, Mutual
information, Co-association based functions and Finite mixture
model, and next explain their advantages, disadvantages and
computational complexity. Finally, we compare the characteristics of
clustering ensembles algorithms such as computational complexity,
robustness, simplicity and accuracy on different datasets in previous
techniques.
Abstract: Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.