Nowadays, data security is one of the most - if not the most important aspects in mobile applications, web and information systems in general. On one hand, this is a result of the vital role of mobile and web applications in our daily life. On the other hand, though, the huge, yet accelerating evolution of computers and software has led to more and more sophisticated forms of threats and attacks which jeopardize user's credentials and privacy. Today's computers are capable of automatically performing authentication attempts replaying recorded data. This fact has brought the challenge of access control to a whole new level, and has urged the researchers to develop new mechanisms in order to prevent software from performing automatic authentication attempts. In this research perspective, the Completely Automatic Public Turing test to tell Computers and Humans Apart (CAPTCHA) has been proposed and widely adopted. However, this mechanism consists of a cognitive intelligence test to reinforce traditional authentication against computerized attempts, thus it puts additional strain on the legitimate user too and, quite often, significantly slows the authentication process. In this paper, we introduce a Completely Automatic Public Physical test to tell Computers and Humans Apart (CAPPCHA) as a way to enhance PIN authentication scheme for mobile devices. This test does not introduce any additional cognitive strain on the user as it leverages only his physical nature. We prove that the scheme is even more secure than CAPTCHA and our experiments show that it is fast and easy for users
Ensemble methods for building improved classifier models have been an important topic in machine learning, pattern recognition and data mining areas, where they have shown great promise. They boast a robustness that has spearheaded their application in many practical classification problems, especially when there is a significant diversity among the ensemble members. Actually, they replace traditional machine learning techniques in many applications and special attention has been devoted to them as a mean to improve the prediction accuracy for problems of high complexity. Several combination rules have been investigated in this context. However, it is claimed that no rule is always better than others for designing an optimal decision. The present study evaluates the performance of two different ensemble methods for protein secondary structure prediction. We focus on weighted opinions pooling and the most common aggregation rules for decisions inference. The ensemble members are accurate protein secondary structure single model predictors namely, Multi-Class Support Vector Machines and Artificial Neural Networks. Experiments are carried out using crossvalidation tests on RS126 and CB513 benchmark datasets. Our results clearly confirm that ensembles are more accurate than a single model and the experimental comparison of the investigated ensemble schemes demonstrates that the newly introduced rule called Exponential Opinion Pool competes Communicated by V. Loia.well against state-of-the-art fixed rules, especially the sum rule which in some cases is able to achieve better performance.
Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier.
Personalization and adaptation to the user profile capability are the hottest issues to ensure ambient assisted living and context awareness in nowadays environments. With the growing healthcare and wellbeing context aware applications, modeling security policies becomes an important issue in the design of future access control models. This requires rich semantics using ontology modeling for the management of services provided to dependant people. However, current access control models remain unsuitable due to lack of personalization, adaptability and smartness to the handicap situation.In this paper, we propose a novel adaptable access control model and its related architecture in which the security policy is based on the handicap situation analyzed from the monitoring of user's behavior in order to grant a service using any assistive device within intelligent environment. The design of our model is an ontology-learning and evolving security policy for predicting the future actions of dependent people. This is reached by reasoning about historical data, contextual data and user behavior according to the access rules that are used in the inference engine to provide the right service according to the user's needs.
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