The Probable and the Provable

In this work it is shown how the behavioral biometrics allows to strengthen security of a personal computer during casual use. The user does not have to be even aware of verification system running in the background. Unfortunately, short passwords do not supply enough data for keystroke dynamics algorithms to be precise enough to keep the way and level the biometrics system requires. Behavioral biometrics cannot grant such authentication level as the other physiological biometric methods, e.g. fingerprint or retina scan. However, their transparency in analyzing data allows to merge methods into multimodal systems with a minimal cost. The benefit of keystroke dynamics is that it can be easily connected with some other biometric methods, especially with other human input interface devices. In this paper an approach to analyze keystroke dynamics along with mouse movement is presented. Even though both of the features are of behavioral character and hence with low repeatability, the results are good and promising for further research and modification.

A Multimodal Biometric User Identification System Based on Keystroke Dynamics and Mouse Movements

Abstract Online learning has become increasingly important, having in mind the latest events, imposed isolation measures and closed schools and campuses. Consequently, teachers and students need to embrace digital tools and platforms, bridge the newly established physical gap between them, and consume education in various new ways. Although literature indicates that the development of intelligent techniques must be incorporated in e-learning systems to make them more effective, the need exists for research on how these techniques impact the whole process of online learning, and how they affect learners’ performance. This paper aims to provide comprehensive research on innovations in e-learning, and present a literature review of used intelligent techniques and explore their potential benefits. This research presents a categorization of intelligent techniques, and explores their roles in e-learning environments. By summarizing the state of the art in the area, the authors outline past research, highlight its gaps, and indicate important implications for practice. The goal is to understand better available intelligent techniques, their implementation and application in e-learning context, and their impact on improving learning in online education. Finally, the review concludes that AI-supported solutions not only can support learner and teacher, by recommending resources and grading submissions, but they can offer fully personalized learning experience. 

/pdf/intelligent-techniques-in-e-learning-a-literature-review-30wb61ql.pdf

Intelligent techniques in e-learning: a literature review

Querying multiple deep web source information is an important issue that may be encountered in many applications. Deep Web is really treasury of new and complementary products offered by many service web providers. It contains more than 90% of information hidden behind web forms in remote databases, while only less than 10% of information in web reached by following hyperlinks called web of surface. In this paper, we address the issue of query answering without access to data. We than propose a new approach which guides interpretation of global query with graphical clues inspired from galaxy and cosmic universe. So that navigation around all semantic entities of global query (e.g. field, concept, query) is brings back to cosmic travel of astronaut around particles of cosmic universe (e.g. stars, galaxy, and super-galaxy). We have done virtual integration of deep web services, i.e. we have merged queries of a number of service web providers. The last step is to transform universal query to universal deep web form. The global query is a data structure which touches meaning of deep Web form, while universal web form touches the interpretation of universal query. These indicators guide visual perception of users through all semantic entities of query. So that, a user friendly web form is necessary to reach global query capability without complexity nor ambiguity, as global deep web form is designed for novice users (e.g. children) and all different ages on internet users. We have evaluated performance of our algorithm on two real-world and standard datasets, and have proved good performance.

Querying deep web data bases without accessing to data

Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users’ activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of these probabilistic graphical models in segmenting and labeling stochastic sequences, their performance is adversely affected by the imperfect quality of data used for the construction of sequences of observation. In this paper, a formalism of the possibilistic theory will be used in order to propose a new approach for observation sequences preparation. The eminent contribution of our approach is to evaluate the effect of possibilistic reasoning—during the generation of observation sequences—on the effectiveness of hidden Markov models and conditional random fields models. Using a dataset containing 51 real manipulations related to three types of learners’ tasks, the preliminary experiments demonstrate that the sequences of observation obtained based on possibilistic reasoning significantly improve the performance of hidden Marvov models and conditional random fields models in the automatic recognition of the e-learning activities.

Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model

This paper proposes a new Information Retrieval Model based on Possibilistic Networks. The model structure integrates most relevant term to term dependence relationships. The approach used to extract the set of these dependencies focuses on local dependencies between terms within each document. The relevance of a document to a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. These two measures are also used for quantifying terms-terms links and terms-documents links. Experiments carried out on three standard document collections show the effectiveness of the model.

Possibilistic Network based Information Retrieval Model

In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a g eneral Bayesian network methodology as the basis for an IR system is difficult to tackle. The problem mainly appears bec ause of the large number of variables involved and the computational efforts needed to both determine the relationships between variables and perform the inference processes. To r esolve these problems, many models have been proposed such as BNR model. Generally, Bayesian network models doesn't consider the fuzziness of natural language in the relevance measure of a document to a given query and possibilistic models doesn't undertake the dependence relations between terms used to index documents. As a first solution we propose a h ybridization of these two models in one that will undertake both the relationship between terms and the intrinsic fuzzin ess of natural language. We believe that the translation of Bayesi an network model from the probabilistic framework to possibili stic one will allow a performance improvement of BNRM.

pdf/pertinent-information-retrieval-based-on-possibilistic-p0lwt19al1.pdf

Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective

Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of these probabilistic graphical models in segmenting and labeling stochastic sequences, their performance is adversely affected by the imperfect quality of data used for the construction of sequences of observation. In this paper, a formalism of the possibilistic theory will be used in order to propose a new approach for observation sequences preparation. The eminent contribution of our approach is to evaluate the effect of possibilistic reasoning during the generation of observation sequences on the effectiveness of hidden Markov models and conditional random fields models. Using a dataset containing 51 real manipulations related to three types of learners tasks, the preliminary experiments demonstrate that the sequences of observation obtained based on possibilistic reasoning significantly improve the performance of hidden Marvov models and conditional random fields models in the automatic recognition of the e-learning activities.

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Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model

Possibilistic Network based Information Retrieval Model

Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective

Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model