1,721,048 research outputs found
Visual cryptography schemes with perfect reconstruction of black pixels
No full textA (k,n)-threshold visual cryptography scheme ((k,n)-threshold VCS, for short) is a method to encode a secret image SI into n shadow images called shares such that any k or more shares enable the "visual" recovery of the secret image, but by inspecting less than k shares one cannot gain any information on the secret image. The "visual" recovery consists of xeroxing the shares onto transparencies, and then stacking them. Any k shares will reveal the secret image without any cryptographic computation. Visual cryptography schemes are characterized by two parameters: The pixel expansion, which is the number of subpixels each pixel of the original image is encoded into, and the contrast which measures the "difference" between a black and a white pixel in the reconstructed image. In this paper we analyze visual cryptography schemes in which the reconstruction of black pixels is perfect, that is, all the subpixels associated to a black pixel are black. We show that the minimum pixel expansion of such schemes can be simply computed by solving a suitable linear programming problem. Moreover, we give a construction for (3,n)-threshold VCS and a construction for (n - 1,n)-threshold VCS. These two constructions improve on the best previously known constructions with respect to the pixel expansion. © 1998 Elsevier Science Ltd. All rights reserved
A Time-Aware Approach for MOOC Dropout Prediction Based on Rule Induction and Sequential Three-Way Decisions
No full textNowadays, Massive Open Online Courses (MOOCs) are adopted by students worldwide. One of the main critical issues often associated with MOOCs is the dropout phenomenon. In other words, the percentage of students abandoning a MOOC-based study path is considered still too high. Therefore, an increasing number of scientific works, coming from several heterogeneous communities (e.g., computer science, data science, statistics, education) propose approaches trying to mitigate such a problem. The majority of the aforementioned works focus on machine learning methods to define classifiers able to be trained and, subsequently, to predict students who are going to abandon a course before it ends. Among such approaches, the ones achieving the best performance use enriched sets of features (to train their models) and produce results that cannot be used to easily clearly characterize the different behaviors of dropping-out and non-dropping-out students. The present work proposes the design of a novel process to train a set of dropout predictors leveraging on a reduced set of features. The underlying idea is to exploit weekly data in order to classify, with acceptable levels of precision, students who are likely going towards dropout or not. In cases of uncertainty, the classification decision is deferred to the next week, when new data is available. Such an approach, which takes care and is aware of the course timeline, offers several advantages. The first one is the chance to build a real-time educational decision support system able to support decision as sufficient information is available (as the time goes on). The second one is to preserve resources and avoiding wasting them with students erroneously classified at risk of dropout. The third one is to allow explicit characterization of dropout-conducing behavior by using a rule mining approach
Sequential Three-Way Decisions for Reducing Uncertainty in Dropout Prediction for Online Courses
No full textMassive Open Online Courses (MOOCs) allow accessing qualitative online educational resources for huge amounts of online students. In this context, the dropout phenomenon is known as a nasty problem faced by several existing studies proposing methods and techniques to make predictions on students who are at risk of dropping out. Although the majority of such studies adopt traditional classification algorithms based on supervised methods, the present work proposes a sequential approach based on Three-Way Decisions and Neighborhood Rough Sets. The underlying idea is to exploit weekly data in order to classify, with high levels of precision, students who are likely going towards dropout or not. In cases of uncertainty, the classification decision is deferred to the next week, when new data is available. Such an approach has the advantage to preserve resources and avoiding wasting them with students erroneously classified at risk of dropout. The sequential application of the approach makes the recall increase as new data is gathered
Targeted Advertising That Protects the Privacy of Social Networks Users
No full textNowadays, the massive use of social media provides useful unstructured knowledge that can be used to enhance the efficacy of online brand marketing campaigns. The unstructured nature of social media content and the relevance of the contextual dimension, like time, stress the requirements for extracting users' interests during the timeline. However, user profiling could have some unpleasant consequences for users' privacy, thus raising the need to define methodologies capable of avoiding privacy leaks despite the exploitation of interactions over social media. This paper presents both an intelligent method of profiling social media users and a privacy protection technique that is designed to match users' profiles and advertisements, and which could be used by advertising agencies. The proposed method performssemantic data analysis for extracting representations of the contents of messages exchanged by users over social media (e.g.,tweets), by exploiting rough set theory. In this way, users' interestis obtained by mining their daily online activity. The proposed framework investigates two-party scenarios, i.e., scenarios composed of a social network owner and an advertising agency willing to promote its client's products through the social network. This paper presents three privacy-preserving matching protocols which enable targeted advertising without compromising the privacy of either the users or the advertisers. Starting from a recently proposed advertisement matching protocol, a private layer was added to ensure that any sensitive information of either party is kept private. In this way, the social network and the advertiser could benefit from a system which allows them to run a matching protocol with the guarantee that sensitive user data (for the social network) and business information (for the advertiser) will not be disclosed. The first two protocols require interaction between the Advertiser and the Online Social Network, while the third one outsources to a semi-trusted service provider some of the computation done during the execution of the advertisement matching. The experimental results are also presented to illustrate the proposed system's good performance to discover potentially interested users given an advertisement as input
A time-driven FCA-based approach for identifying students' dropout in MOOCs
Full text linkIn online learning, the dropout phenomenon is a relevant issue to address with practical solutions. Several data sets stimulate original, and resolutive data analysis approaches, demonstrating the importance of the dropout phenomenon. This study proposes a novel approach to predicting massive online open course (MOOC) students at risk of dropout stressing the need to consider the temporal dimension in the data log. The proposal aims to build a data-driven decision support system able to identify students at risk of dropout based on the conceptualization of such students' behavior and its evolution along the time dimension. The primary theoretical model behind the proposed method is the formal concept analysis, and its temporal extension (i.e., temporal concept analysis) for analyzing timestamped data and carrying out a timed lattice. The main result of the paper is a method to extract behavioral patterns of MOOC students at risk of dropout. Such patterns are defined as Time-based Behavior Rules extracted from the aforementioned timed lattice obtained through the preprocessing of MOOC platform log files. The resulting rule set can be easily integrated for implementing educational DSS, as shown in the last part of the paper. The conducted experiments reveal promising results in terms of F-score and students' monitoring time
Managing Constraints in Role Based Access Control
Full text linkRole-based access control (RBAC) is the most popular access control model currently adopted in several contexts to define security management. Constraints play a crucial role since they can drive the selection of the best representation of the organization's security policies when migrating towards an RBAC system. In this paper, we examine different types of constraints addressing both theoretical aspects and practical considerations. On one side, we define the constrained role mining problem for each constraint type, showing its complexity. On the other hand, we present efficient heuristics adapted to each class of constraints, all derived from the specialization of a general approach for role mining. We show that our techniques improve over previous proposals, offering a complete set of experimentations obtained after the application of the heuristics to standard real-world datasets
Gastrointestinal and nutritional issues in joint hypermobility syndrome/ehlers-danlos syndrome, hypermobility type.
No full textGastrointestinal involvement is a well known complication of Ehlers-Danlos syndromes (EDSs), mainly in form of abdominal emergencies due to intestinal/abdominal vessels rupture in vascular EDS. In the last decade, a growing number of works investigated the relationship between a wide spectrum of chronic gastrointestinal complaints and various EDS forms, among which the hypermobility type (a.k.a. joint hypermobility syndrome; JHS/EDS-HT) was the most studied. The emerging findings depict a major role for gastrointestinal involvement in the health status and, consequently, management of JHS/EDS-HT patients. Nevertheless, fragmentation of knowledge limits its impact on practice within the boundaries of highly specialized clinics. In this paper, literature review on gastrointestinal manifestations in JHS/EDS-HT was carried out and identified papers categorized as (i) case-control/cohort studies associating (apparently non-syndromic) joint hypermobility and gastrointestinal involvement, (ii) case-control/cohort studies associating JHS/EDS-HT and gastrointestinal involvement, (iii) case reports/series on various gastrointestinal complications in (presumed) JHS/EDS-HT, and (iv) studies reporting gastrointestinal features in heterogeneous EDS patients' cohorts. Gastrointestinal manifestations of JHS/EDS-HT were organized and discussed in two categories, including structural anomalies (i.e., abdominal/diaphragmatic hernias, internal organ/pelvic prolapses, intestinal intussusceptions) and functional features (i.e., dysphagia, gastro-esophageal reflux, dyspepsia, recurrent abdominal pain, constipation/diarrhea), with emphasis on practice and future implications. In the second part of this paper, a summary of possible nutritional interventions in JHS/EDS-HT was presented. Supplementation strategies were borrowed from data available for general population with minor modifications in the light of recent discoveries in the pathogenesis of selected JHS/EDS-HT features
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