This study leverages high-quality data from soccer matches to derive a better understanding of the elements contributing to a team's success. Initially, we classically analyzed extensive soccer logs from major European leagues and international tournaments. We have defined a team's technical performance through a vector of features, including goalkeeping, intercepts, tackles, dribbles, and more. The objective of the paper is to classify the state of a match with the labels of win, defeat, and draw. We have not made predictions about any future outcomes, but did focus on understanding the characteristics of the data itself to identify patterns and trends of the match. In doing so, we guessed the match result only after having collected the above feature data for the entire match duration. Thus, our scenario poses a classification problem. We compare different models (SVM, Logit, XGB, and MLP), the last one outperforming the others. Moreover, as a brand-new approach, we analyzed the logs by considering matches of different increasing duration. In particular, the lengths of the matches were the terms of an arithmetic series with a common difference of 5 minutes. In doing so, we have provided a dynamic approach that labels the match outcome every 5 minutes, using an MLP to track the accuracy of the state over the time. The findings have revealed an improved detection of draws, and highlighted that the model accuracy is higher in the early stages but decreases as the match progresses. In both approaches, explainable AI techniques have identified the key predictive features, offering insights into how technical features influence success dynamically throughout a match.

The RBAC model eases the administration of complex organizations and supports the implementation of basic security principles such as least privilege and separation of duty (SoD). In this work, we present PP-SSP, a Post-Processing Heuristic for the Static Separation of Permissions Problem. The heuristic has been designed to decouple role discovery from constraint enforcement phase, so that any existing rolemining algorithm can be selected at the beginning, making then the framework flexible and easily adaptable to different mining techniques. We evaluate the heuristic against a number of public datasets, report the results obtained, and discuss its performance in terms of different metrics, showing its efficiency and effectiveness.

The popularity of the Role-based Access Control (RBAC) model is determined by its flexibility and its adaptability in different contexts, easing the enforcement and the management of security policy. In some cases, different kinds of (cardinality) constraints are considered to adjust and adapt roles and their assignment to best represent the organization’s security policy.However, the process of role mining, whether based on an organizational scenario or on existing permission assignments, is a hard task, since the problem shows NP-hard computational complexity and in case of frequent policy updates, the dynamic adaptation of the roles can be challenging. Then, the only possibility of producing an RBAC model compliant with the security policy is to resort to heuristics, which may return an approximation of the optimal solution.In this paper, we propose an innovative approach to explore the space of the solution based on the bag of word value, which is commonly deployed in the field of document representation and knowledge extraction. We propose different heuristics and validate our approach reporting the results of the application to standard datasets, and providing an evaluation under different metrics and indicators. We show that our technique returns improved results and provides an alternative way to produce valid solutions for constrained RBAC.

The rising popularity of e-commerce has led to the widespread adoption of electronic coupons (e-coupons) due to their convenience and portability.However, traditional e-coupon services that rely on centralized servers often face security concerns.Centralization can result in issues like counterfeit e-coupons, difficulties in proving ownership, and the possibility of double-spending on expired e-coupons.In order to address these challenges, we have developed an innovative e-coupon service that harnesses the power of blockchain technology to enhance security.Our approach involves the creation of a dedicated server that facilitates the e-coupon service and interacts seamlessly with the blockchain system.To ensure the integrity of the e-coupon business logic and implementation information, we have developed a smart contract on an Ethereum-based blockchain system.By leveraging this blockchain infrastructure, we can establish a trustable and transparent environment for e-coupon transactions.Through rigorous experimentation and analysis , we have demonstrated that our proposed service significantly bolsters security while incurring only minimal performance impact when compared to existing e-coupon services.By integrating blockchain technology, we provide a robust solution that effectively addresses the concerns surrounding e-coupon security, paving the way for a more reliable and trustworthy e-commerce experience.

Role-based access control (RBAC) defines the methods complex organizations use to assign their users permissions for accessing restricted resources. RBAC assigns users to roles, where roles determine the resources each user can access. The definition of roles, especially when there is a large number of users and many resources to handle, can be a very difficult and time consuming task. The class of tools and methodologies to elicit roles starting from existing user-permission assignments are referred to as role mining. Sometimes, to let the RBAC model be directly deployable in organizations, role mining can also take into account various constraints, like cardinality and separation of duty. Typically, these constraints are enforced to ease roles' management and their use is justified as role administration becomes convenient. In this paper, we focus on the User-Distribution cardinality constraint which places a restriction the number of users that can be assigned to a given role. In this scenario, we present a simple heuristic that improves over the state-of-the-art. Furthermore, to address a more realistic situation, we provide the User-Distribution model with the additional constraint that avoids the generation of roles sharing identical set of permissions. Similarly, within this context, we describe a heuristic enabling the computation of a solution in the new model. Additionally, we assess both heuristics' performances using real-world datasets.

Nowadays, 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.

Role-based access control (RBAC) has become a de facto standard to control access to restricted resources in complex systems and is widely deployed in many commercially available applications, including operating systems, databases and other softwares. The migration process towards RBAC, starting from the current access configuration, relies on the design of role mining techniques, whose aim is to define suitable roles that implement the given access policies. Some constraints can be used to transform the roles automatically output by the mining procedures and effectively capture the organization’s status under analysis. Such constraints can limit the final configuration characteristics, such as the number of roles assigned to a user, or the number of permissions included in a role, and produce a resulting role set that is effectively usable in real-world situations. In this paper, we consider two constraints: the number of permissions a role can include and the number of roles assigned to any user. In particular, we present two heuristics that produce roles compliant with both constraints and evaluate their performances using both real-world and synthetic datasets.

Massive 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.

In 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.

Role mining techniques are frequently used to derive a set of roles representing the current organization of a company following the RBAC model and simplifying the definition and the implementation of security policies. Constraints on the resulting roles can be defined to have valid roles, that can be efficiently managed, limiting for example the number of permissions included in a role or the users a role can be assigned to. Since the associated problems are NP hard, several heuristics have been developed to find sub-optimal solutions adopting the concurrent or the post-processing approach. In the first case, assignment matrices are obtained satisfying the given constraints during the computation, while in the second case, the intermediate solutions are obtained without considering the constraints, that are enforced successively. In this paper we present two heuristics for the Permission Usage and Role Usage Cardinality Constraints in the post-processing approach: we consider constraints limiting the number of permissions that can be included in a role in the first case, and the number of roles that can include a permission in the second case, refining the roles produced by some other technique (not considering any constraint). For both heuristics we analyze their performance after their application to some standard datasets, showing the improved results obtained w.r.t. state of the art solutions.

Nowadays, 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.

Role-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.

In this paper, we propose an intelligent and private method to profile social network users. The scenario is constituted by the social network platform and an advertiser willing to expose its products to potentially interested users. The intelligence is in the use of text analysis services (like Wikify!) to extract knowledge from the social networks posts and get measure criteria to compare them, by using Rough Set Theory. The private layer guarantees that neither of the parties in the scenario can gain any advantage by knowing information (tastes, users topic adherence, etc...) of the other party.

Constrained role mining aims to define a valid set of roles efficiently representing the organization of a company, easing the management of the security policies. Since the associated problems are NP hard, usually some heuristics are defined to find some sub-optimal solutions. In this paper we define two heuristics for the Permission Distribution and Role Usage Cardinality Constraints in the post processing framework, i.e. refining the roles produced by some other algorithm. We discuss the performance of the proposed heuristics applying them to some standard datasets showing the improvements w.r.t. previously available solutions.

Role Based Access Control (RBAC) models have been adopted in many organizations as the standard way to implement security policies and assign access to restricted resources to roles and roles to users. To capture the business relationships within the organization and efficiently migrate towards RBAC, several role mining techniques have been defined. Constraints on the resulting roles and assignments to users can be imposed to filter out inconsistent situations produced by the automatic algorithm and to better capture the status of the organization. In this paper we are interested in constraints on the number of permissions that can be included in a role and on the number of persons a role can be assigned to. We analyze the problem and propose a couple of heuristics. The heuristics have been applied to standard datasets to validate their performance.

Nowadays, large shopping malls provide tools to help and boost customers to buy products. Some of these tools melt down digital operations with physical ones executed by customers into blended commerce experiences. On the other hand, ambient intelligence (AmI) represents a paradigm focused on equipping physical environments to define ergonomic spaces for people interacting with computer-based localized services which are ubiquitously accessible. In this context, we propose a framework based on cellular automata (CA), a very well known formal computational model, suitable to abstract services deployed into an AmI-based environment preserving certain privacy levels of shoppers’ information. CA-based algorithms are advantageous because they are distributed, scalable, on-line and require low costs to be deployed. This work proposes a recent application of CA, namely Cellular ANTomata, to implement a service by which shoppers are guided to find the suitable offerings for items in their shopping lists. A further result provided by this paper is the instantiation of a protocol for privacy-preserving shopping experience in the shopping mall.

In this paper we extend a previous recent work on Ambient Intelligence, deployed into a scenario of Intelligence Shopping Malls, with a privacy layer. In fact nowadays, in the Ambient Intelligence context, privacy issues are more and more considered an urgent and main issue to take care of. The success of this permeated ubiquitous intelligence seems to be strongly correlated to how much the scenario is able to protect the privacy and the rights of the users.The Intelligence Shopping Mall is a physical environment for commerce equipped with sensors and actuators for supporting shoppers. These latters have a wish list of the items to buy. Once in the mall, the wish list should be disclosed to steer the shopper towards the right shop selling the wished item. Anyway, from shops' point of view, shopping lists contain valuable information about shoppers. Indeed, from shopping lists one could easily infer users' personal preferences or tendency (e.g., users' lifestyle) that could be used for marketing purpose. Hence, shopping lists could reveal shoppers' sensitive information. In this paper, to preserve the shoppers' privacy without limiting the possibility to guide users towards shops selling the sought products, we propose an efficient and efficacious privacy preserving protocol. Using such a protocol, shops can steer shoppers towards the shops selling the desired items without knowing the items in their shopping lists (excluding the items bought in the shop itself).

This paper emphasize on, how to improve security of biometric systems with the help of signatures using multilayer multishare approach of hierarchical visual cryptography. Hierarchical visual cryptography is defined on the basis of visual cryptography. Here we propose model, which highlights a novel approach of An Automated System for Signature Recognition and Authentication from Multishare Based Image Database. It creates shares of a signature image and achieve its encryption/decryption after going through visual cryptography. The overall effort of the proposed scheme is the achievement of creating multiple shares at multiple level. Our objective is to improve security, accuracy reliability and efficiency of signature image using hierarchical visual cryptography. KeywordsVisual Cryptography, Handwritten Signature, Multi-layer Multi-Shares, Hierachical Visual Cryptography, Signature Recognition.

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the Jaccard similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.