Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions

The current study focuses on systematically analyzing the recent advances in the field of Multimodal eXplainable Artificial Intelligence (MXAI). In particular, the relevant primary prediction tasks and publicly available datasets are initially described. Subsequently, a structured presentation of the MXAI methods of the literature is provided, taking into account the following criteria: a) The number of the involved modalities, b) The stage at which explanations are produced, and c) The type of the adopted methodology (i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are discussed. Finally, a comprehensive analysis of current challenges and future research directions is provided.Comment: 26 pages, 11 figure

Blockchain-based recommender systems: Applications, challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Συστήματα συστάσεων με εφαρμογές στον πραγματικό κόσμο

Based on the 2012 ACM Computing Classification System (ACM CCS), recommender systems are sub-category of Information Systems and Information Retrieval, bridging concepts from those two categories. Recommender systems are basically tools that traditionally predict the likelihood of a user’s preference for an item. They are typically used in a variety of applications such as movies, music, news, books, research articles, search queries, social tags, and products in general. The goal of such a system is to understand the user’s preferences and taste and then recommend items that the user is likely to enjoy or find useful. Modern recommender systems reveal an expansion on the application areas such systems are being used for various scenarios. This brings us to what we call in the context of this dissertation “recommender systems with real-life applications” (or real-life recommender systems in short), that forms the main research direction of this dissertation. The domain of research is the subject of “Recommender Systems”, focusing though on a problem with a greater amount of challenges and extensions. The scope of this research is to provide a new type of recommender systems that spans beyond the traditional virtual frameworks of social networks and web applications. Real-life recommender systems are fed with real-world data (i.e. user trajectories, IoT sensor data, etc.), extract patterns that are correspond to user habits and recommend real-life actions to the user. This thesis, attempts to lay down the fundamental characteristics of such systems and provides a hands-on development of a demo framework for the case of energy consumption and sustainability, providing energy-related action recommendations for reducing users’ energy consumption.Με βάση το από 2012 Σύστημα Κατηγοριοποίησης Υπολογιστικών Όρων της ACM (ACM Computing Classification System, ACM CCS), τα συστήματα συστάσεων είναι υπο-κατηγορία των Πληροφοριακών Συστημάτων και των συστημάτων Ανάκτησης Πληροφοριών, γεφυρώνοντας έννοιες και από τις δύο παραπάνω κατηγορίες. Τα συστήματα συστάσεων είναι ουσιαστικά εργαλεία που παραδοσιακά προβλέπουν την πιθανότητα προτίμησης ενός χρήστη για ένα αντικείμενο. Συνήθως χρησιμοποιούνται σε μια ποικιλία εφαρμογών για παραγωγή συστάσεων, όπως ταινίες, μουσική, ειδήσεις, βιβλία, ερευνητικά άρθρα, ερωτήματα αναζήτησης, ετικέτες κοινωνικής δικτύωσης και προϊόντα γενικότερα. Ο στόχος ενός τέτοιου συστήματος είναι να κατανοήσει τις προτιμήσεις του χρήστη και στη συνέχεια να προτείνει αντικείμενα που ο χρήστης είναι πιθανό να προτιμήσει ή να βρει χρήσιμα. Τα σύγχρονα συστήματα συστάσεων αποκαλύπτουν μια επέκταση στους τομείς εφαρμογής που χρησιμοποιούνται τέτοια συστήματα για διάφορα σενάρια. Αυτό μας φέρνει σε αυτό που στο πλαίσιο αυτής της διατριβής ονομάζουμε «Συστήματα συστάσεων με εφαρμογές στον πραγματικό κόσμο» (ή Συστήματα Συστάσεων για τον πραγματικό κόσμο), το οποίο αποτελεί την κύρι α ερευνητική κατεύθυνση αυτής της διατριβής. Το πεδίο έρευνας της παρούσας εργασίας είναι τα “Συστήματα Συστάσεων”, εστιάζοντας όμως σε ένα πρόβλημα με πολύ μεγαλύτερες προκλήσεις και προεκτάσεις. Σκοπός αυτής της έρευνας είναι ο ορισμός και η περιγραφή ενός νέου τύπου συστημάτων συστάσεων που επεκτείνονται πέρα από τα παραδοσιακά εικονικά πλαίσια των κοινωνικών δικτύων και των εφαρμογών Ιστού. Τα συστήματα συστάσεων πραγματικού κόσμου τροφοδοτούνται με δεδομένα πραγματικού κόσμου (π.χ. τροχιές χρηστών, δεδομένα αισθητήρων IoT, κ.λπ.), εξάγουν πρότυπα (patterns) τα οποία αντιστοιχούν σε ανθρώπινες συνήθειες και προτείνουν ενέργειες στον πραγματικό κόσμο. Η παρούσα διατριβή επιχειρεί να καθορίσει τα θεμελιώδη χαρακτηριστικά τέτοιων συστημάτων και παρέχει μια πρακτική ανάπτυξη ενός πλαισίου επίδειξης για την περίπτωση παροχής συστάσεων για δράσεις ενεργειακής απόδοσης με σκοπό τη μείωση της κατανάλωσης ενέργειας από τους χρήστες

Detecting Search and Rescue Missions from AIS Data

In this work we present a tool that automatically detects SAR missions in the sea, by employing Automatic Identification System (AIS) data streams. The approach defines three steps to be taken: a) trajectory compression for affordable real time analysis in the presence of big data; b) detection of sub-operations to which a SAR mission is actually decomposed, and; c) synthesis of multiple vessels' inferred behavior to determine an ongoing SAR mission and its details

Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark

A model for predicting room occupancy based on motion sensor data

When designing a large scale IoT ecosystem, it is important to provide economical solutions at all levels, from sensors and actuators to the software used for analytics and orchestration. It is of equal importance to provide non-intrusive solutions that do not violate users' privacy, but above all, it is important to guarantee the accuracy and integrity of the provided solution. In this work, we present a research prototype solution that has been developed as part of an ongoing project called (EM)3. The project involves IoT sensors and actuators, realtime data analytics modules and cutting edge recommendation algorithms in an ecosystem that improves energy efficiency in office buildings. The main concept of the (EM)3 is to recommend energy saving actions at the right moment to the right user. At the core of the (EM)3 vision is to detect when is the right moment for an energy saving action and sensors play a vital role in this. This article focuses on the model that predicts room occupancy using only data from a motion sensor. The predictions of the model, are used to trigger automations and notifications that turn-off office devices (e.g. air conditioning, lights, monitors, etc.) as soon as the office becomes empty, or a few minutes before this happens, in order to further promote efficient energy consumption habits. The evaluation of the model, using data from a camera sensor for validation, demonstrates a very low error rate and a very short delay on the detection of when the room is actually empty. 2020 IEEE.ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu