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

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. a large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our aI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. a supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine. 2013 IEEE.The statements made herein are solely the responsibility of the authors. This work was supported in part by the National Priorities Research Program (NPRP) from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 10-0130-170288.Scopu

Techno-economic assessment of building energy efficiency systems using behavioral change: A case study of an edge-based micro-moments solution

Energy efficiency based on behavioral change has attracted increasing interest in recent years, although, solutions in this area lack much needed techno-economic analysis. That is due to the absence of both prospective studies and consumer awareness. To close such gap, this paper proposes the first techno-economic assessment of a behavioral change-based building energy efficiency solution, to the best of the authors' knowledge. From the one hand, the technical assessment is conducted through (i) introducing a novel edge-based energy efficiency solution; (ii) analyzing energy data using machine learning tools and micro-moments, and producing intelligent, personalized, and explainable action recommendations; and (iii) proceeding with a technical evaluation of four application scenarios, i.e., data collection, data analysis and anomaly detection, recommendation generation, and data visualization. On the other hand, economic assessment is performed by examining the marketability potential of the proposed solution via a market and research analysis of behavioral change-based systems for energy efficiency applications. Also, various factors impacting the commercialization of the final product are investigated before providing recommended actions to ensure its potential marketability via conducting a Go/No-Go evaluation. In conclusion, the proposed solution is designed at a low cost and can save up to 28%-68% of the consumed energy, which results in a Go decision to commercialize the technology. 2021 Elsevier LtdThis 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

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

Using big data and federated learning for generating energy efficiency recommendations

Internet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on the cloud. They collect data from a multitude of sensors, process it in real-time on the cloud and deliver immediate actions to sets of actuators that are installed locally. In this work, we present the (EM)3 project (Consumer Engagement towards Energy Saving Behaviour by Means of Exploiting Micro Moments and Mobile Recommendation Systems), which combines data collection, information abstraction, timed recommendations for energy saving actions and automations that promote energy saving in a household or office setup. The advantage of the (EM)3 project is that each room or office setup is controlled locally on an edge device, thus removing the need to share data to the cloud. The current article details on the data and information processing aspects of the (EM)3 solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. It also demonstrates the scalability of the solution by simulating a simple scenario of distributed data collection and processing on the edge nodes, which takes advantage of federated learning in order to adapt to the needs of multiple users without exposing their privacy. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.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