The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Sécurité étendue de la cryptographie fondée sur les réseaux euclidiens

Lattice-based cryptography is considered as a quantum-safe alternative for the replacement of currently deployed schemes based on RSA and discrete logarithm on prime fields or elliptic curves. It offers strong theoretical security guarantees, a large array of achievable primitives, and a competitive level of efficiency. Nowadays, in the context of the NIST post-quantum standardization process, future standards may ultimately be chosen and several new lattice-based schemes are high-profile candidates. The cryptographic research has been encouraged to analyze lattice-based cryptosystems, with a particular focus on practical aspects. This thesis is rooted in this effort.In addition to black-box cryptanalysis with classical computing resources, we investigate the extended security of these new lattice-based cryptosystems, employing a broad spectrum of attack models, e.g. quantum, misuse, timing or physical attacks. Accounting that these models have already been applied to a large variety of pre-quantum asymmetric and symmetric schemes before, we concentrate our efforts on leveraging and addressing the new features introduced by lattice structures. Our contribution is twofold: defensive, i.e. countermeasures for implementations of lattice-based schemes and offensive, i.e. cryptanalysis.On the defensive side, in view of the numerous recent timing and physical attacks, we wear our designer’s hat and investigate algorithmic protections. We introduce some new algorithmic and mathematical tools to construct provable algorithmic countermeasures in order to systematically prevent all timing and physical attacks. We thus participate in the actual provable protection of the GLP, BLISS, qTesla and Falcon lattice-based signatures schemes.On the offensive side, we estimate the applicability and complexity of novel attacks leveraging the lack of perfect correctness introduced in certain lattice-based encryption schemes to improve their performance. We show that such a compromise may enable decryption failures attacks in a misuse or quantum model. We finally introduce an algorithmic cryptanalysis tool that assesses the security of the mathematical problem underlying lattice-based schemes when partial knowledge of the secret is available. The usefulness of this new framework is demonstrated with the improvement and automation of several known classical, decryption-failure, and side-channel attacks.La cryptographie fondée sur les réseaux euclidiens représente une alternative prometteuse à la cryptographie asymétrique utilisée actuellement, en raison de sa résistance présumée à un ordinateur quantique universel. Cette nouvelle famille de schémas asymétriques dispose de plusieurs atouts parmi lesquels de fortes garanties théoriques de sécurité, un large choix de primitives et, pour certains de ses représentants, des performances comparables aux standards actuels. Une campagne de standardisation post-quantique organisée par le NIST est en cours et plusieurs schémas utilisant des réseaux euclidiens font partie des favoris. La communauté scientifique a été encouragée à les analyser car ils pourraient à l’avenir être implantés dans tous nos systèmes. L’objectif de cette thèse est de contribuer à cet effort.Nous étudions la sécurité de ces nouveaux cryptosystèmes non seulement au sens de leur résistance à la cryptanalyse en “boîte noire” à l’aide de moyens de calcul classiques, mais aussi selon un spectre plus large de modèles de sécurité, comme les attaques quantiques, les attaques supposant des failles d’utilisation, ou encore les attaques par canaux auxiliaires. Ces différents types d’attaques ont déjà été largement formalisés et étudiés par le passé pour des schémas asymétriques et symétriques pré-quantiques. Dans ce mémoire, nous analysons leur application aux nouvelles structures induites par les réseaux euclidiens. Notre travail est divisé en deux parties complémentaires : les contremesures et les attaques.La première partie regroupe nos contributions à l’effort actuel de conception de nouvelles protections algorithmiques afin de répondre aux nombreuses publications récentes d’attaques par canaux auxiliaires. Les travaux réalisés en équipe auxquels nous avons pris part on abouti à l’introduction de nouveaux outils mathématiques pour construire des contre-mesures algorithmiques, appuyées sur des preuves formelles, qui permettent de prévenir systématiquement les attaques physiques et par analyse de temps d’exécution. Nous avons ainsi participé à la protection de plusieurs schémas de signature fondés sur les réseaux euclidiens comme GLP, BLISS, qTesla ou encore Falcon.Dans une seconde partie consacrée à la cryptanalyse, nous étudions dans un premier temps de nouvelles attaques qui tirent parti du fait que certains schémas de chiffrement à clé publique ou d’établissement de clé peuvent échouer avec une faible probabilité. Ces échecs sont effectivement faiblement corrélés au secret. Notre travail a permis d’exhiber des attaques dites « par échec de déchiffrement » dans des modèles de failles d’utilisation ou des modèles quantiques. Nous avons d’autre part introduit un outil algorithmique de cryptanalyse permettant d’estimer la sécurité du problème mathématique sous-jacent lorsqu’une information partielle sur le secret est donnée. Cet outil s’est avéré utile pour automatiser et améliorer plusieurs attaques connues comme des attaques par échec de déchiffrement, des attaques classiques ou encore des attaques par canaux auxiliaires

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC