Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean

PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology

With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature. Given the high promise of accurate PCD technologies, we develop, PALMAR, a multiple-inhabitant activity recognition system by employing efficient signal processing and novel machine learning techniques to track individual person towards developing an adaptive multi-inhabitant tracking and HAR system. More specifically, we propose (i) a voxelized feature representation-based real-time PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive Order Hidden Markov Model based multi-person tracking and crossover ambiguity reduction techniques and (iii) novel adaptive deep learning-based domain adaptation technique to improve the accuracy of HAR in presence of data scarcity and diversity (device, location and population diversity). We experimentally evaluate our framework and systems using (i) a real-time PCD collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants, (ii) one publicly available 3D LiDAR activity data (28 participants) and (iii) an embedded hardware prototype system which provided promising HAR performances in multi-inhabitants (96%) scenario with a 63% improvement of multi-person tracking than state-of-art framework without losing significant system performances in the edge computing device.Comment: Accepted in IEEE International Conference on Computer Communications 202

Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks

Intégration de la blockchain à l'Internet des objets

L'Internet des objets (IdO) est en train de transformer l'industrie traditionnelle en une industrie intelligente où les décisions sont prises en fonction des données. L'IdO interconnecte de nombreux objets (ou dispositifs) qui effectuent des tâches complexes (e.g., la collecte de données, l'optimisation des services, la transmission de données). Toutefois, les caractéristiques intrinsèques de l'IdO entraînent plusieurs problèmes, tels que la décentralisation, une faible interopérabilité, des problèmes de confidentialité et des failles de sécurité. Avec l'évolution attendue de l'IdO dans les années à venir, il est nécessaire d'assurer la confiance dans cette énorme source d'informations entrantes. La blockchain est apparue comme une technologie clé pour relever les défis de l'IdO. En raison de ses caractéristiques saillantes telles que la décentralisation, l'immuabilité, la sécurité et l'auditabilité, la blockchain a été proposée pour établir la confiance dans plusieurs applications, y compris l'IdO. L'intégration de la blockchain a l'IdO ouvre la porte à de nouvelles possibilités qui améliorent intrinsèquement la fiabilité, la réputation, et la transparence pour toutes les parties concernées, tout en permettant la sécurité. Cependant, les blockchains classiques sont coûteuses en calcul, ont une évolutivité limitée, et nécessitent une bande passante élevée, ce qui les rend inadaptées aux environnements IdO à ressources limitées. L'objectif principal de cette thèse est d'utiliser la blockchain comme un outil clé pour améliorer l'IdO. Pour atteindre notre objectif, nous relevons les défis de la fiabilité des données et de la sécurité de l'IdO en utilisant la blockchain ainsi que de nouvelles technologies émergentes, notamment l'intelligence artificielle (IA). Dans la première partie de cette thèse, nous concevons une blockchain qui garantit la fiabilité des données, adaptée à l'IdO. Tout d'abord, nous proposons une architecture blockchain légère qui réalise la décentralisation en formant un réseau superposé où les dispositifs à ressources élevées gèrent conjointement la blockchain. Ensuite, nous présentons un algorithme de consensus léger qui réduit la puissance de calcul, la capacité de stockage, et la latence de la blockchain. Dans la deuxième partie de cette thèse, nous concevons un cadre sécurisé pour l'IdO tirant parti de la blockchain. Le nombre croissant d'attaques sur les réseaux IdO, et leurs graves effets, rendent nécessaire la création d'un IdO avec une sécurité plus sophistiquée. Par conséquent, nous tirons parti des modèles IA pour fournir une intelligence intégrée dans les dispositifs et les réseaux IdO afin de prédire et d'identifier les menaces et les vulnérabilités de sécurité. Nous proposons un système de détection d'intrusion par IA qui peut détecter les comportements malveillants et contribuer à renforcer la sécurité de l'IdO basé sur la blockchain. Ensuite, nous concevons un mécanisme de confiance distribué basé sur des contrats intelligents de blockchain pour inciter les dispositifs IdO à se comporter de manière fiable. Les systèmes IdO existants basés sur la blockchain souffrent d'une bande passante de communication et d’une évolutivité limitée. Par conséquent, dans la troisième partie de cette thèse, nous proposons un apprentissage machine évolutif basé sur la blockchain pour l'IdO. Tout d'abord, nous proposons un cadre IA multi-tâches qui exploite la blockchain pour permettre l'apprentissage parallèle de modèles. Ensuite, nous concevons une technique de partitionnement de la blockchain pour améliorer l'évolutivité de la blockchain. Enfin, nous proposons un algorithme d'ordonnancement des dispositifs pour optimiser l'utilisation des ressources, en particulier la bande passante de communication.Abstract : The Internet of Things (IoT) is reshaping the incumbent industry into a smart industry featured with data-driven decision making. The IoT interconnects many objects (or devices) that perform complex tasks (e.g., data collection, service optimization, data transmission). However, intrinsic features of IoT result in several challenges, such as decentralization, poor interoperability, privacy issues, and security vulnerabilities. With the expected evolution of IoT in the coming years, there is a need to ensure trust in this huge source of incoming information. Blockchain has emerged as a key technology to address the challenges of IoT. Due to its salient features such as decentralization, immutability, security, and auditability, blockchain has been proposed to establish trust in several applications, including IoT. The integration of IoT and blockchain opens the door for new possibilities that inherently improve trustworthiness, reputation, and transparency for all involved parties, while enabling security. However, conventional blockchains are computationally expensive, have limited scalability, and incur significant bandwidth, making them unsuitable for resource-constrained IoT environments. The main objective of this thesis is to leverage blockchain as a key enabler to improve the IoT. Toward our objective, we address the challenges of data reliability and IoT security using the blockchain and new emerging technologies, including machine learning (ML). In the first part of this thesis, we design a blockchain that guarantees data reliability, suitable for IoT. First, we propose a lightweight blockchain architecture that achieves decentralization by forming an overlay network where high-resource devices jointly manage the blockchain. Then, we present a lightweight consensus algorithm that reduces blockchain computational power, storage capability, and latency. In the second part of this thesis, we design a secure framework for IoT leveraging blockchain. The increasing number of attacks on IoT networks, and their serious effects, make it necessary to create an IoT with more sophisticated security. Therefore, we leverage ML models to provide embedded intelligence in the IoT devices and networks to predict and identify security threats and vulnerabilities. We propose a ML intrusion detection system that can detect malicious behaviors and help further bolster the blockchain-based IoT’s security. Then, we design a distributed trust mechanism based on blockchain smart contracts to incite IoT devices to behave reliably. Existing blockchain-based IoT systems suffer from limited communication bandwidth and scalability. Therefore, in the third part of this thesis, we propose a scalable blockchain-based ML for IoT. First, we propose a multi-task ML framework that leverages the blockchain to enable parallel model learning. Then, we design a blockchain partitioning technique to improve the blockchain scalability. Finally, we propose a device scheduling algorithm to optimize resource utilization, in particular communication bandwidth