Reservoir Computing (RC) is an emerging machine learning paradigm where a fixed kernel, built from a randomly connected "reservoir" with sufficiently rich dynamics, is capable of expanding the problem space in a non-linear fashion to a higher dimensional feature space. These features can then be interpreted by a linear readout layer that is trained by a gradient descent method. In comparison to traditional neural networks, only the output layer needs to be trained, which leads to a significant computational advantage. In addition, the short term memory of the reservoir dynamics has the ability to transform a complex temporal input state space to a simple non-temporal representation. Adaptive real-time systems are multi-stage decision problems that can be used to train an agent to achieve a preset goal by performing an optimal action at each timestep. In such problems, the agent learns through continuous interactions with its environment. Conventional techniques to solving such problems become computationally expensive or may not converge if the state-space being considered is large, partially observable, or if short term memory is required in optimal decision making. The objective of this thesis is to use reservoir computers to solve such goal-driven tasks, where no error signal can be readily calculated to apply gradient descent methodologies. To address this challenge, we propose a novel reinforcement learning approach in combination with reservoir computers built from simple Boolean components. Such reservoirs are of interest because they have the potential to be fabricated by self-assembly techniques. We evaluate the performance of our approach in both Markovian and non-Markovian environments. We compare the performance of an agent trained through traditional Q-Learning. We find that the reservoir-based agent performs successfully in these problem contexts and even performs marginally better than Q-Learning agents in certain cases. Our proposed approach allows to retain the advantage of traditional parameterized dynamic systems in successfully modeling embedded state-space representations while eliminating the complexity involved in training traditional neural networks. To the best of our knowledge, our method of training a reservoir readout layer through an on-policy boot-strapping approach is unique in the field of random Boolean network reservoirs.

Desktops, laptops, smartphones, tablets, and the Kinect, oh my! With so many devices available to the average consumer, the limitations and pitfalls of each interface are becoming more apparent. Swimming in devices, users often have to stop and think about how to interact with each device to accomplish the current tasks at hand. The goal of this thesis is to minimize user cognitive effort in handling multiple devices by creating a context aware hybrid interface. The context aware system will be explored through the hybridization of gesture and touch interfaces using a multi-touch coffee table and the next-generation Microsoft Kinect. Coupling gesture and touch interfaces creates a novel multimodal interface that can leverage the benefits of both gestures and touch. The hybrid interface is able to utilize the more intuitive and dynamic use of gestures, while maintaining the precision of a tactile touch interface. Joining these two interfaces in an intuitive and context aware way will open up a new avenue for design and innovation.

<p>REQUIREMENTS ANALYSIS FOR A CONTEXT-AWARE MULTI-AGENCY EMERGENCY RESPONSE SYSTEM</p> / <p>Society faces many natural and man-made disasters which can have a large impact in terms of deaths, injuries, monetary losses, psychological distress, and economic effects. Society needs to find ways to prevent or reduce the negative impact of these disasters as much as possible. Information systems have been used to assist emergency response to a certain degree in some cases. However, there is still a lack of understanding on how to build an effective emergence response system. To identify the basic requirements of such systems, a grounded theory research method is used for data collection and analysis. Data from firsthand interviews and observations was combined with literature and analyzed to discover several emergent issues and concepts regarding disaster response. The issues and concepts were organized into four categories: i) context-awareness; ii) multi-party relationships; iii) task-based coordination; and iv) information technology support, which together identified the needs of disaster response coordination. Using evidence from the data, these factors were related to one another to develop a framework for context-aware multi-party coordination systems (CAMPCS). This study contributes to the field of emergency management as the framework represents a comprehensive theory for disaster response coordination that can guide future research on emergency management coordination.</p> / Doctor of Philosophy (PhD)

The Big Data revolution brought an increasing availability of data sets of unprecedented scales, enabling researchers in machine learning and data mining communities to escalate in learning from such data and providing data-driven insights, decisions, and predictions. However, on their journey, they are faced with numerous challenges, including dealing with missing observations while learning from such data or making predictions on previously unobserved or rare (“tail”) examples, which are present in a large span of domains including climate, medical, social networks, consumer, or computational advertising domains. In this thesis, we address this important problem and propose tools for handling partially observed or completely unobserved data by exploiting information from its context. Here, we assume that the context is available in the form of a network or sequence structure, or as additional information to point-informative data examples. First, we propose two structured regression methods for dealing with missing values in partially observed temporal attributed graphs, based on the Gaussian Conditional Random Fields (GCRF) model, which draw power from the network/graph structure (context) of the unobserved instances. Marginalized Gaussian Conditional Random Fields (m-GCRF) model is designed for dealing with missing response variable value (labels) in graph nodes, whereas Deep Feature Learning GCRF is able to deal with missing values in explanatory variables while learning feature representation jointly with learning complex interactions of nodes in a graph and together with the overall GCRF objective. Next, we consider unsupervised and supervised shallow and deep neural models for monetizing web search. We focus on two sponsored search tasks here: (i) query-to-ad matching, where we propose novel shallow neural embedding model worLd2vec with improved local query context (location) utilization and (ii) click-through-rate prediction for ads and queries, where Deeply Supervised Semantic Match model is introduced for dealing with unobserved and tail queries click-through-rate prediction problem, while jointly learning the semantic embeddings of a query and an ad, as well as their corresponding click-through-rate. Finally, we propose a deep learning approach for ranking investigators based on their expected enrollment performance on new clinical trials, that learns from both, investigator and trial-related heterogeneous (structured and free-text) data sources, and is applicable to matching investigators to new trials from partial observations, and for recruitment of experienced investigators, as well as new investigators with no previous experience in enrolling patients in clinical trials. Experimental evaluation of the proposed methods on a number of synthetic and diverse real-world data sets shows surpassing performance over their alternatives. / Computer and Information Science

No / Rapid advances in software systems, wireless networks, and embedded devices have led to the development of a pervasive and mobile cyberspace that provides an infrastructure for anywhere/anytime service provisioning in different domains such as engineering, commerce, education, and entertainment. This style of service provisioning enables users to freely move between geographical areas and to continuously access information and conduct online transactions. However, such a high mobility may cause performance and reliability problems during the execution of transactions. For example, the unavailability of sufficient bandwidth can result in failure of transactions when users move from one area (cell) to another. We present a context-aware transaction model that dynamically adapts to the users' needs and execution environments. Accordingly, we develop a new mobility management scheme that ensures seamless connectivity and reliable execution of context-aware transactions during mobility of users. The proposed scheme is designed and developed using a combination of different queuing models. We conduct various experiments in order to show that the proposed scheme optimizes the mobility management process and increases the throughput of context-aware transactions.

In this dissertation, we propose service-oriented sensor-actuator networks (SOSANETs) as a new paradigm for building the next generation of customizable, open, interoperable sensor-actuator networks. In SOSANETs, nodes expose their capabilities to applications in the form of service profiles. A node's service profile consists of a set of services (i.e., sensing and actuation capabilities) that it provides and the quality of service (QoS) parameters associated with those services (delay, accuracy, freshness, etc.). SOSANETs provide the benefits of both application-specific SANETs and generic SANETs. We first define a query model and an architecture for SOSANETs. The proposed query model offers a simple, uniform query interface whereby applications specify sensing and actuation queries independently from any specific deployment of the underlying SOSANET. We then present μRACER (Reliable Adaptive serviCe-driven Efficient Routing), a routing protocol suite for SOSANETs. μRACER consists of three routing protocols, namely, SARP (Service-Aware Routing Protocol), CARP (Context-Aware Routing Protocol), and TARP (Trust-Aware Routing Protocol). SARP uses an efficient service-aware routing approach that aggressively reduces downstream traffic by translating service profiles into efficient paths. CARP supports QoS by dynamically adapting each node's routing behavior and service profile according to the current context of that node, i.e. number of pending queries and number and type of messages to be routed. Finally, TARP achieves high end-to-end reliability through a scalable reputation-based approach in which each node is able to locally estimate the next hop of the most reliable path to the sink. We also propose query optimization techniques that contribute to the efficient execution of queries in SOSANETs. To evaluate the proposed service-oriented architecture, we implemented TinySOA, a prototype SOSANET built on top of TinyOS with uRACER as its routing mechansim. TinySOA is designed as a set of layers with a loose interaction model that enables several cross-layer optimization options. We conducted an evaluation of TinySOA that included a comparison with TinyDB. The obtained empirical results show that TinySOA achieves significant improvements on many aspects including energy consumption, scalability, reliability and response time. / Ph. D.

Computer Architecture is entering an era where hundreds of Processing Elements (PE) can be integrated onto single chips even as decades-long, steady advances in instruction, thread level parallelism are coming to an end. And yet, conventional methods of parallelism fail to scale beyond 4-5 PE's, well short of the levels of parallelism found in the human brain. The human brain is able to maintain constant real time performance as cognitive complexity grows virtually unbounded through our lifetime. Our underlying thesis is that contextual categorization leading to simplified algorithmic processing is crucial to the brains performance efficiency. But, since the overheads of such reorganization are unaffordable in real time, we also observe the critical role of sleep and dreaming in the lives of all intelligent beings. Based on the importance of dream sleep in memory consolidation, we propose that it is also responsible for contextual reorganization. We target mobile device applications that can be personalized to the user, including speech, image and gesture recognition, as well as other kinds of personalized classification, which are arguably the foundation of intelligence. These algorithms rely on a knowledge database of symbols, where the database size determines the level of intelligence. Essential to achieving intelligence and a seamless user interface however is that real time performance be maintained. Observing this, we define our chief performance goal as: Maintaining constant real time performance against ever increasing algorithmic and architectural complexities. Our solution is a method for Malleable Contextual Partitioning (MCP) that enables closer personalization to user behavior. We conceptualize a novel architectural framework, the Dream Architecture for Lateral Intelligence (DALI) that demonstrates the MCP approach. The DALI implements a dream phase to execute MCP in ideal MISD parallelism and reorganize its architecture to enable contextually simplified real time operation. With speech recognition as an example application, we show that the DALI is successful in achieving the performance goal, as it maintains constant real time recognition, scaling almost ideally, with PE numbers up to 16 and vocabulary size up to 220 words. / Master of Science

In this thesis, we present ContextNet, a novel general object detection framework for incorporating context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to predefined classes and regressing a bounding-box around it without using any information about the corresponding scene. ContextNet is based on an intuitive idea of having cues about the general scene (e.g., kitchen and library), and changes the priors about presence/absence of some object classes. We provide a general means for integrating this notion in the decision process about the given ROI by using a pretrained network on the scene recognition datasets in parallel to a pretrained network for extracting object-level features for the corresponding ROI. Using comprehensive experiments on the PASCAL VOC 2007, we demonstrate the effectiveness of our design choices, the resulting system outperforms the baseline in most object classes, and reaches 57.5 mAP (mean Average Precision) on the PASCAL VOC 2007 test set in comparison with 55.6 mAP for the baseline. / MS

L’objectif de cette thèse est d’apporter une réponse à la problématique d’adaptation automatique et dynamique des services numériques au sein des réseaux du futur (NGN). Pour cela, notre contribution s’articule autour d’un intergiciel qui prend en charge les fonctions de gestion du contexte pour les services numériques ; ceci pour permettre l’émergence de services numériques sensibles au contexte dans les réseaux du futur. L’originalité de ces travaux dans les NGN se situe dans le modèle de conception pour des services numériques sensibles au contexte où l’on découple la logique métier du service numérique de la logique de gestion du contexte. Plus spécifiquement nous définissons la notion de signature de contexte, propre à chaque service numérique, qui permet d’identifier les situations d’usage du service numérique et accompagne l’intergiciel dans le choix de la modalité d’exécution la plus performante. Elle est liée au service numérique sensible au contexte dès sa conception. Lors du déploiement du service dans le serveur d’applications, la signature de contexte est désolidarisée de la logique métier et se voit insérée dans la base de connaissance de l’intergiciel. La signature fait ainsi le lien entre l’intergiciel et le service numérique. Elle permet d’activer l’ensemble des fonctions de traitement du contexte que nous avons modélisées sous la forme d’une chaîne de traitement du contexte. Cette approche assure une séparation des deux logiques qui composent un service numérique sensible au contexte. Les concepteurs de tels services peuvent alors se concentrer sur le développement de la logique métier sans se soucier des fonctions de gestion du contexte / The era of fixed-mobile convergence is imminent, thanks to the Next Generation Network (NGN) paradigm. Such approaches encompass converged networks where users can use any kind of terminals and network access technologies.An utmost issue for NGN is to provide multimodal digital services, since the user’s terminal or network access are expected to change over time during his own session. This means services must be able to shift from one form of service delivery to another according to context updates. We propose to implement context awareness for digital services in NGNs by providing context-awareness to application servers hosted in a NGN. These services running on applications servers (AS) are consumed through a network using the client-server paradigm. In this situation, the context is composed of information about the network and features of the end-user terminal. We explain and implemente a novel architecture that makes AS taking into account the context-awareness of the digital service they host. The architecture employs application ontology in order to monitor the applications’ context. Moreover, when a new context-aware application is deployed on the application server, its context-logic (a set of SWRL rules which rely the domain ontology called context signature) is extracted from the application bundle. Each SWRL rule encodes a context-aware threshold for the application. When a rule is triggered at runtime, the application server notifies the application, so that the application change its service delivery modality, as the current context favors another service delivery modality different than the current one

<p>This work describes a design of a multimedia content delivery system based on context, to provide multimedia information and other services according to the user location and his preferences. It focuses on mobility and the problem of different coherent and cohesive presentations depending on the available resources of the presentation environment.</p>