Learning dialogue POMDP model components from expert dialogues

Un système de dialogue conversationnel doit aider les utilisateurs humains à atteindre leurs objectifs à travers des dialogues naturels et efficients. C'est une tache toutefois difficile car les langages naturels sont ambiguës et incertains, de plus le système de reconnaissance vocale (ASR) est bruité. À cela s'ajoute le fait que l'utilisateur humain peut changer son intention lors de l'interaction avec la machine. Dans ce contexte, l'application des processus décisionnels de Markov partiellement observables (POMDPs) au système de dialogue conversationnel nous a permis d'avoir un cadre formel pour représenter explicitement les incertitudes, et automatiser la politique d'optimisation. L'estimation des composantes du modelé d'un POMDP-dialogue constitue donc un défi important, car une telle estimation a un impact direct sur la politique d'optimisation du POMDP-dialogue. Cette thèse propose des méthodes d'apprentissage des composantes d'un POMDPdialogue basées sur des dialogues bruités et sans annotation. Pour cela, nous présentons des méthodes pour apprendre les intentions possibles des utilisateurs à partir des dialogues, en vue de les utiliser comme états du POMDP-dialogue, et l'apprendre un modèle du maximum de vraisemblance à partir des données, pour transition du POMDP. Car c'est crucial de réduire la taille d'état d'observation, nous proposons également deux modèles d'observation: le modelé mot-clé et le modelé intention. Dans les deux modèles, le nombre d'observations est réduit significativement tandis que le rendement reste élevé, particulièrement dans le modele d'observation intention. En plus de ces composantes du modèle, les POMDPs exigent également une fonction de récompense. Donc, nous proposons de nouveaux algorithmes pour l'apprentissage du modele de récompenses, un apprentissage qui est basé sur le renforcement inverse (IRL). En particulier, nous proposons POMDP-IRL-BT qui fonctionne sur les états de croyance disponibles dans les dialogues du corpus. L'algorithme apprend le modele de récompense par l'estimation du modele de transition de croyance, semblable aux modèles de transition des états dans un MDP (processus décisionnel de Markov). Finalement, nous appliquons les méthodes proposées à un domaine de la santé en vue d'apprendre un POMDP-dialogue et ce essentiellement à partir de dialogues réels, bruités, et sans annotations.Spoken dialogue systems should realize the user intentions and maintain a natural and efficient dialogue with users. This is however a difficult task as spoken language is naturally ambiguous and uncertain, and further the automatic speech recognition (ASR) output is noisy. In addition, the human user may change his intention during the interaction with the machine. To tackle this difficult task, the partially observable Markov decision process (POMDP) framework has been applied in dialogue systems as a formal framework to represent uncertainty explicitly while supporting automated policy solving. In this context, estimating the dialogue POMDP model components is a signifficant challenge as they have a direct impact on the optimized dialogue POMDP policy. This thesis proposes methods for learning dialogue POMDP model components using noisy and unannotated dialogues. Speciffically, we introduce techniques to learn the set of possible user intentions from dialogues, use them as the dialogue POMDP states, and learn a maximum likelihood POMDP transition model from data. Since it is crucial to reduce the observation state size, we then propose two observation models: the keyword model and the intention model. Using these two models, the number of observations is reduced signifficantly while the POMDP performance remains high particularly in the intention POMDP. In addition to these model components, POMDPs also require a reward function. So, we propose new algorithms for learning the POMDP reward model from dialogues based on inverse reinforcement learning (IRL). In particular, we propose the POMDP-IRL-BT algorithm (BT for belief transition) that works on the belief states available in the dialogues. This algorithm learns the reward model by estimating a belief transition model, similar to MDP (Markov decision process) transition models. Ultimately, we apply the proposed methods on a healthcare domain and learn a dialogue POMDP essentially from real unannotated and noisy dialogues

Active Learning with Semi-Supervised Support Vector Machines

A significant problem in many machine learning tasks is that it is time consuming and costly to gather the necessary labeled data for training the learning algorithm to a reasonable level of performance. In reality, it is often the case that a small amount of labeled data is available and that more unlabeled data could be labeled on demand at a cost. If the labeled data is obtained by a process outside of the control of the learner, then the learner is passive. If the learner picks the data to be labeled, then this becomes active learning. This has the advantage that the learner can pick data to gain specific information that will speed up the learning process. Support Vector Machines (SVMs) have many properties that make them attractive to use as a learning algorithm for many real world applications including classification tasks. Some researchers have proposed algorithms for active learning with SVMs, i.e. algorithms for choosing the next unlabeled instance to get label for. Their approach is supervised in nature since they do not consider all unlabeled instances while looking for the next instance. In this thesis, we propose three new algorithms for applying active learning for SVMs in a semi-supervised setting which takes advantage of the presence of all unlabeled points. The suggested approaches might, by reducing the number of experiments needed, yield considerable savings in costly classification problems in the cases when finding the training data for a classifier is expensive

An Ordered Bag Semantics for SQL

Semantic query optimization is an important issue in many contexts of databases including information integration, view maintenance and data warehousing and can substantially improve performance, especially in today's database systems which contain gigabytes of data. A crucial issue in semantic query optimization is query containment. Several papers have dealt with the problem of conjunctive query containment. In particular, some of the literature admits SQL like query languages with aggregate operations such as sum/count. Moreover, since real SQL requires a richer semantics than set semantics, there has been work on bag-semantics for SQL, essentially by introducing an interpreted column. One important technique for reasoning about query containment in the context of bag semantics is to translate the queries to alternatives using aggregate functions and assuming set semantics. Furthermore, in SQL, order by is the operator by which the results are sorted based on certain attributes and, clearly, ordering is an important issue in query optimization. As such, there has been work done in support of ordering based on the application of the domain. However, a final step is required in order to introduce a rich semantics in support. In this work, we integrate set and bag semantics to be able to reason about real SQL queries. We demonstrate an ordered bag semantics for SQL using a relational algebra with aggregates. We define a set algebra with various expressions of interest, then define syntax and semantics for bag algebra, and finally extend these definitions to ordered bags. This is done by adding a pair of additional interpreted columns to computed relations in which the first column is used in the standard fashion to capture duplicate tuples in query results, and the second adds an ordering priority to the output. We show that the relational algebra with aggregates can be used to compute these interpreted columns with sufficient flexibility to work as a semantics for standard SQL queries, which are allowed to include order by and duplicate preserving select clauses. The reduction of a workable ordered bag semantics for SQL to the relational algebra with aggregates - as we have developed it - can enable existing query containment theory to be applied in practical query containment

Access Control Administration with Adjustable Decentralization

Access control is a key function of enterprises that preserve and propagate massive data. Access control enforcement and administration are two major components of the system. On one hand, enterprises are responsible for data security; thus, consistent and reliable access control enforcement is necessary although the data may be distributed. On the other hand, data often belongs to several organizational units with various access control policies and many users; therefore, decentralized administration is needed to accommodate diverse access control needs and to avoid the central bottleneck. Yet, the required degree of decentralization varies within different organizations: some organizations may require a powerful administrator in the system; whereas, some others may prefer a self-governing setting in which no central administrator exists, but users fully manage their own data. Hence, a single system with adjustable decentralization will be useful for supporting various (de)centralized models within the spectrum of access control administration. Giving individual users the ability to delegate or grant privileges is a means of decentralizing access control administration. Revocation of arbitrary privileges is a means of retaining control over data. To provide flexible administration, the ability to delegate a specific privilege and the ability to revoke it should be held independently of each other and independently of the privilege itself. Moreover, supporting arbitrary user and data hierarchies, fine-grained access control, and protection of both data (end objects) and metadata (access control data) with a single uniform model will provide the most widely deployable access control system. Conflict resolution is a major aspect of access control administration in systems. Resolving access conflicts when deriving effective privileges from explicit ones is a challenging problem in the presence of both positive and negative privileges, sophisticated data hierarchies, and diversity of conflict resolution strategies. This thesis presents a uniform access control administration model with adjustable decentralization, to protect both data and metadata. There are several contributions in this work. First, we present a novel mechanism to constrain access control administration for each object type at object creation time, as a means of adjusting the degree of decentralization for the object when the system is configured. Second, by controlling the access control metadata with the same mechanism that controls the users’ data, privileges can be granted and revoked to the extent that these actions conform to the corporation’s access control policy. Thus, this model supports a whole spectrum of access control administration, in which each model is characterized as a network of access control states, similar to a finite state automaton. The model depends on a hierarchy of access banks of authorizations which is supported by a formal semantics. Within this framework, we also introduce the self-governance property in the context of access control, and show how the model facilitates it. In particular, using this model, we introduce a conflict-free and decentralized access control administration model in which all users are able to retain complete control over their own data while they are also able to delegate any subset of their privileges to other users or user groups. We also introduce two measures to compare any two access control models in terms of the degrees of decentralization and interpretation. Finally, as the conflict resolution component of access control models, we incorporate a unified algorithm to resolve access conflicts by simultaneously supporting several combined strategies

Optimal Witnessing of Healthcare IoT Data Using Blockchain Logging Contract

Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. There is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this paper, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: (1) We develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; (2) We then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; (3) Lastly, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than two dollars for one-hour witnessing service.Comment: 12 pages, 12 figure

NeuroSpeech

NeuroSpeech is a software for modeling pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. Although it was developed to model dysarthric speech signals from Parkinson's patients, its structure allows other computer scientists or developers to include other pathologies and/or measures. Different tasks can be performed: (1) modeling of the signals considering the aforementioned speech dimensions, (2) automatic discrimination of Parkinson's vs. non-Parkinson's, and (3) prediction of the neurological state according to the Unified Parkinson's Disease Rating Scale (UPDRS) score. The prediction of the dysarthria level according to the Frenchay Dysarthria Assessment scale is also provided

Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease

Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UPDRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores

Wearables in medicine

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches