A new language model based on possibility theory

Nous n'avons pas encore la date officielle de parutionInternational audienceLanguage modeling is a very important step in several NLP applications. Most of the current language models are based on probabilistic methods. In this paper, we propose a new language modeling approach based on the possibility theory. Our goal is to suggest a method for estimating the possibility of a word-sequence and to test this new approach in a machine translation system.We propose a word-sequence possibilistic measure, which can be estimated from a corpus. We proceeded in two ways: first, we checked the behaviour of the newapproach compared with the existing work. Second, we compared the new language model with the probabilistic one used in statistical MTsystems. The results, in terms of the METEOR metric, show that the possibilistic-language model is better than the probabilistic one. However, in terms of BLEU and TER scores, the probabilistic model remains better

Toward Sensor-Based Context Aware Systems

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information

Image annotation and retrieval based on multi-modal feature clustering and similarity propagation.

The performance of content-based image retrieval systems has proved to be inherently constrained by the used low level features, and cannot give satisfactory results when the user\u27s high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. In this thesis we propose a system for image retrieval that has three mains components. The first component of our system consists of a novel possibilistic clustering and feature weighting algorithm based on robust modeling of the Generalized Dirichlet (GD) finite mixture. Robust estimation of the mixture model parameters is achieved by incorporating two complementary types of membership degrees. The first one is a posterior probability that indicates the degree to which a point fits the estimated distribution. The second membership represents the degree of typicality and is used to indentify and discard noise points. Robustness to noisy and irrelevant features is achieved by transforming the data to make the features independent and follow Beta distribution, and learning optimal relevance weight for each feature subset within each cluster. We extend our algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. We also outline a semi-supervised version of the proposed algorithm. In the second component of our system consists of a novel approach to unsupervised image annotation. Our approach is based on: (i) the proposed semi-supervised possibilistic clustering; (ii) a greedy selection and joining algorithm (GSJ); (iii) Bayes rule; and (iv) a probabilistic model that is based on possibilistic memebership degrees to annotate an image. The third component of the proposed system consists of an image retrieval framework based on multi-modal similarity propagation. The proposed framework is designed to deal with two data modalities: low-level visual features and high-level textual keywords generated by our proposed image annotation algorithm. The multi-modal similarity propagation system exploits the mutual reinforcement of relational data and results in a nonlinear combination of the different modalities. Specifically, it is used to learn the semantic similarities between images by leveraging the relationships between features from the different modalities. The proposed image annotation and retrieval approaches are implemented and tested with a standard benchmark dataset. We show the effectiveness of our clustering algorithm to handle high dimensional and noisy data. We compare our proposed image annotation approach to three state-of-the-art methods and demonstrate the effectiveness of the proposed image retrieval system

On the Quantum-like Contextuality of Ambiguous Phrases

Language is contextual as meanings of words are dependent on their contexts. Contextuality is, concomitantly, a well-defined concept in quantum mechanics where it is considered a major resource for quantum computations. We investigate whether natural language exhibits any of the quantum mechanics' contextual features. We show that meaning combinations in ambiguous phrases can be modelled in the sheaf-theoretic framework for quantum contextuality, where they can become possibilistically contextual. Using the framework of Contextuality-by-Default (CbD), we explore the probabilistic variants of these and show that CbD-contextuality is also possible

Advances in transfer learning methods based on computational intelligence

Traditional machine learning and data mining have made tremendous progress in many knowledge-based areas, such as clustering, classification, and regression. However, the primary assumption in all of these areas is that the training and testing data should be in the same domain and have the same distribution. This assumption is difficult to achieve in real-world applications due to the limited availability of labeled data. Associated data in different domains can be used to expand the availability of prior knowledge about future target data. In recent years, transfer learning has been used to address such cross-domain learning problems by using information from data in a related domain and transferring that data to the target task. The transfer learning methodology is utilized in this work with unsupervised and supervised learning methods. For unsupervised learning, a novel transfer-learning possibilistic c-means (TLPCM) algorithm is proposed to handle the PCM clustering problem in a domain that has insufficient data. Moreover, TLPCM overcomes the problem of differing numbers of clusters between the source and target domains. The proposed algorithm employs the historical cluster centers of the source data as a reference to guide the clustering of the target data. The experimental studies presented here were thoroughly evaluated, and they demonstrate the advantages of TLPCM in both synthetic and real-world transfer datasets. For supervised learning, a transfer learning (TL) technique is used to pre-train a CNN model on posture data and then fine-tune it on the sleep stage data. We used a ballistocardiography (BCG) bed sensor to collect both posture and sleep stage data to provide a non-invasive, in-home monitoring system that tracks changes in the subjects' health over time. The quality of sleep has a significant impact on health and life. This study adopts a hierarchical and none-hierarchical classification structure to develop an automatic sleep stage classification system using ballistocardiogram (BCG) signals. A leave-one-subject-out cross-validation (LOSO-CV) procedure is used for testing classification performance in most of the experiments. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks DNNs are complementary in their modeling capabilities, while CNNs have the advantage of reducing frequency variations, LSTMs are good at temporal modeling. Polysomnography (PSG) data from a sleep lab was used as the ground truth for sleep stages, with the emphasis on three sleep stages, specifically, awake, rapid eye movement (REM), and non-REM sleep (NREM). Moreover, a transfer learning approach is employed with supervised learning to address the cross-resident training problem to predict early illness. We validate our method by conducting a retrospective study on three residents from TigerPlace, a retirement community in Columbia, MO, where apartments are fitted with wireless networks of motion and bed sensors. Predicting the early signs of illness in older adults by using a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease care costs. Illness prediction is based on sensor data and uses algorithms such as support vector machine (SVM) and k-nearest neighbors (kNN). One of the most significant challenges related to the development of prediction algorithms for sensor networks is the use of knowledge from previous residents to predict new ones' behaviors. Each day, the presence or absence of illness was manually evaluated using nursing visit reports from a homegrown electronic medical record (EMR) system. In this work, the transfer learning SVM approach outperformed three other methods, i.e., regular SVM, one-class SVM, and one-class kNN.Includes bibliographical references (pages 114-127)

Organizing Contextual Knowledge for Arabic Text Disambiguation and Terminology Extraction.

Ontologies have an important role in knowledge organization and information retrieval. Domain ontologies are composed of concepts represented by domain relevant terms. Existing approaches of ontology construction make use of statistical and linguistic information to extract domain relevant terms. The quality and the quantity of this information influence the accuracy of terminologyextraction approaches and other steps in knowledge extraction and information retrieval. This paper proposes an approach forhandling domain relevant terms from Arabic non-diacriticised semi-structured corpora. In input, the structure of documentsis exploited to organize knowledge in a contextual graph, which is exploitedto extract relevant terms. This network contains simple and compound nouns handled by a morphosyntactic shallow parser. The noun phrases are evaluated in terms of termhood and unithood by means of possibilistic measures. We apply a qualitative approach, which weighs terms according to their positions in the structure of the document. In output, the extracted knowledge is organized as network modeling dependencies between terms, which can be exploited to infer semantic relations.We test our approach on three specific domain corpora. The goal of this evaluation is to check if our model for organizing and exploiting contextual knowledge will improve the accuracy of extraction of simple and compound nouns. We also investigate the role of compound nouns in improving information retrieval results

Early detection of health changes in the elderly using in-home multi-sensor data streams

The rapid aging of the population worldwide requires increased attention from health care providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen, so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this work, we investigate a methodology for learning data distribution from streaming data and tracking the evolution of the behavior trajectories over long periods (years) using high dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, our tracking algorithm in the original high dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data, real-world data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over ten years. We used the TigerPlace electronic health records (EHR) to understand the residents' behavior patterns and to evaluate and explain the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the EHR, providing strong support for a prospective deployment of the approach.Includes bibliographical references

New Graphical Model for Computing Optimistic Decisions in Possibility Theory Framework

This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic networks: the first one expresses agent's knowledge while the second one encodes agent's preferences representing a qualitative utility. We then propose an efficient algorithm for computing optimistic optimal decisions using our new model for representing possibilistic decision making under uncertainty. We show that the computation of optimal decisions comes down to compute a normalization degree of the junction tree associated with the graph resulting from the fusion of agent's beliefs and preferences. This paper also proposes an alternative way for computing optimal optimistic decisions. The idea is to transform the two possibilistic networks into two equivalent possibilistic logic knowledge bases, one representing agent's knowledge and the other represents agent's preferences. We show that computing an optimal optimistic decision comes down to compute the inconsistency degree of the union of the two possibilistic bases augmented with a given decision