3,084 research outputs found

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

    Get PDF
    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

    Visualisation of bioinformatics datasets

    Get PDF
    Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset

    Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

    Get PDF
    This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

    Random Projection in Deep Neural Networks

    Get PDF
    This work investigates the ways in which deep learning methods can benefit from random projection (RP), a classic linear dimensionality reduction method. We focus on two areas where, as we have found, employing RP techniques can improve deep models: training neural networks on high-dimensional data and initialization of network parameters. Training deep neural networks (DNNs) on sparse, high-dimensional data with no exploitable structure implies a network architecture with an input layer that has a huge number of weights, which often makes training infeasible. We show that this problem can be solved by prepending the network with an input layer whose weights are initialized with an RP matrix. We propose several modifications to the network architecture and training regime that makes it possible to efficiently train DNNs with learnable RP layer on data with as many as tens of millions of input features and training examples. In comparison to the state-of-the-art methods, neural networks with RP layer achieve competitive performance or improve the results on several extremely high-dimensional real-world datasets. The second area where the application of RP techniques can be beneficial for training deep models is weight initialization. Setting the initial weights in DNNs to elements of various RP matrices enabled us to train residual deep networks to higher levels of performance

    Exploiting word embeddings for modeling bilexical relations

    Get PDF
    There has been an exponential surge of text data in the recent years. As a consequence, unsupervised methods that make use of this data have been steadily growing in the field of natural language processing (NLP). Word embeddings are low-dimensional vectors obtained using unsupervised techniques on the large unlabelled corpora, where words from the vocabulary are mapped to vectors of real numbers. Word embeddings aim to capture syntactic and semantic properties of words. In NLP, many tasks involve computing the compatibility between lexical items under some linguistic relation. We call this type of relation a bilexical relation. Our thesis defines statistical models for bilexical relations that centrally make use of word embeddings. Our principle aim is that the word embeddings will favor generalization to words not seen during the training of the model. The thesis is structured in four parts. In the first part of this thesis, we present a bilinear model over word embeddings that leverages a small supervised dataset for a binary linguistic relation. Our learning algorithm exploits low-rank bilinear forms and induces a low-dimensional embedding tailored for a target linguistic relation. This results in compressed task-specific embeddings. In the second part of our thesis, we extend our bilinear model to a ternary setting and propose a framework for resolving prepositional phrase attachment ambiguity using word embeddings. Our models perform competitively with state-of-the-art models. In addition, our method obtains significant improvements on out-of-domain tests by simply using word-embeddings induced from source and target domains. In the third part of this thesis, we further extend the bilinear models for expanding vocabulary in the context of statistical phrase-based machine translation. Our model obtains a probabilistic list of possible translations of target language words, given a word in the source language. We do this by projecting pre-trained embeddings into a common subspace using a log-bilinear model. We empirically notice a significant improvement on an out-of-domain test set. In the final part of our thesis, we propose a non-linear model that maps initial word embeddings to task-tuned word embeddings, in the context of a neural network dependency parser. We demonstrate its use for improved dependency parsing, especially for sentences with unseen words. We also show downstream improvements on a sentiment analysis task.En els darrers anys hi ha hagut un sorgiment notable de dades en format textual. Conseqüentment, en el camp del Processament del Llenguatge Natural (NLP, de l'anglès "Natural Language Processing") s'han desenvolupat mètodes no supervistats que fan ús d'aquestes dades. Els anomenats "word embeddings", o embeddings de paraules, són vectors de dimensionalitat baixa que s'obtenen mitjançant tècniques no supervisades aplicades a corpus textuals de grans volums. Com a resultat, cada paraula del diccionari es correspon amb un vector de nombres reals, el propòsit del qual és capturar propietats sintàctiques i semàntiques de la paraula corresponent. Moltes tasques de NLP involucren calcular la compatibilitat entre elements lèxics en l'àmbit d'una relació lingüística. D'aquest tipus de relació en diem relació bilèxica. Aquesta tesi proposa models estadístics per a relacions bilèxiques que fan ús central d'embeddings de paraules, amb l'objectiu de millorar la generalització del model lingüístic a paraules no vistes durant l'entrenament. La tesi s'estructura en quatre parts. A la primera part presentem un model bilineal sobre embeddings de paraules que explota un conjunt petit de dades anotades sobre una relaxió bilèxica. L'algorisme d'aprenentatge treballa amb formes bilineals de poc rang, i indueix embeddings de poca dimensionalitat que estan especialitzats per la relació bilèxica per la qual s'han entrenat. Com a resultat, obtenim embeddings de paraules que corresponen a compressions d'embeddings per a una relació determinada. A la segona part de la tesi proposem una extensió del model bilineal a trilineal, i amb això proposem un nou model per a resoldre ambigüitats de sintagmes preposicionals que usa només embeddings de paraules. En una sèrie d'avaluacións, els nostres models funcionen de manera similar a l'estat de l'art. A més, el nostre mètode obté millores significatives en avaluacions en textos de dominis diferents al d'entrenament, simplement usant embeddings induïts amb textos dels dominis d'entrenament i d'avaluació. A la tercera part d'aquesta tesi proposem una altra extensió dels models bilineals per ampliar la cobertura lèxica en el context de models estadístics de traducció automàtica. El nostre model probabilístic obté, donada una paraula en la llengua d'origen, una llista de possibles traduccions en la llengua de destí. Fem això mitjançant una projecció d'embeddings pre-entrenats a un sub-espai comú, usant un model log-bilineal. Empíricament, observem una millora significativa en avaluacions en dominis diferents al d'entrenament. Finalment, a la quarta part de la tesi proposem un model no lineal que indueix una correspondència entre embeddings inicials i embeddings especialitzats, en el context de tasques d'anàlisi sintàctica de dependències amb models neuronals. Mostrem que aquest mètode millora l'analisi de dependències, especialment en oracions amb paraules no vistes durant l'entrenament. També mostrem millores en un tasca d'anàlisi de sentiment

    Temporospatial Context-Aware Vehicular Crash Risk Prediction

    Get PDF
    With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

    Advances in knowledge discovery and data mining Part II

    Get PDF
    19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p
    corecore