Data Mining Practical Machine Learning Tools and Techniques

From the Publisher:
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

Art : Art in which the subject matter has been simplified or distorted to the point that it may or may not be easily descended. Acrylic: Resin that, when mixed with water and pigment, forms an inorganic and quick drying medium. Acrylic paint therefore is a fast drying synthetic paint made from acrylic resin. Action Painting: The technique of dripping and splashing paint onto canvases stretched on the floor Aesthetic: Pertaining to the appreciation of the beautiful as opposed to the functional or utilitarian, and, by extention, to the appreciation of any form of art. Aesthetic value: The impact of a work of art on our senses, intellect, and emotions. Analogous colours:Pairs of colour such as yellow and orange, that are adjudscent to each other on the colour wheel. Acqatint : A print making process that includes etching and that permits broad areas of black and gray tones. Air brush: Atomizer operated by compressed air used for spraying on paint. Art Deco: A popular art and design style of the 1920s and 30s, and characterized by its intergration of organic and geometric forms. Art: The special expression of ideas, feelings and values in visual forms. Art criticism: A special concentrated way of looking at a piece of art with a purpose to recieve maximum enjoyment and meaning from it. Abstract Expressionism: A twentieth-century painting style in which artists applied paint freely to huge canvases in an effort to show feelings and emotions rather than realistic subject matter.Expressionism: A twentieth-century painting style in which artists applied paint freely to huge canvases in an effort to show feelings and emotions rather than realistic subject matter.

The Ministry of Education

Robust Human Activity Recognition using smartwatches and smartphones

This paper presents the development of a Human Activity Recognition (HAR) system that uses a network of nine inertial measurement units situated in different body parts. Every unit provides 3D (3-dimension) acceleration, 3D angular velocity, 3D magnetic field orientation, and 4D quaternions. This system identifies 33 different physical activities (walking, running, cycling, lateral elevation of arms, etc.). The system is composed of two main modules: a feature extractor for obtaining the most relevant characteristics from the inertial signals every second, and a machine learning algorithm for classifying between the different activities. This paper focuses on the feature extractor module, evaluating several types of features and proposing different normalization approaches. This paper also analyses the performance of every sensor included in the inertial measurement units. The main experiments have been done using a public available dataset named REALDISP Activity Recognition dataset. This dataset includes recordings from 17 subjects performing 33 different activities in three different scenarios. Final results demonstrate that the proposed HAR system significantly improves the classification accuracy compared to previous works on this dataset. For the best configuration, the system accuracy is 99.1%. This system has been also evaluated with the OPPORTUNITY dataset obtaining competitive results.

/pdf/feature-extraction-for-robust-physical-activity-recognition-igoy1jj1s7.pdf

Feature extraction for robust physical activity recognition

Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) for PD detection from drawing movements. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The inputs to the CNN are the module of the Fast Fourier’s transform in the range of frequencies between 0 Hz and 25 Hz. We analyzed the discrimination capability of different directions during drawing movements obtaining the best results for X and Y directions. This analysis was performed using a public dataset: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset. The best results obtained in this work showed an accuracy of 96.5%, a F1-score of 97.7%, and an area under the curve of 99.2%.

https://www.mdpi.com/2079-9292/8/8/907/pdf

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

This paper provides improved confidence assessment for detection of word-level speech recognition errors, out of domain utterances and incorrect concepts in the CU Communicator system. New features from the speech understanding component are proposed for confidence annotation at the utterance and concept levels. We consider a neural network to combine all features in each level. Using the data collected from a live telephony system, it is shown that 53.2% of incorrectly recognized words, 53.2% of out of domain utterances and 50.1% of incorrect concepts are detected at a 5% false rejection rate. In addition, the confidence measures are used to improve the word recognition accuracy. Several hypotheses from different speech recognizers are compiled into a word-graph. The word-graph is searched for the hypothesis with the best confidence. We report a 14.0% relative word error rate reduction after this confidence rescoring.

/pdf/confidence-measures-for-spoken-dialogue-systems-3k3fhatfe3.pdf

Confidence measures for spoken dialogue systems

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.

Rubén San-Segundo

Papers

Robust Human Activity Recognition using smartwatches and smartphones

Feature extraction for robust physical activity recognition

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

Confidence measures for spoken dialogue systems

A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression