Penalizing unknown words’ emissions in hmm pos tagger based on Malay affix morphemes

The challenge in unsupervised Hidden Markov Model (HMM) training for a POS tagger isthat the training depends on an untagged corpus; the only supervised data limiting  possible tagging of words is a dictionary. Therefore, training cannot properly map  possible tags. The exact morphemes of prefixes, suffixes and circumfixes in the   agglutinative Malay language is examined to assign unknown words’ probable tags based on linguistically meaningful affixes using a morpheme-based POS guessing algorithm for tagging. The algorithm has been integrated into Viterbi algorithm which uses HMM trained parameters for tagging new sentences. In the experiment, this tagger is first, uses character-based prediction to handle unknown words; next, uses morpheme-based POS guessing algorithm; lastly, combination of the first and second.Keywords: Malay POS tagger; morpheme-based; HMM

INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH

Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes

RV4JaCa - Runtime Verification for Multi-Agent Systems

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. MAS have been used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. We demonstrate the implementation of a monitor that aims to control this dialogue flow in a MAS that communicates with the user through natural language to aid decision-making in hospital bed allocation

The development of the quaternion wavelet transform

The purpose of this article is to review what has been written on what other authors have called quaternion wavelet transforms (QWTs): there is no consensus about what these should look like and what their properties should be. We briefly explain what real continuous and discrete wavelet transforms and multiresolution analysis are and why complex wavelet transforms were introduced; we then go on to detail published approaches to QWTs and to analyse them. We conclude with our own analysis of what it is that should define a QWT as being truly quaternionic and why all but a few of the “QWTs” we have described do not fit our definition

Predicting Short-Term Traffic Congestion on Urban Motorway Networks

Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction. Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems. The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based meta-heuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Método para la evaluación de usabilidad de sitios web transaccionales basado en el proceso de inspección heurística

La usabilidad es considerada uno de los factores más importantes en el desarrollo de productos de software. Este atributo de calidad está referido al grado en que, usuarios específicos de un determinado aplicativo, pueden fácilmente hacer uso del software para lograr su propósito. Dada la importancia de este aspecto en el éxito de las aplicaciones informáticas, múltiples métodos de evaluación han surgido como instrumentos de medición que permiten determinar si la propuesta de diseño de la interfaz de un sistema de software es entendible, fácil de usar, atractiva y agradable al usuario. El método de evaluación heurística es uno de los métodos más utilizados en el área de Interacción Humano-Computador (HCI) para este propósito debido al bajo costo de su ejecución en comparación otras técnicas existentes. Sin embargo, a pesar de su amplio uso extensivo durante los últimos años, no existe un procedimiento formal para llevar a cabo este proceso de evaluación. Jakob Nielsen, el autor de esta técnica de inspección, ofrece únicamente lineamientos generales que, según la investigación realizada, tienden a ser interpretados de diferentes maneras por los especialistas. Por tal motivo, se ha desarrollado el presente proyecto de investigación que tiene como objetivo establecer un proceso sistemático, estructurado, organizado y formal para llevar a cabo evaluaciones heurísticas a productos de software. En base a un análisis exhaustivo realizado a aquellos estudios que reportan en la literatura el uso del método de evaluación heurística como parte del proceso de desarrollo de software, se ha formulado un nuevo método de evaluación basado en cinco fases: (1) planificación, (2) entrenamiento, (3) evaluación, (4) discusión y (5) reporte. Cada una de las fases propuestas que componen el protocolo de inspección contiene un conjunto de actividades bien definidas a ser realizadas por el equipo de evaluación como parte del proceso de inspección. Asimismo, se han establecido ciertos roles que deberán desempeñar los integrantes del equipo de inspectores para asegurar la calidad de los resultados y un apropiado desarrollo de la evaluación heurística. La nueva propuesta ha sido validada en dos escenarios académicos distintos (en Colombia, en una universidad pública, y en Perú, en dos universidades tanto en una pública como en una privada) demostrando en todos casos que es posible identificar más problemas de usabilidad altamente severos y críticos cuando un proceso estructurado de inspección es adoptado por los evaluadores. Otro aspecto favorable que muestran los resultados es que los evaluadores tienden a cometer menos errores de asociación (entre heurística que es incumplida y problemas de usabilidad identificados) y que la propuesta es percibida como fácil de usar y útil. Al validarse la nueva propuesta desarrollada por el autor de este estudio se consolida un nuevo conocimiento que aporta al bagaje cultural de la ciencia.Tesi