Development of an Adaptive Environmental Management System for Lejweleputswa District: A Participatory Approach through Fuzzy Cognitive Maps

Published ThesisEnvironmental pollution caused by mines within the district of Lejweleputswa in Free State is a major contributor to health issues and the inability to grow crops within the mining communities. Mining industries continue to develop environmental management systems/plans to mitigate the impact their operations has on the society. Even with these plans, there are still issues of environmental pollution affecting the society. Though there are Information Communication and Technology (ICT) based pollution monitoring solutions, their use is dismal due to lack of appreciation or understanding of how they disseminate information. Furthermore, non-adopting community members are being regarded as inherently conservative or irrational, but these community members argue that the recommendations and technologies brought to them are not always appropriate to their circumstances. There was concern that local people’s knowledge of their environment, farming systems, and their social as well as economic situation had been ignored and underestimated when ICTs solutions are being implemented (Warburton & Martin, 1999). Another challenge is that there is no station to monitor pollution for small communities such as Nyakallong in the district. This result in mining communities depending on their own local knowledge to observe and monitor mining related environmental pollution. However, this local knowledge has never been tested scientifically or analysed to recognize its usability or effectiveness. Mining companies tend to ignore this knowledge from the communities as it is treated like common information with no much scientific value. As a step towards verifying or validating this local knowledge, fuzzy cognitive maps were used to model, analyse and represent this linguistic local knowledge. Although this local knowledge assists in mitigating environmental pollution, incorporating it with scientific knowledge will improve its relevance, trustworthiness and acceptability by majority of community members and policy-makers. Information and Communication Technologies (ICTs) can accelerate this integration; this is the focus of this research. The increased usages of Information Technology being witnessed today makes it the most important factor for the world to depend on for solutions to many of today’s and tomorrow’s problems. These solutions make use of various forms for dissemination purposes, one of the most versatile dissemination device is a mobile phone since majority of the world’s population do own a mobile phone. In this way information is easily accessible by almost everyone that needs it. A novel environmental management solution was designed to work within the mining communities of Lejweleputswa. The research started off by designing a unique integration framework that creates the much-needed link between local knowledge and scientific knowledge. The framework was then converted into an adaptable environmental pollution management system prototype made up of three components; (1) gathering environmental pollution knowledge; (2) environmental monitoring and; (3) environmental dissemination and communication. To achieve sustainability, relevance and acceptability, local knowledge was integrated in each of the three components while mobile phones were used as both input and output devices for the system. In order to facilitate collection and conservation of local knowledge on environmental monitoring, an elaborate android-based mobile application was developed. Wireless sensor-based gas sensor boards were acquired, and deployed as a compliment to conventional monitoring stations, they were used to gather scientific knowledge. To allow for public access to the system’s data, a web portal and an SMS-based component were also implemented. In order to collect local knowledge from community, a case study of Nyakallong community in Lejweleputswa was carried out. On completion of the system prototype, it was evaluated by participants from the community; 90% of respondents gave a score of ‘excellent ‘

Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning

Most of the world's languages suffer from the paucity of annotated data. This curbs the effectiveness of supervised learning, the most widespread approach to modelling language. Instead, an alternative paradigm could take inspiration from the propensity of children to acquire language from limited stimuli, in order to enable machines to learn any new language from a few examples. The abstract mechanisms underpinning this ability include 1) a set of in-born inductive biases and 2) the deep entrenchment of language in other perceptual and cognitive faculties, combined with the ability to transfer and recombine knowledge across these domains. The main contribution of my thesis is giving concrete form to both these intuitions. Firstly, I argue that endowing a neural network with the correct inductive biases is equivalent to constructing a prior distribution over its weights and its architecture (including connectivity patterns and non-linear activations). This prior is inferred by "reverse-engineering" a representative set of observed languages and harnessing typological features documented by linguists. Thus, I provide a unified framework for cross-lingual transfer and architecture search by recasting them as hierarchical Bayesian neural models. Secondly, the skills relevant to different language varieties and different tasks in natural language processing are deeply intertwined. Hence, the neural weights modelling the data for each of their combinations can be imagined as lying in a structured space. I introduce a Bayesian generative model of this space, which is factorised into latent variables representing each language and each task. By virtue of this modular design, predictions can generalise to unseen combinations by extrapolating from the data of observed combinations. The proposed models are empirically validated on a spectrum of language-related tasks (character-level language modelling, part-of-speech tagging, named entity recognition, and common-sense reasoning) and a typologically diverse sample of about a hundred languages. Compared to a series of competitive baselines, they achieve better performances in new languages in zero-shot and few-shot learning settings. In general, they hold promise to extend state-of-the-art language technology to under-resourced languages by means of sample efficiency and robustness to the cross-lingual variation.ERC (Consolidator Grant 648909) Lexical Google Research Faculty Award 201

Contribution to improve mobility uses through context-awareness

Dey, in his paper “Towards a Better Understanding of Context and Context-Awareness”, argues that context-awareness is important in applications in which the user’s context changes rapidly, such as in mobile environments for ubiquitous computing. In his paper, Dey defines context as “any information that can be used to characterize the situation of an entity”. In mobile environments, the entity is the mobile device itself. The device is both pervasive and person-­centric; it can continuously capture information about its users and their context through its sensors. The use of context has gained importance in ubiquitous computing since the 1990s, and the technique has recently been used in mobile devices to improve their uses and applications. For mobile context-awareness to become a reality, further research is required, particularly in the field of context prediction, which can expand the possibilities of context-awareness applications by expanding the applications’ situation awareness. In this PhD dissertation, we focus on the use of data obtained through mobile device sensors and user behavior to derive and predict context to improve mobility for both the users’ experience and for the applications’ functionality. We contribute to context-­aware mobile computing by showing how mobile devices can automatically learn from the user’s context and can adapt to improve the mobile experience. We begin our work with a state-­‐of-­‐the-­‐art analysis of “context-awareness” proposals for mobile systems and applications and of the current tools used to infer context from the existing environmental variables. In this dissertation, we analyze the existing gaps in mobile environments and propose solutions to resolve these issues. We first define “context-­awareness” and propose an architecture to predict context from a mobility perspective. Numerous definitions of context, context-­awareness and architectures exist, but few focus exclusively on mobility. Moreover, all of the definitions are oriented towards context inference rather than towards a prediction of future context. We develop a model that captures, processes and unifies variables from heterogeneous sources for use by a machine-­learning algorithm that infers and predicts the context. We also test and benchmark several machine-­learning algorithms in our architecture so that we can recommend those algorithms that we consider most appropriate for inferring context in mobility environments. We propose the combination of on-­‐line prediction algorithms and classifier algorithms to enhance context derivation with future context prediction. We evaluate our proposal utilizing real data from the Reality Mining project, which captures data from the daily mobile usage of c.100 Nokia smart phones during an academic year. We conclude with an example of how to apply our proposed architecture and model, and we demonstrate its enrichment of the search experience with a mobile device by including a “context-awareness” module in mobile search engines. We use Bing as the search engine for all of our search examples. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Describe Dey, en su artículo “Towards a Better Understanding of Context and Context-Awareness” cómo la percepción del contexto (context-awareness) cobra importancia en las aplicaciones en las que el contexto del usuario cambia con rapidez, como es el caso en los entornos móviles de la computación ubicua. Dey, en su artículo, define contexto como “cualquier información que pueda usarse para caracterizar la situación de una entidad”. En entornos móviles, dicha entidad es el dispositivo móvil en sí mismo. Este aparato, al ser ubicuo y centrado en las personas, puede captar continuamente información tanto de los usuarios como de su contexto a través de sus sensores. El uso del contexto ha cobrado importancia en entornos de computación ubicua desde la década de los 90, y esta técnica se ha empleado en dispositivos móviles para mejorar su utilización y aplicación. Para que el área de percepción de contexto se convierta en una realidad, se necesita más investigación, sobre todo en el área de predicción de contexto que amplíe las posibilidades de las aplicaciones que usan información de su contexto. En esta tesis doctoral, nos centramos en el uso de los datos obtenidos de los sensores del móvil y en el comportamiento del usuario, para deducir el contexto presente predecir el contexto futuro, mejorando así la usabilidad del móvil y las funcionalidades de sus aplicaciones. Contribuimos a la computación de percepción del contexto móvil demostrando cómo los dispositivos móviles pueden aprender automáticamente sobre el contexto en el que está el usuario y adaptarse al mismo para mejorar la experiencia de movilidad. Comenzamos nuestro trabajo realizando un estudio del estado del arte de propuestas de percepción de contexto para sistemas y aplicaciones móviles, así como de las herramientas para intuir el contexto a partir de variables existentes del entorno. Analizamos las carencias que tienen en su aplicación al área de la movilidad y hacemos propuestas de cómo resolverlas a lo largo de la tesis. Primero sentamos las bases de la tesis definiendo el concepto de percepción de contexto (“context-awarenes”) y realizamos una propuesta de arquitectura de derivación del contexto actual y predicción del contexto futuro desde un punto de vista de un entorno móvil. Existen muchas definiciones de contexto, percepción de contexto y arquitecturas, pero hay pocas orientadas exclusivamente a movilidad. Además todas se centran en la derivación del contexto actual en vez de hacerlo en la predicción del contexto futuro. Desarrollamos un modelo que nos permite captar, procesar y unificar variables de fuentes heterogéneas para que puedan ser utilizadas por el algoritmo de aprendizaje automático para intuir y predecir contexto. También probamos y referenciamos varios algoritmos de aprendizaje automático para poder recomendar los algoritmos que consideramos más apropiados para intuir contexto en entornos de movilidad. Hacemos una propuesta de mejora en la que combinamos los algoritmos de predicción en línea con los algoritmos de clasificación para poder así predecir el contexto futuro además del contexto actual intuido por el clasificador. Evaluamos nuestra propuesta con datos reales de uso del móvil disponibles en el proyecto “Reality Mining”, en el cual se captan datos de uso diario de móviles de aproximadamente 100 Smartphones Nokia usados por estudiantes universitarios durante un año académico. Finalmente concluimos dando un ejemplo de cómo aplicar nuestra arquitectura y el modelo propuesto demostrando como enriquece la experiencia de búsqueda en un dispositivo móvil el hecho de incluir un módulo de percepción de contexto en los buscadores móviles. Usamos el buscador Bing para todos los ejemplos de búsquedas

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality