Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Active Learning for Text Classification

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques

The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal speech data can be collected, the risk of malicious use of voice-cloning and speaker/gender/pathological/etc. recognition has increased. This thesis proposes solutions for anonymizing speech and evaluating the degree of the anonymization. In this work, anonymization refers to making personal speech data unlinkable to an identity while maintaining the usefulness (utility) of the speech signal (e.g., access to linguistic content). We start by identifying several challenges that evaluation protocols need to consider to evaluate the degree of privacy protection properly. We clarify how anonymization systems must be configured for evaluation purposes and highlight that many practical deployment configurations do not permit privacy evaluation. Furthermore, we study and examine the most common voice conversion-based anonymization system and identify its weak points before suggesting new methods to overcome some limitations. We isolate all components of the anonymization system to evaluate the degree of speaker PPI associated with each of them. Then, we propose several transformation methods for each component to reduce as much as possible speaker PPI while maintaining utility. We promote anonymization algorithms based on quantization-based transformation as an alternative to the most-used and well-known noise-based approach. Finally, we endeavor a new attack method to invert anonymization.Comment: PhD Thesis Pierre Champion | Universit\'e de Lorraine - INRIA Nancy | for associated source code, see https://github.com/deep-privacy/SA-toolki

Global leadership: An Analysis of three Leadership Competency Models in Multinational Corporations

At a time of rapid business globalisation when leaders are required to operate in diverse international environments, it is essential for multinational corporations to appreciate the complexities leaders face and support individuals in developing the requisite competencies. How then can leaders move from one-dimensional to cross-cultural models of global leadership to encourage more fluid and contextualised international business operations? This thesis examines extant leadership competency models (LCMs) in three multinational companies - selected from across Europe and the US – and attempts to understand how effectively these models translate across different regions and cultures. Such examination is based on semi-structured, in-depth qualitative interviews with 38 middle management and HR leaders who work across various cultural contexts in the three corporations. The underlying thesis of the study – that national culture impacts on the implementation and interpretation of LCMs – is built into analysis that highlights the ethnocentric nature of these models. For LCMs to effectively enhance leadership in global businesses, it is argued that cultural literacy and a global mindset are fundamental to LCM development. This study fills a gap in existing research that has rarely given systematic attention to the enactment of universal LCMs in multinational organisations. It will be the purpose of this work to judge the effectiveness of leadership competencies in a cross-cultural context, and to set the ground rules for the development of multinational LCMs in the futur

Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential

Computer-aided storytelling: effects on emergent literacy of preschool-aged children in an EFL context

This thesis presents the results of a study involving very young learners of English as a foreign language in Spain. In Phase I of the study, the feasibility of developing a series of guidelines that could be used to develop English lessons based on ICT-stories and communicative tasks was investigated. In Phase II, the lessons derived from the use of the guidelines were implemented with the purpose of exploring the development of story-related emergent literacy skills of a group of 3- and 4 year-old children receiving daily English lessons. The study has extended previous research studies regarding emergent literacy development in first and second language contexts by studying a foreign .language context. The children were asked to retell stories viewed and the transcribed retellings were analysed under the Narrative Scoring Scheme. The children participants showed signs of developmental changes throughout the duration of the course. However, there were marked individual differences in these changes. Further investigation is suggested to study the reasons that might provide insight into why the children developed understanding of story structure in such high variations. Results showed that the ICT-stories motivated the children and as a consequence, their level of participation in the lesson improved. Additionally, teachers found the use of ICT as a positive strategy to enhance young children's learning environment, but they said that implementing ICT-based projects in the young learner classroom necessarily raises issues of school's provision for training and technical support as well as considerations of teacher:child ratios. From the children's perspective, an evaluation of the lesson tasks via a survey showed positive reactions to the inclusion of ICT in the English lesson. Findings of the study show how the young learner English curriculum could consider children's development of emergent literacy skills as a result of learning English via ICT enhanced stories. They also show how ICT integrated in the foreign language curriculum can motivate young children and provide meaning to the activity of learning English at a time when their mother tongue is still developing. The study also yielded unexpected results related to how young children address a task in which a story has to be organised in sequence. These results present interesting research opportunities that could be explored further in the light of theories of cognitive development

Learners in transition: a longitudinal study of seven People's Republic of China students at the National University of Singapore

This doctorate thesis reports on a qualitative research project to investigate the English learning experiences of seven People’s Republic of China (PRC) students during nearly five years of studies at the National University of Singapore (NUS). The research questions for this longitudinal, multiple-case study are: 1. What are some key learner characteristics of PRC students and what transitions, if any, do they make in their English learning journey as a result of studying in NUS? 2. What pedagogical implications can I draw from the findings? For the purpose of triangulation, four instruments were used to collect data in two stages. In the students’ first year in NUS, the instruments were learner diaries and face-to-face interviews while those used in the students’ final year, were email interviews and an autobiography. This research design facilitated the broad to narrow approach adopted for the study, and made possible the collection of increasingly more in-depth data. The analysis of the initial data, through coding, categorising and summarising, was carried out alongside the collection of the later data, similar to the grounded theory approach. However, this study also started with some a priori categories culled from literature and a decade’s teaching and research experience associated with PRC students. The findings indicate that the traditional Chinese culture of learning as well as the NUS L2 context had an influence on the seven participants’ key learner characteristics. These students underwent a variety of transitions in their beliefs, strategies, motivation, affective dimension, and identity, agency and investment. However, certain aspects of their key learner characteristics also remained stable. Based on these findings, pedagogical implications were drawn and limitations stated to teachers of PRC learners to better equip themselves and their students to successfully navigate the latter’s transition from EFL to ESL/EIL contexts