Approximation-based feature selection and application for algae population estimation

Q. Shen and R. Jensen, 'Approximation-based feature selection and application for algae population estimation,' Applied Intelligence, vol. 28, no. 2, pp. 167-181, 2008. Sponsorship: EPSRC RONO: EP/E058388/1

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Data-Driven Text Generation using Neural Networks & Provenance is Complicated and Boring — Is there a solution?

Title: Data-Driven Text Generation using Neural Networks Speaker: Pavlos Vougiouklis, University of Southampton Abstract: Recent work on neural networks shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks. This talk will focus on the Natural Language Generation (NLG) problem and, more specifically, on the extend to which neural network language models could be employed for context-sensitive and data-driven text generation. In addition, a neural network architecture for response generation in social media along with the training methods that enable it to capture contextual information and effectively participate in public conversations will be discussed. Speaker Bio: Pavlos Vougiouklis obtained his 5-year Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2013. He was awarded an MSc degree in Software Engineering from the University of Southampton in 2014. In 2015, he joined the Web and Internet Science (WAIS) research group of the University of Southampton and he is currently working towards the acquisition of his PhD degree in the field of Neural Network Approaches for Natural Language Processing. Title: Provenance is Complicated and Boring — Is there a solution? Speaker: Darren Richardson, University of Southampton Abstract: Paper trails, auditing, and accountability — arguably not the sexiest terms in computer science. But then you discover that you've possibly been eating horse-meat, and the importance of provenance becomes almost palpable. Having accepted that we should be creating provenance-enabled systems, the challenge of then communicating that provenance to casual users is not trivial: users should not have to have a detailed working knowledge of your system, and they certainly shouldn't be expected to understand the data model. So how, then, do you give users an insight into the provenance, without having to build a bespoke system for each and every different provenance installation? Speaker Bio: Darren is a final year Computer Science PhD student. He completed his undergraduate degree in Electronic Engineering at Southampton in 2012.

Knowledge acquisition through conceptual clustering : a theoretical framework and an algorithm for partitioning data into conjunctive concepts /

"May 1980."

Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Entrepreneurial growth : the role of international knowledge acquisition as moderated by firm age

In line with repeated recent calls for research on specific forms of growth rather than on an undifferentiated notion of “total growth,” our study contributes to the understanding of entrepreneurial growth. By this we mean growth through expansion into new geographic markets and/or via the introduction of new products or services. Building on Penrose's theory of the growth of the firm and on the research streams she has in part inspired, we investigate the impact of knowledge acquisition from international markets on entrepreneurial growth both at home and abroad. We further suggest that the effects of international knowledge acquisition on entrepreneurial growth will vary with firm age. Utilizing longitudinal data on 138 small and medium-sized enterprises (SMEs), we find that the acquisition of knowledge from international markets fuels growth through market development, and that this effect is stronger for international expansion than domestic expansion. Our results also show that firm age negatively moderates the relationship between international knowledge acquisition and entrepreneurial growth via the introduction of new products or services. Specifically, international knowledge acquisition has a positive effect on growth via new products/services development in young firms, but a negative effect in mature firms. We assume this reflects changes over time in how international knowledge is managed.

Knowledge acquisition in supply chain partnerships: the role of power

Knowledge is recognised as an important source of competitive advantage and hence there has been increasing academic and practitioner interest in understanding and isolating the factors that contribute to effective knowledge transfer between supply chain actors. The literature identifies power as a salient contributor to the effective operation of a supply chain partnership. However, there is a paucity of empirical research examining how power among actors influences knowledge acquisition and in turn the performance of supply chain partners. The aim of this research is to address this gap by examining the relationship between power, knowledge acquisition and supply chain performance among the supply chain partners of a focal Chinese steel manufacturer. A structured survey was used to collect the necessary data. Two conceptually independent variables – ‘availability of alternatives’ and ‘restraint in the use of power’ – were used to assess actual and realised power, respectively. Controlling for contingencies, we found that the flow of knowledge increased when supply chain actors had limited alternatives and when the more powerful actor exercised restraint in the use of power. Moreover, we found a positive relationship between knowledge acquisition and supply chain performance. This paper enriches the literature by empirically extending our understanding of how power affects knowledge acquisition and performance.

An investigation into knowledge acquisition idiosyncrasies in Ghanaian universities

Purpose This paper aims to fill the research and knowledge gap in knowledge management studies in Ghana. Knowledge acquisition is one of the unexploited areas in knowledge management literature, especially in the Ghanaian context. This study tries to ascertain the factors affecting knowledge acquisition in Ghanaian universities. Design/methodology/approach The study used the quantitative approach. The cross-sectional survey was adopted as the research design. A questionnaire consisting of Likert scale questions was used to collect data from the respondents. The items and the constructs were derived from the extant literature. The questionnaire was sent to 350 respondents, out of which 250 were returned fully completed. Data were quantitatively analysed using descriptive methods and factor analysis. Findings This study provides empirical evidence about the factors affecting knowledge acquisition in Ghanaian universities. Findings from the study show that programme content, lecturers’ competence, student academic background and attitude and facilities for teaching and learning influence knowledge acquisition in Ghanaian universities. Research limitations/implications Although the study seeks to generalize the findings, this should be cautiously done, as some scholars have advocated for large sample size. Nonetheless, there are some studies that have used sample size less than the one used in this study. Practical implications The study takes notice of the need for Ghanaian universities to use modern facilities and infrastructures such as electronic libraries and information technology equipment and also provide reading rooms to enhance teaching and learning. Originality/value Studies looking at knowledge acquisition in Ghanaian universities are virtually non-existent, and this study provides empirical findings on the factors affecting knowledge acquisition in Ghanaian universities.

Evaluation of an educational handbook as a knowledge-acquisition strategy for mastectomized women

This descriptive, cross-sectional and quantitative study presents an analysis of knowledge acquired by mastectomized women concerning breast cancer after reading an educational handbook. The sample was composed of 125 women. Data were collected in a specialized cancer facility in three phases: preparatory, operational I and operational II. As to the knowledge acquired, the posttest showed an 11% increase in the number of correct answers compared to the pretest. The most frequent correct answer regarded a question asking the name of the surgery (97.60%) while the question concerning breast reconstruction obtained the lowest number of correct answers (58.40%). Answers to all the questions significantly improved in the posttest, with the exception of a question addressing breast reconstruction (p=0.754). The assessment of knowledge showed positive results after reading, suggesting that cognition is essential to understanding and adhering to guidance, thus the handbook is a favorable resource to be used in the rehabilitation of mastectomized women.

A data driven study of relationships between relief and farmland abandonment in a Mediterranean region

The environmental, cultural and socio-economic causes and consequences of farmland abandonment are issues of increasing concern for researchers and policy makers. In previous studies, we proposed a new methodology for selecting the driving factors in farmland abandonment processes. Using Data Mining and GIS, it is possible to select those variables which are more significantly related to abandonment. The aim of this study is to investigate the application of the above mentioned methodology for finding relationships between relief and farmland abandonment in a Mediterranean region (SE Spain).We have taken into account up to 28 different variables in a single analysis, some of them commonly considered in land use change studies (slope, altitude, TWI, etc), but also other novel variables have been evaluated (sky view factor, terrain view factor, etc). The variable selection process provides results in line with the previous knowledge of the study area, describing some processes that are region specific (e.g. abandonment versus intensification of the agricultural activities). The European INSPIRE Directive (2007/2/EC) establishes that the digital elevation models for land surfaces should be available in all member countries, this means that the research described in this work can be extrapolated to any European country to determine whether these variables (slope, altitude, etc) are important in the process of abandonment.

Data driven ontology evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Knowledge acquisition from L2 specialist texts

This research aims to investigate knowledge acquisition and concept formation in the domain of economics and business studies through a foreign language, English, from the very initial to the very final stage of development in the context of Higher Education in Turkey. It traces both the processes as well as the product of acquisition in order to provide a detailed picture of how knowledge acquisition occurs. It aims to explore ways in which the acquisition process can be facilitated and promoted while prospective students of the Department of Economics and Business Administration receive a language training programme, following the completion of which they will join their academic community which offers part of its courses through the English language. The study draws upon (some) theories of mental representation of knowledge, such as schema, frame and script. The concept of discourse community with its characteristics is investigated, enculturation of prospective students to acquire knowledge of their domain through L2 is explored, and the crucial role of the constructivist theory in relation to knowledge acquisition is highlighted. The present study was conducted through a process of enculturation taking place partly at the language centre of Çukurova University and partly at the target discourse community. The data utilised for initiating knowledge acquisition was obtained by establishing a corpus of economics and business texts, which the learners are expected to read during their academic courses utilising computerised technology. The method of think aloud protocols was used to analyse processes taking place in knowledge acquisition, while the product of what was acquired was investigated by means of written recall protocols. It has been discovered that knowledge acquisition operates on the basis of analogical and to a certain extent metaphorical reasoning. The evidence obtained from the think aloud protocols showed that neophytes were able to acquire fundamental concepts of their future domain by reaching the level of shared understanding with the members of their target community of the faculty. Diaries and questionnaire analyses demonstrated that enculturation facilitated learners' transition from the language centre into the target community. Analyses of the written recall protocols and examinations from the post-enculturation stage of the research showed that neophytes' academic performances in their target community were much higher than those of their non-enculturated counterparts. Processes learners go through and strategies they spontaneously make use of, especially while acquiring knowledge of a specific domain through L2 have so far remained unexplored research areas. The present research makes a potential contribution to the language and knowledge acquisition theories by examining closely and systematically the language and the strategies they employ in acquiring such knowledge. The research findings offer useful implications to English language teaching at language schools. Language teachers are provided with useful guidelines as to how they can provide prospective students of a particular academic community with an experience of acquiring fundamental concepts of their discipline before they become members of their target community.

Data-Driven Motion Detection and Characterization in PET Brain Scans Using List Mode

Head motion during a Positron Emission Tomography (PET) brain scan can considerably degrade image quality. External motion-tracking devices have proven successful in minimizing this effect, but the associated time, maintenance, and workflow changes inhibit their widespread clinical use. List-mode PET acquisition allows for the retroactive analysis of coincidence events on any time scale throughout a scan, and therefore potentially offers a data-driven motion detection and characterization technique. An algorithm was developed to parse list-mode data, divide the full acquisition into short scan intervals, and calculate the line-of-response (LOR) midpoint average for each interval. These LOR midpoint averages, known as “radioactivity centroids,” were presumed to represent the center of the radioactivity distribution in the scanner, and it was thought that changes in this metric over time would correspond to intra-scan motion.

Several scans were taken of the 3D Hoffman brain phantom on a GE Discovery IQ PET/CT scanner to test the ability of the radioactivity to indicate intra-scan motion. Each scan incrementally surveyed motion in a different degree of freedom (2 translational and 2 rotational). The radioactivity centroids calculated from these scans correlated linearly to phantom positions/orientations. Centroid measurements over 1-second intervals performed on scans with ~1mCi of activity in the center of the field of view had standard deviations of 0.026 cm in the x- and y-dimensions and 0.020 cm in the z-dimension, which demonstrates high precision and repeatability in this metric. Radioactivity centroids are thus shown to successfully represent discrete motions on the submillimeter scale. It is also shown that while the radioactivity centroid can precisely indicate the amount of motion during an acquisition, it fails to distinguish what type of motion occurred.