COSNet : a cost sensitive neural network for semi-supervised learning in graphs

The semi-supervised problem of learning node labels in graphs consists, given a partial graph labeling, in inferring the unknown labels of the unlabeled vertices. Several machine learning algorithms have been proposed for solving this problem, including Hopfield networks and label propagation methods; however, some issues have been only partially considered, e.g. the preservation of the prior knowledge and the unbalance between positive and negative labels. To address these items, we propose a Hopfield-based cost sensitive neural network algorithm (COSNet). The method factorizes the solution of the problem in two parts: 1) the sub- network composed by the labelled vertices is considered, and the net- work parameters are estimated through a supervised algorithm; 2) the estimated parameters are extended to the subnetwork composed of the unlabeled vertices, and the attractor reached by the dynamics of this subnetwork allows to predict the labeling of the unlabeled vertices. The proposed method embeds in the neural algorithm the \u201da priori\u201d knowl- edge coded in the labelled part of the graph, and separates node labels and neuron states, allowing to differentially weight positive and nega- tive node labels. Moreover, COSNet introduces an efficient cost-sensitive strategy which allows to learn the near-optimal parameters of the net- work in order to take into account the unbalance between positive and negative node labels. Finally, the dynamics of the network is restricted to its unlabeled part, preserving the minimization of the overall objective function and significantly reducing the time complexity of the learning algorithm. COSNet has been applied to the genome-wide prediction of gene function in a model organism. The results, compared with those ob- tained by other semi-supervised label propagation algorithms and super- vised machine learning methods, show the effectiveness of the proposed approach

Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. It then proceeds to investigate current applications of deep learning techniques in the field of financial market predictions and also in the specific area of portfolio management. A series of successively deeper neural network models were then developed and assessed in terms of their ability to accurately predict whether a sample of stocks would either outperform or underperform the selected benchmark index. The predictions generated by these models were then used to guide the adjustment of portfolio weightings to implement and forward test an enhanced indexing strategy on a hypothetical stock portfolio

A methodology for evaluating utilisation of mine planning software and consequent decision-making strategies in South Africa

Mine planning software has and continues to contribute to the development of the South African mining industry. As mine planning software usage continues to be more widespread, it is imperative that a methodology to evaluate mine planning software utilisation for enhanced decision-making strategies in South Africa is established. An existing online database available on the website link http://db.mining.wits.ac.za was developed prior to this study in September 2012 (initial data collection date). However, the database only acted as a snapshot of mine planning software data repository and lacked a framework to evaluate utilisation of mine planning software in the South African mining industry. In this thesis, a methodology was developed to measure the utilisation of mine planning software to enhance decision-making strategies in the South African mining industry. The methodology for the evaluation of utilisation of mine planning software in various commodity sectors was developed on the basis of three variables, namely, commodity, functionality, and time factor, as a key evaluation criteria. Even though the calculations can be done on any commodity in a similar manner, in this research, calculations were only performed on four different commodities, namely coal, diamond, gold and platinum group metals which are the most significant minerals in South Africa. Six functionalities namely Geological Data Management, Geological Modelling and Resource Estimation, Design and Layout, Scheduling, Financial Valuation and Optimisation were applied on the four different commodities using two different time-stamps (September 2012 and April 2014). The following software providers availed information that was used to populate the database: Geovia, MineRP Solutions, Sable, RungePincockMinarco, Maptek, Cyest Technology and CAE Mining. Note that the CAE Mining data was only made available in April 2014 (second data collection date). However, the results indicated that the market leaders in terms of mine planning software utilisation in South Africa differs, depending on the commodity that is being mined as well as the functionality that is being used. In addition, this thesis also proposed a framework to estimate the future use of mine planning software on an evolving dataset by considering the fact that the database will be continually updated in the future. By using Artificial Neural Networks (ANN), specifically supervised learning, time-series analyses were performed. Results from the time-series analyses were used to establish the framework for estimating the future use of mine planning software utilisation in the South African mining industry. By using this newly developed framework, utilisation of the various mine planning software was measured leading to the formulation of different decision-making strategies for the various mine planning software stakeholders. By using this newly developed framework to estimate and measure mine planning software utilisation, and proposing a framework for time-series analyses on an evolving dataset, this thesis serves a number of beneficiaries; firstly, the South African mining industry to position themselves better by acquiring optimal combination of mine planning software that is being used in South Africa so that they can improve their production levels, secondly, tertiary education institutions and mining consulting firms which make use of mine planning software, and lastly, the aforementioned software providers by strategically positioning themselves in a limited mine planning software market. However, this newly developed framework could be used by involved parties for corporate strategic decision-making

Data mining industry : emerging trends and new opportunities

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2000."May 2000."Includes bibliographical references (leaves 170-179).by Walter Alberto Aldana.M.Eng

International Conference Management, Business and Economics

UBT Annual International Conference is the 9th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: Art and Digital Media Agriculture, Food Science and Technology Architecture and Spatial Planning Civil Engineering, Infrastructure and Environment Computer Science and Communication Engineering Dental Sciences Education and Development Energy Efficiency Engineering Integrated Design Information Systems and Security Journalism, Media and Communication Law Language and Culture Management, Business and Economics Modern Music, Digital Production and Management Medicine and Nursing Mechatronics, System Engineering and Robotics Pharmaceutical and Natural Sciences Political Science Psychology Sport, Health and Society Security Studies This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event

From unstructured HTML to structured XML: how XML supports financial knowledge management on internet.

by Yuen Lok-tin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 88-95).Abstracts in English and Chinese.ABSTRACT --- p.I摘要 --- p.IIIACKNOWLEDGEMENT --- p.VTABLE OF CONTENTS --- p.VILIST OF FIGURES --- p.VIIILIST OF TABLES --- p.IXChapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Objectives --- p.2Chapter 1.3 --- Organization --- p.4Chapter 2 --- LITERATURE REVIEW & THEORETICAL FOUNDATION --- p.6Chapter 2.1 --- "Data, Information and Knowledge" --- p.6Chapter 2.2 --- Knowledge Management --- p.7Chapter 2.3 --- Information Transparency and Efficiency --- p.10Chapter 2.3.1 --- Transparency --- p.11Chapter 2.3.2 --- Efficiency --- p.13Chapter 2.4 --- extensible markup language (XML) --- p.14Chapter 3 --- DIGITAL FINANCIAL INFORMATION AND ISSUES --- p.16Chapter 3.1 --- Managing Financial Information on the Internet --- p.17Chapter 3.2 --- Existing Electronic Financial Filing Systems --- p.20Chapter 3.3 --- Financial Document Disclosure Model --- p.21Chapter 3.4 --- Interaction Between Information Producers and Consumers --- p.23Chapter 3.5 --- Gluing All Together --- p.26Chapter 4 --- IDEAL ELECTRONIC FINANCIAL DISCLOSURE SYSTEM --- p.27Chapter 4.1 --- Structure and Representation of Knowledge --- p.28Chapter 4.2 --- Content Creation --- p.33Chapter 5 --- PROPOSED APPROACH --- p.36Chapter 5.1 --- Preliminary XML Data Dictionary --- p.36Chapter 5.2 --- Creation of XML Tags --- p.40Chapter 5.2.1 --- Statistical Information Retrieval --- p.41Chapter 5.2.2 --- Accounting and Auditing Practice --- p.43Chapter 5.2.3 --- Investors´ةFeedback --- p.44Chapter 5.3 --- Value-Added Services --- p.45Chapter 6 --- DESIGN AND DEVELOPMENT OF ELFFS-XML --- p.49Chapter 6.1 --- Stages of ELFFS-XML --- p.49Chapter 6.1.1 --- Information Creation --- p.49Chapter 6.1.2 --- Information Collection/Storage --- p.50Chapter 6.1.3 --- Knowledge Generation --- p.51Chapter 6.1.4 --- Knowledge Dissemination/Presentation --- p.52Chapter 6.1.5 --- Feedback --- p.52Chapter 6.2 --- Components of ELFFS-XML --- p.53Chapter 6.2.1 --- Data Source Abstraction Layer --- p.55Chapter 6.2.2 --- Storage Abstraction Layer --- p.57Chapter 6.2.3 --- Logic Layer --- p.61Chapter 6.2.4 --- Presentation Layer --- p.63Chapter 7 --- EVALUATING ELFFS-XML --- p.66Chapter 7.1 --- Comparison with Other Financial Information Disclosure Systems --- p.66Chapter 7.2 --- Users' Evaluation --- p.70Chapter 7.3 --- Systems Efficiency --- p.71Chapter 7.4 --- XML Tag Generation Approach Performance Evaluation --- p.73Chapter 8 --- CONCLUSION AND FUTURE RESEARCH --- p.78APPENDIX I SURVEY ON INVESTMENT PATTERN --- p.80APPENDIX II CORE ELFFS-XML DTD --- p.84APPENDIX III PERFORMANCE RELATED XML TAGS --- p.86BIBLIOGRAPHY --- p.8