A forward constrained selection algorithm for probabilistic neural network

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

A multi-level probabilistic neural network

Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is introduced. The MLPNN uses an incremental constructive approach, i.e. it grows level by level. The construction algorithm of the MLPNN is proposed such that the classification accuracy monotonically increases to ensure that the classification accuracy of the MLPNN is higher than or equal to that of the traditional PNN. Numerical examples are included to demonstrate the effectiveness of proposed new approach.

Mid-term evaluation of African Technology Policy Studies Network Phase VI Strategic Plan: for the period January 1, 2009 to December 31, 2010

The African Technology Policy Studies Network (ATPS) is a multidisciplinary network of researchers, private sector actors, policymakers and civil society. ATPS has the vision to become the leading international centre of excellence and reference in science, technology and innovation (STI) systems research, training and capacity building, communication and sensitization, knowledge brokerage, policy advocacy and outreach in Africa. It has a Regional Secretariat in Nairobi Kenya, and operates through national chapters in 29 countries (including 27 in Africa and two Chapters in the United Kingdom and USA for Africans in the Diaspora) with an expansion plan to cover the entire continent by 2015. The ATPS Phase VI Strategic Plan aims to improve the understanding and functioning of STI processes and systems to strengthen the learning capacity, social responses, and governance of STI for addressing Africa's development challenges, with a specific focus on the Millennium Development Goals (MDGs). A team of external evaluators carried out a midterm review to assess the effectiveness and efficiency of the implementation of the Strategic Plan for the period January 1, 2009 to December 31, 2010. The evaluation methodology involved multiple quantitative and qualitative methods to assess the qualitative and quantitative inputs (human resources, financial resources, time, etc.) into ATPS activities (both thematic and facilitative) and their tangible and intangible outputs, outcomes and impacts. Methods included a questionnaire survey of ATPS members and stakeholders, key informant interviews, and focus group discussions (FGDs) with members in six countries. Effectiveness of Programmes Under all six strategic goals, very good progress has been made towards planned outputs and outcomes. This is evidenced by key performance indicators (KPIs) generated from desk review, ratings from the survey respondents, and the themes that run through the FGDs. Institutional and Programme Cost Effectiveness Institutional Effectiveness: assessment of institutional effectiveness suggests that adequate management frameworks are in place and are being used effectively and transparently. Also technical and financial accounting mechanisms are being followed in accordance with grant agreements and with global good practice. This is evidenced by KPIs generated from desk review. Programme Cost Effectiveness: assessment of cost-effectiveness of execution of programmes shows that organisational structure is efficient, delivering high quality, relevant research at relatively low cost by international standards. The evidence includes KPIs from desk review: administrative costs to programme cost ratio has fallen steadily, to around 10%; average size of research grants is modest, without compromising quality. There is high level of pro bono input by ATPS members. ATPS Programmes Strategic Evaluation ATPS research and STI related activities are indeed unique and well aligned with STI issues and needs facing Africa and globally. The multi-disciplinary and trans-boundary nature of the research activities are creating a unique group of research scientists. The ATPS approach to research and STI issues is paving the way for the so called Third Generation University (3GU). Understanding this unique positioning, an increasing number of international multilateral agencies are seeking partnership with ATPS. ATPS is seeing an increasing level of funding commitments by Donor Partners. Recommendations for ATPS Continued Growth and Effectiveness On-going reform of ATPS administrative structure to continue The on-going reforms that have taken place within the Board, Regional Secretariat, and at the National Chapter coordination levels are welcomed. Such reform should continue until fully functional corporate governance policy and practices are fully established and implemented across the ATPS governance structures. This will further strengthen ATPS to achieve the vision of being the leading STI policy brokerage organization in Africa. Although training in corporate governance has been carried out for all sectors of ATPS leadership structure in recent time, there is some evidence that these systems have not yet been fully implemented effectively within all the governance structures of the organization, especially at the Board and National chapter levels. Future training should emphasize practical application with exercises relevant to ATPS leadership structure from the Board to the National Chapter levels. Training on Transformational Leadership - Leading a Change Though a subject of intense debate amongst economists and social scientists, it is generally agreed that cultural mindsets and attitudes could enhance and/or hinder organizational progress. ATPS’s vision demands transformational leadership skills amongst its leaders from the Board members to the National Chapter Coordinators. To lead such a change, ATPS leaders must understand and avoid personal and cultural mindsets and value systems that hinder change, while embracing those that enhance it. It requires deliberate assessment of cultural, behavioural patterns that could hinder progress and the willingness to be recast into cultural and personal habits that make for progress. Improvement of relationship amongst the Board, Secretariat, and National Chapters A large number of ATPS members and stakeholders feel they do not have effective communications and/or access to Board, National Chapter Coordinators and Regional Secretariat activities. Effort should be made to improve the implementation of ATPS communication strategy to improve on information flows amongst the ATPS management and the members. The results of the survey and the FGDs suggest that progress has been made during the past two years in this direction, but more could be done to ensure effective flow of pertinent information to members following ATPS communications channels. Strategies for Increased Funding for National Chapters There is a big gap between the fundraising skills of the Regional Secretariat and those of the National Coordinators. In some cases, funds successfully raised by the Secretariat and disbursed to national chapters were not followed up with timely progress and financial reports by some national chapters. Adequate training in relevant skills required for effective interactions with STI key policy players should be conducted regularly for National Chapter coordinators and ATPS members. The ongoing training in grant writing should continue and be made continent-wide if funding permits. Funding of National Chapters should be strategic such that capacity in a specific area of research is built which, with time, will not only lead to a strong research capacity in that area, but also strengthen academic programmes. For example, a strong climate change programme is emerging at University of Nigeria Nsukka (UNN), with strong collaborations with Universities from neighbouring States. Strategies to Increase National Government buy-in and support for STI Translating STI research outcomes into policies requires a great deal of emotional intelligence, skills which are often lacking in the first and second generation universities. In the epoch of the science-based or 2GUs, governments were content with universities carrying out scientific research and providing scientific education. Now they desire to see universities as incubators of new science- or technology-based commercial activities, whether by existing firms or start-ups. Hence, governments demand that universities take an active and leading role in the exploitation of their knowledge and they are willing to make funds available to support such activities. Thus, for universities to gain the attention of national leadership they must become centres of excellence and explicit instruments of economic development in the knowledge-based economy. The universities must do this while working collaboratively with government departments, parastatals, and institutions and dedicated research establishments. ATPS should anticipate these shifting changes and devise programmes to assist both government and universities to relate effectively. New administrative structures in member organizations to sustain and manage the emerging STI multidisciplinary teams Second Generation universities (2GUs) tend to focus on pure science and often do not regard the application of their know-how as their task. In contrast, Third Generation Universities (3GUs) objectively stimulate techno-starters – students or academics – to pursue the exploitation or commercialisation of the knowledge they generate. They view this as being equal in importance to the objectives of scientific research and education. Administratively, research in the 2GU era was mainly monodisciplinary and departments were structured along disciplines. The emerging interdisciplinary scientific teams with focus on specific research areas functionally work against the current mono-disciplinary faculty-based, administrative structure of 2GUs. For interdisciplinary teams, the current faculty system is an obstacle. There is a need for new organisational forms for university management that can create responsibilities for the task of know-how exploitation. ATPS must anticipate this and begin to strategize solutions for their member institutions to transition to 3Gus administrative structure, otherwise ATPS growth will plateau, and progress achieved so far may be stunted.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

The link between temporal attention and emotion: a playground for psychology, neuroscience, and plausible artificial neural networks

In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion.

Ubiquitous grid resource monitoring

In any wide-area distributed system there is a need to communicate and interact with a range of networked devices and services ranging from computer-based ones (CPU, memory and disk), to network components (hubs, routers, gateways) and specialised data sources (embedded devices, sensors, data-feeds). In order for the ensemble of underlying technologies to provide an environment suitable for virtual organisations to flourish, the resources that comprise the fabric of the Grid must be monitored in a seamless manner that abstracts away from the underlying complexity. Furthermore, as various competing Grid middleware offerings are released and evolve, an independent overarching monitoring service should act as a corner stone that ties these systems together. GridRM is a standards-based approach that is independent of any given middleware and that can utilise legacy and emerging resource-monitoring technologies. The main objective of the project is to produce a standardised and extensible architecture that provides seamless mechanisms to interact with native monitoring agents across heterogeneous resources.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

Mulsemedia: state of the art, perspectives, and challenges

Mulsemedia—multiple sensorial media—captures a wide variety of research efforts and applications. This article presents a historic perspective on mulsemedia work and reviews current developments in the area. These take place across the traditional multimedia spectrum—from virtual reality applications to computer games—as well as efforts in the arts, gastronomy, and therapy, to mention a few. We also describe standardization efforts, via the MPEG-V standard, and identify future developments and exciting challenges the community needs to overcome.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

FORCe: fully online and automated artifact removal for brain-computer interfacing

A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing. The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g. electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged auto-mutual information clustering (LAMIC) and Fully automated statistical thresholding (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.