An improved memory management scheme for large scale graph computing engine GraphChi

GraphChi is the first reported disk-based graph engine that can handle billion-scale graphs on a single PC efficiently. GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs. With the novel technique of parallel sliding windows (PSW) to load subgraph from disk to memory for vertices and edges updating, it can achieve data processing performance close to and even better than those of mainstream distributed graph engines. GraphChi mentioned that its memory is not effectively utilized with large dataset, which leads to suboptimal computation performances. In this paper we are motivated by the concepts of 'pin ' from TurboGraph and 'ghost' from GraphLab to propose a new memory utilization mode for GraphChi, which is called Part-in-memory mode, to improve the GraphChi algorithm performance. The main idea is to pin a fixed part of data inside the memory during the whole computing process. Part-in-memory mode is successfully implemented with only about 40 additional lines of code to the original GraphChi engine. Extensive experiments are performed with large real datasets (including Twitter graph with 1.4 billion edges). The preliminary results show that Part-in-memory mode memory management approach effectively reduces the GraphChi running time by up to 60% in PageRank algorithm. Interestingly it is found that a larger portion of data pinned in memory does not always lead to better performance in the case that the whole dataset cannot be fitted in memory. There exists an optimal portion of data which should be kept in the memory to achieve the best computational performance.

TQM in e-Learning: a Self-assessment Model and Questionnaire

Organizations are seeking new, integrated systems that enable rapid changes through early identification of opportunities and problems, tracking of progress against plans, flexible allocation of resources to achieve goals, and consistent operations. Total Quality Management (TQM) is an overall business strategy. It means that all activities of the company will be focused on satisfying all stakeholders of the company. TQM can be realised by using the EFQM model. The EFQM model is a tool that organizations may use as a framework for self-evaluation that enables an organization to identify its strengths and areas for improvement and the extent to which its operations and results are in line with the characteristics of an excellent organization. We focus on a training organisation or to the learning department of an organization. So we are limiting the EFQM model to the training /learning activities. We can apply EFQM perfect on the level of an activity (business line) of a company. We selected the main criteria for which the learner can play the role of assessor. So only three main criteria left: the enabling resources, the enabling processes and the (learning) results for the learner. We limited the last one to “learning results” based on the Kirkpatrick model.

Learning from openness:the dynamics of breadth in external innovation linkages

We explore how openness in terms of external linkages generates learning effects, which enable firms to generate more innovation outputs from any given breadth of external linkages. Openness to external knowledge sources, whether through search activity or linkages to external partners in new product development, involves a process of interaction and information processing. Such activities are likely to be subject to a learning process, as firms learn which knowledge sources and collaborative linkages are most useful to their particular needs, and which partnerships are most effective in delivering innovation performance. Using panel data from Irish manufacturing plants, we find evidence of such learning effects: establishments with substantial experience of external collaborations in previous periods derive more innovation output from openness in the current period. © 2013 The Authors. Strategic Management Journal published by John Wiley & Sons Ltd.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Extending the GUIDE@HAND Tourist Guide Application with QR Codes for Museum Learning

The following paper attempts to encompass the opportunities for applying QR codes for museums and exhibits through the example of the Hungarian Museum of Environmental Protection and Water Management (Esztergom, Hungary). Besides providing interactivity in the museum for the mobile phone generation through the utilization of a device and a method that they are familiar with, it is important to explain how and why it is worthwhile to “adorn” the exhibits with these codes. In this paper we also touch upon the technical issues of how an existing mobile phone application can be incorporated into and used for the presentation of the museum.

The essentials of knowledge management

This book reviews the field of Knowledge Management, taking a holistic approach that includes both "soft" and "hard" aspects. It provides a broad perspective on the field, rather than one based on a single viewpoints from Computer Science or Organizational Learning, offering a comprehensive and integrated conception of Knowledge Management. The chapters represent the best Knowledge Management articles published in the 21st century in Knowledge Management Research & Practice and the European Journal of Information Systems, with contributors including Ikujiro Nonaka, Frada Burstein, and David Schwartz. Most of the chapters contribute significantly to practise as well as theory.

Automated Data Retrieval in Distance Learning Quality

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Teaching Statistics to Engineers: Learning from Experiential Data

The purpose of the work is to claim that engineers can be motivated to study statistical concepts by using the applications in their experience connected with Statistical ideas. The main idea is to choose a data from the manufacturing factility (for example, output from CMM machine) and explain that even if the parts used do not meet exact specifications they are used in production. By graphing the data one can show that the error is random but follows a distribution, that is, there is regularily in the data in statistical sense. As the error distribution is continuous, we advocate that the concept of randomness be introducted starting with continuous random variables with probabilities connected with areas under the density. The discrete random variables are then introduced in terms of decision connected with size of the errors before generalizing to abstract concept of probability. Using software, they can then be motivated to study statistical analysis of the data they encounter and the use of this analysis to make engineering and management decisions.

E-blogspot in the teaching of strategic management

E-learning and e-learning applications/tools are available to all educators thanks to the evolution of technology and the internet. Although a great variety of technologies are available it is not always obvious how these can be integrated in traditional teaching to support and enhance the learning experience. The majority of the existing literature proposes the use of blogging as an activity that students should do in order to increase their active participation in learning. This article presents the use of blogspots in the teaching of Strategic Management as a tool used to create greater linkages between theory and practice, discussing the evolution of its utilisation in my modules, the current state of use and a series of reflections on experience gained from its use so far. Overall, I have found that there is limited literature on how blogging could link to teaching activities and its utilisation should be viewed as learning by doing which is evaluated and improved by critical reflection of the user.

Implementing a knowledge management system within an NHS hospital:a case study exploring the roll-out of an Electronic Patient Record (EPR)

This research aims to contribute to understanding the implementation of knowledge management systems (KMS) in the field of health through a case study, leading to theory building and theory extension. We use the concept of the business process approach to knowledge management as a theoretical lens to analyse and explore how a large teaching hospital developed, executed and practically implemented a KMS. A qualitative study was conducted over a 2.5 year period with data collected from semi-structured interviews with eight members of the strategic management team, 12 clinical users and 20 patients in addition to non-participant observation of meetings and documents. The theoretical propositions strategy was used as the overarching approach for data analysis. Our case study provides evidence that true patient centred approaches to supporting care delivery with a KMS benefit from process thinking at both the planning and implementation stages, and an emphasis on the knowledge demands resulting from: the activities along the care pathways; where cross-overs in care occur; and knowledge sharing for the integration of care. The findings also suggest that despite the theoretical awareness of KMS implementation methodologies, the actual execution of such systems requires practice and learning. Flexible, fluid approaches through rehearsal are important and communications strategies should focus heavily on transparency incorporating both structured and unstructured communication methods.

Organisational learning, climate change and the tragedy of the commons in China

This paper ends with a brief discussion of climate change and suggests that a practical solution would be to transfer much of the current air, sea and long-haul trucking of intercontinental freight between China and Europe (and the USA) to maglev systems. First we review the potential of Asian knowledge management and organisational learning and contrast this against Western precepts finding that there seems to be little incentive to 'look after one's fellows' in China (and perhaps across Asia) outside of tight personal guanxi networks. This is likely to be the case in the intense production regions of China where little time is allowed for 'organisational learning' by the staff and there is little incentive to initiate 'knowledge management' by senior managers. Thus the 'tragedy of the commons' will be enacted by individuals, township, and provincial leaders upwards to top ministers - no one will care for the climate or pollution, only for their own group and their wealth creation prospects. Copyright © 2011 Inderscience Enterprises Ltd.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

Country strategic risk and knowledge management:a proposed framework for improvement

The world is in a period of reflection about social and economic models. In particular there is a review of the capacities that countries have for improving their competitiveness. The experiences in a society are part of the process of learning and knowledge development in that society: especially in the development of communities. Risks appear continually in the process of the search for, analysis and implementation of solutions to problems. This paper discusses the issues related to the improvement of productivity and knowledge in a society, the risk that poor or even declining productivity brings to the communities and the need to develop people that support the decision making process in communities.The approach to improve the communities' development is through the design of a research programme in knowledge management based on distance learning. The research programme implementation is designed to provide value added to the decisions in communities in order to use collective intelligence, solve collective problems and to achieve goals that support local solutions. This program is organized and focused on four intelligence areas, artificial, collective, sentient and strategic. These areas are productivity related and seek to reduce the risk of lack of competitiveness through formal and integrated problem analysis. In a country such as Colombia, where different regions face varying problems to solve and there is a low level of infrastructure, the factors of production such as knowledge, skilled labour and "soft" infrastructure can be a way to develop the society.This entails using the local physical resources adequately for creating value with the support of people in the region to lead the analysis and search for solutions in the communities. The paper will describe the framework and programme and suggest how it could be applied in Colombia.

Cognitive-behavioural phenotype in a group of girls from 1.2 to 12 years old with the Incontinentia Pigmenti syndrome:recommendations for clinical management

Incontinentia Pigmenti (IP, OMIM#308300) is a rare X-linked genomic disorder (about 1,400 cases) that affects the neuroectodermal tissue and Central Nervous System (CNS). The objective of this study was to describe the cognitive-behavioural profile in children in order to plan a clinical intervention to improve their quality of life. A total of 14 girls (age range: from 1 year and 2 months to 12 years and 10 months) with IP and the IKBKG/NEMO gene deletion were submitted to a cognitive assessment including intelligence scales, language and visuo-spatial competence tests, learning ability tests, and a behavioural assessment. Five girls had severe to mild intellectual deficiencies and the remaining nine had a normal neurodevelopment. Four girls were of school age and two of these showed no intellectual disability, but had specific disabilities in calculation and arithmetic reasoning. This is the first description of the cognitive-behavioural profile in relation to developmental age. We stress the importance of an early assessment of learning abilities in individuals with IP without intellectual deficiencies to prevent the onset of any such deficit.