Designing Interfaces to Support Collaboration in Information Retrieval

Information retrieval systems should acknowledge the existence of collaboration in the search process. Collaboration can help users to be more effective in both learning systems and in using them. We consider some issues of viewing interfaces to information retrieval systems as collaborative notations and how to build systems that more actively support collaboration. We describe a system that embodies just one kind of explicit support; a graphical representation of the search process that can be manipulated and discussed by the users. By acknowledging the importance of other people in the search process, we can develop systems that not only improve help-giving by people but which can lead to a more robust search activity, more able to cope with, and indeed exploit, the failures of any intelligent agents used

Improving instructional effectiveness with computer‐mediated communication

This study explores the use of asynchronous Computer‐Mediated Communication (CMC) in the delivery of instructional content, and points up the interaction among learners, as well as between learners and instructors. The instructional content in the project described was available to learners online as Microsoft Word documents, with email being used for communicating within the student group. Many students, as well as some of the instructors, felt uncomfortable with the flexibility and openness that a CMC environment allowed. However, once familiar with this process of instruction and interaction, learners were able to work consistently at their own pace, and understand that instructors are interested in every individual learner's opinion and in the collective views of the group. It was evident that a CMC‐based instructional delivery system, when carefully planned, has the potential to facilitate that outcome, and to improve instructional effectiveness

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Computer Applications as Mediators of Design and Use

The present report constitutes together with 21 submitted papers the author's doctor's dissertation. This dissertation summarizes an understanding of computers as the materials that we shape in design, on the one hand, and the artifacts that we use, in work and other everyday activities on the other. The presented work is primarily methodological and design-oriented, i.e. it is concerned with changing computer applications and with understanding them as changing and as part of change

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

A Comparison of Teaching Models in the West and in China

Models of teaching commonly used in the West and in China are analyzed and compared, using an analytical approach that systematically considers different aspects of the models. The purpose of the exploration is three-fold: (a) to create better understanding of both Chinese and Western models, for mutual insight and to strengthen the development of pedagogical theory building in China; (b) to guide a joint project between the Netherlands and China relative to the development computer-related learning resources for China; and (c) to contribute to better overall understanding of how instructional resources can be adapted for use in both Western and Chinese situations. The analysis provides a contribution for each of these goals

An evaluation framework to drive future evolution of a research prototype

The Open Source Component Artefact Repository (OSCAR) requires evaluation to confirm its suitability as a development environment for distributed software engineers. The evaluation will take note of several factors including usability of OSCAR as a stand-alone system, scalability and maintainability of the system and novel features not provided by existing artefact management systems. Additionally, the evaluation design attempts to address some of the omissions (due to time constraints) from the industrial partner evaluations. This evaluation is intended to be a prelude to the evaluation of the awareness support being added to OSCAR; thus establishing a baseline to which the effects of awareness support may be compared

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial