Editorial Special Issue on Enhancement Algorithms, Methodologies and Technology for Spectral Sensing

The paper is an editorial issue on enhancement algorithms, methodologies and technology for spectral sensing and serves as a valuable and useful reference for researchers and technologists interested in the evolving state-of-the-art and/or the emerging science and technology base associated with spectral-based sensing and monitoring problem. This issue is particularly relevant to those seeking new and improved solutions for detecting chemical, biological, radiological and explosive threats on the land, sea, and in the air

Diverse perceptions of smart spaces

This is the era of smart technology and of ‘smart’ as a meme, so we have run three workshops to examine the ‘smart’ meme and the exploitation of smart environments. The literature relating to smart spaces focuses primarily on technologies and their capabilities. Our three workshops demonstrated that we require a stronger user focus if we are advantageously to exploit spaces ascribed as smart: we examined the concept of smartness from a variety of perspectives, in collaboration with a broad range of contributors. We have prepared this monograph mainly to report on the third workshop, held at Bournemouth University in April 2012, but do also consider the lessons learned from all three. We conclude with a roadmap for a fourth (and final) workshop, which is intended to emphasise the overarching importance of the humans using the spac

The elicitation of key performance indicators of e-government providers: A bottom-up approach

Copyright @ 2013 EMCIS.Delivering an adequate e-Government service (e-service) is becoming more of a necessity in today's digital world. In order to improve e-services and increase the engagement of both users' and providers' side, studies on the performance evaluation of such provided e-services are taking places. However a clear identification of the key performance indicators from the e-Government providers’ side is not well explored. This shortcoming hampers the conduct of a holistic evaluation of an e-service provision from the perspective of its stakeholders in order to improve e-services as well as to increase e-services take-ups. In this paper, a systematic process to identify indicators is implemented based on a bottom-up approach. The process used three focus-group meetings with providers, users, and academics in Qatar, Lebanon and UK to collect, identify and validate key indicators from the perspective of e-services’ providers. The approach resulted in the identification of five factors levels (service, technology, employees, policy and management and social responsibilities) with fifteen sub-categories of SMART variables. Hence, leading to the development of a new model, STEPS, that can fully explain and predict e-government success from the providers’ point of view. It will work as a strategic management tool to align various stakeholders on common goal and values based on evidence based evaluation of e-services using smart measurable indicators for the improvement of an e-service at the engagement level in the field of e-government. In addition, other fields can benefit from the outcome of this work, such as logistics service providers, who make their services available across new and existing relationships between the Internet commerce firms, their customers, and their vendors

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering