An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Accountability and Value for Money: A Framework for Exploring the Relationship in Private Finance Initiative Contracts

There is an implicit assumption in the UK Treasury’s publications on public-private partnerships (PPP) – also more commonly known in the United Kingdom as private finance initiative (PFI) - that accountability and value for money (VFM) are related concepts. While recent academic studies on PPP/PFI (from now on as PFI) have focused on VFM, there is a notable absence of studies exploring the ‘presumed’ relationships between accountability and VFM. Drawing on Dubnick’s (Dubnick and Romzek, 1991, 1993; Dubnick, 1996, 1998, 2003, 2005; Dubnick and Justice, 2002) framework for accountability and PFI literature, we develop a research framework for exploring potential relationships between accountability and VFM in PFI projects by proposing alternative accountability cultures, processes and mechanisms for PFI. The PFI accountability model is then exposed to four criteria - warrantability, tractability, measurability and feasibility. Our preliminary interviews provide us guidance in identifying some of the cultures, processes and mechanisms indicated in our model which should enable future researchers to test not only the UK Government’s claimed relationships between accountability and VFM using more specific PFI empirical data, but also a potential relationship between accountability and performance in general.

Using smart people to form future mobile wireless networks

The concept of a body-to-body network, where smart communicating devices carried or worn by a person are used to form a wireless network with devices situated on other nearby persons. New innovations in this area will see the form factor of smart devices being modified, so that they may be worn on the human body or integrated into clothing, in the process creating a new generation of smart people. Applications of body-to-body networking will extend well beyond the support of cellular and Wi-Fi networks. They will also be used in short-range covert military applications, first responder applications, team sports and used to interconnect body area networks (BAN). Security will be a major issue as routing between multiple nodes will increase the risk of unauthorized access and compromise sensitive data. This will add complexity to the medium access layer (MAC) and network management. Antennas designed to operate in body centric communications systems may be broadly categorized as on- or off-body radiators, according to their radiation pattern characteristics when mounted on the human body.