Key influences on motivations for utility cycling (cycling for transport to and from places)

Issue addressed: 

The influence of organisational commitment, job involvement and utility perceptions on trainees’ motivation to improve work through learning

Workplace training is a key strategy often used by organisations to optimise performance. Further, trainee motivation is a key determinant of the degree to which the material learned in a training programme will be transferred to the workplace, enhancing the performance of the trainee. This study investigates the relationship between several components of the Revised Human Resource Development (HRD) Evaluation and Research Model. This model provides a framework for diagnosing and understanding the causal influences of HRD intervention outcomes on training effectiveness. Data were obtained from an online questionnaire completed by 105 employees of various organisations. Findings revealed that affective organisational commitment, job involvement and utility perceptions are predictors of motivation to learn and transfer learning. An interaction effect was found, with increased affective organisational commitment predicting greater motivation to learn when training was of lower perceived utility. These findings suggest that the design and delivery of training should emphasise the relevance and utility of the programme in order to encourage greater trainee motivation and maximise return on investment. Additionally, implementing strategies aimed at promoting organisational commitment would appear beneficial.

Investigating Facebook groups through a random graph model

Facebook disseminates messages for billions of users everyday. Though there are log files stored on central servers, law enforcement agencies outside of the U.S. cannot easily acquire server log files from Facebook. This work models Facebook user groups by using a random graph model. Our aim is to facilitate detectives quickly estimating the size of a Facebook group with which a suspect is involved. We estimate this group size according to the number of immediate friends and the number of extended friends which are usually accessible by the public. We plot and examine UML diagrams to describe Facebook functions. Our experimental results show that asymmetric Facebook friendship fulfills the assumption of applying random graph models.

Preserving utility in social network graph anonymization

To protect from privacy disclosure, the social network graph is modified in order to hide the information that potentially be used to disclose person's identity. However, when the social network graph is changed, it is a great challenge to balance between the privacy gained and the loss of data utility. In this paper, we address this problem. We propose a new graph topological-based metric to improve utility preservation in social network graph anonymization. We compare the proposed approach with the amount-of-edge-change metric that popularly used in most of previous works. Experimental evaluation shows that our approach generates anonymized social network with improved utility preservation.

Network traffic clustering using random forest proximities

The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for the task, which performs clustering based on Random Forest (RF) proximities instead of Euclidean distances. The approach consists of two steps. In the first step, we derive a proximity measure for each pair of data points by performing a RF classification on the original data and a set of synthetic data. In the next step, we perform a K-Medoids clustering to partition the data points into K groups based on the proximity matrix. Evaluations have been conducted on real-world Internet traffic traces and the experimental results indicate that the proposed approach is more accurate than the previous methods.

The potential utility of Daoist theory and practice in psychology

This thesis attempted to demonstrate the potential for adaptation from the Chinese philosophical tradition of Daoism into modern psychology. The candidate was able to succesfully demonstrate a link between Daoist thinking style and reduced stress levels, especially in Chinese individuals.

Cost-utility analysis of different treatments for post-traumatic stress disorder in sexually abused children

Post-traumatic stress disorder (PTSD) is diagnosed in 20% to 53% of sexually abused children and adolescents. Living with PTSD is associated with a loss of health-related quality of life. Based on the best available evidence, the NICE Guideline for PTSD in children and adolescents recommends cognitive behavioural therapy (TF-CBT) over non-directive counselling as a more efficacious treatment.

Secure universal designated verifier signature without random oracles

In Asiacrypt 2003, the concept of universal designated verifier signature (UDVS) was introduced by Steinfeld, Bull, Wang and Pieprzyk. In the new paradigm, any signature holder (not necessarily the signer) can designate the publicly verifiable signature to any desired designated verifier (using the verifier’s public key), such that only the designated verifier can believe that the signature holder does have a valid publicly verifiable signature, and hence, believes that the signer has signed the message. Any other third party cannot believe this fact because this verifier can use his secret key to create a valid UDVS which is designated to himself. In ACNS 2005, Zhang, Furukawa and Imai proposed the first UDVS scheme without random oracles. In this paper, we give a security analysis to the scheme of Zhang et al. and propose a novel UDVS scheme without random oracles based on Waters’ signature scheme, and prove that our scheme is secure under the Gap Bilinear Diffie Hellman assumption

Engineering a distributed infrastructure for large-scale cost-effective content dissemination over urban vehicular networks

This paper proposes a practical and cost-effective approach to construct a fully distributed roadside communication infrastructure to facilitate the localized content dissemination to vehicles in the urban area. The proposed infrastructure is composed of distributed lightweight low-cost devices called roadside buffers (RSBs), where each RSB has the limited buffer storage and is able to transmit wirelessly the cached contents to fast-moving vehicles. To enable the distributed RSBs working toward the global optimal performance (e.g., minimal average file download delays), we propose a fully distributed algorithm to determine optimally the content replication strategy at RSBs. Specifically, we first develop a generic analytical model to evaluate the download delay of files, given the probability density of file distribution at RSBs. Then, we formulate the RSB content replication process as an optimization problem and devise a fully distributed content replication scheme accordingly to enable vehicles to recommend intelligently the desirable content files to RSBs. The proposed infrastructure is designed to optimize the global network utility, which accounts for the integrated download experience of users and the download demands of files. Using extensive simulations, we validate the effectiveness of the proposed infrastructure and show that the proposed distributed protocol can approach to the optimal performance and can significantly outperform the traditional heuristics.