Saving bitrate vs. pleasing users : where is the break-even point in mobile video quality?

This paper presents a comprehensive study to find the most efficient bitrate requirement to deliver mobile video that optimizes bandwidth, while at the same time maintains good user viewing experience. In the study, forty participants were asked to choose the lowest quality video that would still provide for a comfortable and long-term viewing experience, knowing that higher video quality is more expensive and bandwidth intensive. This paper proposes the lowest pleasing bitrates and corresponding encoding parameters for five different content types: cartoon, movie, music, news and sports. It also explores how the lowest pleasing quality is influenced by content type, image resolution, bitrate, and user gender, prior viewing experience, and preference. In addition, it analyzes the trajectory of users’ progression while selecting the lowest pleasing quality. The findings reveal that the lowest bitrate requirement for a pleasing viewing experience is much higher than that of the lowest acceptable quality. Users’ criteria for the lowest pleasing video quality are related to the video’s content features, as well as its usage purpose and the user’s personal preferences. These findings can provide video providers guidance on what quality they should offer to please mobile users.

Engaging new digital locals with interactive urban screens to collaboratively improve the city

Local governments struggle to engage time poor and seemingly apathetic citizens, as well as the city’s young digital natives, the digital locals. This project aims at providing a lightweight, technological contribution towards removing the hierarchy between those who build the city and those who use it. We aim to narrow this gap by enhancing people’s experience of physical spaces with digital, civic technologies that are directly accessible within that space. This paper presents the findings of a design trial allowing users to interact with a public screen via their mobile phones. The screen facilitated a feedback platform about a concrete urban planning project by promoting specific questions and encouraging direct, in-situ, real-time responses via SMS and twitter. This new mechanism offers additional benefits for civic participation as it gives voice to residents who otherwise would not be heard. It also promotes a positive attitude towards local governments and gathers information different from more traditional public engagement tools.

Composite SaaS placement and resource optimization in Cloud computing using evolutionary algorithms

Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms.

Bending and blurring blended learning boundaries [Extended Abstract]

This presentation presents a blended learning model that provides greater opportunity for learning to be self-managed and personalized.

Bond performance of CFRP strengthened steel members subjected to axial compression

This paper focuses on the use of externally bonded Carbon Fiber Reinforced Polymer (CFRP) materials to strengthen steel plates subjected to compression. A fully slender steel section was selected in this test programme. CFRP strengthened steel plates and non strengthened plates were tested to fail under compressive load. The middle part of the strut was strengthened using CFRP sheet. The length of the strengthened zone was varied. Eight specimens were tested in this test programme. The test results showed a significant strength gain of 47% and delaying of lateral torsional buckling failure mode of strengthened members. This study confirms that there is great potential to increase the short term performance of CFRP strengthened steel structure under axial compression.

Towards reliable perception for unmanned ground vehicles in challenging conditions

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

'Explain in plain English' questions revisited : data structures problems

Recent studies have linked the ability of novice (CS1) programmers to read and explain code with their ability to write code. This study extends earlier work by asking CS2 students to explain object-oriented data structures problems that involve recursion. Results show a strong correlation between ability to explain code at an abstract level and performance on code writing and code reading test problems for these object-oriented data structures problems. The authors postulate that there is a common set of skills concerned with reasoning about programs that explains the correlation between writing code and explaining code. The authors suggest that an overly exclusive emphasis on code writing may be detrimental to learning to program. Non-code writing learning activities (e.g., reading and explaining code) are likely to improve student ability to reason about code and, by extension, improve student ability to write code. A judicious mix of code-writing and code-reading activities is recommended.

Fire performance and design of cold-formed steel wall systems

Traditionally, the fire resistance rating of Light gauge steel frame (LSF) wall systems is based on approximate prescriptive methods developed using limited fire tests. These fire tests are conducted using standard fire time-temperature curve given in ISO 834. However, in recent times fire has become a major disaster in buildings due to the increase in fire loads as a result of modern furniture and lightweight construction, which make use of thermoplastics materials, synthetic foams and fabrics. Therefore a detailed research study into the performance of load bearing LSF wall systems under both standard and realistic design fires on one side was undertaken to develop improved fire design rules. This study included both full scale fire tests and numerical studies of eight different LSF wall systems conducted for both the standard fire curve and the recently developed realistic design fire curves. The use of previous fire design rules developed for LSF walls subjected to non-uniform elevated temperature distributions based on AISI design manual and Eurocode 3 Parts 1.2 and 1.3 was investigated first. New simplified fire design rules based on AS/NZS 4600, North American Specification and Eurocode 3 Part 1.3 were then proposed with suitable allowances for the interaction effects of compression and bending actions. The importance of considering thermal bowing, magnified thermal bowing and neutral axis shift in the fire design was also investigated and their effects were included. A spread sheet based design tool was developed based on the new design rules to predict the failure load ratio versus time and temperature curves for varying LSF wall configurations. The accuracy of the proposed design rules was verified using the fire test and finite element analysis results for various wall configurations, steel grades, thicknesses and load ratios under both standard and realistic design fire conditions. A simplified method was also proposed to predict the fire resistance rating of LSF walls based on two sets of equations developed for the load ratio-hot flange temperature and the time-temperature relationships. This paper presents the details of this study on LSF wall systems under fire conditions and the results.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.