‘Making it real’: evaluating the impact of service user and carer involvement in social work education

Service user and carer involvement (SUCI) in social work education in England is required by the profession’s regulator, the Health and Care Professions Council. However, a recent study of 83 HEIs in England reported that despite considerable progress in SUCI, there is no evidence that the learning derived from it is being transferred to social work practice. In this article we describe a study that examines the question: ‘What impact does SUCI have on the skills, knowledge and values of student social workers at the point of qualification and beyond?’ Students at universities in England and Northern Ireland completed online questionnaires and participated in focus groups, spanning a period immediately pre-qualification and between six to nine months post-qualification. From our findings, we identify four categories that influence the impact of service user involvement on students’ learning: student factors; service user and carer factors; programme factors; and practice factors; each comprises of a number of sub-categories. We propose that the model developed can be used by social work educators, service user and carer contributors and practitioners to maximise the impact of SUCI. We argue that our findings also have implications for employment-based learning routes and post-qualifying education.

FPGA based Streaming Image Processing Processor (SIPPro) architecture to accelerate data intensive applications

Current data-intensive image processing applications push traditional embedded architectures to their limits. FPGA based hardware acceleration is a potential solution but the programmability gap and time consuming HDL design flow is significant. The proposed research approach to develop “FPGA based programmable hardware acceleration platform” that uses, large number of Streaming Image processing Processors (SIPPro) potentially addresses these issues. SIPPro is pipelined in-order soft-core processor architecture with specific optimisations for image processing applications. Each SIPPro core uses 1 DSP48, 2 Block RAMs and 370 slice-registers, making the processor as compact as possible whilst maintaining flexibility and programmability. It is area efficient, scalable and high performance softcore architecture capable of delivering 530 MIPS per core using Xilinx Zynq SoC (ZC7Z020-3). To evaluate the feasibility of the proposed architecture, a Traffic Sign Recognition (TSR) algorithm has been prototyped on a Zedboard with the color and morphology operations accelerated using multiple SIPPros. Simulation and experimental results demonstrate that the processing platform is able to achieve a speedup of 15 and 33 times for color filtering and morphology operations respectively, with a significant reduced design effort and time.

Signal Reliability Improvement Using Selection Combining Based Macro-Diversity for Off-Body Communications At 868 MHz

This paper investigates the potential improvement in signal reliability for outdoor short-range off-body communications channels at 868 MHz using the macro-diversity offered by multiple co-located base stations. In this study, ten identical hypothetical base stations were positioned equidistantly around the perimeter of a rectangle of length 6.67 m and width 3.3 m. A body worn node was placed on the central chest region of an adult male. Five scenarios, each considering different user trajectories, were then analyzed to test the efficacy of using macro-diversity when the desired link is subject to shadowing caused by the human body. A number of selection combining based macro-diversity configurations consisting of four and then ten base stations were considered. It was found that using a macro-diversity system consisting of four base stations (or equivalently signal branches), a maximum diversity gain of 22.5 dB could be obtained while implementing a 10-base station setup this figure could be improved to 25.2 dB.

Retrieving Similar Discussion Forum Threads: A Structure based Approach

Online forums are becoming a popular way of finding useful
information on the web. Search over forums for existing discussion
threads so far is limited to keyword-based search due
to the minimal effort required on part of the users. However,
it is often not possible to capture all the relevant context in a
complex query using a small number of keywords. Examplebased
search that retrieves similar discussion threads given
one exemplary thread is an alternate approach that can help
the user provide richer context and vastly improve forum
search results. In this paper, we address the problem of
finding similar threads to a given thread. Towards this, we
propose a novel methodology to estimate similarity between
discussion threads. Our method exploits the thread structure
to decompose threads in to set of weighted overlapping
components. It then estimates pairwise thread similarities
by quantifying how well the information in the threads are
mutually contained within each other using lexical similarities
between their underlying components. We compare our
proposed methods on real datasets against state-of-the-art
thread retrieval mechanisms wherein we illustrate that our
techniques outperform others by large margins on popular
retrieval evaluation measures such as NDCG, MAP, Precision@k
and MRR. In particular, consistent improvements of
up to 10% are observed on all evaluation measures

Container-based Cloud Virtual Machine Benchmarking

With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight - time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed.

In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near real-time on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite - Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.

Energy Detection based Spectrum Sensing over Two-wave with Diffuse Power Fading Channels

One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This article investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse than Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing as well as square law selection diversity reception are derived. These expressions are used to analyze the behavior of ED-based spectrum sensing over moderate, severe and extreme fading conditions, and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. Our results indicate that TWDP fading conditions can significantly degrade the sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable us to identify the limits of ED-based spectrum sensing and quantify the trade-offs between detection performance and energy efficiency for cognitive radio systems deployed within confined environments such as in-vehicular wireless networks.

Learning about probability from text and tables: Do color coding and labeling through an interactive-user interface help?

Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly assigned to learn about probabilistic reasoning from one of 4 computer-based lessons generated from a 2 (color coding/no color coding) by 2 (labeling/no labeling) between-subjects design. Learners added the labels or color coding at their own pace by clicking buttons in a computer-based lesson. Participants' eye movements were recorded while viewing the lesson. Labeling was beneficial for learning, but color coding was not. In addition, labeling, but not color coding, increased attention to important information in the table and time with the lesson. Both labeling and color coding increased looks between the text and corresponding information in the table. The findings provide support for the multimedia principle, and they suggest that providing labeling enhances learning about probabilistic reasoning from text and tables