A tutorial task and tertiary courseware model for collaborative learning communities

RAED provides a computerised infrastructure to support the development and administration of Vicarious Learning in collaborative learning communities spread across multiple universities and workplaces. The system is based on the OASIS middleware for Role-based Access Control. This paper describes the origins of the model and the approach to implementation and outlines some of its benefits to collaborative teachers and learners

Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi

A web system trace model and its application to web design

Traceability analysis is crucial to the development of web-centric systems, particularly those with frequent system changes, fine-grained evolution and maintenance, and high level of requirements uncertainty. A trace model at the level of the web system architecture is presented in this paper to address the specific challenges of developing web-centric systems. The trace model separates the concerns of different stakeholders in the web development life cycle into viewpoints; and classifies each viewpoint into structure and behaviour. Tracing relationships are presented along two dimensions: within viewpoints; and among viewpoints. Examples of tracing relationships are presented using UML. This trace model is demonstrated through its application to the design of a commercial web project using a web-design process. The design artifacts in each activity are transformed based on the artifacts tracing relationship in the trace model. The model provides mechanisms for verification of consistency, completeness and coverage within each viewpoint and the connectedness across viewpoints

Using synchronized lightweight state observers to minimise wireless sensor resource utilisation

A major trend in the evolution of the Web is the rapidly growing numbers of web-enabled sensors which provide a rich ability to monitor and control our physical environment. The devices are often cheap, lightweight, rapidly deployed and densely interconnected. The current dominant models of Web-based data monitoring are not well-adapted to the operational needs of these devices, particularly in terms of resource utilization. In this paper we describe an approach to the optimization of the resources utilized by these devices based on the use of synchronized state-observers. By embedding state observers with a minimized footprint into both the sensors and the monitoring Web client, we show that it is possible to minimize the utilization of limited sensor resources such as power and bandwidth, and hence to improve the performance and potential applications of these devices

Separation of concerns: A web application architecture framework

Architecture frameworks have been extensively developed and described within the literature. These frameworks typically support and guide organisations during system planning, design, building, deployment and maintenance. Their main pupose is to provide clarity to the different modelling perspectives, abstractions, and domains of consideration within system development. In dpoing so they allow improved clarity with regard to the connections between the different models, and the selection of models tht are most likely to capture salient features of the system. In this paper we present an Architectural Framework which takes into account the specific characteristics of web systems. The framework is based around a two dimensional matrix. One dimension separates the concerns of different participants of the web system into perspectives. The second dimension classifies each perspective into development abstractions: structure (what), behaviour (how), location (where) and pattern. The framework is illustrated through examples from the development of a commercial web application

STUDY OF SPREAD CODES WITH BLOCK SPREAD OFDM

This paper presents the study undertaken with block spread OFDM and compares three spreading matrices. The matrices include the Hadamard, Rotated Hadamard and Mutually Orthogonal Complementary Sets of Sequences (MOCSS). The study is carried out for block lengths of M = 2, M = 4 and M = 8 and it shows that all the spreading matrices show improvement and a better performance over the conventional OFDM over frequency selective channel as expected. As the size ofM increased the spreading matrices which have better orthogonal qualities show greater improvemen

Rehabilitation strategies to overcome quadriceps weakness for athletes with anterior cruciate ligament (ACL) reconstruction

This poster was presented at the Great Plains Honors Conference in Canyon, Texas.https://scholarworks.uttyler.edu/student_posters/1018/thumbnail.jp

Feature-Guided Black-Box Safety Testing of Deep Neural Networks

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. Most existing approaches for crafting adversarial examples necessitate some knowledge (architecture, parameters, etc.) of the network at hand. In this paper, we focus on image classifiers and propose a feature-guided black-box approach to test the safety of deep neural networks that requires no such knowledge. Our algorithm employs object detection techniques such as SIFT (Scale Invariant Feature Transform) to extract features from an image. These features are converted into a mutable saliency distribution, where high probability is assigned to pixels that affect the composition of the image with respect to the human visual system. We formulate the crafting of adversarial examples as a two-player turn-based stochastic game, where the first player's objective is to minimise the distance to an adversarial example by manipulating the features, and the second player can be cooperative, adversarial, or random. We show that, theoretically, the two-player game can con- verge to the optimal strategy, and that the optimal strategy represents a globally minimal adversarial image. For Lipschitz networks, we also identify conditions that provide safety guarantees that no adversarial examples exist. Using Monte Carlo tree search we gradually explore the game state space to search for adversarial examples. Our experiments show that, despite the black-box setting, manipulations guided by a perception-based saliency distribution are competitive with state-of-the-art methods that rely on white-box saliency matrices or sophisticated optimization procedures. Finally, we show how our method can be used to evaluate robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.Comment: 35 pages, 5 tables, 23 figure

Diffuse scattered field of elastic waves from randomly rough surfaces using an analytical Kirchhoff theory

We develop an elastodynamic theory to predict the diffuse scattered field of elastic waves by randomly rough surfaces, for the first time, with the aid of the Kirchhoff approximation (KA). Analytical expressions are derived incorporating surface statistics, to represent the expectation of the angular distribution of the diffuse intensity for different modes. The analytical solutions are successfully verified with numerical Monte Carlo simulations, and also validated by comparison with experiments. We then apply the theory to quantitatively investigate the effects of the roughness and the shear-to-compressional wave speed ratio on the mode conversion and the scattering intensity, from low to high roughness within the valid region of KA. Both the direct and the mode converted intensities are significantly affected by the roughness, which leads to distinct scattering patterns for different wave modes. The mode conversion effect is very strong around the specular angle and it is found to increase as the surface appears to be more rough. In addition, the 3D roughness induced coupling between the out-of-plane shear horizontal (SH) mode and the in-plane modes is studied. The intensity of the SH mode is shown to be very sensitive to the out-of-plane correlation length, being influenced more by this than by the RMS value of the roughness. However, it is found that the depolarization pattern for the diffuse field is independent of the actual value of the roughness