Using neo-Piagetian theory, formative in-Class tests and think alouds to better understand student thinking : a preliminary report on computer programming

It is acknowledged around the world that many university students struggle with learning to program (McCracken et al., 2001; McGettrick et al., 2005). In this paper, we describe how we have developed a research programme to systematically study and incrementally improve our teaching. We have adopted a research programme with three elements: (1) a theory that provides an organising framework for defining the type of phenomena and data of interest, (2) data on how the class as a whole performs on formative assessment tasks that are framed from within the organising framework, and (3) data from one-on-one think aloud sessions, to establish why students struggle with some of those in-class formative assessment tasks. We teach introductory computer programming, but this three-element structure of our research is applicable to many areas of engineering education research.

Establishing initial trust in autonomous delay tolerant networks without centralised PKI

A Delay Tolerant Network (DTN) is one where nodes can be highly mobile, with long message delay times forming dynamic and fragmented networks. Traditional centralised network security is difficult to implement in such a network, therefore distributed security solutions are more desirable in DTN implementations. Establishing effective trust in distributed systems with no centralised Public Key Infrastructure (PKI) such as the Pretty Good Privacy (PGP) scheme usually requires human intervention. Our aim is to build and compare different de- centralised trust systems for implementation in autonomous DTN systems. In this paper, we utilise a key distribution model based on the Web of Trust principle, and employ a simple leverage of common friends trust system to establish initial trust in autonomous DTN’s. We compare this system with two other methods of autonomously establishing initial trust by introducing a malicious node and measuring the distribution of malicious and fake keys. Our results show that the new trust system not only mitigates the distribution of fake malicious keys by 40% at the end of the simulation, but it also improved key distribution between nodes. This paper contributes a comparison of three de-centralised trust systems that can be employed in autonomous DTN systems.

Efficient route update and maintenance processes for multipath routing in large-scale industrial wireless sensor networks

Reliable communications is one of the major concerns in wireless sensor networks (WSNs). Multipath routing is an effective way to improve communication reliability in WSNs. However, most of existing multipath routing protocols for sensor networks are reactive and require dynamic route discovery. If there are many sensor nodes from a source to a destination, the route discovery process will create a long end-to-end transmission delay, which causes difficulties in some time-critical applications. To overcome this difficulty, the efficient route update and maintenance processes are proposed in this paper. It aims to limit the amount of routing overhead with two-tier routing architecture and introduce the combination of piggyback and trigger update to replace the periodic update process, which is the main source of unnecessary routing overhead. Simulations are carried out to demonstrate the effectiveness of the proposed processes in improvement of total amount of routing overhead over existing popular routing protocols.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Key Management in Wireless Sensor Networks

This chapter presents a comparative survey of recent key management (key distribution, discovery, establishment and update) solutions for wireless sensor networks. We consider both distributed and hierarchical sensor network architectures where unicast, multicast and broadcast types of communication take place. Probabilistic, deterministic and hybrid key management solutions are presented, and we determine a set of metrics to quantify their security properties and resource usage such as processing, storage and communication overheads. We provide a taxonomy of solutions, and identify trade-offs in these schemes to conclude that there is no one-size-fits-all solution.

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

Dynamic lifecycle strategies for target date retirement funds

Target date retirement funds have gained favor with retirement plan investors in recent years. Typically, these funds initially have a high allocation to stocks but move towards less volatile assets, such as bonds and cash, as the target retirement date approaches. Empirical research has generally found that a switch to low-risk assets prior to retirement can reduce the risk of confronting the most extreme negative outcomes. This article questions the rationale for lifecycle switching based solely on age or target retirement date as is the prevalent practice among target date funds. The authors argue that a dynamic switching strategy, which takes into consideration achieved investment returns, will produce superior returns for most investors compared to conventional lifecycle switching. In this article, the authors put forward a dynamic lifecycle switching strategy that is conditional on the attainment of the plan member's wealth accumulation objective at every stage of switching.

Glider path-planning for optimal sampling of mesocale eddies

Ocean gliders constitute an important advance in the highly demanding ocean monitoring scenario. Their effciency, endurance and increasing robustness make these vehicles an ideal observing platform for many long term oceanographic applications. However, they have proved to be also useful in the opportunis-tic short term characterization of dynamic structures. Among these, mesoscale eddies are of particular interest due to the relevance they have in many oceano-graphic processes.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

Investigation of sensitivity based load ranking with composite load model in power system

Load modeling plays an important role in power system dynamic stability assessment. One of the widely used methods in assessing load model impact on system dynamic response is through parametric sensitivity analysis. Load ranking provides an effective measure of such impact. Traditionally, load ranking is based on either static or dynamic load model alone. In this paper, composite load model based load ranking framework is proposed. It enables comprehensive investigation into load modeling impacts on system stability considering the dynamic interactions between load and system dynamics. The impact of load composition on the overall sensitivity and therefore on ranking of the load is also investigated. Dynamic simulations are performed to further elucidate the results obtained through sensitivity based load ranking approach.

TS-fuzzy controlled DFIG based wind energy conversion system

This paper focuses on the implementation of the TS (Tagaki-Sugino) fuzzy controller for the active power and the DC capacitor voltage control of the Doubly Fed Induction Generator (DFIG) based wind generator. DFIG system is represented by a third-order model where electromagnetic transients of the stator are neglected. The effectiveness of the TS-fuzzy controller on the rotor speed oscillations and the DC capacitor voltage variations of the DFIG damping controller on converter ratings of the DFIG system is also investigated. The results of the time domain simulation studies are presented to elucidate the effectiveness of the TS-fuzzy controller compared with conventional PI controller in the DFIG system. The proposed TS-fuzzy controller can improve the fault ride through capability of DFIG compared to the conventional PI controller

Overview of the INEX 2011 snippet retrieval track

This paper gives an overview of the INEX 2011 Snippet Retrieval Track. The goal of the Snippet Retrieval Track is to provide a common forum for the evaluation of the effectiveness of snippets, and to investigate how best to generate snippets for search results, which should provide the user with sufficient information to determine whether the underlying document is relevant. We discuss the setup of the track, and the evaluation results.

Towards brain-based sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition during a navigation task, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.

V1-inspired features induce a weighted margin in SVMs

Image representations derived from simplified models of the primary visual cortex (V1), such as HOG and SIFT, elicit good performance in a myriad of visual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards why these architectures perform so well. In this paper, we offer a unique perspective to this question by subsuming the role of V1-inspired features directly within a linear support vector machine (SVM). We demonstrate that a specific class of such features in conjunction with a linear SVM can be reinterpreted as inducing a weighted margin on the Kronecker basis expansion of an image. This new viewpoint on the role of V1-inspired features allows us to answer fundamental questions on the uniqueness and redundancies of these features, and offer substantial improvements in terms of computational and storage efficiency.

Towards a secure human-and-computer mutual authentication protocol

We blend research from human-computer interface (HCI) design with computational based crypto- graphic provable security. We explore the notion of practice-oriented provable security (POPS), moving the focus to a higher level of abstraction (POPS+) for use in providing provable security for security ceremonies involving humans. In doing so we high- light some challenges and paradigm shifts required to achieve meaningful provable security for a protocol which includes a human. We move the focus of security ceremonies from being protocols in their context of use, to the protocols being cryptographic building blocks in a higher level protocol (the security cere- mony), which POPS can be applied to. In order to illustrate the need for our approach, we analyse both a protocol proven secure in theory, and a similar proto- col implemented by a �nancial institution, from both HCI and cryptographic perspectives.