Learning Analytics Dashboards (LAD) are the subject of research in a multitude of schools and higher education institutions, but a lack of research into learner-facing dashboards in professional learning has been identified. This study took place in an authentic professional learning context and aims to contribute insights into LAD design by using an academic approach in a practice-based environment. An existing storytelling LAD created to support 81 accountants was evaluated using Technology Acceptance Model, finding a learner expectation for clarity, conciseness, understanding and guidance on next steps. High usage levels and a ‘take what you need’ approach was identified, with all visualizations and automated personalized feedback being considered useful although to varying degrees. Professional learners in this study focus on understanding and acting upon weaknesses rather than celebrating strengths. The lessons for LAD design are to offer choice and create elements which support learners to take action to improve performance at a multitude of time points and levels of success.

There has recently been considerable interest in automatic detection strategies for recognising application layer security threats such as Hypertext Transfer Protocol (HTTP) Slow Denial of-Service (Slow DoS) attacks in Internet of Things (IoT) networks. Most existing approaches however, fail to take cognisance of the substantial resource constraints imposed upon IoT environments, which limits the applicability and deployment of many Slow DoS detection mechanisms. This paper addresses this significant security threat for resource scarce IoT nodes and networks in proposing an accurate and computationally efficient approach to packet-based intrusion detection of HTTP Slow DoS activity. The paper both critically analyses and measures the impact of applying network attribute filtering and packet sampling to reduce the computational overheads on the resource constrained IoT Slow DoS detection node. The unique solution proposed uses a dataset synthesised from a live IoT environment comprising both legitimate and malicious network events in the form of legitimate HTTP traffic and Slow DoS attacks. Experimental results corroborate that combining filtering at the Border Router of only in-bound packets containing no TCP payload with a systematic packet sampling scheme at a sampling ratio of up to 1:64, the processing overheads on the detection node are significantly reduced. The novel contribution presented is a resource efficient solution, garnered by employing systematic sampling to seamlessly and accurately support selective attribute-based intrusion detection of HTTP Slow DoS attacks in IoT networks.

Slow DoS attacks have proven to pose a significant security threat to low-resource IoT devices and networks, because they can be launched by nodes which consume nominal bandwidth and have limited resource capability. This makes such malicious attacks easy to initiate, but difficult to mitigate. There also exists the recurrent likelihood of misclassifying legitimate nodes, which are incurring slow or poor network connectivity, as malicious activity. Existing intrusion detection systems (IDS) for detecting Slow DoS attacks often require the creation of large datasets for post event analysis. A functional disadvantage of this dataset-driven approach is the sheer volume of data required, due to the high number of network attributes and events collated, which precludes an in-line, real-time IDS detection solution for live IoT networks. This paper presents an innovative IDS detection framework for resource constrained IoT networks. Using a set of only four attributes, a two-step analysis of live IoT network events enables Slow DoS attacks, in the form of Slowloris, to be both efficiently and reliably detected in real-time. In addition, this lightweight IDS framework can accurately distinguish between malicious and genuine nodes encountering slow or intermittent network connections.

A major challenge in spectrum sensing for cognitive radio (CR) applications is the very high sampling rates involved, which imposes significant demands on the signal acquisition technology. This has given impetus to applying compressive sensing (CS) as a sub‐Nyquist sampling paradigm for CR‐type wireless signals which exhibit sparsity in certain domains. CS architectures like the random demodulator (RD) and compressive multiplexer (CM) can be used for CR spectral sensing, though both are inherently restricted in terms of the signal classes they can effectively process. To address these limitations, this study presents two unified RD and CM‐based CS architectures that seamlessly integrate precolouring and the multitaper spectral estimator into their respective structures to facilitate efficient sensing of both digitally modulated and narrowband signals, along with popular CR‐access technologies like orthogonal frequency division multiplexing . A significant feature of these unified CS architectures is they do not require a priori knowledge of either the input signal or modulation scheme, while a tristate spectral classifier is introduced to afford notably enhanced spectrum access opportunities for unlicensed secondary users. A critical performance evaluation corroborates that both unified architectures demonstrate consistently superior CS results and robustness across a broad range of CR‐type signals, modulations and access technologies.

With the ubiquity of handheld devices people can now access information and conduct online transactions virtually anywhere and anytime. In such flexible but less reliable environment transaction management technology is believed to provide service reliability and data consistency. Existing models developed for mobile transactions mainly focus on variation of conventional commit procedures and give little or no attention to the context-awareness which has become a crucial part of modern mobile applications. Given the high availability of context-aware services, mobile transactions should not only acquire required services but should also comply with the service context. This paper exploits the service-oriented computing technology and accordingly proposes a novel approach that manages transactions using the contextual information of mobile services. The proposed approach has several advantages. It manages transactions such that they adapt to the required service context as well as user’s needs. It also ensures the reliability of transactions and also increases their resilience to failures.

In this paper, we present our generic three- layered data model for context. The conceptual and physical layers built on top of a XML schema layer constitute the backbone of our approach.

Nowadays various attempts have been proposed to enhance service oriented environments with context information. However, not all the proposed context models are suitable for this type of environments. In this paper, we present a brief overview of the existing context models and study their suitability for service-oriented environments.

Summary form only given. This paper presents an approach for specifying an environment of software agents and Web services. Users in collaboration with software agents compose Web services into high-level business processes denoted by composite services. The participation of Web services in a composite service is based on several selection criteria such as the execution cost of a Web service and the location of the resources on which a Web service is performed. Prior to that selection, the specification approach puts forwards three levels: intrinsic, behavior, and organizational/functional.

This paper presents an agent-based and context-oriented approach that supports the composition of Web services. A Web service is an accessible application that other applications and humans can discover and invoke to satisfy multiple needs. To reduce the complexity featuring the composition of Web services, two concepts are put forward, namely, software agent and context. A software agent is an autonomous entity that acts on behalf of users and the context is any relevant information that characterizes a situation. During the composition process, software agents engage in conversations with their peers to agree on the Web services that participate in this process. Conversations between agents take into account the execution context of the Web services. The security of the computing resources on which the Web services are executed constitutes another core component of the agent-based and context-oriented approach presented in this paper.

This work presents an approach for the dynamic management of the content of several Universal Description, Discovery, and Integration (UDDI) registries. By content, it is meant the announcements of Web services that providers post on various distributed UDDI registries. Unlike other initiatives in the field of Web services, our concerns are the following: availability of several UDDI registries, no predefined communication infrastructure between the UDDI registries, and no centralized component to coordinate the UDDI registries. The solution presented in this paper integrates users and software agents into what we call messengers. When a user is in the vicinity of an UDDI registry, his software agent, which resides in his mobile device, interacts with that registry. The objective is to submit the details on Web services that are stored in the mobile device.