Abstract Data fusion has demonstrated its effectiveness in enhancing information retrieval across various studies. However, advanced fusion methods typically require a dataset with extensive relevance judgments to train optimal model weights, necessitating labor-intensive and costly manual efforts. This study explores efficient methods for generating training data to facilitate affordable relevance judgments and improve fusion model quality. Experiments conducted on six datasets from TREC’s Precision Medicine and Deep Learning tracks reveal that with careful sampling design, near-optimal fusion weights can be achieved using only 5% of the documents compared to the full TREC judgments. This translates to a dataset comprising 20 queries and 500 relevance-judged documents in total. The findings highlight the potential for sophisticated fusion techniques to become more accessible to researchers and practitioners, delivering substantial performance improvements with minimal judgment effort and cost.

This paper underscores the crucial role of video anonymization in smart ports, were data privacy and security hold critical importance. It introduces the Matrix Platform, specifically designed for smart environments, and highlights its strong video anonymization capabilities. The platform employs advanced Video Anonymization techniques to effectively balance the preservation of data confidentiality with the enhancement of port security. Furthermore, the paper discusses how object detection and anonymization methods are strategically employed to protect sensitive information while still allowing access to critical operational details such as cargo and vessel types. Emphasis is placed on video anonymization's pivotal role in strengthening port security by concealing high-value assets and minimizing the risk of exposing sensitive data. By integrating the Matrix Platform, ports gain the capability to proactively manage security risks, safeguard assets, and secure information. This adaptable platform can be deployed in both Edge and Cloud environments, ensuring alignment with the specific needs of smart ports, with a primary focus on data anonymization. In conclusion, as the port industry continues to evolve, this paper asserts that the adoption of video anonymization techniques is fundamental for future growth and development, providing assurance of privacy and security in this dynamic landscape.

IoT ecosystems are rapidly expanding, and device management is emerging as a key challenge due to the scale, complexity, and dynamism of IoT systems. The adoption of autonomous techniques shows promise to alleviate key issues including maintaining organisational security when large volumes of IoT devices are being added and removed from a telecommunications network. Here we propose a proactive IoT device management approach that addresses the need to control network access in a risk-based manner. The proposed system comprises of two novel core components, a Management Platform for IoT (MP-IoT) component and an Intent-Based Microsegmentation (IBMS) component. The MP-IoT component carries out a risk management role and combines with IBMS to provide risk-based network segmentation. The two components work together to proactively manage risks by migrating devices between isolated network segments according to a dynamic assessment of the risk to the system from an individual device. Self-healing techniques may then be used to mitigate risks associated with a device and consequently change the network segment that it resides in. Here we present the key challenges associated with typical IoT environments and demonstrate how they are addressed by the proposed Proactive IoT Device Management architecture. A prototype implementation is also presented to validate the operation of the proposed architecture.

This paper presents an approach to autonomous IoT device lifecycle management for our developed Matrix IoT platform. We discuss our approach for zero touch onboarding, IoT device failure, device end-of-life offboarding and SLAs to support device lifecycle management. We collected timings on the key stages of our proposed onboarding process. The total onboarding time takes on average 6.4 seconds to onboard a device. Therefore, when scaled to many hundreds of devices, there is a very significant time saving benefit to onboarding devices automatically, along with the benefits of reducing human error.

Neighbourhood Renewal Areas (NRAs) are among the most deprived 10% of wards across Northern Ireland. The IM HAPPY (Improving Hopes, Aspirations, Potential & Prospects in the Youth) project seeks to raise aspirations and provide confidence to residents of two NRAs through the promotion of educational engagement and formalised attainment. As part of the initial stages of the project, four bite-sized computing modules (Multimedia Web Development, Introduction to Mobile App Development, Robotics and Effective Social Media) were delivered to classes in approved secondary schools and community centres within the NRAs. A range of teaching methods were employed to encourage the participants to engage with their learning and these methods were adapted to accommodate groups from different age ranges and with different existing computing skills. This paper reflects on the relative success of these four modules in engaging the participants and attempts to identify influencing factors on performances to help shape future policy and direction. Particular focus is given to the Robotics module and the use of LEGO Mindstorms, with additional observations highlighting differences in participant engagement using age and gender as discriminators.

ABSTRACT Akari Software offers a family of software products supporting curriculum management in higher education. The work started with one specific commission, but within 4 years, a small product suite had been developed, with ten organisations using customised variants of those products. In effect, a product family had been created but without the underlying structure and processes needed to ensure the efficient creation and maintenance of variants. This is a common issue faced by software companies as their product range expands. In 2010, Akari began making internal changes to develop a product range infrastructure that was suitable both for its existing products and its plans for future growth. The purpose of this paper is to describe the product line transition that occurred, both to share the experience involved and to identify a number of general lessons learned. The work was supported by a FUSION project, funded by InterTradeIreland, undertaken in collaboration with the University of Ulster. Copyright © 2014 John Wiley & Sons, Ltd.

This paper reports on an investigation into bit-rate adjustment on high capability metro and core routers. The bit-rate is controlled in response to traffic load using EWMA (Exponential Weighted Moving Average), with parameters set to minimize the performance impact of the adjustments. The purpose of the adjustment is to enable energy savings. Our calculations show that 22.5% power saving is achievable by bit rate adjustment without significant performance degradation.

Enterprise IT environments have seen a sharp growth in content use due to the popularity of on-demand data-intensive applications. In turn, the huge demand in content has spawned off major developments such as growth and distribution of computing nodes as well as the adoption of various implementation technologies. Given the complexity brought to the makeup of business computing environments in addressing the above-mentioned factors, the critical planning task of determining the appropriate infrastructure sizes for supporting firm Quality of Service (QoS) guarantees becomes a very challenging undertaking to fulfil. Benchmarking methods are widely employed in calibrating attainable performance in IT solutions, but these have the drawback of presenting output performance metrics as composite measurements that only give an end-to-end perspective. As an enhancement to benchmarking approaches, we explore the use of Performance Monitoring Counters (PMCs) in obtaining detailed operational performance of CPU and memory hardware. Performance Monitoring Counters (PMCs) are onchip registers found on most modern processor hardware. We use PMC-derived measurements to validate cache performance trends that have been derived analytically, and in the course of validations, PMC data is also used to investigate the nature and character of surges in cache miss events, which emerge as the memory load generated by runtime processes increases.