Reviewerly/exHarmony huggingface dataset:
University Teachers’ Perceptions of Networked Learning During the Emergency-Remote-Teaching Period: A Phenomenographically-Informed Inquiry

This paper presents part of the findings of a phenomenographically-informed investigation into higher education (HE) teachers’ perceptions of personal learning networks in the context of the recent Covid-19 emergency-remote-teaching (ERT). The pandemic-induced ERT period has magnified teachers’ use of personal learning networks with an absence of a coherent institutionalized approach to ERT. Subsequently, there has been a diversity of teachers’ networked learning experiences throughout this interruption of the status quo. The present research, therefore, explores the perceptions of 18 Academic English teachers at a leading English-instruction university in Kazakhstan that was forced to move online at the beginning of the concerned pandemic. The chapter reports the teachers’ perceived benefits of using their networks for online teaching and learning—particularly for connecting to resources and people online. The results suggest that teachers perceive the benefits of network use in at least four different ways: (i) enabling flexible access to online resources, (ii) enabling flexible access to others, (iii) facilitating personalized and focused small-scale pedagogical interactions, and (iv) maintaining and developing a sense of belonging to academic communities. The chapter concludes by discussing the implications of the results for teacher professional development contexts and reflecting on challenges associated with the phenomenon.
Sustainable Learning Design: A Case Study of Eight Undergraduate Science Module Interventions

This chapter presents the results of a case study of eight undergraduate science modules investigating factors for efficient and sustainable Learning Design interventions. Using a mixed-methods approach involving educator interviews, student activity data, screening of learning designs, module evaluations, and an efficiency assessment, a total of six factors related to the educator and student perspectives as well as the actualised learning designs are identified. The chapter concludes that the educators’ consideration for the institutional cost-benefit perspective, their perceived usefulness of technology-enhanced learning and buy-in of its related pedagogy, the students’ buy-in of technology-enhanced learning, a consistent networked structure with online activities, reflection exercises and feedback, and the scale and reuse of the design are significant factors for efficient learning designs. Furthermore, the chapter discusses three implication aspects of the identified factors for designing sustainable networked learning: the educator perspective on TEL, students’ networked learning with technology, and the module organisation and institutional aspect.
Hyperspectral Imaging and Analysis of Hydrothermal Alternation Mineralogy in Cuprite, Nevada, USA [Headwall Photonics]

Lab- and drone-based hyperspectral imaging scans of different clays, micas, sulfates, and other alteration products were classified to show the distribution of spectrally active minerals in the exposed upper zone of the Cuprite epithermal system.
On the Optimization of Systems Using AI Metaheuristics and Evolutionary Algorithms

In this chapter, evolutionary computation techniques, algorithms and research are presented for the optimization and allocation problems. Several aspects of continuous optimization, systems security and supply networks (SN) are illustrated. The real-life optimization and security problems in systems, automation, SN and law enforcement are NP-hard optimization problems, thus evolutionary algorithms (EA) that employ metaheuristic methods are useful for solving them. EA gain significant interest in recent years, and this chapter summarizes some of the advances in that field and then summarizes their applications for real-life problems. The rest of this chapter is organized as follows. First, the introduction of the developments of nature-inspired EAs and metaheuristics is described. Then the working principles of genetic algorithms (GA), swarm intelligence, and other nature-inspired optimization algorithms are given. Next, the overview of the various applications that were solved and optimized by EAs is presented. The reader of this chapter will be familiar with the following topics: The state-of-the-art AI algorithms and techniques and their working principle. The way to harness AI for optimization and finding optimal solutions. Controlling and optimizing a collaborative system in real-time while addressing several tasks in a complex environment
The Framework and Applications of Anomalous Subsequence Detection in Streaming Data Analysis and Process Monitoring in Intelligent Manufacturing

In intelligent manufacturing, detecting anomalous signals or time series subsequences and monitoring the process shift are two fundamental tasks to maintain manufacturing quality. Detecting the anomalous signals or process shifting as early as possible is a challenge. This chapter reviews the relevant research works in these two domains and presents the state-of-art methodologies that combine machine learning and data mining techniques to resolve the practical problem in production, named local recurrence rate with robust k-means (LRR-RKMeans) and long short-term memory real-time contrasts control chart (LSTM-RTC). The real-world data in manufacturing were used to evaluate the methods. The methods for anomalous subsequence detection on process monitoring and real-time contrasts control chart for quality control were applied to real-world semiconductor wafer and white wine production cases. The experimental results show that LRR-RKMeans can detect the defective wafer accurately, and LSTM-RTC has a low response delay in noticing process shift in white wine production. The possibility of combining the proposed data analysis framework with an edge-cloud computing system to alleviate the performance boundary in the computational time was further discussed.
Statistical analysis of various splitting criteria for decision trees

Decision trees are frequently used to overcome classification problems in the fields of data mining and machine learning, owing to their many perks, including their clear and simple architecture, excellent quality, and resilience. Various decision tree algorithms are developed using a variety of attribute selection criteria, following the top-down partitioning strategy. However, their effectiveness is influenced by the choice of the splitting method. Therefore, in this work, six decision tree algorithms that are based on six different attribute evaluation metrics are gathered in order to compare their performances. The choice of the decision trees that will be compared is done based on four different categories of the splitting criteria that are criteria based on information theory, criteria based on distance, statistical-based criteria, and other splitting criteria. These approaches include iterative dichotomizer 3 (first category), C[Formula: see text] (first category), classification and regression trees (second category), Pearson’s correlation coefficient based decision tree (third category), dispersion ratio (third category), and feature weight based decision tree algorithm (last category). On eleven data sets, the six procedures are assessed in terms of classification accuracy, tree depth, leaf nodes, and tree construction time. Furthermore, the Friedman and post hoc Nemenyi tests are used to examine the results that were obtained. The results of these two tests indicate that the iterative dichotomizer 3 and classification and regression trees decision tree methods perform better than the other decision tree methodologies.
Augmenting Human-Machine Teaming Through Industrial AR: Trends and Challenges

Industrial Augmented Reality (AR) is an emerging spatial computing technology which involves the use of head-mounted displays or hand-held devices such as tablets or smartphones to superimpose digital content onto the worker’s physical to foster their productivity, learning, and interactions with machines, tools, and other workers. Industrial AR has been adopted in many industries such as manufacturing, healthcare, aerospace, and defense, predominantly for training or remote assistance purposes. Yet, several technical and technological challenges remain to be addressed for industrial AR to evolve from a spatial visualization tool to a more intelligent and adaptive assistive tool that not only augments the spatial and causal reasoning of workers but can also provide them with just-in-time training and support on the job. This chapter provides some technical background on industrial AR and underscores several research and development directions which can potentially materialize this vision.
Design and Development of Collaborative Hub for Safety and Reliability Analysis

Effective utilization of product quality and reliability intellectual capital assists companies to avoid expensive errors, allows for streamlined product development, provides better customer satisfaction, creates better issue/process management and results in more robust and reliable products. For large multinational organizations, it is often challenging to record and reuse innovative problem-solving approaches developed by teams working in different divisions or geographical locations. To address this, we developed a knowledge management system based on the HUBzero platform for the Reliability Engineering (RE) division of a leading consumer goods company. HUBzero is a WEB 2.0 based scientific collaboration platform with various social networking and data management features like user groups, access-controlled file sharing and review mechanism, content tagging, online discussions, wikis and blogs that can be used by researchers and commercial organizations. It allows people in different teams to collaborate in a social networking manner and also saves time by reusing the work already done. The HUB developed by us created a searchable and reusable organizational memory of expert-verified good-quality reliability analyses performed using various RE tools such as Failure Mode Effects and Criticality Analysis, Reliability Growth Analysis and Shakedown Testing. We developed a mechanism of acquiring, publishing and sharing RE analyses in a semi-automated manner by interfacing HUBzero to other software being used in the organization. Post implementation, the number of RE files on HUBzero has been increasing at a steady pace. With growing number of files, effective organization and easy retrieval becomes a challenge for any knowledge management system. To address this, we developed automated tagging of files using linguistically extracted keywords and designed new navigational features based on the metadata information of RE tool files with sorting and filtering capabilities to improve the searchability and discoverability of past analyses.
Implementation and evaluation of NoisyNets to reinforcement learning of automated ICT system design

This paper proposes to apply the method with an additional noisy layer to the structure of the graph neural network for reinforcement learning in automated design technology for information and communication systems. The automated design technology has an elementary problem of huge learning time caused by overestimating a specific configuration because of hardly ever rewards despite huge exploration space with a vast combination of selections, arrangements, and connections. The parametric noise applied to the network is learned with gradient descent and the remaining network weights to reduce this harmful overestimation during learning and increase the design exploration efficiency. The evaluation result showed that using the proposed algorithm for our automated design technology in development could shorten 15% of the episodes needed for learning to converge.
Two-Party Adaptor Signature Scheme Based on IEEE P1363 Identity-Based Signature

Adaptor signature is extended from the standard digital signature, which conceals a secret value in the “pre-signature". The one who knows the secret value can transform it into a completed signature. Adaptor signatures are useful in addressing blockchain scalability issues, and have been used to build blockchain scaling protocols, such as payment channels or atomic swaps. Recently, someone propose two-party adaptor signature to enhance the privacy in application of adaptor signatures. However, we notice that there is no identity-based two-party adaptor signature scheme so far. Based on IEEE P1363 standard identity-based signature scheme, a secure two-party adaptor signature is constructed in this paper. Moreover, under the random oracle model, we analyze the security of our proposed P1363-based two-party adaptor signature. Finally, we make a comparison between our scheme and other adaptor signature schemes in computation and communication overheads, the result shows the costs of our scheme are acceptable.
A Federated Deep Learning Framework for Privacy-Preserving Consumer Electronics Recommendations

Recommender systems (RSs) have proven to be highly effective in guiding consumers towards well-informed purchase decisions for electronics. These systems can provide personalised recommendations that consider individual preferences, past purchases and current market trends by collecting and analysing massive amounts of consumer data. However, RSs have traditionally employed centralised storage of users consumption records and item interactions, which may potentially lead to privacy concerns. In particular, centralised data storage may prove unworkable in the future with the advent of regulations such as the General Data Protection Regulation. In turn, this can lead to an urgent need for decentralised recommendation frameworks for consumer electronics. In this study, we propose a federated learning recommender system (FRS) for the recommendation task in the consumer electronics industry. However, this is rather challenging due to its privacy protection, model scalability and personalisation requirements. First, the federated recommender system for consumer electronics (FRS-CE) adopts an outer product and two proposed feature fusion operations to construct an interaction map between users and items. Second, the FRS-CE uses a lightweight convolution operation to extract high-order features from the interaction map. Finally, the proposed model employs an adaptive aggregation mechanism to update the global model, which enhances the scalability of the system. Extensive experiments conducted on two real-world datasets have demonstrated the effectiveness of the FRS-CE in generating consumer electronics recommendations with privacy protection.
Robust Correlation Filter Learning With Continuously Weighted Dynamic Response for UAV Visual Tracking

Unmanned Aerial Vehicles (UAV) visual tracking has always been a challenging task. Existing correlation filter tracking algorithms typically utilize the Histograms of Oriented Gradients (HOG) and Color Names (CN) method to directly incorporate the extracted target features into the model updating process. However, in low-resolution video quality, it leads to unstable target feature values. To address this limitation, we propose a novel preprocessing technique involving Gaussian denoising. This preprocessing step is designed to enhance the stability of the target’s feature values and make the target’s scale information clearer, thereby improving the tracker’s recognition capability for the target and effectively reducing noise interference. Furthermore, in contrast to other UAV trackers that rely on a singular representation of contextual information, this paper aims to enhance the utilization of historical information. Therefore, we introduce a context-based approach that integrates continuously weighted dynamic response maps from both temporal and spatial perspectives. Our tracker has the ability to adapt to rapid environmental changes during the tracking process while simultaneously reducing the potential risks of model overfitting and distortion. Extensive experiments are conducted on authoritative datasets, including DTB70, UAV123@10fps, and UAVDT, comparing our model against other advanced trackers. The experimental results validate the superior tracking performance and robustness of our tracker.
Human Motion Prediction Based on Space-Time-Separable Graph Convolutional Network

Human motion prediction is a popular method to predict future motion sequences based on past sequences, which is widely used in human-computer interaction. Space-time-separable graph Convolutional Network (STS-GCN) is a conventional mathematical model for human motion prediction. However, the uncertainty of human movements often leads to the problem of significant prediction error in the prediction results. This paper first proposed a Multi-scale STS-GCN (MSTS-GCN) model based on the conventional STS-GCN method to find the relevant factors that affect the prediction results. In our study, the constructed Multi-scale Temporal Convolutional Network (MTCN) decoder effectively reduced the human motion prediction error at specific time nodes. To expand the transmission and utilization performance in a larger receptive field, a Gated Recurrent Unit-TCN decoder was also designed. Finally, a new STS-GCN (NSTS-GCN) human motion prediction model was proposed, which realized the transmission and utilization of motion sequence features under a larger temporal perceptual field. To verify the effectiveness of NSTS-GCN, the Human3.6M dataset, AMASS and 3DPW dataset were tested. The experimental results show that the MPJPE error of the proposed model for human joint prediction at each time node is reduced compared with the conventional STS-GCN model, and the mean reduction was achieved by 3.0mm. All the experimental results validated the effectiveness of the proposed NSTS-GCN model, which further improved the performance of human motion prediction.
A Randomized Sparsity Preserving Projection and its Exponential Approach for Image Recognition

To preserve the sparse representation of the original high-dimensional data, sparsity preserving projection (SPP) is proposed which involves solving a series of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization problems and a large-scale generalized eigenvalue problem. Compared to many other dimensionality reduction techniques, SPP does not contain any model parameters. Besides, it may have some discriminative abilities even though no class labels are provided. However, SPP tends to show signs of fatigue when encountering with the large-scale high-dimensional data sets since the process of solving the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization problems is quite time-consuming. To manage this problem, this paper proposes a randomized SPP (RSPP) via random sketching where the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization problems can be solved efficiently. Furthermore, a preprocessed approach and an exponential approach with a bit of extra efforts are presented to handle the small-sample-size problem which may be occurred in RSPP. Finally, some experiments on image recognition for four authentic data sets are implemented and the corresponding results demonstrate the superiority of the proposed methods.
A Model of Design for Computing Systems: A Categorical Approach

This paper introduces the model of design (MoD), a framework that leverages category theory to study the design and development of computer-driven systems, to the academic and engineering communities dealing with computer systems. The model of design aims to offer a minimal framework for modelling the design and development of embedded computation across domains and abstractions, focusing on functional and extra-functional aspects as well as overarching concerns for automaticity, correctness and reuse. This nuanced approach provides insights into the theory and practice of computer systems design.
Structure-Informed Shadow Removal Networks

Existing deep learning-based shadow removal methods still produce images with shadow remnants. These shadow remnants typically exist in homogeneous regions with low-intensity values, making them untraceable in the existing image-to-image mapping paradigm. We observe that shadows mainly degrade images at the image-structure level (in which humans perceive object shapes and continuous colors). Hence, in this paper, we propose to remove shadows at the image structure level. Based on this idea, we propose a novel structure-informed shadow removal network (StructNet) to leverage the image-structure information to address the shadow remnant problem. Specifically, StructNet first reconstructs the structure information of the input image without shadows and then uses the restored shadow-free structure prior to guiding the image-level shadow removal. StructNet contains two main novel modules: 1) a mask-guided shadow-free extraction (MSFE) module to extract image structural features in a non-shadow-to-shadow directional manner; and 2) a multi-scale feature & residual aggregation (MFRA) module to leverage the shadow-free structure information to regularize feature consistency. In addition, we also propose to extend StructNet to exploit multi-level structure information (MStructNet), to further boost the shadow removal performance with minimum computational overheads. Extensive experiments on three shadow removal benchmarks demonstrate that our method outperforms existing shadow removal methods, and our StructNet can be integrated with existing methods to improve them further.
Advancing Data Center Networks: A Focus on Energy and Cost Efficiency

Data centers serve as the backbone for cloud computing, enterprise services, and infrastructure-based offerings. One area of ongoing research in data center networking focuses on innovating new topologies for large-scale node connectivity. These topologies must incorporate fault-tolerant and efficient routing algorithms. Consequently, the data centers network topology must dynamically adapt to ever-changing application requirements. While traditional topology designs often emphasize scalability, they are typically limited by the necessity for dedicated switches to manage server connections. The development of software technologies that differentiate server and switch roles offers a unique opportunity to reconsider design priorities, paving the way for a more balanced assessment of scalability, energy efficiency, and infrastructure costs. Moreover, certain network topologies fail to be cost-effective due to their structural intricacies, often requiring far more node connections than those that are practically necessary. To address these challenges, we introduce VacoNet: a new flexible data center network topology that organizes nodes into structurally similar clusters, interconnected by a novel physical structure algorithm. Boasting high bisection bandwidth, VacoNet delivers robust network capacity, even when encountering bottlenecks. Furthermore, to connect a given set of nodes, VacoNet uses a minimal number of cables and switches, thereby drastically reducing both infrastructure costs and energy consumption. Simulation results show that VacoNet canreduce error rates by 20% and slashe infrastructure costs by 70% compared to existing solutions. Additionally, it performs tasks 30% faster, underscoring its superior performance.