Reviewerly/exHarmony huggingface dataset:
Don’t Wait, Innovate! Preparing Students and Lecturers in Higher Education for the Future Labor Market

Technological innovations are changing our society at a rapid pace. The expansion of new technologies (e.g., tools and programs) will inevitably change future jobs in the area of, for example, engineering, healthcare, and science. People working in these areas need digital human capital, which is often acquired through education prior to starting a job. As a result, higher education systems around the globe face increasing demands to prepare their students for the changing labor market. To meet these demands, it is essential to focus on both lecturers’ and students’ digital competencies. Teaching professionals will have to learn to do new things using new resources. This goes beyond merely replacing work forms and resources; it is a complex process that demands a deeper way of learning in which routines and underlying knowledge and beliefs are explicitly reconsidered. Attention needs to be paid to how lecturers can gradually and continuously develop their professional competencies in the field of educational innovation with IT, to ensure these practices become embedded in future higher education. In this reflection paper, we will discuss key digital competencies for both students and lecturers. We will also focus on how lecturers develop these competencies through effective professional development (PD) activities. Based on a literature review, we present a model for effective lecturer PD with 29 ‘building blocks’. This model will be used to clarify practical examples of effective lecturer PD aimed at using innovative technology in higher education.
Improving adversarial robustness by learning shared information

We consider the problem of improving the adversarial robustness of neural networks while retaining natural accuracy. Motivated by the multi-view information bottleneck formalism, we seek to learn a representation that captures the shared information between clean samples and their corresponding adversarial samples while discarding these samples’ view-specific information. We show that this approach leads to a novel multi-objective loss function, and we provide mathematical motivation for its components towards improving the robust vs. natural accuracy tradeoff. We demonstrate enhanced tradeoff compared to current state-of-the-art methods with extensive evaluation on various benchmark image datasets and architectures. Ablation studies indicate that learning shared representations is key to improving performance.
Manifold embedded joint geometrical and statistical alignment for visual domain adaptation

Supervised learning algorithms like KNN assume that the training and the testing data come from the same source, and hence, have the same distribution. However, in real life, such instances are rare. Domain adaptation (DA) aims to model a classifier that is invariant to the different features of the training data. Domain invariant features are utilised to reduce distribution differences, ensuring an iterative improvement on the predicted target labels. Existing DA approaches either perform manifold subspace learning or align cross-domain distributions. At the same time, there exists a couple of imperative limitations: (1) degenerated feature transformation, wherein the seemingly domain invariant features are distorted during transformation (2) formulation of a robust objective function that must consider critical objectives in a unified framework to minimize deviations geometrically and statistically. To the best of our knowledge, none of the existing DA approaches address these two limitations simultaneously. Therefore, we propose a novel domain adaptation framework, called Manifold Embedded Joint Geometrical and Statistical Alignment (MEJGSA) for visual domain adaptation to address these limitations. MEJGSA first learns manifold features, and then formulates a robust objective function to reduce divergence between domains geometrically and statistically. We also consider more reliable classifiers to generate pseudo-labels to preserve objective functions such as conditional distributions and target domain discriminant information. Extensive experiments on two widely used cross-domain adaptation datasets demonstrate that MEJGSA exhibits significant improvements in classification accuracy as compared to cutting-edge shallow and deep DA approaches. The MATLAB code for our proposed method will be publicly available at 1 1 https://github.com/rakesh1000/MEJGSA . • A novel domain adaptation approach called MEJGSA is proposed. • MEJGSA reduces the risk of degenerate feature transformation. • A robust objective function is considered to minimize the divergence between the two domains. • More accurate initial pseudo labels are generated. • Thorough experiments like ablation study and feature visualisation underline the validity of MEJGSA.
MSPNet: Multi-stage progressive network for image denoising

Image denoising which aims to restore a high-quality image from the noisy version is one of the most challenging tasks in the low-level computer vision tasks. In this paper, we propose a multi-stage progressive denoising network (MSPNet) and decompose the denoising task into some sub-tasks to progressively remove noise. Specifically, MSPNet is composed of three denoising stages. Each stage combines a feature extraction module (FEM) and a mutual-learning fusion module (MFM). In the feature extraction module, an encoder-decoder architecture is employed to learn non-local contextualized features, and the channel attention blocks (CAB) are utilized to retain the local information of the image. In the mutual-learning fusion module, the criss-cross attention is introduced to balance the image spatial details and the contextualized information. Compared with the state-of-the-art works, experimental results show that MSPNet achieves notable improvements on both objective and subjective evaluations.
Self-supervised anomaly pattern detection for large scale industrial data

Detecting the anomalies in a large amounts of high-dimensional data has been a challenging task. In the Industry 4.0 environment, large-scale high-dimensional monitoring data features the complex pattern of high level semantics. In order to provide enterprise-wide monitoring solutions, it is necessary to identify the high-level semantic patterns of the anomalies in these data without splitting them. Existing end-to-end deep neural networks for time series are capable of recognizing the high-level semantics in natural language or speech signals, but they are barely applied in real-time anomaly detection of industrial data because of the large time costs. In this paper, we leverage the self-supervised contrastive learning methodology and propose a Composite Semantic Augmentation Encoder (CSAE) to provide an appropriate representation of industrial data and implement quick detection of anomalies in industrial application environments. CSAE is a non-sequential deep neural network with two augmentation layers and a mandatory layer. The two layers of data-augmentation are built to expand the size of samples of both low-level semantic anomalies and high-level semantic anomalies, which enables CSAE to discover diverse anomalies and improves its accuracy of high-level semantic pattern recognition. The mandatory layer is built to compress and reserve the temporal information in the industrial data to accelerate the anomaly detection. Therefore, as a non-sequential contrastive learning model, CSAE has faster training convergence than the usual sequence models. The experiment results have verified that CSAE can achieve higher prediction accuracy with less time consumption than existing machine learning models in the tasks of high dimensional anomaly pattern detection.
Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight).Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA), Differential evolution (DE), and, finally, Flower Pollination (FP) algorithms.In phase II, the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT (BA) and Multi-Swarm BAT algorithms.In phase III, the proposed PLTVACIW-PSO is employed to augment the feature selection problem for medical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.
A Multi-Watermarking Algorithm for Medical Images Using Inception V3燼nd燚CT

Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician's diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images.Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians' judgment of patient diagnosis.In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed.This paper proposes a novel watermarking algorithm using the convolutional neural networks (CNN) Inception V3 and the discrete cosine transform (DCT) to address above mentioned problems.First, the medical image is input into the Inception V3 network, which has been structured by adjusting parameters, such as the size of the convolution kernels and the typical architecture of the convolution modules.Second, the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image.At last, the watermarks are encrypted using the logistic map system and hash function, and the keys are stored by a third party.The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark.In the experimental section, multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms.Our NC values for all the images are more than 90% accurate which shows the robustness of the algorithm.Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.
Assessing, Testing and Estimating the Amount of Fine-Tuning by Means of Active Information

A general framework is introduced to estimate how much external information has been infused into a search algorithm, the so-called active information. This is rephrased as a test of fine-tuning, where tuning corresponds to the amount of pre-specified knowledge that the algorithm makes use of in order to reach a certain target. A function $f$ quantifies specificity for each possible outcome $x$ of a search, so that the target of the algorithm is a set of highly specified states, whereas fine-tuning occurs if it is much more likely for the algorithm to reach the target than by chance. The distribution of a random outcome $X$ of the algorithm involves a parameter $\theta$ that quantifies how much background information that has been infused. A simple choice of this parameter is to use $\theta f$ in order to exponentially tilt the distribution of the outcome of the search algorithm under the null distribution of no tuning, so that an exponential family of distributions is obtained. Such algorithms are obtained by iterating a Metropolis-Hastings type of Markov chain, and this makes it possible to compute the their active information under equilibrium and non-equilibrium of the Markov chain, with or without stopping when the targeted set of fine-tuned states has been reached. Other choices of tuning parameters $\theta$ are discussed as well. Nonparametric and parametric estimators of active information and tests of fine-tuning are developed when repeated and independent outcomes of the algorithm are available. The theory is illustrated with examples from cosmology, student learning, reinforcement learning, a Moran type model of population genetics, and evolutionary programming.
A comprehensive and bibliometric review on the blockchain-enabled IoT technology for designing a secure supply chain management system

Abstract Blockchain is a well-known prominent technology that has gotten a lot of interest beyond the financial industry, attracting researchers and practitioners from numerous businesses and fields. Specific uses of blockchain in supply chain management (SCM) are addressed in business practice. By combining two perspectives on blockchain in SCM, this study provides comprehensive knowledge in this field using a bibliometric approach. We will explore the worldwide research trend in related topic areas. By collecting data from the Web of Science, we collected 400 articles related to our research topic from 2016 until early 2021. We eliminated research in the form of technical reports, editorials, comments, and consultancy articles to maintain the quality of the data gathering. VOSviewer is used to create visualization maps based on text and bibliographic information. The examination uncovered helpful information, such as annual publishing and citation patterns, the top research topic, the top authors, and the most supporting funding organizations in this field.
Qualitative Analyses of e-Learning Implementation and Hybrid Teaching during the COVID-19 Pandemic at Spanish Universities

During the COVID-19 pandemic over the academic year 2020–2021, many universities and faculties had to deal with hybrid teaching by combining face-to-face and virtual teaching approaches. The main objective herein considered was to analyze the perceptions of students and teachers from Spanish universities regarding how e-learning has actually been adopted before, during, and after the COVID-19 lockdown. We also wished to know their opinions about the usefulness and applicability of the e-learning and hybrid teaching methodologies regarding their impacts on the teaching–learning process in the university context. A thematic analysis was performed using three discussion groups (two made up of four teachers each, and another comprising five teachers with university management posts). Seventy-nine open questionnaires completed by students were also analyzed. The participants were from eight different Spanish universities: six public and two private. The obtained results revealed a preference for face-to-face teaching over virtual teaching, and the advantages offered by closer interpersonal relationships were stressed. However, the participants also indicated the potential of the e-learning and hybrid teaching methodologies, which they believed complemented one another and reinforced learning personalization. Thus, a specific need for training in the e-learning methodology and hybrid teaching format was apparent.
Responding to sociotechnical controversies in education: a modest proposal toward technical democracy

The use of automated decision-making systems is increasing in education. While the potential impacts of ADM are becoming widely known amongst experts, the perspectives of those impacted by ADM remain peripheral. To broaden expertise and participation, this paper proposes that ADM needs to be considered as a sociotechnical controversy, as part of a technical democracy approach that utilises hybrid forums. Following Callon and colleagues, in this paper, technical democracy refers to the process of learning through uncertainty about sociotechnical controversies, and hybrid forums refer to the specific sites of democratisation. This paper first identifies key uses and concerns with ADM in education. Second, it proposes that restricted capacity for participation can be addressed through technical democracy. Last, it proposes that hybrid forums can create moments of democratisation through shared uncertainty, material politics, and collective experimentation.
Breaking Category Five SPHINCS$$^+$$ with SHA-256

SPHINCS $$^+$$ is a stateless hash-based signature scheme that has been selected for standardization as part of the NIST post-quantum cryptography (PQC) standardization process. Its security proof relies on the distinct-function multi-target second-preimage resistance (DM-SPR) of the underlying keyed hash function. The SPHINCS $$^+$$ submission offered several instantiations of this keyed hash function, including one based on SHA-256. A recent observation by Sydney Antonov on the PQC mailing list demonstrated that the construction based on SHA-256 did not have DM-SPR at NIST category five, for several of the parameter sets submitted to NIST; however, it remained an open question whether this observation leads to a forgery attack. We answer this question in the affirmative by giving a complete forgery attack that reduces the concrete classical security of these parameter sets by approximately 40 bits of security. Our attack works by applying Antonov’s technique to the WOTS $$^+$$ public keys in SPHINCS $$^+$$ , leading to a new one-time key that can sign a very limited set of hash values. From that key, we construct a slightly altered version of the original hypertree with which we can sign arbitrary messages, yielding signatures that appear valid.
A novel robust support vector machine classifier with feature mapping

Support vector machines with different kernel functions are useful for binary classification. In this paper, a novel support vector machine model with feature mapping is proposed. Feature mapping is achieved based on the equivalence of the recently proposed soft quadratic surface support vector machine. In this model, a technique for pre-selecting training points is initially utilized to improve robustness. Then, the feature mapping and kernel trick are combined to handle datasets with different distributions, particularly nonlinearly separable datasets. This model can be transformed into a convex quadratic programming problem, which is efficiently solved by applying the sequential minimal optimization algorithm. Finally, numerical tests on several artificial and benchmark datasets reveal the superior performance of the proposed method over other well-known classification methods.
Cerebras Architecture Deep Dive: First Look Inside the HW/SW Co-Design for Deep Learning : Cerebras Systems

Building and deploying a new class of computer system
Immersive virtual reality applications for design reviews: Systematic literature review and classification scheme for functionalities

The development of immersive virtual reality (IVR) applications for design reviews is a major trend in the design field. While many different applications have been developed, there is little consensus on the functionalities necessary for these applications. This paper proposes a classification scheme for IVR functionalities related to design reviews (DRs), combining conceptual-to-empirical and empirical-to-conceptual strategies. The classification scheme consists of eight class categories (Input, Representation, Navigation, Manipulation, Collaboration, Edit, Creation, and Output), 22 class subcategories, and 55 classes. The classification scheme has been validated by analysing several commercial IVR applications for DRs. As part of the classification scheme development, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilised to review 70 articles that develop IVR applications for DRs. The results from systematic literature reviews suggest the development of solutions that integrate several class categories, are better connected to current design workflows, include various design information, support a DR planning cycle, and support distributed work. The proposed classification scheme helps to orient the future development of IVR applications for DRs and provides a framework to systematically accumulate evidence on the effect of such applications on DRs.
Implications of XR on Privacy, Security and Behaviour: Insights from Experts

Extended-Reality (XR) devices are packed with sensors that allow tracking of users (e.g., behaviour, actions, eye-gaze) and their surroundings (e.g., people, places, objects). As a consequence, XR devices pose significant risks to privacy, security, and our ability to understand and influence the behaviour of users - risks that will be amplified by ever-increasing adoption. This necessitates addressing these concerns before XR becomes ubiquitous. We conducted three focus groups with thirteen XR experts from industry and academia interested in XR, security, and privacy, to investigate current and emerging issues relating to security, privacy, and influencing behaviour. We identified issues such as virtual threats leading to physical harm, missing opting-out methods, and amplifying bias through perceptual filters. From the results we establish a collection of prescient challenges relating to security, privacy and behavioural manipulation within XR and present recommendations working towards developing future XR devices that better support security and privacy by default.
An empirical analysis of the supply chain flexibility using blockchain technology

Building a flexible supply chain can enable the firms to manage their supply chains to adapt effectively to dynamic market demand changes and thus guarantee their accelerated growth in the future. In this vein, this study aims to address several important issues in supply chain management by considering two characteristics of blockchain technology (i.e., information transparency and security of blockchain technology) and exploring the specific conditions under which firms are likely to develop trust in supply chain management. Furthermore, we argue that such supply chain trust is vital to the success of achieving and increasing supply chain flexibility. In addition, we propose that top management teams' digital leadership within the firms plays a vital role in moderating the contribution of each dimension of blockchain technology to supply chain trust. Using data from a large sample of 338 firms in China, we perform structural equation modeling to examine our conceptual framework empirically. Our results highlight and support the idea that blockchain technology's information transparency and security influence the trust-building in a supply chain and supply chain flexibility and articulate the particular importance of digital leadership in explaining the contribution of different blockchain technology characteristics to trust-building. Our study advances the theoretical, empirical, and managerial analysis of critical factors to build trust and achieve flexibility in supply chains.