Reviewerly/exHarmony huggingface dataset:
Improving Learners’ Self-Efficacy in Performing Design Reviews with Virtual Reality

Research on the use of innovative technology, such as virtual reality (VR), in Architecture, Engineering, and Construction (AEC) education, has been growing in the past twenty years. However, such research still requires robust investigation, as few studies have adopted educational psychology theories and rigorous methods. Furthermore, most of the research has focused on the cognitive effects of VR and additional research is also needed to investigate the affective effects, such as motivation, engagement, and self-efficacy. This study aims to evaluate the effects of technology decision-making between immersive virtual reality (IVR) and non-immersive virtual reality (nIVR) setups for a learning activity on AEC learners. For this, three hypotheses are formulated and tested on 165 UK students. Based on the results, both the designed IVR and nIVR learning activities had significant positive effects on learners’ self-efficacy and user experience with no significant difference between IVR and nIVR delivery. With this research, the authors contribute to the growing literature on VR implementation in AEC classrooms by showcasing a study founded on educational psychology theory and by using a rigorous research methodology. Furthermore, this study illustrates the effects that IVR and nIVR have on students’ affective learning and opens the possibility of new research in the field.
An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method

Abstract Extreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However, ELM has some drawbacks such as instability, poor generalizability and overfitting in the case of multicollinearity in the linear model. This paper introduces square-root lasso ELM (SQRTL-ELM) as a novel regularized ELM algorithm to deal with these drawbacks of ELM. A modified version of the alternating minimization algorithm is used to obtain the estimates of the proposed method. Various techniques are presented to determine the tuning parameter of SQRTL-ELM. The method is compared with the basic ELM, RIDGE-ELM, LASSO-ELM and ENET-ELM on six benchmark data sets. Performance evaluation results show that the SQRTL-ELM exhibits satisfactory performance in terms of testing root mean squared error in benchmark data sets for the sake of slightly extra computation time. The superiority level of the method depends on the tuning parameter selection technique. As a result, the proposed method can be considered a powerful alternative to avoid performance loss in regression problems .
Hybrid Uncertainty Calibration for Multimodal Sentiment Analysis

In open environments, multimodal sentiment analysis (MSA) often suffers from low-quality data and can be disrupted by noise, inherent defects, and outliers. In some cases, unreasonable multimodal fusion methods can perform worse than unimodal methods. Another challenge of MSA is effectively enabling the model to provide accurate prediction when it is confident and to indicate high uncertainty when its prediction is likely to be inaccurate. In this paper, we propose an uncertain-aware late fusion based on hybrid uncertainty calibration (ULF-HUC). Firstly, we conduct in-depth research on the issue of sentiment polarity distribution in MSA datasets, establishing a foundation for an uncertain-aware late fusion method, which facilitates organic fusion of modalities. Then, we propose a hybrid uncertainty calibration method based on evidential deep learning (EDL) that balances accuracy and uncertainty, supporting the reduction of uncertainty in each modality of the model. Finally, we add two common types of noise to validate the effectiveness of our proposed method. We evaluate our model on three publicly available MSA datasets (MVSA-Single, MVSA-Multiple, and MVSA-Single-Small). Our method outperforms state-of-the-art approaches in terms of accuracy, weighted F1 score, and expected uncertainty calibration error (UCE) metrics, proving the effectiveness of the proposed method.
Digital Twin of Worker of Nuclear Facility at the Stage of Pre-Shift Control

Introduction. Digital twin. Digital twin in medicine using the example of Philips. Digital twin as an object of medical information space. Digital twin as an abstraction. Digital twin of worker of nuclear facility at the stage of pre-shift control. Conclusion.
Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.
Score-Based Black-Box Adversarial Attack on Time Series Using Simulated Annealing Classification and Post-Processing Based Defense

While deep neural networks (DNNs) have been widely and successfully used for time series classification (TSC) over the past decade, their vulnerability to adversarial attacks has received little attention. Most existing attack methods focus on white-box setups, which are unrealistic as attackers typically only have access to the model’s probability outputs. Defensive methods also have limitations, relying primarily on adversarial retraining which degrades classification accuracy and requires excessive training time. On top of that, we propose two new approaches in this paper: (1) A simulated annealing-based random search attack that finds adversarial examples without gradient estimation, searching only on the l∞-norm hypersphere of allowable perturbations. (2) A post-processing defense technique that periodically reverses the trend of corresponding loss values while maintaining the overall trend, using only the classifier’s confidence scores as input. Experiments applying these methods to InceptionNet models trained on the UCR dataset benchmarks demonstrate the effectiveness of the attack, achieving up to 100% success rates. The defense method provided protection against up to 91.24% of attacks while preserving prediction quality. Overall, this work addresses important gaps in adversarial TSC by introducing novel black-box attack and lightweight defense techniques.
A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

In this study, we proposed a system to reduce the speaker’s suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker’s silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer’s body area on the screen. To closely evaluate the system’s effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system.
IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation

In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP method is provided, outlining its framework and approach. The method leverages a greedy iteration approach to formulate an optimization problem for solving acoustic universal adversarial perturbations, with a new objective function designed to ensure that the attack has higher accuracy in terms of minimizing the perceptibility of adversarial perturbations and increasing the accuracy of successful attacks. The perturbation generation process is described in detail, and the resulting acoustic universal adversarial perturbation is evaluated in both target-attack and no-target-attack scenarios. Experimental analysis and testing were carried out using comparable techniques and dissimilar target models. The findings reveal that the acoustic generality adversarial perturbation produced by the IG-UAP method can obtain effective attack results even when the audio training data sample size is minimal, i.e., one for each category. Moreover, the human ear finds it difficult to detect the loss of original data information and the addition of adversarial perturbation (for the case of a target attack, the ASR values range from 82.4% to 90.2% for the small sample data set). The success rates for untargeted and targeted attacks average 85.8% and 84.9%, respectively.
Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data

Purpose The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy ( n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.
Visualizing high-dimensional loss landscapes with Hessian directions

Abstract Analyzing the geometric properties of high-dimensional loss functions, such as local curvature and the existence of other optima around a certain point in loss space, can help provide a better understanding of the interplay between neural-network structure, implementation attributes, and learning performance. In this paper, we combine concepts from high-dimensional probability and differential geometry to study how curvature properties in lower-dimensional loss representations depend on those in the original loss space. We show that saddle points in the original space are rarely correctly identified as such in the expected lower-dimensional representations if random projections are used. The principal curvature in the expected lower-dimensional representation is proportional to the mean curvature in the original loss space. Hence, the mean curvature in the original loss space determines if saddle points appear, on average, as either minima, maxima, or almost flat regions. We use the connection between expected curvature in random projections and mean curvature in the original space (i.e. the normalized Hessian trace) to compute Hutchinson-type trace estimates without calculating Hessian-vector products as in the original Hutchinson method. Because random projections are not suitable for correctly identifying saddle information, we propose to study projections along the dominant Hessian directions that are associated with the largest and smallest principal curvatures. We connect our findings to the ongoing debate on loss landscape flatness and generalizability. Finally, for different common image classifiers and a function approximator, we show and compare random and Hessian projections of loss landscapes with up to approximately <?CDATA $7\times 10^6$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>7</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>6</mml:mn> </mml:msup> </mml:math> parameters.
Implications of metaverse, virtual reality, and extended reality for development and learning in organizations

Purpose This study investigates the implications and significance of immersive technologies (viz. metaverse, virtual reality, and extended reality) in organizational learning and development. Design/methodology/approach Employing a strategy map (using Biblioshiny) and network analysis (using VOSViewer), the study analyzed a total of 189 Scopus-indexed documents, spanning the period 1997 to 2023. Findings The findings revealed four thematic clusters, knowledge management, training, e-learning, and technology. Each cluster provided insights into the various facets of development and learning in respect to these technologies. The strategy map identified key themes, revealing core ideas, emerging trends, and foundational concepts within the domain. Practical implications Organizations can utilize this study to incorporate metaverse, virtual reality, and extended reality in organizational development, optimizing training and boosting employee experience and productivity. Originality/value While most literature delves into the technical side of immersive technologies, our study uncovers their impact on development and learning in organizations, addressing a critical research gap and offering a guiding framework.
Research on the method of building power transformer fault tree based on fault cases

This paper proposes a method for establishing a power transformer fault tree based on fault cases. The method includes obtaining multiple fault cases of power transformers, dismantling multiple fault cases according to preset dismantling rules, classifying the faults of power transformers according to multiple fault cases, and establishing the basic framework of the fault tree. According to the results of dismantling, we are able to determine the logical relationship between the fault cause and the fault phenomenon of the power transformer. Based on the basic framework of the fault tree and the determined logical relationship, we build a fault tree for power transformers based on fault cases. This method will effectively improve the role of fault cases in the establishment of power transformer fault trees, realize fault identification and diagnosis of power transformers with high efficiency and high practicability, improve the efficiency of transformer fault analysis and disposal, and save power outage time caused by transformer faults.
Fast Hybrid Search for Automatic Model Compression

Neural network pruning has been widely studied for model compression and acceleration, to facilitate model deployment in resource-limited scenarios. Conventional methods either require domain knowledge to manually design the pruned model architecture and pruning algorithm, or AutoML-based methods to search the pruned model architecture but still prune all layers with a single pruning algorithm. However, many pruning algorithms have been proposed and they all differ regarding the importance they attribute to the criterion of filters. Therefore, we propose a hybrid search method, searching for the pruned model architecture and the pruning algorithm at the same time, which automatically finds the pruning ratio and pruning algorithm for each convolution layer. Moreover, to be more efficient, we divide the search process into two phases. Firstly, we search in a huge space with adaptive batch normalization, which is a fast but relatively inaccurate model evaluation method; secondly, we search based on the previous results and evaluate models by fine-tuning, which is more accurate. Therefore, our proposed hybrid search method is efficient, and achieves a clear improvement in performance compared to current state-of-the-art methods, including AMC, MetaPruning, and ABCPruner. For example, when pruning MobileNet, we achieve a 59.8% test accuracy on ImageNet with only 49 M FLOPs, which is 2.6% higher than MetaPruning.
Hidden Tensor Structures

Any single system whose space of states is given by a separable Hilbert space is automatically equipped with infinitely many hidden tensor-like structures. This includes all quantum mechanical systems as well as classical field theories and classical signal analysis. Accordingly, systems as simple as a single one-dimensional harmonic oscillator, an infinite potential well, or a classical finite-amplitude signal of finite duration can be decomposed into an arbitrary number of subsystems. The resulting structure is rich enough to enable quantum computation, violation of Bell’s inequalities, and formulation of universal quantum gates. Less standard quantum applications involve a distinction between position and hidden position. The hidden position can be accompanied by a hidden spin, even if the particle is spinless. Hidden degrees of freedom are, in many respects, analogous to modular variables. Moreover, it is shown that these hidden structures are at the roots of some well-known theoretical constructions, such as the Brandt–Greenberg multi-boson representation of creation–annihilation operators, intensively investigated in the context of higher-order or fractional-order squeezing. In the context of classical signal analysis, the discussed structures explain why it is possible to emulate a quantum computer by classical analog circuit devices.
Algorithm for generating secure communication protocol test sequences under hyperparameter optimization of correlation vector machines

Secure communication protocols are an important means to ensure the security of information transmission, and test sequence generation algorithms are used to evaluate and verify secure communication protocols. To improve the performance and reliability of secure communication protocols, a correlation vector machine hyperparameter optimization algorithm for generating secure communication protocol test sequences is proposed. We build an FSM model for secure communication protocols, analyze the optimization process of vector machine hyperparameters related to the FSM model, and generate a test sequence for secure communication protocols based on this. The experimental results show that the proposed algorithm performs well in generating secure communication protocol test sequences and can effectively improve the efficiency of generating secure communication protocol test sequences.
A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method

The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications.