Reviewerly/exHarmony huggingface dataset:
Towards a Metaverse Workspace: Opportunities, Challenges, and Design Implications

Both enterprises and their employees have globally experienced remote work at an unprecedented scale since the outbreak of COVID-19. As the pandemic becomes less of a threat, some companies have called their employees back to a physical office, citing issues related to working remotely, but many employees have refused to return. Thus, working in the metaverse has gained much attention as an alternative that could complement the weaknesses of completely remote work or even offline work. However, we do not know yet what benefits and drawbacks the metaverse has as a legitimate workspace, because there are few real cases of 1) working in the metaverse and 2) working remotely at such an unprecedented scale. Thus, this paper aims to identify real challenges and opportunities the metaverse workspace presents when compared to remote work by conducting semi-structured interviews and participatory workshops with various employees and company stakeholders (e.g., HR managers and CEOs) who have experienced at least two of three work types: working in a physical office, remotely, or in the metaverse. Consequently, we identified 1) advantages and disadvantages of remote work and 2) opportunities and challenges of the metaverse. We further discuss design implications that may overcome the identified challenges of working in the metaverse.
On the Design of AI-powered Code Assistants for Notebooks

AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks, such as Jupyter, are of particular interest as they provide rich interface affordances that interleave code and output in a manner that allows for both exploratory and presentational work. Despite their popularity, little is known about the appropriate design of code assistants in notebooks. We investigate the potential of code assistants in computational notebooks by creating a design space (reified from a survey of extant tools) and through an interview-design study (with 15 practicing data scientists). Through this work, we identify challenges and opportunities for future systems in this space, such as the value of disambiguation for tasks like data visualization, the potential of tightly scoped domain-specific tools (like linters), and the importance of polite assistants.
TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks

Generative adversarial networks (GANs) have demonstrated remarkable potential in the realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional latent space interpolation and manifold interpolation (GAN-CLS-INT) encounter challenges in generating images that accurately reflect the given text descriptions. To overcome these limitations, we introduce TextControlGAN, a controllable GAN-based model specifically designed for text-to-image synthesis tasks. In contrast to traditional GANs, TextControlGAN incorporates a neural network structure, known as a regressor, to effectively learn features from conditional texts. To further enhance the learning performance of the regressor, data augmentation techniques are employed. As a result, the generator within TextControlGAN can learn conditional texts more effectively, leading to the production of images that more closely adhere to the textual conditions. Furthermore, by concentrating the discriminator’s training efforts on GAN training exclusively, the overall quality of the generated images is significantly improved. Evaluations conducted on the Caltech-UCSD Birds-200 (CUB) dataset demonstrate that TextControlGAN surpasses the performance of the cGAN-based GAN-INT-CLS model, achieving a 17.6% improvement in Inception Score (IS) and a 36.6% reduction in Fréchet Inception Distance (FID). In supplementary experiments utilizing 128 × 128 resolution images, TextControlGAN exhibits a remarkable ability to manipulate minor features of the generated bird images according to the given text descriptions. These findings highlight the potential of TextControlGAN as a powerful tool for generating high-quality, text-conditioned images, paving the way for future advancements in the field of text-to-image synthesis.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si35.svg" display="inline" id="d1e2375"><mml:mi>μ</mml:mi></mml:math>-Net: Medical image segmentation using efficient and effective deep supervision

Although the existing deep supervised solutions have achieved some great successes in medical image segmentation, they have the following shortcomings; (i) semantic difference problem: since they are obtained by very different convolution or deconvolution processes, the intermediate masks and predictions in deep supervised baselines usually contain semantics with different depth, which thus hinders the models' learning capabilities; (ii) low learning efficiency problem: additional supervision signals will inevitably make the training of the models more time-consuming. Therefore, in this work, we first propose two deep supervised learning strategies, U-Net-Deep and U-Net-Auto, to overcome the semantic difference problem. Then, to resolve the low learning efficiency problem, upon the above two strategies, we further propose a new deep supervised segmentation model, called μ-Net, to achieve not only effective but also efficient deep supervised medical image segmentation by introducing a tied-weight decoder to generate pseudo-labels with more diverse information and also speed up the convergence in training. Finally, three different types of μ-Net-based deep supervision strategies are explored and a Similarity Principle of Deep Supervision is further derived to guide future research in deep supervised learning. Experimental studies on four public benchmark datasets show that μ-Net greatly outperforms all the state-of-the-art baselines, including the state-of-the-art deeply supervised segmentation models, in terms of both effectiveness and efficiency. Ablation studies sufficiently prove the soundness of the proposed Similarity Principle of Deep Supervision, the necessity and effectiveness of the tied-weight decoder, and using both the segmentation and reconstruction pseudo-labels for deep supervised learning.
Picking with a robot colleague: A systematic literature review and evaluation of technology acceptance in human–robot collaborative warehouses

Just-in-time delivery, shorter product life cycles, demographic changes, and the Covid-19 pandemic have driven the industrial application of collaborative robots in warehouses. These robots work alongside humans, increasing their productivity and relieving them of repetitive or strenuous tasks. However, human workers can be reluctant to collaborate with robots owing to certain fears; for example, they may be concerned about job loss, stress, expected effort, or risk to physical integrity. These concerns can negatively impact the acceptance of human–robot collaboration (HRC). As the literature on this topic is fragmented, this study analyzes HRC acceptance in warehouses based on the Unified Theory of Acceptance and Use of Technology. We identify, classify, and analyze studies examining HRC acceptance in warehouses using a systematic literature review methodology. A framework is established to guide the analysis of performance expectancy, effort expectancy, (perceived) occupational safety, psychosocial, and legal and privacy factors. The results indicate the importance of corporate infrastructure and consideration of cognitive factors in particular. The findings of this study will support future research on HRC in warehouses and provide guidance for managers regarding HRC applicability.
CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps

The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence of meta-heuristic algorithms. In this paper, we introduce chaotic mapping into the PPE algorithm to propose a new algorithm, the Chaotic-based Phasmatodea Population Evolution (CPPE) algorithm. The chaotic map replaces the initialization population of the original PPE algorithm to enhance performance and convergence. We evaluate the effectiveness of the CPPE algorithm by testing it on 28 benchmark functions, using 12 different chaotic maps. The results demonstrate that CPPE outperforms PPE in terms of both performance and convergence speed. In the performance analysis, we found that the CPPE algorithm with the Tent map showed improvements of 8.9647%, 10.4633%, and 14.6716%, respectively, in the Final, Mean, and Standard metrics, compared to the original PPE algorithm. In terms of convergence, the CPPE algorithm with the Singer map showed an improvement of 65.1776% in the average change rate of fitness value, compared to the original PPE algorithm. Finally, we applied our CPPE to stock prediction. The results showed that the predicted curve was relatively consistent with the real curve.
A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder

During the operation of rotating machinery, the occurrence of unknown fault types makes it impossible for the artificial intelligence-based fault diagnosis model to distinguish. Furthermore, due to the excessive generalization capability of the autoencoder, the unsupervised anomaly detection method based on the autoencoder is difficult to effectively distinguish normal and abnormal samples. To address the above problem, this paper proposed an unsupervised anomaly detection method based on memory augmented temporal convolutional autoencoder (MATCAE). Firstly, a novel temporal convolutional autoencoder model is constructed based on dilated causal convolution, skip connection and autoencoder to facilitate the model to learn the temporal features of the input data, thereby enhancing the model's ability to capture the complex structure of the data. Then, a memory augmented module is designed using a memory matrix and an attention mechanism to expand the distribution interval between the reconstructed samples of normal and abnormal samples and reduce the sample capacity in the overlapping area. Finally, an anomaly detection module based on Euclidean distance, cosine distance and absolute mean square error is designed to improve the reliability of the metric between the input and reconstructed samples. To verify the effectiveness of the proposed method, experimental validation is carried out on a gearbox anomaly detection dataset. The experimental results show that the proposed method has higher anomaly detection accuracy and better noise robustness than other advanced anomaly detection methods, where the average performance metric is improved by 26.86% at the highest and 2.80% at the lowest.
A highly secure image encryption algorithm based on conservative hyperchaotic system and dynamic biogenetic gene algorithms

Aiming at the problem that the current image encryption algorithms generally use dissipative chaos, which leads to the algorithms being vulnerable to reconstruction attack, and the coding rules of image encryption algorithms using biological coding are too simple, this paper proposes an image encryption algorithm based on conservative hyperchaotic and biological gene algorithms. Firstly, a new conservative hyperchaotic system is proposed, which can resist reconstruction attacks, has a larger key space, and can generate pseudorandom sequences with strong randomicity. Secondly, SHA-512 is used to calculate the hash value of plaintext image, and the initial key is generated through the key generation algorithm we devised to improve the sensitivity of the algorithm to the key and plaintext. Finally, new dynamic coding rules are designed to enrich the biological coding rules. In addition, dynamic gene uniform crossover algorithm and dynamic gene mutation algorithm are devised, and pseudorandom sequences dynamically control gene operation, further enriching coding rules and expanding key space, improving the security of the algorithm. According to experimental and analysis results, the image encryption algorithm has high security and efficiency.
Creation of Virtual Reality for Education Purposes

Virtual reality systems have been developed primarily for the entertainment sector. However, they are being increasingly considered as high potential tools for use in industry and education. In this context, schools are now facing a challenge to introduce virtual-reality-supported teaching into their processes. With this in mind, the authors, in their paper, focus on the possibility for using virtual excursions as part of vocational education and training. For this purpose, they analyze the suitability and usability of selected virtual reality systems, as well as relevant camera systems, for the creation of virtual reality software products designed for industrial practice in upper secondary vocational schools’ apprenticeships (vocational education and training). The main results of their analyses are summarized in the form of tabularized SWOT parameters.
BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.
Meta-analyses of effects of augmented reality on educational outcomes over a decade

Since the outbreak of the pandemic, many students have been forced to receive education assisted by augmented reality technologies at home. To investigate the effects of augmented reality technologies on educational outcomes, researchers conducted a meta-analysis using Stata/MP 14.0. The results suggested that augmented reality-assisted education significantly improved learner attitudes towards and learning achievements when compared to non-augmented reality-assisted education. However, the study failed to identify any significant differences in motivation levels between augmented reality-assisted and non-augmented reality-assisted educational models. Several potential reasons were explored to account for this unexpected finding. Future research should consider more comprehensive influencing factors such as learning styles and learner personality to determine the effect of augmented reality technologies on educational outcomes. Additionally, integrating advanced technologies into course designs presents a promising avenue for improving educational outcomes in the future.
Rethinking Homework in the Age of Artificial Intelligence

The evolution of natural language processing techniques has led to the development of advanced conversational tools such as ChatGPT, capable of assisting users with a variety of activities. Media attention has centered on ChatGPT’s potential impact, policy implications, and ethical ramifications, particularly in the context of education. As such tools become more accessible, students across the globe may use them to assist with their homework. However, it is still unclear whether ChatGPT’s performance is advanced enough to pose a serious risk of plagiarism. We fill this gap by evaluating ChatGPT on two introductory and two advanced university-level courses. We find that ChatGPT receives near-perfect grades on the majority of questions in the introductory courses but has not yet reached the level of sophistication required to pass in advanced courses. Moreover, adding a few full stops or typos may fool a machine learning algorithm designed to detect ChatGPT-generated text. These findings suggest that, at least for some courses, current artificial intelligence tools pose a real threat that can no longer be overlooked by educational institutions.
Secure communication through reliable S-box design: A proposed approach using coset graphs and matrix operations

Protection of sensitive information has been always the major security concern since decades to withstand against illegitimate access and usage. Substitution-boxes (S-boxes) are vital components of any modern day cryptographic system that allows us to ensure its resistance to attacks. The prime problem with creating S-box is that we are generally unable to discover a consistent distribution among its numerous features to withstand diverse cryptanalysis attacks. The majority of S-boxes investigated in the literature has good cryptographic defenses against some attacks but are susceptible to others. Keeping these considerations in mind, this paper proposes a novel approach for S-box design based on a pair of coset graphs and a newly defined operation of row and column vectors on a square matrix. Several standard performance assessment criteria are used to evaluate the reliability of proposed approach, and the results demonstrate that the developed S-box satisfies all criterions for being robust for secure communication and encryption.
A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction

Breast cancer is the most common life-threatening cancer in women and one of the leading causes of death. Early diagnosis is one of the best defenses against the spread of breast cancer. Machine learning (ML) tools are now available for cancer detection and prediction. This study presents a comparative assessment of machine learning models for diagnosing breast cancer based on various classification schemes. Our classification methodology is based on well-organized data collection, preparation, transformation, and exploratory analysis (including correlation matrix, histogram, and data distribution). All characteristics are compared with the results of applying the Least Absolute Shrinkage and Selection Operator (LASSO) approach, which selects the most important attributes. Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms have been applied in this study. We have achieved the maximum accuracy of 90.68% by RF compared with LASSO. Similarly, the recall in KNN was 98.80%, the precision in MLP was 92.50%, and the F1 score in RF was 94.60%.
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing

Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to learn effective snow removal. In order to handle the diverse scenes, we propose a multi-scale projection transformer (MSP-Former), which understands and covers a variety of snow degradation features in a multi-path manner, and integrates comprehensive scene context information for clean reconstruction via self-attention operation. For the local details of various snow degradations, the local capture module is introduced in parallel to assist in the rebuilding of a clean image. Such design achieves the SOTA performance on three desnowing benchmark datasets while costing the low parameters and computational complexity, providing a guarantee of practicality.
A Lightweight Object Detection Algorithm for Remote Sensing Images Based on Attention Mechanism and YOLOv5s

The specific characteristics of remote sensing images, such as large directional variations, large target sizes, and dense target distributions, make target detection a challenging task. To improve the detection performance of models while ensuring real-time detection, this paper proposes a lightweight object detection algorithm based on an attention mechanism and YOLOv5s. Firstly, a depthwise-decoupled head (DD-head) module and spatial pyramid pooling cross-stage partial GSConv (SPPCSPG) module were constructed to replace the coupled head and the spatial pyramid pooling-fast (SPPF) module of YOLOv5s. A shuffle attention (SA) mechanism was introduced in the head structure to enhance spatial attention and reconstruct channel attention. A content-aware reassembly of features (CARAFE) module was introduced in the up-sampling operation to reassemble feature points with similar semantic information. In the neck structure, a GSConv module was introduced to maintain detection accuracy while reducing the number of parameters. Experimental results on remote sensing datasets, RSOD and DIOR, showed an improvement of 1.4% and 1.2% in mean average precision accuracy compared with the original YOLOv5s algorithm. Moreover, the algorithm was also tested on conventional object detection datasets, PASCAL VOC and MS COCO, which showed an improvement of 1.4% and 3.1% in mean average precision accuracy. Therefore, the experiments showed that the constructed algorithm not only outperformed the original network on remote sensing images but also performed better than the original network on conventional object detection images.
Organizational frontlines in the digital age: The Consumer–Autonomous Technology–Worker (CAW) framework

While organizational frontlines in the digital age involve complex interactions between consumers, autonomous technology (AT), and frontline workers, research so far largely focuses on the effect of AT on either the consumer or the worker. Bridging the fields of marketing and organizational behavior, we develop the Consumer–Autonomous Technology–Worker (CAW) framework, which reflects the implications of consumer–worker–AT interactions. We consider that AT can be consumer-facing, such as service robots, or worker-facing, such as AT-enabled knowledge-based systems supporting a worker’s decision-making. Drawing on illustrative interviews in hospitality contexts with workers who co-work with robots and the consumers served, we develop research propositions that highlight avenues for future research. We expect consumer–worker relations to strengthen when AT augments instead of replaces the worker. Human leadership is critical for consumers’ and workers’ acceptance of AT, while AT anthropomorphism is less critical in the presence of a human worker.