Reviewerly/exHarmony huggingface dataset:
Student Perspectives on Remote Hardware Labs and Equitable Access in a Post-Pandemic Era

This Research Full Paper builds on a prior study that compared overall student performance between in-hand versus remotely accessible hardware in digital design courses. The COVID-19 pandemic necessitated a global educational shift to emergency online learning that led to rethinking the delivery of engineering labs. The prior study showed that, amidst pandemic-necessitated online learning, student understanding was not impeded by the incorporation of remotely accessible hardware into the course curriculum; rather, using remote hardware resulted in similar or better learning outcomes. In this paper, we analyze the remotely accessible hardware lab through the lens of equity, investigating the student perspective on equitable access and the remote lab experience. The study accomplishes this goal by surveying students of a junior-level digital design course who use a remotely accessible hardware lab for completing their assignments. The survey aims to determine the factors deemed important by today’s learners – those who have experienced remote learning for approximately two years of their educational careers – when considering equitable access and remote labs. Survey questions utilized the multiple-choice, semantic differential scale, and Likert scale formats for quantitative analysis as well as inductive coding of freeform responses for qualitative analysis. Initial findings from the survey are the key considerations of the surveyed students which include Factors of the Remote Experience (FREs) and Factors of Equitable Access (FEAs). FREs and FEAs specifically relate to the Student’s Access to Electronic Devices, the Student’s Environment Outside of Class, the Student’s Schedule, the Student’s Internet Quality, the lab’s Learnability, the lab’s Web Interface Design, the lab’s Convenience, the lab’s Overall Positive Experience, the lab’s Ease of Use, the lab’s Internet Quality, and the lab’s Affordability. Rooted in the online learner’s experience, these results contribute to an improved understanding of how students perceive equitable access to engineering education which shall guide better-informed advancements in the field in a post-pandemic world.
Neural entity alignment with cross-modal supervision

The majority of currently available entity alignment (EA) solutions primarily rely on structural information to align entities, which is biased and disregards additional multi-source information. To compensate for inadequate structural details, this article suggests the SKEA framework, which is a simple but flexible framework for Entity Alignment with cross-modal supervision of Supporting Knowledge. We employ a relational aggregate network to specifically utilize the details about the entity and its neighbors. To overcome the limitations of relational features, two multi-modal encode modules are being used to extract visual and textural information. A new set of potential aligned entity pairs are generated by SKEA in each iteration using the knowledge of two reference modalities, which can enhance the model's supervision. It is important to note that the supporting information used in our framework does not participate in the network's backpropagation, which considerably improves efficiency and differs dramatically from earlier work. In comparison to existing baselines, experiments demonstrate that our proposed framework can incorporate multi-aspect information efficiently and enable supervisory signals from other modalities to transmit to entities. The maximum performance improvement of 5.24% indicates our suggested framework's superiority, especially for sparse KGs.
Hybrid Encryption Scheme for Medical Imaging Using AutoEncoder and Advanced Encryption Standard

Recently, medical image encryption has gained special attention due to the nature and sensitivity of medical data and the lack of effective image encryption using innovative encryption techniques. Several encryption schemes have been recommended and developed in an attempt to improve medical image encryption. The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes. Alternatively, the engagement of intelligent models such as deep learning along with encryption schemes exhibited more effective outcomes, especially when used with digital images. This paper aims to reduce and change the transferred data between interested parties and overcome the problem of building negative conclusions from encrypted medical images. In order to do so, the target was to transfer from the domain of encrypting an image to encrypting features of an image, which are extracted as float number values. Therefore, we propose a deep learning-based image encryption scheme using the autoencoder (AE) technique and the advanced encryption standard (AES). Specifically, the proposed encryption scheme is supposed to encrypt the digest of the medical image prepared by the encoder from the autoencoder model on the encryption side. On the decryption side, the analogous decoder from the auto-decoder is used after decrypting the carried data. The autoencoder was used to enhance the quality of corrupted medical images with different types of noise. In addition, we investigated the scores of structure similarity (SSIM) and mean square error (MSE) for the proposed model by applying four different types of noise: salt and pepper, speckle, Poisson, and Gaussian. It has been noticed that for all types of noise added, the decoder reduced this noise in the resulting images. Finally, the performance evaluation demonstrated that our proposed system improved the encryption/decryption overhead by 50–75% over other existing models.
Experience with Using BBC Micro:Bit and Perceived Professional Efficacy of Informatics Teachers

Our study is focused on the perceived professional efficacy of informatics in-service teachers with the experience of using micro-controller BBC micro:bit. In Slovakia, teaching using hardware is not typical. In addition, many teachers do not teach programming. BBC micro:bit is designed to be a tool for computer science (CS) teachers that should make a significant contribution to the innovation of CS teaching and enable CS teachers to implement CS lessons. The following research questions were asked. Q1: Is there a difference in the perceived efficacy to use teaching strategies based on experience with the micro:bit? Q2: Is there a relationship between the perceived efficacy of using teaching strategies and experience using the micro:bit? The research sample comprised N = 388 CS teachers employed in Slovak schools from the available selection. The research sample included CS teachers who participated in the project called ENTER. All participants have a grant, weekly online practices, supporting materials, and also consultant for implementation of a new teaching strategy. This study’s findings indicate that the use of a microcontroller such as the micro:bit has a positive impact on self-efficacy for instructional strategies, but not for classroom management.
Industrial Anomaly Detection with Skip Autoencoder and Deep Feature Extractor

Over recent years, with the advances in image recognition technology for deep learning, researchers have devoted continued efforts toward importing anomaly detection technology into the production line of automatic optical detection. Although unsupervised learning helps overcome the high costs associated with labeling, the accuracy of anomaly detection still needs to be improved. Accordingly, this paper proposes a novel deep learning model for anomaly detection to overcome this bottleneck. Leveraging a powerful pre-trained feature extractor and the skip connection, the proposed method achieves better feature extraction and image reconstructing capabilities. Results reveal that the areas under the curve (AUC) for the proposed method are higher than those of previous anomaly detection models for 16 out of 17 categories. This indicates that the proposed method can realize the most appropriate adjustments to the needs of production lines in order to maximize economic benefits.
True and false recognition in MINERVA 2: Extension to sentences and metaphors

Arndt and Hirshman (1998) used MINERVA 2 to simulate true and false recognition in DRM-style lists and found that the model was able to capture many features of the empirical data. Here, we first replicate their simulations, but using empirically structured vectors derived from Latent Semantic Analysis rather than the randomly generated vectors characteristic of MINERVA 2. We report that the model still captures the DRM effect with fewer free parameters. We then extend our analyses to true and false recognition for full sentences and metaphorical expressions. Using a simple bag-of-words representation for sentences, we find that the MINERVA 2 model captures classic sentence false recognition findings from Bransford and Frank (1971) and a more recent finding from Reid and Katz (2018a) that demonstrates false recognition of unstudied sentences that share a metaphorical but not literal theme to studied sentences. These simulations provide evidence that an instance-based memory model, when amalgamated with structured semantic representations from a distributional semantic model, can account for true and false recognition across different types of language experiences.
GesPlayer: Using Augmented Gestures to Empower Video Players

In this paper, we introduce GesPlayer, a gesture-based empowered video player that explores how users can experience their hands as an interface through gestures. We provide three semantic gestures based on the camera of a computer or other smart device to detect and adjust the progress of video playback, volume, and screen brightness, respectively. Our goal is to enable users to control video playback simply by their gestures in the air, without the need to use a mouse or keyboard, especially when it is not convenient to do so. Ultimately, we hope to expand our understanding of gesture-based interaction by understanding the inclusiveness of designing the hand as an interactive interface, and further broaden the state of semantic gestures in an interactive environment through computational interaction methods.
Deep Adaptive Sampling and Reconstruction Using Analytic Distributions

We propose an adaptive sampling and reconstruction method for offline Monte Carlo rendering. Our method produces sampling maps constrained by a user-defined budget that minimize the expected future denoising error. Compared to other state-of-the-art methods, which produce the necessary training data on the fly by composing pre-rendered images, our method samples from analytic noise distributions instead. These distributions are compact and closely approximate the pixel value distributions stemming from Monte Carlo rendering. Our method can efficiently sample training data by leveraging only a few per-pixel statistics of the target distribution, which provides several benefits over the current state of the art. Most notably, our analytic distributions' modeling accuracy and sampling efficiency increase with sample count, essential for high-quality offline rendering. Although our distributions are approximate, our method supports joint end-to-end training of the sampling and denoising networks. Finally, we propose the addition of a global summary module to our architecture that accumulates valuable information from image regions outside of the network's receptive field. This information discourages sub-optimal decisions based on local information. Our evaluation against other state-of-the-art neural sampling methods demonstrates denoising quality and data efficiency improvements.
Good and bad self-excitation: Asymmetric self-exciting jumps in Bitcoin returns

Recent studies find jumps in cryptocurrencies such as Bitcoin, however, little is known about the behavior of self-exciting jump clustering. Using high frequency data, we investigate the characteristics of self-exciting jumps in Bitcoin returns. First, we find strong asymmetry in self-excitation, which is triggered, on average, more by bad (negative) jumps than good (positive) jumps. Second, when discriminating bear and bull markets, we find negative asymmetry in bear markets but no evidence of positive asymmetry in bull markets. Third, self-excitation has asymmetric aftershock effects, where aftershocks triggered by bad self-excitation are more persistent than aftershocks triggered by good self-excitation. The findings presented in this paper have important implications in risk management and asset pricing for Bitcoin.
GLCM: Global–Local Captioning Model for Remote Sensing Image Captioning

Remote sensing image captioning (RSIC), which describes a remote sensing image with a semantically related sentence, has been a cross-modal challenge between computer vision and natural language processing. For visual features extracted from remote sensing images, global features provide the complete and comprehensive visual relevance of all the words of a sentence simultaneously, while local features can emphasize the discrimination of these words individually. Therefore, not only global features are important for caption generation but also local features are meaningful for making the words more discriminative. In order to make full use of the advantages of both global and local features, in this article, we propose an attention-based global-local captioning model (GLCM) to obtain global-local visual feature representation for RSIC. Based on the proposed GLCM, the correlation of all the generated words and the relation of each separate word and the most related local visual features can be visualized in a similarity-based manner, which provides more interpretability for RSIC. In the extensive experiments, our method achieves comparable results in UCM-captions and superior results in Sydney-captions and RSICD which is the largest RSIC dataset.
Effects of a Modern Virtual Reality 3D Head-Mounted Display Exergame on Simulator Sickness and Immersion Under Specific Conditions in Young Women and Men: Experimental Study

Many young adults do not reach the World Health Organization's minimum recommendations for the amount of weekly physical activity. The virtual reality 3D head-mounted display (VR 3D HMD) exergame is a technology that is more immersive than a typical exercise session. Our study considers gender differences in the experience of using VR games for increasing physical activity.The aim of this study was to examine the differences in the effects of VR 3D HMD gaming in terms of immersion, simulator sickness, heart rate, breathing rate, and energy expenditure during two 30-minute sessions of playing an exergame of increasing intensity on males and females.To examine the effects of the VR 3D HMD exergame, we experimented with 45 participants (23 males and 22 females) exercising with VR 3D HMD Oculus Quest 1, hand controllers, and Zephyr BioHarness 3.0. Players exercised according to the Audio Trip exergame. We evaluated the immersion levels and monitored the average heart rate, maximum heart rate, average breathing rate, maximum breathing rate, and energy expenditure in addition to simulator sickness during two 30-minute exergame sessions of increasing intensity.Audio Trip was well-tolerated, as there were no dropouts due to simulator sickness. Significant differences between genders were observed in the simulator sickness questionnaire for nausea (F2,86=0.80; P=.046), oculomotor disorders (F2,86=2.37; P=.010), disorientation (F2,86=0.92; P=.040), and total of all these symptoms (F2,86=3.33; P=.04). The measurements after the first 30-minute VR 3D HMD exergame session for all the participants showed no significant change compared to the measurements before the first 30-minute exergame session according to the total score. There were no gender differences in the immersion (F1,43=0.02; P=.90), but the measurements after the second 30-minute exergame session showed an increase in the average points for immersion in women and men. The increase in the level of immersion in the female group was higher than that in the male group. A significant difference between genders was observed in the average breathing rate (F2,86=1.44; P=.04), maximum breathing rate (F2,86=1.15; P=.047), and energy expenditure (F2,86=10.51; P=.001) measurements. No gender differences were observed in the average heart rate and maximum heart rate measurements in the two 30-minute sessions.Our 30-minute VR 3D HMD exergame session does not cause simulator sickness and is a very immersive type of exercise for men and women users. This exergame allows reaching the minimum recommendations for the amount of weekly physical activity for adults. The second exergame session resulted in simulator sickness in both groups, more noticeably in women, as reflected in the responses in the simulator sickness questionnaire. The gender differences observed in the breathing rates and energy expenditure measurements can be helpful when programming VR exergame intensity in future research.
Learning by Seeing More Classes

Traditional pattern recognition models usually assume a fixed and identical number of classes during both training and inference stages. In this paper, we study an interesting but ignored question: can increasing the number of classes during training improve the generalization and reliability performance? For a k-class problem, instead of training with only these k classes, we propose to learn with k+m classes, where the additional m classes can be either real classes from other datasets or synthesized from known classes. Specifically, we propose two strategies for constructing new classes from known classes. By making the model see more classes during training, we can obtain several advantages. First, the added m classes serve as a regularization which is helpful to improve the generalization accuracy on the original k classes. Second, this will alleviate the overconfident phenomenon and produce more reliable confidence estimation for different tasks like misclassification detection, confidence calibration, and out-of-distribution detection. Lastly, the additional classes can also improve the learned feature representation, which is beneficial for new classes generalization in few-shot learning and class-incremental learning. Compared with the widely proved concept of data augmentation (dataAug), our method is driven from another dimension of augmentation based on additional classes (classAug). Comprehensive experiments demonstrated the superiority of our classAug under various open-environment metrics on benchmark datasets.
Mixed-Cultural Speech for Intelligent Virtual Agents - the Impact of Different Non-Native Accents Using Natural or Synthetic Speech in the English Language

This paper presents an exploratory study investigating the impact of non-native accented speech on the perception of Intelligent Virtual Agents (IVAs). In an online study, native English speakers watched a video of an IVA holding a monologue whilst speaking English with either a Spanish, Hindi or Mandarin accent that was either recorded by native speakers of that respective language (natural speech) or synthetically generated (synthetic speech). The results showed a significant impact of naturalness of speech on the IVAs perceived warmth and a significant interaction of accent and naturalness of speech on its perceived competence. The naturalness of speech impacted the participants' perception of the IVA as a non-native speaker of English, and the correctness of the attributed mother tongue in the Spanish and the Mandarin accent condition. These results are a valuable contribution to research on mixed-cultural IVAs in general and non-native speech as a cultural cue more specifically.
MSBPR: A multi-pairwise preference and similarity based Bayesian personalized ranking method for recommendation

For addressing the "One-Class Collaborative Filtering" (OCCF) problem in recommendation systems, in which the obtained user information is all single-type positive feedback, the current mainstream methods are all based on the idea of pairwise preference learning. The Bayesian Personalized Ranking (BPR) method is a classical representative of such an idea. However, the assumption in BPR, that "users tend to prefer items that they have once interacted", may not always hold in reality. This is because for non-interacted items, a user may have different perspectives, such as potentially favorite, dislike, or something in between. For mitigating the above-mentioned issue, this paper proposes a Multi-pairwise preference and Similarity based BPR method, termed as MSBPR for brevity. MSBPR utilizes additional auxiliary feedback information to excavate and infer deeper-level user preferences. Subsequently, MSBPR borrows ideas from traditional item/user-based collaborative filtering methods to further divide non-interacted items from the angle of item/user, respectively. Afterwards, MSBPR constructs four preferences on top of the divided items, and accordingly builds up the multiple pairwise preference assumption. To optimize MSBPR, we derive an efficient learning algorithm based on the stochastic gradient descent algorithm. The computational complexity of MSBPR is also theoretically analyzed. Comprehensive experimental results demonstrate the effectiveness and efficiency of MSBPR over ten state-of-the-art methods on six benchmark real-world datasets.
Blockchain diffusion: the role of consulting firms

Blockchain is one of the most promising technologies with a highly disruptive potential, yet many aspects of this innovation remain unclear in the current body of research. One of them is the whole adoption and diffusion process of blockchain technology, which needs more attention from researchers. This study has adopted a qualitative method to investigate the role of consultants in blockchain diffusion, relying on data from 14 semi-structured interviews conducted with blockchain consultants. The various roles of these key players were identified and challenges related to the technology analyzed by means of the Diffusion of Innovation Theory (DOI) and the Technology – Organization – Environment (TOE) framework. According to the results obtained, consultants' roles range from technology brokers to quality filters. The same results also revealed that the most important barriers to blockchain adoption and diffusion include the lack of knowledge and understanding of the technology, which contributes to promoting unconscious or forced adoption. This paper sheds light on how consulting firms and blockchain-oriented start-ups operate to impact the diffusion of blockchain while dismantling barriers to adoption.
Principals of Optimal Cloud Resource Management

Cloud data centers are complex systems consisting of a number of subsystems, such as computing, telecommunication and engineering subsystems. Each of these subsystems has its own optimization tasks. The paper provides an overview of the main directions and tasks of resource optimization in cloud computing. Particular attention is paid to the computing subsystem, where optimization tasks such as initial and dynamic placement of virtual machines, optimization of resource scaling processes and migration of virtual machines and containers are solved. Based on the aims of the cloud computing participants, in this work the principles of optimal resource management, such as the principle of stability, energy efficiency and individuality, are formulated for the first time. The results of the work on the formation of the main cycle of computing work to optimize the process of managing cloud data center resources are presented.