Reviewerly/exHarmony huggingface dataset:
Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach

Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying and segmenting different structures. In this study, we employ the latest developments in 3D semi-supervised learning to create cutting-edge models for the 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductors scans. We illustrate our approach to locating the region of interest of the structures, their individual components, and their void defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefit of contrastive learning in the data pre-selection step for our detection model and multi-scale Mean Teacher training paradigm in 3D semantic segmentation to achieve better performance compared with the state of the art. Our extensive experiments have shown that our method achieves competitive performance and is able to outperform by up to 16% on object detection and 7.8% on semantic segmentation. Additionally, our automated metrology package shows a mean error of less than 2 μm for key features such as Bond Line Thickness and pad misalignment.
Geochemistry of ore minerals from Chelopech deposit: I. Trace elements in pyrite

The concentrations of trace elements in pyrite from blocks 8, 19-SE, 19-NW, 19-NE, 19-SW, 103-W, 103-E, 144, 146, 147, 149-south, 150, and 151 of copper-gold high sulphidation Chelopech deposit were determined by LA-ICP-MS. It was found that the pyrites from the individual blocks are enriched in different degrees of “impurity” elements: the highest contents of Ag, Au, Zn, Hg, and In are typical to block 19-NE; Se, Bi, and Ti to block 19-SE; As, Mn, Tl, V, and Cd to block 19-SW; Co and Ni to block 144; Sb and Sn to block 146; Cu to block 149-south; Pb, Te, and Ga to block 103-E and Cr to block 103-W. The obtained results also show that in collomorhic pyrites the variations in the contents of trace elements are always very large and are rather due to adsorption processes than isomorphism. In addition, pyrites are also enriched in poisonous trace elements, especially from the lower level of the northern blocks, which, however, also have the highest values of gold and silver.
An Assessment of the Banking Sector Development in Economic Performance: A Case of Selected Countries

Purpose: The study examines economic growth and banking sector development in some G8 countries (United States, United Kingdom, Canada, Japan, and Germany) and three African countries (Nigeria, Ghana, and South Africa). Approach/Methodology/Design: Study objectives include filling the gap occasioned by a lack of literature on this topic, especially as it concerns the selected countries. As a check for stationarity, we used the Levin-Lin-Chu and Im-Peseran-Shun unit root tests. In addition to Pedroni, long-run relationships between variables are also tested. Because the study is a cross-country study, it was necessary to perform the Hausman test to determine if random effect panel analysis is consistent and effective and to test long-run cointegration using the ARDL Bound test. Findings: According to the results, banking sector development, and exchange rate contribute positively to economic growth while CPI contributes negatively. In contrast, the results indicate a long-run relationship between economic growth, banking, and other determinants. Originality/value: The study recommends that G8 countries and most African countries consider improving their banking sector and incorporating it into their economic development as one of the determinants.
Proof-theoretic Semantics for Intuitionistic Multiplicative Linear Logic

This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic (IMLL). The starting point is a review of Sandqvist's B-eS for intuitionistic propositional logic (IPL), for which we propose an alternative treatment of conjunction that takes the form of the generalized elimination rule for the connective. The resulting semantics is shown to be sound and complete. This motivates our main contribution, a B-eS for IMLL, in which the definitions of the logical constants all take the form of their elimination rule and for which soundness and completeness are established.
Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.
An automated monitoring system of urban pollution using geochemical and magnetic parameters

An automated monitoring system of urban pollution using geochemical and magnetic parameters
Analysis of Key Factors for XR Extended Reality Immersive Art Experience

We examined the perception and preference of extended reality (XR) immersive art experiences from the exhibition at the National Taiwan Science Education Center between October 2021 and February 2022. Various research methods including a questionnaire survey, expert interviews, on-site observation survey, and triangulation were used to analyze qualitative and quantitative results. Structural Product and Service Solutions Principal Component Analysis (SPSS PCA) was also used to analyze and validate a sample of 219 participants. The findings indicated that the participants’ preference and ranking results were in line with the Teamlab Planets exhibition held at the National Taiwan Science Education Center. The key factors of immersive experience were charm and elegance, artistic and technological innovation, creativity and professionalism, trendy and cool, and eye-catching and exquisite immersion. These results served as a reference and contributed to understanding the current trends in immersive art experience design.
Operational Matrix of New Shifted Wavelet Functions for Solving Optimal Control Problem

The present work concerns with presenting an explicit formula for new shifted wavelet (NSW) functions. Wavelet functions have many applications and advantages in both applied and theoretical fields. They are formulated with different orthogonal polynomials to construct new techniques for treating some problems in sciences, and engineering. A new important differentiation property of NSW in terms of NSW themselves is obtained and proved in this paper. Then it is utilized together with the state parameterization technique to find solution of optimal control problem (OCP) approximately. The suggested method converts the OCP into a quadratic programming problem, which can be easily determined on computer. As a result, the approximate solution closes with the exact solution even with a small number of NSW utilized in estimation. The error bound estimation for the proposed method is also discussed. Some test numerical examples are solved to demonstrate the applicability of the suggested method. For comparison, the exact known solutions against the obtained approximate results are listed in Tables.
On the Long-Term Effects of Continuous Keystroke Authentication

One of the main challenges in deploying a keystroke dynamics-based continuous authentication scheme on smartphones is ensuring low error rates over time. Unstable false rejection rates (FRRs) would lead to frequent phone locks during long-term use, and deteriorating attack detection rates would jeopardize its security benefits. The fact that it is undesirable to train complex deep learning models directly on smartphones or send private sensor data to servers for training present unique deployment constraints, requiring on-device solutions that can be trained fully on smartphones. To improve authentication accuracy while satisfying such real-world deployment constraints, we propose two novel feature engineering techniques: (1) computation of pair-wise correlations between accelerometer and gyroscope sensor values, and (2) on-device feature extraction technique to compute dynamic time warping (DTW) distance measurements between autoencoder inputs and outputs via transfer-learning. Using those two feature sets in an ensemble blender, we achieved 6.4 percent equal error rate (EER) in a public dataset. In comparison, blending two state-of-the-art solutions achieved 14.1 percent EER in the same test settings. Our real-world dataset evaluation showed increasing FRRs (user frustration) over two months; however, through periodic model retraining, we were able to maintain average FRRs around 2.5 percent while keeping attack detection rates around 89 percent. The proposed solution has been deployed in the latest Samsung Galaxy smartphone series to protect secure workspace through continuous authentication.
Understanding and Detecting Hateful Content Using Contrastive Learning

The spread of hate speech and hateful imagery on the Web is a significant problem that needs to be mitigated to improve our Web experience. This work contributes to research efforts to detect and understand hateful content on the Web by undertaking a multimodal analysis of Antisemitism and Islamophobia on 4chan’s /pol/ using OpenAI’s CLIP. This large pre-trained model uses the Contrastive Learning paradigm. We devise a methodology to identify a set of Antisemitic and Islamophobic hateful textual phrases using Google’s Perspective API and manual annotations. Then, we use OpenAI’s CLIP to identify images that are highly similar to our Antisemitic/Islamophobic textual phrases. By running our methodology on a dataset that includes 66M posts and 5.8M images shared on 4chan’s /pol/ for 18 months, we detect 173K posts containing 21K Antisemitic/Islamophobic images and 246K posts that include 420 hateful phrases. Among other things, we find that we can use OpenAI’s CLIP model to detect hateful content with an accuracy score of 0.81 (F1 score = 0.54). By comparing CLIP with two baselines proposed by the literature, we find that CLIP outperforms them, in terms of accuracy, precision, and F1 score, in detecting Antisemitic/Islamophobic images. Also, we find that Antisemitic/Islamophobic imagery is shared in a similar number of posts on 4chan’s /pol/ compared to Antisemitic/Islamophobic textual phrases, highlighting the need to design more tools for detecting hateful imagery. Finally, we make available (upon request) a dataset of 246K posts containing 420 Antisemitic/Islamophobic phrases and 21K likely Antisemitic/Islamophobic images (automatically detected by CLIP) that can assist researchers in further understanding Antisemitism and Islamophobia.
Halting the Decay of Talk

We investigate how people with atypical bodily capabilities interact within virtual reality (VR) and the way they overcome interactional challenges in these emerging social environments. Based on a videographic multimodal single case analysis, we demonstrate how non-speaking VR participants furnish their bodies, at-hand instruments, and their interactive environment for their practical purposes. Our findings are subsequently related to renewed discussions of the relationship between agency and environment, and the co-constructed nature of situated action. We thus aim to contribute to the growing vocabulary of atypical interaction analysis and the broader context of ethnomethodological conceptualizations of unorthodox and fractured interactional ecologies.
Study of Factors Influencing Consumer to Adopt Cryptocurrency

Cryptocurrencies have become one of the most traded financial assets in the last decade. The first half of 2022 will not be easily forgotten by the crypto faithful as it shifted investor sentiment after a monster cryptocurrency rally in 2021 that saw bitcoin (BTC) and altcoins scale multiple record peaks. Just as users were getting comfortable with their passive DeFi incomes, Terra and its non-collaterised stablecoin collapsed in a sensational turn of events. Three Arrows Capital (3AC), a crypto hedge fund that managed assets worth about $10bn at its peak, went bankrupt as falling crypto prices forced liquidations of collateralized loans and leveraged trading positions across the industry, aggravating the sell-off. Investor sentiment plummeted as crypto lending platforms like Celsius and Babel suspended withdrawals for clients. Investor sentiment plummeted as crypto lending platforms like Celsius and Babel suspended withdrawals for clients. The future of cryptocurrency still remains unclear. Given all these mess, we would like to know what factors could actually influence consumer adoption of cryptocurrency from here on out.The purpose of this research is to study factors influencing consumer to adopt cryptocurrency. These factors include seven independent variables: Convenience (CV), Popularity (PL), Usefulness (UF), Credibility (CD), Recommendations (RC), Price Stability (PS) and Risk (RK) and one dependent variable: Crypto Behavior (BH). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that Since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .047 (&lt;.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .934 (&gt;.90), the model seems to fit well according to the descriptive measures of fit. More importantly Popularity (PL), Usefulness (UF), Credibility (CD), Recommendations (RC) and Risk (RK) seem to have significant effects on influencing consumer to adopt cryptocurrency due to their p-values are both less than .05. That means as long as cryptocurrency is becoming more popular, useful, credible and highly recommended by key stakeholders (friends &amp; family, professionals and influencers) with better risk management, consumer will be more welcome to adopt cryptocurrency as both day-to-day currency and investment alternative. One interesting finding is that Price Stability (PS) of cryptocurrency that we’ve seen over and over again especially post-covid doesn’t seem to significantly impact much of consumer adoption.
XR Programmers Give Their Perspective on How XR Technology can be Effectively Utilised in High-Performance Sport

Abstract Background The successful use of extended reality (XR) in sport is highly dependent on the extent to which it can represent the perception–action couplings that exist in the performance setting. However, there are many unknowns regarding the effectiveness of XR technology which is limiting its adoption in sport. Therefore, providing high-performance sporting organisations with more information about the efficacy and utility of XR, specifically its strengths and limitations, is warranted. Results The results provide insight into the limitations of XR and how those limitations are likely to reduce the effectiveness of XR for training motor skills. The participants described opportunities provided by XR for measuring athlete performance and highlighted several practical applications for enhancing athlete and coaching performance. Using artificial intelligence (AI) for training tactical decision-making and creating new movement solutions was also a key finding. Conclusions The use of XR in sport is in its infancy, and more research is required to establish a deeper understanding of its utility and efficacy. This research provides sporting organisations, coaches, athletes, and XR technology companies with insights into where XR technology can have the greatest positive impact on performance in sport.
Absorptive Capabilities as Determinants of Economic Sustainability of Small and Medium-Scale Enterprises

Purpose – Drawing from the Absorptive Capabilities theory, this paper examines the role played by dimensions of absorptive capabilities (i.e., knowledge acquisition, knowledge assimilation, knowledge transformation, and knowledge exploitation) on the economic sustainability of SMEs in Nigeria. Design/methodology/approach – This study uses a cross-sectional research design to collect quantitative data from 343 owners/managers of SMEs in Plateau State. Structural equation modeling through the use of the Analysis of Moments of Structures (AMOS) software, version 23 was employed to test the hypotheses. Findings – Research results indicate that all the dimensions of absorptive capabilities exert significant influence on the economic sustainability of SMEs. Thus the dimensions of absorptive capabilities serve as the antecedents of the economic sustainability of SMEs could be realized. Limitations- The focus of this study is on SMEs in Plateau State alone. The use of a cross-sectional design may also undermine the results of the study. Implications – This work adds to existing research on the subject of sustainability by revealing that dimensions of absorptive capabilities are necessary for the attainment of economic sustainability. Originality – Based on current literature, studies on the dimensions of absorptive capabilities as predictor variables are limited. No study has been conducted on the influence of absorptive capabilities using four dimensions, on the economic sustainability of SMEs. Thus it is a pioneering study in terms of both content and context.
Training deep learning models for cell image segmentation with sparse annotations

Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELE-PHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.
Higher Education 4.0: brief considerations

Higher Education 4.0 is an educational approach that is based on the integration of digital technologies in all aspects of the educational process. This approach seeks to transform higher education through the personalization of learning, the focus on relevant competencies for the 21st century, the encouragement of collaborative and networked learning, and the flexibility and accessibility to adapt to the changing needs of students. Higher Education 4.0 implies the use of tools such as artificial intelligence, virtual reality and online learning, to improve the quality of teaching, learning and administrative management. This approach seeks to prepare students to face the challenges of the world of work and society, fostering skills such as critical thinking, problem solving, and collaboration. In summary, Higher Education 4.0 seeks to take advantage of the potential of digital technologies to provide a more personalized, relevant and accessible education.
The effect of big data governance on financial technology in Jordanian commercial banks: The mediation role of organizational culture

Big data has become much more widely used in recent years, particularly in the banking sector. Banks have begun to use big data to enhance customer experience, optimize operations, and create novel products and services. This study investigates the effect of big data governance on financial technology in Jordanian commercial banks. The study focuses on the four dimensions of big data governance: strategy and planning, ethics and social responsibility, data quality and management, and infrastructure and architecture. Additionally, the study considers the mediating role of organizational culture in the relationship between big data governance and financial technology. Primary data were collected from 250 IT employees working in Jordanian commercial banks, and the data were analyzed using PLS-SEM. The study findings indicate that big data governance has a significant positive effect on financial technology in Jordanian commercial banks. Additionally, it has been discovered that organizational culture partially mediates the relationship between big data governance and financial technology, emphasizing the significance of creating a culture that encourages the efficient use of data in fintech. In the context of Jordanian commercial banks, this study presents empirical evidence for the connection between big data governance and financial technology. The results indicate that big data governance measures should be applied while taking organizational culture into account as a potential mediator of the effects on financial technology. This study offers guidance on how to successfully integrate big data governance policies in commercial banks to advance financial technology for bank managers and policymakers.