Reviewerly/exHarmony huggingface dataset:
Performing a Knowledge Audit Within a South African Chemical Manufacturer

Despite the increased efforts of organisations to implement knowledge management (KM) initiatives, many fail. Performing a knowledge audit before embarking on KM activities increases the probability of success. In this interpretive case study, the authors applied a modified version of the knowledge audit methodology developed by Perez-Soltero et al. to a South African (SA) veterinary medicine, fine chemical, and pharmaceutical manufacturer. The authors engaged members of the organisation in focus group sessions and individual interviews to identify knowledge assets related to core processes within the organisation. They used the data from the focus groups and individual interviews to identify and articulate many of the knowledge assets at the core of the organisation's current success. In addition, the process of conducting a knowledge audit and making the steps explicit while adjusting for context, may inform researchers and practitioners in terms of knowledge audit approach.
Board 174: Stakeholder Views in Building a Sustainable Engineering Learning Ecosystem: Afterschool Green Energy, Robotics, and Automation (Work in Progress)

Abstract This research was part of the first year of a National Science Foundation funded project aimed at promoting high school students' interest in green energy, robotics, automation and post-secondary engineering and technology study. High school teachers, undergraduate majors in STEM areas, and community based non-profit organizations were involved in this afterschool engineering program for high school students with the goal of broadening participation among minoritized groups in engineering and engineering technology. This study investigated how these different stakeholders' views aligned and diverged about (1) the characteristics of STEM engagement, and (2) the factors that influence the development of engineering identities. The purpose of this investigation was to uncover the relationships between community members' viewpoints, community assets, and the positionality of the project personnel.
Data Mesh Meets Blockchain

Abstract Effective dataset management is crucial for enterprises to make informed decisions and remain competitive. However, centralized dataset management approaches often result in poor scalability, unclear governance, inaccessible data silos, and duplication of efforts. This paper proposes a distributed blockchain-based framework inspired by the data mesh architecture to address these challenges. Our proposed framework leverages blockchain’s decentralized nature to enable efficient and transparent dataset sharing across enterprise business domains. By turning datasets into digital assets and business domains into peers, our framework utilizes blockchain smart contracts to allow business domains to view, request, and share datasets. In this paper, we describe the details of our framework, and we analyze it from scalability, accessibility, security, and data governance perspectives. To validate our framework, we provide a proof-of-concept implementation with a publicly available source code.
Predicting Team Function Using Bayesian and Cognitive Diagnostic Modeling Approaches

Abstract Team-based learning is commonly used in engineering introductory courses. As students of a team may be from vastly different backgrounds, academically and non-academically, it is important for faculty members to know what aid or hinder team success. The dataset that is used in this paper includes student personality inputs, self-and-peer-assessments of teamwork, and perceptions of teamwork outcomes. Using this information, we developed several bayesian models that are able to predict if a team is working well. We also constructed and estimated Q-matrices which are crucial in explaining the relationship between latent traits and students' characteristics in cognitive diagnostic models. The prediction and diagnostic models are able to help faculty members and instructors to gain insights into finding ways to separate students into teams more effectively so that students have a positive team-based learning experience.
The Dilemma of the Copyrights of Artificial Intelligence

Artificial intelligence (AI) and intellectual property (IP) share some key similarities, such as uncertainty in predictions, processing a massive amount of data, and machine learning. Yet, they also differ from each other. This paper provides background information on how these two domains have evolved over time. It also highlights how Saudi Arabia's IP system differs from those of other countries. Furthermore, this article explores the relationship between AI and IP and their application in copyright. This study is significant as it helps identify the challenges and opportunities that AI presents with respect to IP in terms of copyright. Finally, this article makes recommendations that will help protect both AI and IP.
Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

Reversible data hiding (RDH) is a technique that embeds secret data into digital media while preserving the integrity of the original media and the secret data. RDH has a wide range of application scenarios in industrial image processing, such as intellectual property protection and data integrity verification. However, with the increasing prevalence of color images in industrial applications, traditional RDH methods for grayscale images are inadequate to meet the requirements of image fidelity. This paper proposes an RDH method for color images based on channel reference mapping (CRM) and adaptive pixel prediction. Initially, the CRM mode for a color image is established based on the pixel variation correlation between the RGB channels. Then, the pixel local complexity context is adaptively selected using the CRM mode. Next, each pixel value is adaptively predicted based on the features and characteristics of adjacent pixels and reference channels, and then data is embedded by expanding the prediction error. Finally, we compare seven existing RDH algorithms on the standard image dataset and the Kodak dataset to validate the advantages of our method. The experimental results demonstrate that our approach achieves average peak signal-to-noise ratio (PSNR) values of 63.61 and 60.53 dB when embedding 20,000 and 40,000 bits of data, respectively. These PSNR values surpass those of other RDH methods. These findings indicate that our method can effectively preserve the visual quality of images even under high embedding capacities.
Assessing Priorities in Engineering Design Through Augmented Reality

Abstract Abstract Authentic learning is fast becoming one of the most effective educational instructional methods for teaching engineering education. One of the issues that students must face with transitioning into the industry is the application of school knowledge to their real-life practice. Engineering students are expected to think critically, come up with original solutions, and collaborate with other team members on projects. Often, traditional teaching methods are not enough to build these capacities in young engineers, which has led to the introduction of new instructional methods such as the incorporation of virtual reality as an authentic learning technique. Several authentic learning techniques have been used in the past to introduce students to virtual reality. While VR substitutes reality, transporting you to a different environment, AR enhances reality by superimposing information onto what is already visible. Hence, incorporating AR in authentic learning activity has the potential to help understand how users view the real world and make informed decisions to improve workplace decisions. This paper serves as a literature review for a larger study that investigates how engineering students set priorities in engineering design to assess their preparedness for industry. The larger study seeks to answer the following research questions: • How do students perceive their undergraduate education prepares them to set priorities in engineering design?" • How do authentic learning activities through AR change the way students think about design? To answer the research questions, the advancement of teaching framework on professional preparation across professions known as the "three apprenticeships model" will be used as a guiding theoretical lens. To explore the research purpose, 10 civil engineering undergraduate students from a university in the Midwest will be assessed on their knowledge via an authentic learning activity to redesign a children's playground using augmented reality based on five assessment metrices (cost, fun, sustainability, time, and safety). Feedback will be gathered through semi-structured interviews and analyzed through thematic analysis. The objective of this study is to give students exposure into real-world practice to understand industry requirements even before they graduate from school. This paper will present a brief literature review on the topic, while more details about the methodology, results, and future directions will be presented in a future paper.
Challenges in the Application of Artificial Intelligence in Education for Sustainable Engineering

The aim of this chapter is to provide university educators with a range of ideas and alternatives for the use of artificial intelligence (AI) tools in their teaching to integrate concepts of sustainability, which are currently highly demanded in the professional activity. Artificial intelligence has revolutionized many areas of economic activity, offering new solutions and/or optimizing certain processes. Training students in these tools is essential for successful integration into the labor market in a technologically evolving era. This training must be carried out in coordination with educational policies, and within a teaching program that includes ethical aspects in the handling of these tools. Artificial intelligence can be key to improving sustainability in multiple areas of engineering as shown in various examples and ideas in this work.
Artificial Intelligence in Chess-Playing Automata

This chapter presents the results of a study for improving the performance of the quiescence phase of Alpha-Beta (α-β) search. The Minimax algorithm's α-β enhancement enhances depth-first search performance by optimizing solutions in near best-first order, thereby reducing the computational effort from O(bd) to O(√bd) where b is the branching factor of the game-tree and d is the depth of the search. This research uses a full breath search to delay the asymptotic behavior of the combinatorial explosion to five levels of depth. A narrow width search involves expanding solutions involving material exchange, pawn promotion, or king-in-check until the position reaches quiescence without any material exchanges or promotions. When quiescence is reached, the evaluation function scores the leaf nodes of the game-tree. This chapter's research shows that α-β pruning is enhanced when a solution without material exchange or promotion is attempted first during the quiescence phase of α-β search which applies to chess playing programs as well.
Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algorithm. The main reason for this is that differential evolution is highly sensitive to the scaling factor and crossover rate parameters. In this study, a novel adaptation technique is proposed which uses the success rate to replace the popular success history-based adaptation for scaling factor tuning. In particular, the scaling factor is sampled with a Cauchy distribution, whose location parameter is set as an nth order root of the current success rate, i.e., the ratio of improved solutions to the current population size. The proposed technique is universal and can be applied to any differential evolution variant. Here it is tested with several state-of-the-art variants of differential evolution, and on two benchmark sets, CEC 2017 and CEC 2022. The performed experiments, which include modifications of algorithms developed by other authors, show that in many cases using the success rate to determine the scaling factor can be beneficial, especially with relatively small computational resource.
Does Economic Uncertainty Obstruct the Financial Inclusion Projects of the Central Bank of India

Financial inclusion provides swift and economical financial services to the remotest population and helps in generating funds. However, researchers conclude that economic events often obstruct the expansion of financial services. Keeping in view the importance of financial inclusion, the present chapter attempts to investigate how economic uncertainty influences financial inclusion initiatives of the Central Bank of India. Moreover, the chapter also investigates the moderating role of governance on the interconnection of financial inclusion and economic uncertainty. To this end, advanced econometric methodologies are employed to conduct an exhaustive empirical analysis on the dataset from 2000-2021. The empirical outcome infers that high economic uncertainty impedes the financial inclusion. However, when the indicators of governance interact with economic uncertainty, it moderates the negative influence of economic policy uncertainty on financial inclusion. The finding assists in understanding the interrelation of financial inclusion, economic uncertainty, and governance.
Smartphone and STEM

Covid-19 has also had a significant impact on schools, the use of distance learning has raised questions already present, in particular with respect to the meaning of tools and technologies. Leaving aside the aspects related to the use of the network and those of communication, the authors want to provide a brief overview of the fundamental issues related to the use of a smartphone for STEM teaching. A theme that sees two opposing positions (pros and cons) colliding, often unavailable for discussion and dialogue. Without taking a position, the text tackles the problem from three points of view: the hardware, the apps, and some possible activities that can be associated with the main functions activated by the students.
Motivating factors for blockchain technology adoption: a theoretical analysis from the perspective of supply chain collaboration

Purpose This study aims to identify the driving factors that influence blockchain technology adoption in the context of a supply chain (SC), considering three dimensions: technology, transactions and collaboration. Design/methodology/approach An integrative systematic literature review of previous studies was conducted. Using three main dimensions: technology, transactions and SC collaboration, supported by the unified theory of acceptance and use of technology, transaction cost economics (TCE) and concepts of SC collaboration, the authors categorized factors that contributed to blockchain technology in SC in the extant literature and proposed a theoretical model that covers these three dimensions. Findings The findings reveal that the information sharing category – related to the SC collaboration dimension – is the category with the greatest number of motivating factors for blockchain adoption in the SC context, followed by performance expectancy and behavioral uncertainty. Research limitations/implications The review considers papers published until 2021 obtained from a specific database. Originality/value This study focuses on filling the research gap concerning technology adoption as it considers the interconnection formed by two organizations, interorganizational transactions and SC collaboration, using complementary theories to explain the phenomenon.
A Vision Enhancement and Feature Fusion Multiscale Detection Network

Abstract In the field of object detection, there is often a high level of occlusion in real scenes, which can very easily interfere with the accuracy of the detector. Currently, most detectors use a convolutional neural network (CNN) as a backbone network, but the robustness of CNNs for detection under cover is poor, and the absence of object pixels makes conventional convolution ineffective in extracting features, leading to a decrease in detection accuracy. To address these two problems, we propose VFN (A Vision Enhancement and Feature Fusion Multiscale Detection Network), which first builds a multiscale backbone network using different stages of the Swin Transformer, and then utilizes a vision enhancement module using dilated convolution to enhance the vision of feature points at different scales and address the problem of missing pixels. Finally, the feature guidance module enables features at each scale to be enhanced by fusing with each other. The total accuracy demonstrated by VFN on both the PASCAL VOC dataset and the CrowdHuman dataset is better than that of other methods, and its ability to find occluded objects is also better, demonstrating the effectiveness of our method.The code is available at https://github.com/qcw666/vfn .
A Concept of a Plug-In Simulator for Increasing the Effectiveness of Rescue Operators When Using Hydrostatically Driven Manipulators

The introduction of Unmanned Ground Vehicles (UGVs) into the field of rescue operations is an ongoing process. New tools, such as UGV platforms and dedicated manipulators, provide new opportunities but also come with a steep learning curve. The best way to familiarize operators with new solutions are hands-on courses but their deployment is limited, mostly due to high costs and limited equipment numbers. An alternative way is to use simulators, which from the software side, resemble video games. With the recent expansion of the video game engine industry, currently developed software becomes easier to produce and maintain. This paper tries to answer the question of whether it is possible to develop a highly accurate simulator of a rescue and IED manipulator using a commercially available game engine solution. Firstly, the paper describes different types of simulators for robots currently available. Next, it provides an in-depth description of a plug-in simulator concept. Afterward, an example of a hydrostatic manipulator arm and its virtual representation is described alongside validation and evaluation methodologies. Additionally, the paper provides a set of metrics for an example rescue scenario. Finally, the paper describes research conducted in order to validate the representation accuracy of the developed simulator.
A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.
Meta: XR-AR-MR and Mirror World Technologies Business Impact of Metaverse

Metaverse offers opportunities for companies to create innovative business models by providing a virtual platform where they can sell products and services and interact with customers in new and unique ways. Extended Reality (XR), Augmented Reality (AR), Mixed Reality (MR) and Mirror World technologies are important technological pathways for the metaverse. The metaverse concept is changing organizational structures with new opportunities and shaping the workforce for the future environment. The purpose of this review article is to discuss the concept of metanomics, the business implications of the metaverse in the context of the economy, business and employee-based value, future workforce, B2B opportunities and new business models. The concept of metaptation was associated with the metaverse in later studies to be conceptualized within the framework of adaptation theory. This review concludes by asserting that as the metaverse continues to evolve, driven by advancements in XR, AR, MR, and Mirror World technologies, it will not only revolutionize traditional business models but also necessitate a new paradigm of metaptation, where businesses must continuously adapt to harness the full potential of the metaverse in the realms of economic growth, innovative B2B opportunities, workforce development, and customer engagement. In order to be successful in this dynamic change process, businesses should adopt a strategic approach to adopting these innovative technologies. These strategic steps of businesses will contribute to gaining competitive advantage by enabling them to make the most of the commercial potential of the Metaverse.
Graph transformer against graph perturbation by flexible-pass filter

Graph perturbation hinders graph models in real applications, and thus, defense methods against graph perturbation have been attracting increasing attention. However, current defense methods limit expressiveness and demand expert knowledge. To overcome these issues, in this paper, we propose a flexible-frequency graph transformer, building on the powerful expressive ability of self-attention. Specifically, we design a frequency-extraction self-attention with three heads to extract multi-frequency representations, i.e., low-frequency representation, hybrid-frequency representation, and high-frequency representation. We then design an adaptive fusion method to combine diverse representations for outputting a flexible-frequency representation. This improves the model’s expressive ability by using comprehensive information to enhance defense ability against graph perturbation. To achieve this, we adaptively capture the robust graph filters within self-attention, eliminating the need for expert knowledge. To further enhance the effectiveness of self-attention, we incorporate graph learning to capture graph information before conducting node representation learning through self-attention. Furthermore, we theoretically analyze the feasibility of our proposed method. Extensive experimental results demonstrate that our proposed method offers a dynamic and effective defense against graph perturbation compared to existing state-of-the-art methods.