Data Mesh Meets Blockchain

Abstract: 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: 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

Abstract: 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

Abstract: 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 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

Abstract: 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

Abstract: 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.


Fostering Global Engineers through the Study of the Humanities: Assessment of the Course "Science and Religion in Japan" from a Racial Equity Perspective

Abstract: Abstract This was an exploratory study to examine the effectiveness of the Spring 2022 course 'Science and Religion in Japan' from an engineering education perspective. Technological innovations are occurring through the fusion of science and engineering fields into new cutting-edge disciplines. Such a world requires engineers with broad knowledge, a global perspective and an appreciation of racial equity, being capable of working in interdisciplinary teams. In the pre-survey conducted in the Spring 2022 semester, the average total MGUDS-S scores for European, South American, and Asian students did not show much difference: 64.33, 65.25, and 65.17 respectively, while their average scores varied in the post-survey: 73.00, 80.25, and 67.83. Interestingly, South American students' total scores showed a greater increase in the post-survey than that of Europeans. Whenever similar assessments are conducted in any of our mobility programs and when comparing each of the three MGUDS-S subscales, namely 'Diversity of Contact (DC)', 'Relativistic Appreciation (RA)', and 'Comfort with Difference (CD)', 'CD' scores are always the lowest, suggesting that many students feel somewhat uncomfortable interacting with people of different ethnic and cultural backgrounds. It is noteworthy that 'CD' scores for Asian students showed a slight decrease in the post-survey. Increasing 'CD' scores will be the challenge for the Fall 2022 semester. The course 'Science and Religion in Japan' is included as one of the humanities topics in a newly launched research-based English degree engineering program. It has been well received by both the research-based program students and engineer-majored international students. The class size is approximately 25 every semester, consisting of students mainly from Europe, Asia, and South America. Therefore, class discussion is always conducted through a racial equity lens, as participants are taking part in the conversation from diverse standpoints born from their different cultures, religions and personal backgrounds. Below are the expected learning outcomes of this course: 1) To gain knowledges of the different cultures, religions, and races that played important roles in Japanese history. 2) To assess the value of equity in history. 3) To understand the importance of interdisciplinary approaches through the humanities course. To enhance students' understanding of the class concepts, every semester two guest speakers are invited to give special lectures on their topics of their expertise: 1) One demonstrates the importance of philosophical thought and religion, and how these worldviews have influenced the concept of 'Humanoid robots' in present-day Japanese society. 2) The other addresses the importance of engineers having the mindset that they should apply technical expertise solely for peaceful purposes. Our research questions are: 1) Can the students' global competency be increased more after the Fall 2022 semester? *Using the MGUDS-S. 2) Does including 'Science and Religion in Japan' in an engineering education curriculum help students to gain flexibility, an appreciation of equity, and a greater richness of ideas? - These aspects are required if students are to become 'Global engineers'. *Using students' survey. 3) Are their expected learning outcomes being achieved, or are there any unexpected by-products gained from this course? *Using students' survey. (499 words)


Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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.


Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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.


Laplace Expansion for Bartlett--Nanda--Pillai Test Statistic and Its Error Bound

Abstract: We construct asymptotic expansions for the distribution function of the Bartlett--Nanda--Pillai statistic under the condition that the null linear hypothesis is valid in a multivariate linear model. Computable estimates of the accuracy of approximation are obtained via the Laplace approximation method, which is generalized to integrals for matrix-valued functions.


An Introduction to SGTPPR: Sparse Geochemical Tectono‐Magmatic Setting Probabilistic MembershiP DiscriminatoR

Abstract: Abstract We present a new and easy‐to‐use geochemical tectono‐magmatic setting discriminator to calculate the probability of membership (the Sparse Geochemical Tectono‐magmatic setting Probabilistic membershiP discriminatoR, SGTPPR) that runs in Excel. It outputs the probability of membership for eight different tectono‐magmatic settings (mid‐ocean ridge, oceanic island, oceanic plateau, continental flood basalt province, intra‐oceanic arc, continental arc, island arc, and back‐arc basin) for a given volcanic rock sample based on major and selected trace element contents (SiO 2 , TiO 2 , Al 2 O 3 , Fe 2 O 3 , MgO, CaO, K 2 O, Na 2 O, Rb, Sr, Y, Zr, Nb, and Ba). We consider all possible ratios and multiplications of these contents, in addition to the contents themselves, which improves the discrimination accuracy. We use a statistical method called sparse multinomial logistic regression to construct a robust and predictive discrimination model. By imposing the sparsity, only a small number of essential variables are included in the model. The variables are objectively extracted from 287 possible geochemical variables, including all possible ratios and multiplications of the major and trace element contents. The constructed model exhibits a high classification ability, indicating that tectonic discrimination using major and selected trace elements yields a high classification ability when ratios and multiplications are considered. The system outputs the relative weights of the variables (i.e., contents, and ratios and multiplications of contents) of the input geochemical data to the calculated membership probabilities. This information can be used to evaluate and interpret the results. We apply the model to multiple samples of a geological unit, to determine the tectonic setting.


Label-efficient object detection via region proposal network pre-training

Abstract: Self-supervised pre-training, based on the pretext task of instance discrimination, has fueled the recent advance in label-efficient object detection. However, existing studies focus on pre-training only a feature extractor network to learn transferable representations for downstream detection tasks. This leads to the necessity of training multiple detection-specific modules from scratch in the fine-tuning phase. We argue that the region proposal network (RPN), a common detection-specific module, can additionally be pre-trained towards reducing the localization error of multi-stage detectors. In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance. We evaluate the efficacy of our approach on benchmark object detection tasks and additional downstream tasks, including instance segmentation and few-shot detection. In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance, with largest gains found in label-scarce settings.


Sample diversity selection strategy based on label distribution morphology for active label distribution learning

Abstract: Labeling a sample in label distribution learning is highly expensive because it involves several labels at the same time and also requires assigning an exact value as the significance of each label. Therefore, active learning, which lowers the labeling cost by actively querying the labels of the most useful data, becomes especially critical for label distribution learning. Most of the known active query algorithms are for multi-label learning, and applying them directly to label distribution learning will lose some key supervisory information and thus fail to yield satisfactory experimental results. In this paper, we propose a sample diversity selection strategy based on the label distribution morphology, which can select diverse samples with different distribution morphologies from a large number of unlabeled samples for active querying. First, we use the feature space to construct a dissimilarity matrix that describes the pairwise dissimilarity among the unlabeled samples in order to pick a subset of samples that are representative of the unlabeled dataset. Second, using the information about the label distribution morphologies provided by the predicted labels of the unlabeled samples, we design a diversity loss score for each unlabeled sample. This score reflects the degree of difference between the sample and the labeled training sample. Finally, we use a convex optimization method to select valuable samples that are diverse from the labeled samples and represent the distribution of the unlabeled samples. The results of the comparison experiments demonstrate the effectiveness of our approach.


Improving generalized zero-shot learning via cluster-based semantic disentangling representation

Abstract: Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class. In this paper, we propose a Cluster-based Semantic Disentangling Representation (CSDR) method to improve GZSL by alleviating the problems of domain shift and semantic gap. First, we cluster the seen data into multiple clusters, where the samples in each cluster belong to several original seen categories, so as to facilitate fine-grained semantic disentangling of visual feature vectors. Then, we introduce representation random swapping and contrastive learning based on the clustering results to realize the disentangling semantic representations of semantic-unspecific, class-shared, and class-unique. The fine-grained semantic disentangling representations show high intra-class similarity and inter-class discriminability, which improve the performance of GZSL by alleviating the problem of domain shift. Finally, we construct the visual-semantic embedding space by the variational auto-encoder and alignment module, which can bridge the semantic gap by generating strongly discriminative unseen class samples. Extensive experimental results on four public data sets prove that our method significantly outperforms state-of-the-art methods in generalized and conventional settings.


A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges

Abstract: Abstract Several decades ago, the medicine supply chain (MSC) transferred the medicines from the manufacturer to the end‐consumer and kept all records in a manual register. The manual intermediary management of MSC and medicine data often leads to issues like unauthorized third parties participating in the process and illegally tempering medicine data. As a result of this medicine temperament, end users get counterfeit medicine that poses severe consequences for patients' health. Over time, manual data management and intermediaries transform into digital platforms that track, manage, and exchange data in real‐time. Real‐time data exchange opens attackers up to target MSCs, access medicine data illegally, and modify medicine conditions, locations, and specifications. With the objective of this, the proposed survey identifies security and privacy issues and discusses security solutions. This security solution involves various data security and privacy frameworks such as micro‐segmentation, zero trust model, and many other software‐based security solutions. Moreover, The proposed survey explores radio frequency identification for medicine tracking in which each intermediary transforms the medicine location over the internet. In contrast, the Internet of Things is used to exchange medicine temperature conditions in real‐time. Furthermore, cybersecurity‐based solutions help protect against malicious threats, and blockchain keeps data private and temper‐proof. Artificial intelligence provides machine learning and deep learning models for analyzing large amounts of data to generate insights from the MSC data. Therefore, this survey addresses the various security and privacy issues and provides solutions that help researchers carry out work in this domain.


Board 353: On ACCESS Program Support for Students’ Academic Successin the Cybersecurity Field

Abstract: Abstract The goal of the NSF S-STEM funded program "Attracting and Cultivating Cybersecurity Experts and Scholars through Scholarships" (ACCESS) is to increase the cybersecurity-related degree completion of high-achieving undergraduate students with demonstrated financial need and thus help address the tremendous unmet need for highly skilled cybersecurity experts. The ACCESS program has successfully awarded scholarships to three cohorts of students and has achieved its goal to increase the annual enrollment of students in the B.S. and Area of Emphasis in Cybersecurity at West Virginia University. Specifically, the enrollment has more than doubled in only two years (from 2020 to 2022). Furthermore, the ACCESS scholarship recipients have greater diversity than their peers enrolled in the cybersecurity field at West Virginia University. Over the last three years, the ACCESS program provided numerous co-curricular activities and student support services and has strengthen its partnerships with employers from the public and private sectors. Students' feedback, which was provided via anonymous surveys and focus groups discussions conducted by an external evaluation team, was overwhelmingly positive and highlighted numerous benefits of the ACCESS program to students' education and future professional careers.


A new frontier in dashboard design: Evaluating an innovative meta‐modelling approach through expert insights

Abstract: Abstract Context Data visualizations and dashboards are essential in disseminating information to broad audiences. However, designing these instruments is complex due to the multitude of involved factors. Theoretical frameworks are therefore critical to guide the design and implementation of data visualizations. Objective This article presents the approach used to pinpoint key features that impact the creation of information visualizations, organize these features using meta‐modelling, and validate the final artefact through expert judgement. Method Meta‐modelling facilitates the distillation of characteristics and functionalities critical for the development of information systems. Utilizing this method, a dashboard meta‐model was constructed in preceding research, yielding a framework of abstract components and connections to delineate specific products. This meta‐model serves as a robust tool for the automated generation of information visualizations and dashboards. Nonetheless, to confirm its quality and efficacy, it underwent a thorough quality assessment and expert evaluation to identify and address potential shortcomings. Results The validation process resulted in positive ratings and constructive feedback to enhance the meta‐model's present iteration. Experts mainly highlighted the meta‐model's complexity and the clarity of certain entities, noting potential confusion for non‐experts. Conclusions While the meta‐model is confirmed to be a coherent and pertinent tool, it requires additional clarifications to simplify its complexity. Addressing this, graphical interfaces have been developed to enable users to interact with the dashboard meta‐model—and to create dashboard instances—through straightforward interactions.


NFT Based Secure Platform for Copyright Images

Abstract: Artists are digitally disenfranchised, rampant online sharing and ease of copying make protecting their work from unauthorized use an uphill battle. Digital art is effortlessly duplicated, shared, and manipulated without credit or compensation, even leading to individuals profiting off stolen work. This widespread issue, particularly plaguing social media, demands a solution. We propose a blockchain-based platform utilizing Non-Fungible Tokens (NFTs) to empower artists. By creating NFTs for their art, they claim irrefutable ownership, authenticity, and copyright, enabling secure sales via cryptocurrencies and combating unauthorized use. This innovative approach empowers artists to thrive in the digital age


A Systematic Survey of Multilingual Speech Transcription and Translation

Abstract: This paper presents an innovative initiative dedicated to revolutionizing multilingual communication by leveraging state-of-the-art technologies such as Artificial Intelligence (AI), NLP, or natural language processing and Machine Learning. With a primary focus on Indian languages, the research aims to develop an advanced system capable of seamlessly transcribing speech across diverse linguistic landscapes. Through the incorporation of cutting-edge algorithms and parallel processing techniques, the proposed system facilitates real-time transcription and translation of multiple languages concurrently. Rigorous experimentation and analysis demonstrate the efficiency of the developed framework in breaking down language barriers and fostering inclusive communication. Furthermore, the paper emphasizes the cultural significance of this technology in promoting global connectivity and celebrating linguistic diversity. Ultimately, this research underscores the transformative potential of technology in facilitating cross-cultural understanding and enabling meaningful interactions within a multilingual society


Board 363: Promoting the Dispositional Dimension of Competency in Undergraduate Computing Programs

Abstract: Abstract The Computing Curricula 2020 (CC2020) report, issued by the ACM and IEEE Computer Society, identified knowledge, skills, and dispositions as the three main components of competency for undergraduate programs in computer engineering, computer science, cybersecurity, information systems, information technology, and software engineering, as well as data science. As earlier generations of curricular guidelines in computing have described knowledge and skills to some extent, the notion of dispositions is relatively new to computing. Dispositions are cultivable behaviors, such as adaptability, meticulousness, and self-directedness, that are desirable in the workplace. Multiple employer surveys and interviews confirm that dispositions are as crucial for success in the workplace as the knowledge and skills students develop in their academic programs of study. As such, the CC2020 report describes eleven dispositions that are expected of competent computing graduates. These are distinct and separate from the technical knowledge and disciplinary skills of computing and engineering. Dispositions are also distinct from baseline or cross-disciplinary skills, such as critical thinking, problem-solving, teamwork, and communication. In contrast, dispositions are inherently human characteristics that describe individual qualities and behavioral patterns that lead to professional success. Dispositions are learnable, not necessarily teachable. This work-in-progress paper motivates dispositions within computing disciplines and presents the background of this approach. It also discusses the use of reflection exercises and vignettes in understanding, promoting, and fostering behavioral patterns that undergraduate computing students identify as related to dispositions they experience in the course. Preliminary data and results from the study are also presented.


Multimedia Messaging for Social Change: Assessing the Impact of Multimedia Integration in Development Communication Campaigns

Abstract: This research explores the transformative potential of multimedia messaging in development communication campaigns, with a focus on its impact on social change. As technology continues to advance, multimedia integration, encompassing elements such as text, images, audio, and video, has become a powerful tool for conveying messages and influencing behaviour. This study aims to assess the effectiveness and implications of incorporating multimedia into development communication initiatives. The paper examines various multimedia formats utilized in social change campaigns and assesses their influence on target audiences. By reviewing existing literature and case studies the study aims to identify patterns, challenges, and success factors associated with multimedia messaging. Additionally, attention is given to the role of digital platforms and social media in amplifying the reach and engagement of multimedia campaigns. Key considerations include the cognitive and emotional impact of multimedia messages, audience receptivity, and the potential for behaviour change. The research also investigates ethical considerations related to multimedia communication in the context of social change, addressing issues such as cultural sensitivity, inclusivity, and the potential for unintended consequences. Findings from this research will contribute valuable insights to the field of development communication, guiding practitioners, and researchers in optimizing the design and implementation of multimedia messaging strategies for effective social change. The overarching goal is to enhance our understanding of the dynamic interplay between multimedia communication and its ability to drive positive societal transformations.


Remote sensing image feature matching via graph classification with local motion consistency

Abstract: Feature matching is a classic challenge in the computer vision field. In this paper, we propose an innovative graph classification method based on neighborhood motion consistency to eliminate erroneous matches. Specifically, we transform the coordinates of feature matching points into vectors on a unified scale. For a given match, we construct a graph centered around the match and incorporating neighboring matches. Node attributes are designed to represent the similarity between the vector of the central node and those of its neighbors. To facilitate this, we develop a lightweight graph attention neural network dedicated to graph property classification, thereby predicting the accuracy of the match under consideration. To effectively train the model, we employ a random cropping strategy to generate a plethora of diverse graphs for classifier training. We evaluate our method on datasets encompassing translational remote sensing data, rotational and scaled remote sensing imagery produced via random cropping, and nonrigid fisheye datasets. Our algorithm demonstrates superior performance to current state-of-the-art methods.


A comparative study for the assessment of marker-less mixed reality applications for the operator training

Abstract: With the rising concept of Industry 5.0, the worker's needs and well-being are placed at the center of the production process. The use of extended reality can be exploited to provide the operator with interactive training, allowing independent, step-by-step learning in the real environment, with instructional digital contents, superimposed over the machinery in the workplace. This kind of training promotes the operator's skills, and consequently empowers the industrial competitiveness. The aim of this paper is to assess and compare the effectiveness of two different marker-less mixed reality (MR) applications to train the operator in the wire harness activities over a tractor's drivetrain. An application based on Azure Spatial Anchors by Microsoft and another one based on the CAD model of the tractor's drivetrain have been developed and tested through the comparison with the traditional paper-based instructions. On-field trials have been accomplished with 10 end-users. User experience, usability, perceived workload, and training effectiveness have been assessed through specific questionnaires and by analyzing user performances. Even if the Anchors-based app showed better outcomes in terms of effectiveness and acceptability, both MR applications resulted in good usability. Operators' willingness of using this technology as a standard tool for training and support emerged.


A copula formulation for multivariate latent Markov models

Abstract: Abstract We specify a general formulation for multivariate latent Markov models for panel data, where outcomes are possibly of mixed-type (categorical, discrete, continuous). Conditionally on a time-varying discrete latent variable and covariates, the joint distribution of outcomes simultaneously observed is expressed through a parametric copula. We therefore do not make any conditional independence assumption. The observed likelihood is maximized by means of an expectation–maximization algorithm. In a simulation study, we argue how modeling the residual contemporary dependence might be crucial in order to avoid bias in the parameter estimates. We illustrate through an original application to assessment of poverty through direct and indirect indicators in a cohort of Italian households.


Impact of digital literacy on academic achievement: Evidence from an online anatomy and physiology course

Abstract: The increasing prevalence of online courses has highlighted the importance of digital literacy for students. This study investigates the relationship between digital literacy and academic achievement among students who participate in an online course on anatomy and physiology. The study also evaluates how different aspects of digital literacy, such as age and previous education in natural science, affect students’ grades. Using logistic regression analysis, data from five areas of digital literacy that were assessed among participants with different educational backgrounds are analyzed. The results show that some aspects of digital literacy are more crucial for academic success in the online course. Students with a natural science background exhibited higher levels of digital literacy, emphasizing the importance of considering previous education in supporting students’ digital skills in online courses. The study also reveals that students were proficient in self-assessing their own digital literacy, enabling easy evaluation of the collective digital literacy within the course and facilitating targeted interventions for all students, regardless of their initial digital literacy levels. This study underscores the importance of digital literacy in online education. It highlights the specific areas of digital literacy that strongly contribute to academic achievement and emphasizes the positive impact of previous education in natural science on students’ digital skills. These findings suggest that instructors should consider these factors when designing and delivering online courses to ensure equal opportunities for students to enhance their digital literacy.


An alternative, versatile, high‐tolerance design of a modified <scp>Richter–Slevogt</scp> camera, using standard glasses

Abstract: Abstract Prime‐focus catadioptric astrographs have been used for a long time in various astronomical applications. The prime advantage offered by them is the capability to produce remarkably wide fields of view, and hence generate a huge amount of data in relatively less observation time. An emerging application of such wide‐field astrographs is in the form of telescope arrays. While this has been implemented mostly, using commercial refractive lenses, low‐cost catadioptric objectives can be used as an alternative for wide‐field or high‐contrast array applications. Commercial catadioptric systems are generally available as modifications of Schmidt and Maksutov systems, that too, mostly in the Cassegrain configuration. Here, we present a low‐cost alternative prime focus camera design of Richter – Slevogt configuration, which is in turn an extension of the Houghton correctors. The Richter – Slevogt design has the potential for a very high performance due to several degrees of freedom. The presented one is a 150 mm aperture, system, providing 3.5° (circular) diffraction‐limited FOV (strehl ), using only standard glasses, N‐BK7 and F2. We present the performance analysis, tolerance sensitivity, and statistical (Monte‐Carlo) analysis for this design. Potential applications of the system, other than as an array are also briefly discussed.


Empowering complex-valued data classification with the variational quantum classifier

Abstract: The evolution of quantum computers has encouraged research into how to handle tasks with significant computation demands in the past few years. Due to the unique advantages of quantum parallelism and entanglement, various types of quantum machine learning (QML) methods, especially variational quantum classifiers (VQCs), have attracted the attention of many researchers and have been developed and evaluated in numerous scenarios. Nevertheless, most of the research on VQCs is still in its early stages. For instance, as a consequence of the mathematical constraints imposed by the properties of quantum states, the majority of research has not fully taken into account the impact of data formats on the performance of VQCs. In this paper, considering a significant number of data in the real world exist in the form of complex numbers, i.e., phasor data in power systems and the result of Fourier transform on image processing, we develop two categories of data encoding methods, including coupling data encoding and splitting data encoding. This paper features the coupling data encoding method to encode complex-valued data in a way of amplitude encoding. By leveraging the property of quantum states living in a complex Hilbert space, the complex-valued data is embedded into the amplitude of quantum states to comprehensively characterize complex-valued information. Optimizers will be utilized to iteratively tune a parameterized ansatz, with the aim of minimizing the value of loss functions defined with respect to the specific classification task. In addition, distinct factors in VQCs have been explored in detail to investigate the performance of VQCs, including data encoding methods, loss functions, and optimizers. The experimental result shows that the proposed data encoding method outperforms other typical encoding methods on a given classification task. Moreover, different loss functions are tested, and the capability of finding the minimum value is evaluated for gradient-free and gradient-based optimizers, which provides valuable insights and guidelines for practical implementations.


A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation

Abstract: Abstract With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.


A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm

Abstract: Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.


Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

Abstract: This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.


“Dirclustering”: a semantic clustering approach to optimize website structure discovery during penetration testing

Abstract: Dirbusting is a technique used to brute force directories and file names on web servers while monitoring HTTP responses in order to enumerate server contents. Such a technique uses lists of common words to discover the hidden structure of the target website. Dirbusting typically relies on response codes as discovery conditions to find new pages. It is widely used in web application penetration testing, an activity that allows companies to detect website vulnerabilities. Dirbusting techniques are both time and resource-consuming, and innovative approaches have never been explored in this field. We hence propose "Dirclustering", an advanced technique to optimize the dirbusting process by leveraging semantic clustering. Specifically, we use semantic clustering techniques to organize wordlist items in different groups according to their semantic meaning. The created clusters are used in an ad-hoc implemented next-word intelligent strategy. This paper demonstrates that clustering techniques outperform the commonly used brute-force methods. Performance is evaluated by testing eight different web applications. Results show a performance increase that is up to 50% for each of the conducted experiments.


Model of collaborative information retrieval behavior using information systems among academic researchers of medical sciences

Abstract: Aim The purpose of this study was to analyze the collaborative information retrieval (CIR) behavior among faculty members of research centers based in the University of Medical Sciences to present a structural model. Methodology The type of this experimental study was based on a pretest-posttest with the control group. Data were collected using a questionnaire, and the participants were selected by stratified random sampling. Out of a total of 73 participants, there were 42 people in the intervention group and 31 participants in the control group. Findings The variables of awareness, information sharing, division of labor, and team communication had a significant positive relationship with the team collaboration (p < 0.05), collaboration had effect on the evaluation of retrieved information with a significant positive relationship (p < 0.05). Conclusion Based on the findings, CIR is the set of activities that can include factors such as the ability to share the obtained knowledge, analyze the retrieved information, communicate, and collaborative awareness. The structural model confirmed our findings that developing the interactive and synergies capabilities of research team members to increase collaboration through a network-based structure reduces the loss of stored collective knowledge and also accelerates the process of access to information resources, and required content. Originality This research has carried on as the one of the first studies on collaborative information retrieval behavior with an experimental method in Iran and many others countries. A considerable point in the present study was assessing the nature of CIR behavior from the perspective of human behaviors, which was mentioned as a very important approach in information science. Accordingly, a human behavioral approach to information retrieval was considered and strengthened, regardless of technical aspects. Another strength of this study was the model extracted from our findings which have emphasized specifically CIR as specific and dynamic activities.


Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things

Abstract: Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.


The semiotics of emojis in advertising: An integrated quantitative and qualitative examination of emotional versus functional ad dynamics

Abstract: Abstract In today's marketplace, consumers encounter a multitude of advertisements incorporating emojis. This research undertook two complementary studies to assess how individuals appraise emotional versus functional advertisements containing emojis, examining outcomes like processing fluency, claim believability, attitude toward the ad, and purchase intention. Study 1 employed a factorial design, resulting in four experimental conditions: emotional ads without emojis, functional ads without emojis, emotional ads with emojis, and functional ads with emojis. Study 2 delved deeper, offering a qualitative exploration of consumer perceptions and sentiments concerning emojis in advertising contexts. Grounded in the Emotion as social information theory, results indicated that within the sports drink advertising domain, emotional advertising without emojis led to enhanced ad and brand attitudes, purchase intentions, claim believability, and information processing when contrasted with conditions incorporating emojis.


MA‐CAT: Misclassification‐Aware Contrastive Adversarial Training

Abstract: Vulnerability to adversarial examples poses a significant challenge to the secure application of deep neural networks. Adversarial training and its variants have shown great potential in addressing this problem. However, such approaches, which directly optimize the decision boundary, often result in overly complex adversarial decision boundaries that are detrimental to generalization. To deal with this issue, a novel plug‐and‐play method known as Misclassification‐Aware Contrastive Adversarial Training (MA‐CAT) from the perspective of data distribution optimization is proposed. MA‐CAT leverages supervised decoupled contrastive learning to cluster nature examples within the same class in the logit space, indirectly increasing the margins of examples. Moreover, by taking into account the varying difficulty levels of adversarial training for different examples, MA‐CAT adaptively customizes the strength of adversarial training for each example using an instance‐wise misclassification‐aware adaptive temperature coefficient. Extensive experiments on the CIFAR‐10, CIFAR‐100, and SVHN datasets demonstrate that MA‐CAT can be easily integrated into existing models and significantly improves robustness with minimal computational cost.


An energy-efficient image filtering interpolation algorithm using domain-specific dynamic reconfigurable array processor

Abstract: With the development of micro-LED displays, the bicubic interpolation algorithm has become a critical technology. It is widely utilized in micro-LED displays to enhance the scaled details of video frames. However, the produced image exhibits the blurring effect and boundary artifacts after scaling. To address the above problems, we propose an image filtering interpolation algorithm. The smooth and Laplace filters are pre-filters to enhance image edges and reduce noise. The four corner-aligned bicubic interpolation algorithm has the high-quality effect of scaling. Secondly, to enhance the processing speed and energy-efficient of the image filtering interpolation algorithm, a domain-specific dynamic reconfigurable array processor is also proposed, which contains a 8 × 8 processing element array with two types of heterogeneous processing elements. The processor supports the energy-efficient acceleration of the image filtering interpolation algorithm. To reduce hardware costs, we integrate the attributes of the smooth filter and the Laplace filter into a simplified composite filter. Besides, we utilize the efficient alternating line caching technology to eliminate the power consumption of repeatedly reading data through reorchestrates data cache order and keep reused data. The experiment results show that the peak signal-to-noise ratio of the proposed image filtering interpolation algorithm is 0.32 dB better than the state-of-the-art methods. In the implementation of the HLMC 55 nm process, the energy-efficient of the proposed processor is 23,661 MPixels/J. Moreover, the processing rate achieves 583.5 MPixels/s for three DRAP, enough to 3840 × 2160@60fps. Using alternating line caching technology, the reconfigurable array processor reduces 37.5% of line buffer resources.


Revisiting technology-assisted language learning affordances for language components: a decade survey

Abstract: Given the important technology-assisted language learning (TALL)-empowered affordances, a comprehensive understanding of how these TALL tools facilitate learning across different language components, e.g., phonology, morphology, syntax, semantics, and pragmatics, could not only present the panoramic scenery of the subject matter but also inform pedagogy for applied linguistics researchers and practitioners. Despite the increasing attention being paid to the affordances of a particular TALL platform, there is still a lack of more holistic research practice to the affordances for the five language components. To bridge the gap, this study aims to systematically and quantitively revisit the TALL-empowered affordances with respect to these language components. To this end, a total of valid 201 articles published from 2010 to 2023 were selected and coded based on the proposed inclusion/exclusion criteria. Results revealed that different TALL platforms (viz. computer, mobile, tablet, virtual reality, and robot) help improve students' language learning performance and efficacy among the five language components. The implications of the study, in perfect tune with the distance language learning experiences in the post COVID-19 pandemic era, justify the need to take full advantage of the TALL affordances and accelerate the move toward a more technology-friendly field of study.


Bidirectional Obstacle Avoidance Enhancement‐Deep Deterministic Policy Gradient: A Novel Algorithm for Mobile‐Robot Path Planning in Unknown Dynamic Environments

Abstract: Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle‐avoidance reactions from the state and action. The cross‐attention mechanism is incorporated to enhance the attention to valuable obstacle‐avoidance information. Meanwhile, the obstacle‐avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. The results show that the proposed method is suitable for path planning tasks in unknown environments and has an excellent obstacle‐avoidance learning capability.


Alternative method to reveal encoded images via Gaussian distribution functions

Abstract: In this work, we present an image processing approach that proved to be effective to reveal an image that was codified by different substitution boxes. The image was hidden by applying a different substitution box on each line producing an image with uniform distribution. Once the original image was codified through this procedure, the codified picture presented a shapeless uniform grey tone due to encoding. In order to reveal the image several assumptions were made, the most important of which was to consider that the gray tones of the original image could be reproduced by a normal o Gaussian distribution or by a superposition of several Gaussian functions. This assumption was implemented at the level of each line of the image by replacing the encoded pixels with those generated by a Gaussian distribution. Among the main findings of this work, the following are worth highlighted: (i) According to our research, for the first time Gaussian distribution functions are used to reveal encoded images, without the need to decode them; (ii) a general technique was implemented to reveal an encoded image, which is applicable to encoded images in which a different encoding rule was applied to each row of the original image; (iii) the values generated by the Gaussian functions were used to replace the pixel values of the encoded image, but their original positions were maintained; (iv) Image enhancement, including contrast, gray tone balance and feature definition, was achieved either by varying the number of Gaussian functions, or dividing the image in zones, or by adjusting pixels 0 and 255, among other resources used. Finally, it is also worth noting that this is a general procedure applicable to decode any image encoded using substitution boxes, as long as the original positions of the encoded pixels are preserved. This method has considerable simplicity and the best results were obtained when applied to high-resolution images.


Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Abstract: Abstract Accurately estimated reference evapotranspiration (ET 0 ) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET 0 estimation with missing R s measurements. However, its application is usually restricted by the two fundamental coefficients ( a and b ) of the A‐P formula. This paper proposes a new method for estimating ET 0 with missing R s by combining machine learning with physical‐based P‐M models (PM‐ET 0 ). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in R s and ET 0 estimates at 80 national R s measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET 0 (ML‐ET 0 ) to compare with the PM‐ET 0 . Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET 0 estimation without R s measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a . The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest R s and ET 0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both R s and ET 0 , and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET 0 outperformed the PM‐ET 0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET 0 . However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing R s measurements.


Blueprints from plane to space: outlook of next‐generation three‐dimensional histopathology

Abstract: Abstract Here, we summarize the literature relevant to recent advances in three‐dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light‐sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross‐disciplinary collaboration and innovation.


Public order offences: how feasible is their applicability to online hate?

Abstract: As an area of law in the UK, public order offences are almost entirely useless on social media. This set of offences (ss. 4, 4A and 5 of the Public Order Act 1986) was aimed to address any behaviour or expressive activities, either oral or written, carried out in a context of physical proximity to the victim. In principle, the foundational base of public order offences runs the risk of becoming blurred if we extend their applicability to hateful messages online and, therefore, to any impersonal way of acting. Consequently, only 13% and 14% of the hate crimes committed online in 2016/17 and 2017/18 in England & Wales involved public order offences. Therefore, there is a certain resistance based on the adequacy of these offences to the online environment without requiring the message to be audible or visible to someone, as a matter of immediacy/proximity. We will explain how this glimmer of hope has lasted over time amid fierce opposition to broaden the scope of application of public order offences beyond traditional public forums, such as disturbances triggering in a city's main square.


App Crafting for An Educational Institution

Abstract: In the modern world, internet tools like Google Classroom, Gmail, Google Forms, WhatsApp, and others have become more and more important for teacher-student communication and information exchange. We created an app that works with all platforms, including tablets, computers, and mobile phones, to meet the various needs of educational institutions. All devices are easily supported by the Android app for websites optimized for mobile and tablets. This app satisfies all of the standards of the school by offering extensive information on attendance, exam schedules, lecture notes, cost data, event notifications, and online assessments. The newest technologies, such as Flutter and Dart with Firebase integration, were used in the development of this application. We also developed a desktop-friendly web application. Both the app and this website are linked to the same


Meeting Transcriber

Abstract: In a world where collaboration is increasingly digital, the efficiency and security of meetings have become paramount. Our project introduces a comprehensive online meeting platform extension that leverages advanced technologies to enhance the meeting experience. Using BERT for data classification, question answering, and summarization, along with an advanced Language Model (LLM) for data protection, we address common challenges such as information overload, language barriers, limited searchability, and a lack of actionable insights. This extension aims to revolutionize meetings, making them more productive, collaborative, and secure


Algorithm to satisfy <i>l</i>‐diversity by combining dummy records and grouping

Abstract: Abstract Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the ‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.


Quantum computer based feature selection in machine learning

Abstract: Abstract The problem of selecting an appropriate number of features in supervised learning problems is investigated. Starting with common methods in machine learning, the feature selection task is treated as a quadratic unconstrained optimisation problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. The different results in small problem instances are compared. According to the results of the authors’ study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, the authors compare the convergence behaviour of the QUBO methods via quantum computing with classical stochastic optimisation methods. Due to persisting error rates, the classical stochastic optimisation methods are still superior.


Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

Abstract: Abstract The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF 6 gas flow, and O 2 gas flow were collected during a silicon etching process with SF 6 , O 2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.


Reference‐Frame‐Independent Mode‐Pairing Quantum Key Distribution with Advantage Distillation

Abstract: Abstract The coordination between distance and the secure key rate is one of the main challenges in the practical application of quantum key distribution (QKD). Mode‐pairing quantum key distribution is one of the schemes that can surpass the secret key capacity for repeaterless QKD. However, the protocol utilizes phase to encode the information, which leads to the problem of active stabilization in the interferometer. In this paper, a reference‐frame‐independent mode‐pairing quantum key distribution (RFI‐MP‐QKD) is proposed as an effective scheme to solve this problem. Moreover, the performance of the RFI‐MP‐QKD protocol is improved by applying the Advantage Distillation (AD) method in data post‐processing, which separates the highly correlated raw key bits from the weakly correlated information. The simulation results show that the secure key rate of RFI‐MP‐QKD has almost no degradation for reference frame deviation angles of . Compared to RFI‐MP‐QKD without AD method, the AD method decreases the quantum bit error rate from 0.04 to 0.012 and increases the maximum transmission distance from 406 to 450 km. The scheme proposed is expected to facilitate the practical implementation of RFI‐MP‐QKD, especially in cases of concerning reference frame alignment and high channel loss.


A Journey Through Open Access Publishing in Aquatic Sciences

Abstract: Abstract Publishing scientific work is as important as conducting research. The rise of online Open Access (OA) publications that are immediately available to readers everywhere free of charge has transformed the publishing landscape in the past decades. I offer a translation of this article into Brazilian Portuguese as my own attempt to improve access to scientific content (Supplementary Information, SI). In this article, I introduce OA publishing in the context of the aquatic sciences. More specifically, I talk about how OA came to be, the different types of OA publications and copyright licenses, the benefits and pitfalls of OA publishing, and how to pay for gold OA publications.


Adaptive micro- and macro-knowledge incorporation for hierarchical text classification

Abstract: Hierarchical text classification (HTC) aims to classify a text into multiple categories organized in a hierarchical structure. The state-of-the-art HTC methods usually employ graph networks, where label graphs are constructed and label representation is learned to interact with text representations for classification. In general, label graphs are built on the intrinsic label hierarchy, label semantic similarity, or label co-occurrence. Such graphs have been proven to be effective, but they only exploit knowledge from training data or simple label descriptions, without considering the vast external knowledge in the open sources. Actually, external knowledge from open sources could bring in complementary information to enhance the label graph’s representation power. Motivated by the above considerations, we explore the use of external knowledge for improving HTC in this paper. We categorize knowledge into micro-knowledge and macro-knowledge, which are defined as the fundamental concepts related to a single class label and the correlations among class labels, respectively. For tailor-made incorporation of the two types of knowledge into representation learning and classification, we propose Adaptive Micro- and Macro-Knowledge Incorporation for Hierarchical Text Classification (AMKI-HTC) model in this paper. The micro-knowledge incorporation helps capture class-relevant keywords in the text and hence produce discriminative representations While the macro-knowledge incorporation improves the accuracy of label graphs. finally a confidence maximization fusion strategy is developed for adaptive aggregation of multi-view features. extensive experiments on three benchmark HTC datasets demonstrate that AMKI-HTC consistently outperforms state-of-the-art models.


Research on industrial risk early warning algorithms based on edge computing and multimodal data

Abstract: Abstract To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy.


Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Abstract: Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or (LD)–based summary statistics perform well in estimating selection.


AI Fitness Model using Deep Learning

Abstract: The project “AI Fitness Model Using Deep Learning (YOLOv5)” aims inorder to transform the fitness business by leveraging state-of-the-art deep learning techniques, specifically YOLOv5 (You Only Look Once version 5), to develop an advanced and efficient fitness tracking system. The model is designed to accurately detect and analyze human poses, movements, and exercise routines in real-time using computer vision. By employing YOLOv5’s object detection capabilities, the AI fitness model can identify key body points, track exercise execution, and provide personalized feedback to users, enhancing their workout experience. This creative strategy not only facilitates automated performance monitoring but also enables the creation of adaptive and dynamic fitness routines tailored to individual needs, fostering improved engagement and results in the realm of health and wellness


Using Stochastic Point Pattern Analysis to Track Regional Orientations of Magmatism During the Transition to Cenozoic Extension and Rio Grande Rifting, Southern Rocky Mountains

Abstract: Abstract The southern Rocky Mountains in Colorado and northern New Mexico hosted intracontinental magmatism that developed during a tectonic transition from shortening (Laramide orogeny, ca. 75 to 40 Ma) through extension and rifting. We present a novel approach that uses stochastic weighted bootstrap simulations of a large set of new and historical geochronology data to better understand how regional anisotropies responsible for focusing magma emplacement evolved through time. This technique can detect subtle trends in directional distributions, including multi‐modal orientations, and can be filtered from regional to local scales. Our results indicate that magmatism followed first the northeast trend of the Colorado mineral belt between 75 and 40 Ma and deviated afterward. These deviations vary depending on the scale of the analysis. At the smallest scale we evaluated (<75 km), the orientation of magmatism from 45 to 30 Ma rotated counter‐clockwise before aligning with the north‐south trend of the modern Rio Grande rift. Larger, regional‐scale analyses indicate magma centers between 40 to 35 Ma and 25 to 20 Ma were dominantly oriented southwest‐northeast, whereas magmatism between 35 and 25 Ma had north‐south orientation. The large areal footprint of magmatism and shifting regional patterns suggest that ancient zones of weakness in the North American lithosphere accommodated magma flow at different moments in time, rather than controlled by a retreating interface of the Farallon and North American plates.


Revolutionizing Travel: An Introduction to a Voice-Enabled Flight Booking System Powered by Machine Learning

Abstract: Numerous websites exist that enable flight booking with various customizations for a particular user. Technology is used to make things easier and convenient at every turn. So, the developments in this field aim at being user friendly and accessible to the user by providing certain features that ensure user interaction occurs at ease. One of these features at disposal is a voice-controlled input system for an online flight reservation website or online travel agencies. This feature is added using voice-to-text system and a machine learning algorithm is used to extract relevant information such as destination, dates and preferences from the user’s voice input and show relevant results for the same. This trait aids in understanding user preferences and provide personalized recommendations, streamlining the user experience by presenting relevant content


Adaptive Trajectories’ Constant False Alarm Rate Mirror Filters and Elevation Angle Evaluation for Multiple-Input Multiple-Output Radar-Based Hand Gesture Recognition

Abstract: Gesture recognition technology has been quickly developed in the field of human–computer interaction. The multiple-input multiple-output (MIMO) radar is popular in gesture recognition because of its notable spatial resolution. This work proposes a MIMO radar-based hand gesture recognition algorithm with low complexity. We leverage low-complexity adaptive signal processing to extract trajectory information and minimize noise to create a system that can be applied in real-world applications with small training datasets. First, a spectrum analysis is utilized on range-Doppler maps (RDMs), and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness of noise. Then, the features related to the distance, speed, direction, and elevation angle of the moving object are determined using the proposed adaptive signal analysis techniques. For classification, the random forest algorithm is implemented. The proposed system can precisely distinguish and identify eight gestures, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anti-clockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.


Navigating the digital world: development of an evidence-based digital literacy program and assessment tool for youth

Abstract: Abstract The rapid expansion of digital connectivity has provided youth with wide-ranging access to digital platforms for communication, entertainment, and education. In light of this profound shift, there have been growing concerns about online safety, data privacy, and cybersecurity. A critical factor influencing the ability of youth to responsibly navigate digital platforms is digital literacy. While digital literacy programs have been implemented in various regions worldwide, significant disparities remain not only in overall digital literacy levels, but also the assessment of digital literacy initiatives. To address these challenges, an environmental scan and literature review were conducted to identify existing digital literacy programs in Canada developed specifically for youth, as well as digital literacy assessment tools, respectively. The search encompassed peer-reviewed articles, organizational curricula, and assessment measures indexed in various databases and organization websites. The environmental scan identified 15 programs targeting key components of digital literacy such as data safety, cyberbullying, and digital media. The literature review identified 12 digital literacy assessment tools. Based on the findings, data were synthesized from shortlisted programs and assessment tools to inform the development of both a new digital literacy program and assessment tool to complement the youth-focused program. The new program focuses on four key components: (1) digital fluency, (2) digital privacy and safety, (3) ethics and empathy, and (4) consumer awareness. A 15-item assessment tool was also developed consisting of 4–5 questions specific to each program component. Given the growing importance of digital competencies, a youth-focused program and assessment tool are crucial for understanding and addressing digital literacy among this vulnerable cohort. This program's adaptability allows for customization across sociodemographic target groups, including culturally diverse and geographically remote communities—an aspect that has the potential to enhance digital literacy across settings. Implementing digital literacy programs can better prepare youth for an increasingly digital world, while minimizing potential risks associated with technology use.


The Effectiveness of Psychoeducation on Depression Literacy and Psychological Well-being of University Students

Abstract:
Today’s modern life is a challenging issue for human beings, particularly those who have mental illnesses like depression, requiring awareness-related programs for increasing their Depression Literacy (D-Lit) and Psychological Well-being (PWB). To mark the gap, we planned to investigate the effect of web-based psychoeducation on literacy regarding depression and psychological well-being. A total of 100 depressed students were included in the sampling frame, purposively selected based on some inclusion and exclusion criteria from Jagannath University. The study was accomplished by following a pretest-posttest with a control group design where data were collected using Depression, Depression Literacy (D-Lit), and Psychological Well-being questionnaires along with a personal information form. The data were analyzed by applying an independent sample t-test, paired sample t-test, and ANCOVA. Results revealed that the level of D-Lit and PWB of the depressed students significantly increased in the post-test as compared to the pre-test in the experimental group, but no significant changes in the control group, indicating psychoeducation has an effect on increasing D-Lit and PWB. This finding explored that psychoeducation can be an immediate awareness program for improving depression-related knowledge and psychological well-being in the community. The applications of these outcomes for investigation and practice are discussed.
 Jagannath University Journal of Science, Volume 10, Number I, Jun 2023, pp. 1−10


A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

Abstract: The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.