Reviewerly/exHarmony huggingface dataset:
Laplace Expansion for Bartlett--Nanda--Pillai Test Statistic and Its Error Bound

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 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

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

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

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 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 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 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

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

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 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

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

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

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 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

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.