Reviewerly/exHarmony huggingface dataset:
A Linear Algebra Story: How We Reconstructed a Matrix from its Eigenvalues

By telling this story, we discuss how to blend research problems into the classrooms to enhance the curriculum starting already with the linear algebra course. In particular, we present an elementary approach to the reconstruction of persymmetric Jacobi matrices from their eigenvalues. This work is done in collaboration with and for undergraduate students taking linear algebra. We wrote it with a thought in mind to fill the gaps and to show the depth of results still keeping it within the boundaries of the undergraduate underclassman level. So if a student reads it and the web supplements, it will be completely understandable and by comparing to the original paper [Citation2] the student would realize how to break down the results and proofs to fully comprehend them. As for educators, we hope this would show an example of how to present an establish knowledge and modern theories by making a problem feasible for the broader undergraduate audience. We also briefly discuss the motivation of studying the problem in the prism of modern theories. Namely, we link this algorithm to the perfect quantum transfer problem.
Modernization as Lifeline for Quality of Education

As the rapid increase in technology has proved the advancement of mobile governance and giving easy access to students on their on-hand technologies, it improvises the quality of their education as well. Ten Asian countries—China, Pakistan, India, Bangladesh, Indonesia, Iran, Turkey, Vietnam, Philippines, and Japan—have been selected for the study. The data has been collected over a period of 27 years, which has been very much adequate to use the A.R.D. L technique. ADF unit root tests as well as cointegration tests are run to analyze the long-run relation. The quality has paved its path on the betterment of education. It implies there is a long-run relation of quality of education (QE), electronic governance (EG), mobile governance (MG), and distance education (DE) keeping the population growth (PG) and literacy rate (LR) in control for these countries as there has been modernization over these years.
Assistive Technology and Secure Communication for AI-Based E-Learning

Assistive systems are growing rapidly with assistive technology (AT) in all sectors and organizations around the world. To protect the content and resources of an assistive system, AT instructors need to ensure they are secure. The aim of this research is to enhance the learning capacity, training facility, self-learning, security of E-learning links, and reusability of e-learning tools. Here, the accessibility of these links and services for improving the assistive system depends on the emerging AT. The method proposed for this research is artificial intelligence (AI) which enhances all aspects of assistive systems. A virtual learning environment (VLE) is one of the academic E-learning tools integrated with AI which enhances the learning capacity and other required facilities. As a result, low-cost assistive tools with easily upgradable solutions according to the individual learning disability are identified. Further, the results attained are analyzed, discussed, and the performance of identified assistive tools with AI concepts is summarised.
Security and dependability analysis of blockchain systems in partially synchronous networks with Byzantine faults

Blockchains enhance trust in decentralized systems through components like distributed consensus, peer-to-peer communication, and trustless computing. Deployed in real networks, they must ensure security and dependability at each level. This paper introduces security and dependability concepts in three key layers: consensus protocols, network infrastructures, and smart contract applications, derived from distributed systems theory and adapted to blockchains. It evaluates these attributes across eight famous blockchains, assuming realistic network deployments and Byzantine fault scenarios to reveal system strengths and weaknesses. The aim is to establish a comprehensive framework for blockchain assessments, providing insight into design choices and potential security issues.
Smart Government and E-Voting

Electronic voting is a topic that has been developing with the idea of applying technology and facilitating the process, however, the main objective has always been the reliability and confidentiality of the vote. The possibility of applying an emerging technology such as Blockchain has been visualized due to its characteristics such as immutability, that is, it cannot be changed or altered, however, some studies have found challenges for its implementation. This chapter addresses the state of the art of electronic voting, the strategy of electronic voting in Smart-Government, the security and transparency of electronic voting through Blockchain, as well as the structure of the blockchain. In addition, the operation of smart contracts as a tool for implementation and important concepts such as consensus algorithms are explained.
Lesson Engagement and Outlier Extraction Analytics Through Learning Log Classification

With the spread of online distance learning and digital teaching materials, research analyzing the learning logs from learning management systems and uncovering knowledge that can be used to improve classes is becoming increasingly important. The interquartile method and the interquartile range were applied to the classification of learning patterns, the creation of cluster heat maps, and to outlier detection to improve the robustness of this research. Comparison of extraction methods also clarified that multiple methods were necessary to extract outliers. The analysis results of learning log classification, cluster heat mapping, and outlier detection can also be used to explain the basis of insufficient student effort when teachers make academic interventions. The experimental data showed that learners in need of academic intervention can be categorized by visualizing class engagement with a cluster heat map and outlier extraction.
Optimizing floor plans of accessible restrooms in elderly long-term care facilities: a path planning approach

Navigating narrow restrooms is challenging for the elderly using wheelchairs or walkers (EUWW). Existing research focuses on the convenience of furniture use in the restroom and overlooks the convenience of mobility between furniture pieces. However, the floor plans of accessible restrooms determine the mobility path of EUWW, affecting mobility convenience. Thus, to improve mobility, floor plans should be evaluated to shorten movement distances and reduce bends. Experiments in constructed environments were expensive. This study instead proposes a novel path-planning based method for accessible restroom floor plan evaluation, simulating mobility paths in a virtual environment, and evaluating how the floor plans of accessible restrooms affect mobility and convenience. The results showed that: (1) wheelchair users, who approach the toilet from the rear, and walker users find opposite wall toilet-sink arrangements convenient. While Wheelchair users, who approach the toilet from the side or front, find it convenient if the toilet and sink are situated on different walls at a 90° angle. Toilets and sinks on the same wall are inconvenient. (2) Corner sinks and toilets reduce EUWW's turning angles. (3) The farther the sink is from the side wall, the more convenient it is for EUWW. (4) The 45° side approach is the most convenient for moving, followed by the frontal approach, and lastly the 90° approach. Theoretically, this study uses path planning algorithms, simulating the movement of EUWW with varying capabilities and offering a new perspective for barrier-free architectural design. Practically, this study provides recommendations for optimizing accessible restroom floor plans.
Enhancing Automated Scoring of Math Self-Explanation Quality Using LLM-Generated Datasets: A Semi-Supervised Approach

In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the large language model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model’s accuracy. Interestingly, there is an optimal limit to how many synthetic self-explanation data can benefit the system. Exceeding this limit does not further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.
Topological photonics: Fundamental concepts, recent developments, and future directions

The authors offer an overview of fundamental concepts in topological photonics and a categorization of recent advancements in this growing field across linear, nonlinear, and quantum regimes. They provide a detailed exploration of future directions, enduring challenges, and elusive questions surrounding the application of topological ideas in photonics systems.
Elective Educational Course “Anthropology of Corporeality”: Soft Skills as a Part of Specialists’ Education at a Technical University

In recent years, there has been a discussion in Russian higher education about the introduction of additional self-development modules focused on the formation of critical thinking, emotional intelligence, self-presentation and intercultural communication skills, as well as other components of personal growth. It should be noted that most Russian universities and employers do not have a common standard for the soft skills development and assessment. The purpose of this work is to study the aspects of training students of engineering and humanitarian specialties in the process of their education at a technical university. The important role of universal (supra-professional) skills, also known as soft skills, is emphasized. The elective course “Anthropology of corporeality” developed by the author was tested in the largest Russian university – Saint Petersburg Polytechnic University of Peter the Great. The discipline contributes to the formation of a holistic understanding of the of intercultural, gender, emotional, non-verbal, social communication specific based on the universal competencies of higher education. The main methodological concepts of the course are based on the general logic of anthropogenesis, cultural genesis and sociogenesis, therefore this scientific problem is of a fundamental and humanitarian nature.
Interactive Media for Make-Up Black and White Photo Color (IMFM) as Medium Study : Activity and Efectiviness Outcomes

Education will produce scholarly, intellectual and technological human resources to increase competitiveness. Black and white photo and color makeup is one of the core competencies of a beautician. There are still limitations in learning black and white photo and color makeup in the Department of Makeup and Beauty, Faculty of Tourism and Hospitality, Padang State University. Students are still struggling to master the basics of black and white and color photo makeup and have not been able to apply materials and cosmetics correctly. This study aims to develop interactive multimedia learning media for black and white photo and color makeup. This research is a development using the 4-D method (define, design, develop and disseminate). Quality and practicality were observed in teachers, students and practicing professionals. The results show the following; (1) The validity of interactive multimedia as a valid medium (2) The practicality of interactive multimedia as a practical medium based on the responses of teachers and students after the test (3) Effective efficacy in increasing the learning activities of the students with a very good category and the learning results of the students before and after using interactive multimedia as a medium. Based on the findings of this study, it is concluded that interactive multimedia as a medium is valid, practical and effective to be used as a learning medium in black and white photo and color makeup lessons.
TIC: text-guided image colorization using conditional generative model

Abstract Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, a novel deep network has been proposed that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. The proposed model has been evaluated using different metrics like SSIM, PSNR, LPISPS and achieved scores of 0.917, 23.27,0.223, respectively. These quantitative metrics have shown that the proposed method outperforms the SOTA techniques in most of the cases.
A comparative dataset: Bridging COVID-19 and other diseases through epistemonikos and CORD-19 evidence

The COVID-19 pandemic has underlined the need for reliable information for clinical decision-making and public health policies. As such, evidence-based medicine (EBM) is essential in identifying and evaluating scientific documents pertinent to novel diseases, and the accurate classification of biomedical text is integral to this process. Given this context, we introduce a comprehensive, curated dataset composed of COVID-19-related documents. This dataset includes 20,047 labeled documents that were meticulously classified into five distinct categories: systematic reviews (SR), primary study randomized controlled trials (PS-RCT), primary study non-randomized controlled trials (PS-NRCT), broad synthesis (BS), and excluded (EXC). The documents, labeled by collaborators from the Epistemonikos Foundation, incorporate information such as document type, title, abstract, and metadata, including PubMed id, authors, journal, and publication date. Uniquely, this dataset has been curated by the Epistemonikos Foundation and is not readily accessible through conventional web-scraping methods, thereby attesting to its distinctive value in this field of research. In addition to this, the dataset also includes a vast evidence repository comprising 427,870 non-COVID-19 documents, also categorized into SR, PS-RCT, PS-NRCT, BS, and EXC. This additional collection can serve as a valuable benchmark for subsequent research. The comprehensive nature of this open-access dataset and its accompanying resources is poised to significantly advance evidence-based medicine and facilitate further research in the domain.
Applications of trace element and isotope geochemistry in revealing origin and evolution of thermal spring water and ore deposit

Abstract Geochemistry is normally referred to as the study of Earth’s chemistry by exploring properties of materials and natural processes from deep beneath the Earth’s surface to the upper atmosphere, demonstrating the natural phenomena nowadays and discovering the history of nature. This study describes a subtopic of geochemistry: inorganic isotopes and trace elements, and discusses the application of the isotopes and trace elements in studies of geothermal spring water and ore deposit. The study of water samples in Hengjing, China was accomplished with the help of gas isotope analysis. The carbon isotope ratios and gas compositions indicate that the thermal waters are mostly sourced from meteoric and mantle sources in this area. The He isotope could evolute from mantle fluid, and CO2 is a product of the carbonate decomposition of rocks at high temperatures. The other study, conducted on the Zhaoxian ore deposit, revealed the origin and processes of formation of the different compositions of the ore. The magmatic-hydrothermal activity in this tectonically active area contributes much to the mineralization process of the ore, as shown by the analysis of trace elements of pyrite. The fluid inclusion He-Ar isotope shows that the ore was characterized by crust-mantle mixing with a little participation of meteoric water.
Learning generalizable visual representation via adaptive spectral random convolution for medical image segmentation

Medical image segmentation models often fail to generalize well when applied to new datasets, hindering their usage in clinical practice. Existing random-convolution-based domain generalization approaches, which involve randomizing the convolutional kernel weights in the initial layers of CNN models, have shown promise in improving model generalizability. Nevertheless, the indiscriminate introduction of high-frequency noise during early feature extraction may pollute the critical fine details and degrade the model's performance on new datasets. To mitigate this problem, we propose an adaptive spectral random convolution (ASRConv) module designed to selectively randomize low-frequency features while avoiding the introduction of high-frequency artifacts. Unlike prior arts, ASRConv dynamically generates convolution kernel weights, enabling more effective control over feature frequencies than randomized kernels. Specifically, ASRConv achieves this selective randomization through a novel weight generation module conditioned on random noise inputs. The adversarial domain augmentation strategy guides the weight generation module in adaptively suppressing high-frequency noise during training, allowing ASRConv to improve feature diversity and reduce overfitting to specific domains. Extensive experimental results show that our proposed ASRConv method consistently outperforms the state-of-the-art methods, with average DSC improvements of 3.07% and 1.18% on fundus and polyp datasets, respectively. We also qualitatively demonstrate the robustness of our model against domain distribution shifts. All these results validate the effectiveness of the proposed ASRConv in learning domain-invariant representations for robust medical image segmentation.
Implicit space pose consistent transfer network for deep face verification

Unconstrained face recognition has achieved state-of-the-art performance with the help of the powerful representation capabilities of deep convolutional neural networks. However, face recognition always suffers from other intra-class imbalances due to the influence of pose variations present in the real world, which significantly degrades the performance, especially for tilted face recognition under surveillance videos, which is rarely studied. Tilted face recognition is more challenging than frontal and profile face recognition due to the effect of self-obscuration. To this end, we consider promoting the frontalization of tilted faces in the implicit space, while preserving identity information. In this paper, we propose an implicit spatial pose consistent transfer network (PCTN) that exploits the prior guidance of pose information to enhance the model's ability to perceive pose. Specifically, we first develop the multi-path attention block (MPAB), which is used as the main block for feature embedding in deep networks. Such a design is employed to enhance the relevance of multi-layer convolutional features and provide a powerful foundation for the deep asymptotic transfer of feature frontalization. Then, the pose consistent transfer module (PCTM) combines pose labels and deep embedding features to learn robust discriminative information. PCTM consists of two parts: the pose guidance block (PGB) explores the implicit nonlinear feature mapping from pose faces to frontal faces in feature space, and the attention compensation module (ACM) aims to exploit the complementarity of dense and sparse features, enhancing pose-related features and suppressing pose-unrelated features in terms of both spatial-wise and channel-wise. Furthermore, we investigate the performance comparison on datasets with multiple pose variations without performance degradation. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed method.
A machine learning-based Anomaly Detection Framework for building electricity consumption data

A suboptimal management or system malfunction can often lead to abnormal energy consumptions in buildings, which result in a significant waste of energy. For this reason, the adoption of advanced monitoring systems, based on Machine Learning (ML) and visualization techniques, is crucial to avoid possible deviations from the baselineenergy consumption. However, the historical data on which analyses are based generally do not report the occurrence of anomalies. Therefore, the application of supervised ML techniques is limited and unsupervised approaches are favored. Moreover, domain experts find most Machine Learning (ML) techniques hard to interpret, and thus find it difficult to contextualize anomalies. To overcome these issues, this work proposes a machine learning-based Anomaly Detection Framework (ADF) that involves the use of two complementary semi-supervised ML applications to obtain a highly interpretable and accurate detection of anomalies. Both techniques use Symbolic Aggregate approXimation (SAX) encoding to extract the most relevant information from load profiles. The aim of the first approach is to maximize the interpretability of the definition and distinction between anomalous and normal behavior. This is achieved using a Classification And Regression Tree (CART), albeit at the expense of a coarser output granularity. The second approach exploits an Multi-Layer Perceptron (MLP) algorithm to obtain a higher and more accurate output resolution, although it leads to a less interpretable definition of any anomalous behavior. The ADF has been applied to a real case study using electricity consumption data provided by a large telecommunications service provider. The results show that combining both ML models enhances the accuracy and interpretability of the detected anomalies.