Reviewerly/exHarmony huggingface dataset:
A hybrid transformer–sequencer approach for age and gender classification from in-wild facial images

The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, human–computer interaction, etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed model’s performance is compared with several state-of-the-art models proposed so far. An improvement of approximately 10% and 6% over the state-of-the-art implementations for age and gender classification, respectively, is noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems.
A system for monitoring the microclimate parameters of premises based on the Internet of Things and edge devices

Recent years have been characterized by the rapid development of Internet of Things (IoT) and edge device technologies. Systems of various purposes with IoT elements and edge devices are increasingly finding practical use in people’s lives, the IoT element base is expanding and improving, which makes it possible to develop effective measuring systems, including those with feedback control. An essential role in ensuring people’s lives is played by the microclimate of the premises where people live, work, and study. As you know, the excess or decrease of the environmental microclimate relative to the norm negatively affects the physiological state of a person, his performance, and concentration and reduces the efficiency of work and training. Therefore, in this work, the problem of round-the-clock monitoring of the microclimate of classrooms is solved by developing an autonomous IoT system using edge devices to measure climatic parameters such as temperature, relative humidity, carbon dioxide level in the air, and the concentration of light air ions with data recording on a smartphone and saving on a remote server. The principles of building microclimate monitoring systems are presented, the requirements for the system are set, the criteria for choosing the elemental base and the technical characteristics of each component are given. The structure of the air ion concentration sensor developed by the author and the method of measuring the air ion concentration in the room are also described. The structural diagram of the developed microclimate parameters monitoring system is also presented. The development is part of a system for studying the influence of microclimate parameters on the physiological state of applicants for education. The results obtained in the work will allow development measures to ensure the necessary normal conditions for training in confined spaces. Research conducted using the developed system will allow better formation of student learning conditions in order to achieve maximum performance indicators.
Grouped Multilayer Practical Byzantine Fault Tolerance Algorithm: A Practical Byzantine Fault Tolerance Consensus Algorithm Optimized for Digital Asset Trading Scenarios

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.
LEARNERS’ PERCEPTION TOWARDS AUDIO–VISUAL (AV) RESOURCES USED IN LECTURE CLASSES

In the contemporary world, visual elements play a pivotal role due to the prevalence of virtual opportunities and information technology. Consequently, television and videos have not only become sources of entertainment but also tools that are actively harnessed across various domains of human activity, including education. The utilization of audio-visual aids has emerged as the most effective approach for enhancing teaching outcomes and facilitating the optimal dissemination of knowledge. Therefore, it is undeniable that technological devices wield a substantial influence, contributing to a dynamic and impactful information system. This research endeavour aimed to assess the efficacy of audio-visual aids in the educational process at the university level. To achieve this objective, a questionnaire was devised as the primary research instrument for gathering insights from students. The study was conducted within the context of lecture classes in the Moradabad district, UP. A total of one hundred two (102) students took part in the investigation, with male students comprising 47.05% and female students comprising 52.95% of the participant group. The study's findings strongly indicate that the incorporation of audio-visuals as a pedagogical approach stimulates cognitive engagement and enhances the learning environment within a classroom setting.
Gamification as a Growing Trend in Higher Education: Prospects and Problems

The article deals with the phenomenon of gamification and the problems of im­plementing game practices in higher education. Based on the information about consumer habits, style features of behavior, etc., gamification becomes a fairly effective IT tool that contributes to achieving the goals of one or another project by way of constructing artificially player motivation, i.e., in fact, programming for a certain behavior pattern. The paper notes that the Western trend of mass in­clusion of games and game technologies in curricula has become a significant reference point for domestic educational policy, and gamification is now dis­cussed as a new, alternative way of organizing learning in educational institu­tions of our country, which allows to significantly influence student behavior and the effectiveness of learning outcomes. Analyzing possible consequences and trying to take into account the explicit and latent risks, the author concludes that the most alarming trend of gamification implementation in the educational process is the creation of unprecedented opportunities for collecting data on student be­havior in the digital educational space. Tracking reactions and psychoemotional state of young people in the course of gamified learning, the use of artificial in­telligence technologies, neural networks, the use of learning action analysis tech­nologies, digital footprint analysis – all these may help to build and improve the behavior management strategy required by customers. Here we see the pro­spects of making a testing range for large-scale work to create additional means of control, standardization and correction with the view to improve the tech­niques of manipulating the consciousness and behavior of the masses of people. In the author’s opinion, the transformation of learning into a computer game will promote not only a significant deformation of the inner meaning of pedagogics, but also the transformation of student youth into an object of various manipu­lations using the high-human methodology, which widely applies the tools of smart technology.
A framework for generating anomaly analysis comments in DHI interpretation report

Efficient and high-quality writing of DHI interpretation reports is critical to realize the instructive value of time-series DHI data, in which anomaly analysis of DHI indicators is the basis of diagnosing problems and giving suggestions for dairy farms. However, this work is labour intensive and knowledge-driven, which is easy to cause inadequate even incorrect analysis due to the lack of DHI specialists. Aiming to the subtasks of automatically generating comments of static anomaly and dynamic change trend of DHI indicators, we proposed a framework to tackle them separately. Firstly, we employed a series of anomaly degree matrixes to deal with the first subtask. Secondly, we proposed an improved end-to-end data-to-text model which incorporates with three pre-processing methods, attention mechanism and contrastive penalty to deal with the second subtask. Experimental results show that our proposed model outperforms the baselines and related works both in terms of fluency and correctness of generated comments, which is suitable for the preliminary description of DHI indicators.
Emotional Intelligence and Its Important Role

Systems prioritizing intelligence or academic values harm students. Lack of emotional control makes children tend to behave rudely and lack empathy. Worse, to the point of bullying behavior. This study aims to examine more about the emotional intelligence of elementary school students. The method in this study uses the method of literature review. The results of this study show that high-grade elementary school students begin to realize that the harsh expression of emotions is not welcome; they begin to learn to control the expression of their emotions. Positive emotions can lead students to optimism and good self-efficacy. Positive emotions stimulate happiness hormones so that feelings become calmer and the brain can think better, so students are fully attentive to a series of learning processes. Lack of emotional control makes students not enthusiastic to follow the learning process. It can be concluded that emotional intelligence needs to be instilled since elementary school to support academic achievement and success.
Prediction of automobile insurance fraud claims using machine learning

Automobile insurance fraud is a significant issue for insurance firms, causing financial losses and higher premiums for policyholders. This study aims to create a predictive model for accurately identifying potential vehicle insurance fraud claims. Understanding fraud detection processes and operationalizing information communication technology is crucial for implementing corrective actions, but personally reviewing insurance claims is time-consuming and costly. This study explored machine learning algorithms to detect fraudulent vehicle insurance claims. The research evaluated AdaBoost, XGboostNB, SVM, LR, DT, ANN, and RF. AdaBoost and XGBoost classifiers outperformed other models with 84.5% classification accuracy, while LR classifiers performed poorly with balanced and unbalanced data. The ANN classifier performed better with unbalanced data. Performance evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the models. The results demonstrate the effectiveness of machine learning in distinguishing between genuine and fraudulent claims, providing insurance companies with a powerful tool to proactively combat fraud and improve their overall risk management strategies. The findings of this research contribute to the insurance industry’s efforts to enhance fraud detection systems, reduce financial losses, and offer more competitive insurance premiums to honest policyholders.
Adapting the Harrow-Hassidim-Lloyd algorithm to quantum many-body theory

Rapid progress in developing near- and long-term quantum algorithms for quantum chemistry has provided us with an impetus to move beyond traditional approaches and explore new ways to apply quantum computing to electronic structure calculations. In this work, we identify the connection between quantum many-body theory and a quantum linear solver, and implement the Harrow-Hassidim-Lloyd (HHL) algorithm to make precise predictions of correlation energies for light molecular systems via the (nonunitary) linearized coupled cluster theory, where the term ``light molecular systems'' refers to those molecules whose constituent atoms have low atomic number. For the purposes of practical computations, we make suitable changes to the HHL framework. This entails two aspects: (1) Adapt, prescribing a novel scaling approach that allows one to scale any arbitrary Hermitian matrix, $A$, that in turn dictates the controlled-rotation angles without having to precompute the eigenvalues of $A$, and yet achieve a reasonably high precision in $|x\ensuremath{\rangle}$, and (2) Lite, for which we devise techniques that reduce the depth of the relevant quantum circuit. In this context, we introduce the following variants of HHL for different eras of quantum computing: AdaptHHLite in its appropriate forms for noisy intermediate-scale quantum (NISQ), late-NISQ, and the early fault-tolerant eras, as well as AdaptHHL for the fault-tolerant quantum computing era. We demonstrate the ability of the NISQ variant of AdaptHHLite to capture correlation energy precisely, while simultaneously being resource lean, using simulation as well as the 11-qubit IonQ quantum hardware.
Implementation of Virtual Worlds to Promote Distance Practice Teachers' Participation in the English Learning Process

Distance education has adopted innovative technology-assisted approaches to improve teacher engagement, with virtual worlds emerging as a potentially promising tool for enhancing the participation of distance practice teachers in the learning process. In this context, the purpose of this study is to investigate the incidence of using virtual worlds as a resource to increase distance practice teachers’ participation in the learning process. An action research design that incorporated a combination of both quantitative and qualitative research was used to carry out the study. The techniques applied to collect the information were a survey, an interview, and an observation checklist. The sample consisted of 116 participants (44 male and 72 female) whose ages ranged from 25 to 35 years. They were enrolled in the English major of a distance program at a private university in a southern city in Ecuador. They participated actively in 16 synchronic activities during the intervention period (one per week). The results showed that the use of virtual worlds allowed pre-service teachers to increase the frequency of their participation during synchronic classes. Besides, practice English teachers had positive perceptions regarding the use of virtual worlds as a resource for learning because the sense of presence provided by the platform motivated them to participate in the activities. From the findings of the study, it is recommended that further research consider the use of virtual worlds for fostering participation in on-site learning environments to further triangulate the results of the study and for comparative analysis.
Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&amp;E Histologic Assessment

Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, and fixed tissues. Our objective was to determine whether PARS could provide an image quality sufficient for the diagnostic assessment of breast cancer needle core biopsies (NCB). We PARS imaged and virtually H&E stained seven independent unstained formalin-fixed paraffin-embedded breast NCB sections. These identical tissue sections were subsequently stained with standard H&E and digitally scanned. Both the 40× PARS and H&E whole-slide images were assessed by seven breast cancer pathologists, masked to the origin of the images. A concordance analysis was performed to quantify the diagnostic performances of standard H&E and PARS virtual H&E. The PARS images were deemed to be of diagnostic quality, and pathologists were unable to distinguish the image origin, above that expected by chance. The diagnostic concordance on cancer vs. benign was high between PARS and conventional H&E (98% agreement) and there was complete agreement for within-PARS images. Similarly, agreement was substantial (kappa > 0.6) for specific cancer subtypes. PARS virtual H&E inter-rater reliability was broadly consistent with the published literature on diagnostic performance of conventional histology NCBs across all tested histologic features. PARS was able to image unstained tissues slides that were diagnostically equivalent to conventional H&E. Due to its ability to non-destructively image fixed and fresh tissues, and the suitability of the PARS output for artificial intelligence assistance in diagnosis, this technology has the potential to improve the speed and accuracy of breast cancer diagnosis.
Optimizing Data Security with Hybrid Scheme Based on LSB and DWT

One of the most popular techniques in image steganography is the Least Significant Bit (LSB) operation, which involves inserting secret data into the cover image's pixels' least significant bit. However, the amount of secret data that can be concealed in the cover image depends on the number of bits used for embedding. In this paper, a novel hybrid steganographic system based on Discrete Wavelet Transform (DWT) and Least Significant Bit (LSB) for image steganography is proposed. The proposed scheme aims to optimize data security by utilizing LSB and DWT techniques to embed secret data into an image robustly and efficiently. The present paper focuses on evaluating the algorithm performance by comparing the encrypted image quality using metrics such as PSNR, RMSE, and SSIM. The experimental results showed that the suggested method outperforms the existing LSB-based in terms of security performance. These results indicated that the proposed method consistently outperforms the conventional LSB-based method across all bit depths tested, with an overall improvement of 14.12% in PSNR, 41.87% in RMSE, and 4.02% in SSIM over the traditional LSB. The proposed method enhanced the PSNR values, reduced the RMSE values, and increased the SSIM values for all bit depths tested, indicating higher image fidelity, less distortion, and better preservation of structural similarity of the original image.
Cross-domain Detection Transformer based on Spatial-aware and Semantic-aware Token Alignment

Detection transformers such as DETR [1] have recently exhibited promising performance for many object detection tasks, but the generalization ability of those methods is still quite limited for cross-domain adaptation scenarios. To address the cross-domain issue, a straightforward method is to perform token alignment with adversarial training in transformers. However, its performance is often unsatisfactory because the tokens in detection transformers are quite diverse and represent different spatial and semantic information. In this paper, we propose a new method for cross-domain detection transformers called spatial-aware and semantic-aware token alignment (SSTA). Specifically, we take advantage of the characteristics of cross-attention as used in the detection transformer and propose spatial-aware token alignment (SpaTA) and semantic-aware token alignment (SemTA) strategies to guide the token alignment across domains. For spatial-aware token alignment, we extract the information from the cross-attention map (CAM) to align the distribution of tokens according to their attention to object queries. For semantic-aware token alignment, we inject the category information into the cross-attention map and construct domain embedding to guide the learning of a multi-class discriminator to model the category relationship and achieve category-level token alignment during the entire adaptation process. We conduct extensive experiments on several widely-used benchmarks, and the results clearly show the effectiveness of our proposed approach over existing state-of-the-art methods.
Aggregating Coin Find Data to the ARIADNE Portal. Challenges of using the Getty AAT for a specialist domain

Integration of data on coin finds into the ARIADNE portal presents a number of challenges, partly related to the use of the Getty AAT as a controlled vocabulary, in particular its incomplete coverage and hierarchical structure. Further problems arise from the fact that the majority of the data are being provided not from primarily numismatic projects and institutions, but rather from disparate archaeological resources that integrate a wide range of artefacts and records. As a result, they often focus less on the peculiarities of using the AAT for coin finds and the difficulties that arise from its use. This article illustrates how this can lead to quite disparate and inconsistent mappings of data to the ARIADNE portal. Potential solutions such as aligning the AAT to the established vocabulary of Nomisma.org, or even implementing the latter in the portal, as well as implementing a standard mapping for coin finds, are discussed. Also addressed are the possibilities for complex, granular searches using the SPARQL endpoint in the ARIADNElab Virtual Research Area.
Following the Thread: Integrating SORAN's Japanese Dataset into ARIADNE

The Comprehensive Database of Archaeological Site Reports in Japan (SORAN) is an online index of domestic archaeological excavations operated by the Nara National Research Institute for Cultural Properties (NABUNKEN). SORAN emerged as a response to the need to improve the findability and accessibility of Japanese archaeological grey literature and the information contained therein. NABUNKEN joined the ARIADNEplus project in 2019 and finished integrating SORAN's metadata into the ARIADNE Catalogue in 2022. In this article we give a short overview of how archaeological data, especially fieldwork reports, are produced in Japan. Next, we summarise the history of SORAN and the nature of its dataset. Finally, we explain the steps taken to transform the Japanese dataset to allow its integration in the ARIADNE Catalogue.
Achievements of the ARIADNE Initiative for Archaeological Data Sharing and Research

Following a brief introduction to the ARIADNE initiative, this article presents selected achievements of the initiative with the ARIADNEplus project. It addresses the extension and support of the ARIADNE community, the activities promoting FAIR data in archaeology, and the standardisation of datasets based on the CIDOC CRM and the domain vocabularies Getty AAT and PeriodO. It considers the ARIADNE Portal as an effective data access and research tool, and the development of Virtual Research Environments as a new innovative approach.