Reviewerly/exHarmony huggingface dataset:
Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

Eye-gaze direction-tracking technology is used in fields such as medicine, education, engineering, and gaming. Stability, accuracy, and precision of eye-gaze direction-tracking are demanded with simultaneous upgrades in response speed. In this study, a method is proposed to improve the speed with decreases in the system load and precision in the human pupil orbit model (HPOM) estimation method. The new method was proposed based on the phenomenon that the minor axis of the elliptical-deformed pupil always pointed toward the rotational center presented in various eye-gaze direction detection studies and HPOM estimation methods. Simulation experimental results confirmed that the speed was improved by at least 74 times by consuming less than 7 ms compared to the HPOM estimation. The accuracy of the eye’s ocular rotational center point showed a maximum error of approximately 0.2 pixels on the x-axis and approximately 8 pixels on the y-axis. The precision of the proposed method was 0.0 pixels when the number of estimation samples (ES) was 7 or less, which showed results consistent with those of the HPOM estimation studies. However, the proposed method was judged to work conservatively against the allowable angle error (AAE), considering that the experiment was conducted under the worst conditions and the cost used to estimate the final model. Therefore, the proposed method could estimate HPOM with high accuracy and precision through AAE adjustment according to system performance and the usage environment.
RITUAL: a Platform Quantifying the Trustworthiness of Supervised Machine Learning

This demo presents RITUAL, a platform composed of a novel algorithm and a Web application quantifying the trustworthiness level of supervised Machine and Deep Learning (ML/DL) models according to their fairness, explainability, robustness, and accountability. The algorithm is deployed on a Web application to allow users to quantify and compare the trustworthiness of their ML/DL models. Finally, a scenario with ML/DL models classifying network cyberattacks demonstrates the platform applicability.
MR image synthesis using Riemannian geometry constrained in VAE

Deep generative models are used to learn nonlinear data distributions across a set of latent variables and a generator function which maps the latent variables to the input space. In this paper, we propose a deep generative model based on variational auto-encoders (VAE) with Riemannian geometry for synthetizing MRI images. Riemannian geometry is used as a constraint to extract the most relevant and structuring features in latent space. This approach is applied on Brats20 data. Quantitative comparisons between a classical VAE and our method are carried out to evaluate the quality of the generated images. The results demonstrate the superiority of our approach.
Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects

This paper proposes a pseudo-haptic interface that depicts the virtual weights of dumbbells in a virtual gymnasium. When a user performs a dumbbell biceps curl, he/she fixes the elbow joint as a standard joint and lifts the dumbbell, with its movement trajectory represented as a circular arc. The trajectories and velocity of dumbbell bicep curls differ depending on human physiological characteristics. Therefore, the proposed system provides an adaptable exercise area and force visualization of virtual dumbbells using a velocity-based pseudo-haptic interface and computer vision-based tracking method. The system recognizes the position and rotation of joints related to a dumbbell biceps curl with the implementation of density-based spatial clustering of applications with noise (a clustering algorithm) and resizes the radius and angle of an integrated force circular gauge. Furthermore, when a user lifts a dumbbell, the system recognizes, using linear regression, the current position and lifting force of the virtual dumbbell and visualizes the current lifting force with a guided movement trajectory to match the lifting force. Experimental results show that the proposed pseudo-haptic interface increased weight perception and usability by up to 30% compared to conventional methods (p &lt; 0.05).
OMECDN: A Password-Generation Model Based on an Ordered Markov Enumerator and Critic Discriminant Network

At present, static text passwords are still the most widely-used identity authentication method. Password-generation technology can generate large-scale password sets and then detect the defects in password-protection mechanisms, which is of great significance for evaluating password-guessing algorithms. However, the existing password-generation technology cannot ignore low-quality passwords in the generated password set, which will lead to low-efficiency password guessing. In this paper, a password-generation model based on an ordered Markov enumerator and critic discriminant network (OMECDN) is proposed, where passwords are generated via an ordered Markov enumerator (OMEN) and a discriminant network according to the probability of the combination of passwords. OMECDN optimizes the performance of password generation with a discriminative network based on the good statistical properties of OMEN. Moreover, the final password set is formed by the selected passwords with a higher score than the preset threshold, which guarantees the superiority of the hit rate of almost all ranges of combinations of passwords over the initial password set. Finally, the experiments show that OMECDN achieves a qualitative improvement in hit rate metrics. In particular, regarding the generation of 107 passwords on the RockYou dataset, the matching entries of the password set generated by the OMECDN model are 25.18% and 243.58% higher than those generated by the OMEN model and the PassGAN model, respectively.
Parallel Dense Merging Network with Dilated Convolutions for Semantic Segmentation of Sports Movement Scene

In the field of scene segmentation, the precise segmentation of object boundaries in sports movement scene images is a great challenge.The geometric information and spatial information of the image are very important, but in many models, they are usually easy to be lost, which has a big influence on the performance of the model.To alleviate this problem, a parallel dense dilated convolution merging Network (termed PDDCM-Net) was proposed.The proposed PDDCMNet consists of a feature extractor, parallel dilated convolutions, and dense dilated convolutions merged with different dilation rates.We utilize different combinations of dilated convolutions that expand the receptive field of the model with fewer parameters than other advanced methods.Importantly, PDDCM-Net fuses both low-level and high-level information, in effect alleviating the problem of accurately segmenting the edge of the object and positioning the object position accurately.Experimental results validate that the proposed PDDCM-Net achieves a great improvement compared to several representative models on the COCO-Stuff data set.
THE INFLUENCE OF INTERACTIVE TECHNOLOGY “WORLD CAFE” ON THE FORMATION THE TYPES OF THINKING, THE LEVELS OF CREATIVITY AND THE LEVEL OF EDUCATIONAL PUPILS’ ACHIEVEMENTS OF THE BIOLOGY IN THE 8TH FORM

At the article researched the effectiveness of the implementation of the innovative and interactive technology “World Cafe” which was implemented in the educational process in biology in the eighth form based on the Khmelnytskyi educational complex. The relevance of the relevant topic is that increasing requirements to the level of professionalism of the modern biology teacher, his competencies, the metodical organization and the introduction of the latest high-quality learning and teaching technologies, which will contribute to maintaining the competitiveness of both the teacher himself and the student, are fundamentally changing. In the conditions of modern changes in education, the quality depends significantly on the interaction between the teacher and the student. Therefore, interactive teaching methods are acquire relevance today. Exactly they who need not only active feedback between the teacher and the pupil, but also the organization of interaction between the pupils themselves, which unites the pupils and gives them the opportunity to work in a group (solve complex problems together, model different options for solving various biological tasks etc). The implementation of the innovative and interactive technology “World Cafe” provides an opportunity to involve every pupil in active work, through the formation of a comfortable atmosphere of openness, ease and psychological safety, that is providing a feeling of “Equal to equal”. The undeniable advantages of the implementation of the innovative and interactive technology “World Cafe” are: conducting a fruitful dialogue between completely different pupils on different topics; the discussion of biological material focuses on consideration don’t wide, but professional-specific issues; facilitating the exchange of opinions in various formats; expanding the boundaries of knowledge to more global situations, for example; the number of possible participants varies from 3 to ten; duration from 40 minutes to several hours, depending on the number and complexity of questions; management of the degree of spontaneity and spontaneity of discussions; does not require special equipment, can be carried out both indoors and outdoors. This approach to education promotes the development of pupils’ cognitive activity.&#x0D; Keywords: the type of thinking, the level of creativity, the level of educational achievements, innovative technologies, interactive methods, “World Cafe” technology, interactive learning.
Investigating the digital skills of undergraduate students in terms of various variables

It is argued that, young people growing up surrounded by digital technologies intuitively acquire digital skills and therefore do not need digital education or training. For this reason, with the belief that students already have digital skills, digital literacy education courses are reduced or removed in educational institutions. Therefore, it is necessary to emphasize the importance of development of digital skills in the education. The purpose of this research is to examine the digital skills of students in terms of various variables. This research was conducted as a survey research. The participant group of the research consists of 423 undergraduate students who have just started to study at a university. According to the results of the research, the digital skill levels of the students who attend the IT courses are significantly higher than the students who do not. Digital skills were significantly higher for the male students than for the female students. In addition, the digital skills of the students who took the IT courses from the teacher trained in teaching digital skills were significantly higher than the students who took the course from teachers from other professions.
An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed. The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics). 109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system. Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.
TIF: Trajectory and Information Flow Coupling Mechanism for Behavior Analysis in Autonomous Driving

The significant achievements have been made in crowd detection and tracking due to the advancement of artificial intelligence in the autonomous driving. However, the image-based methods have strict requirements for the collection conditions of video, and the development of the new generation of flexible fabrics has become potential sensors to perceive context. In this paper, an intelligent fabric space enabled by multi-sensing sensors is established to track the motion objects. We propose a behavior analysis pipeline including the modules of data preparation, trajectory coupling, motion scenario segmentation, and motion pattern measurement to capture the crowd information from micro-level and macro-level over the intelligent fabric space. After making preprocess for the multi-sensing data, a coupling mechanism is formulated to fuse the video-based trajectory and fabric-based trajectory. And an automatic motion scenario segmentation model divides the surrounding scenario into main-crowd, sub-crowd, and background according to the motion behavior. Further, we define measurement metrics to analyze the motion pattern for the different crowds. Extensive experiments prove that our proposed methods effectively fuse multiple trajectories and realize the crowd segmentation and the motion description. This will greatly help autonomous vehicles and control system perceive the surrounding pedestrians and the environment to make precise driving decisions.
Two-View Mammogram Synthesis from Single-View Data Using Generative Adversarial Networks

While two-view mammography taking both mediolateral-oblique (MLO) and cranio-caudual (CC) views is the current standard method of examination in breast cancer screening, single-view mammography is still being performed in some countries on women of specific ages. The rate of cancer detection is lower with single-view mammography than for two-view mammography, due to the lack of available image information. The goal of this work is to improve single-view mammography’s ability to detect breast cancer by providing two-view mammograms from single projections. The synthesis of novel-view images from single-view data has recently been achieved using generative adversarial networks (GANs). Here, we apply complete representation GAN (CR-GAN), a novel-view image synthesis model, aiming to produce CC-view mammograms from MLO views. Additionally, we incorporate two adaptations—the progressive growing (PG) technique and feature matching loss—into CR-GAN. Our results show that use of the PG technique reduces the training time, while the synthesized image quality is improved when using feature matching loss, compared with the method using only CR-GAN. Using the proposed method with the two adaptations, CC views similar to real views are successfully synthesized for some cases, but not all cases; in particular, image synthesis is rarely successful when calcifications are present. Even though the image resolution and quality are still far from clinically acceptable levels, our findings establish a foundation for further improvements in clinical applications. As the first report applying novel-view synthesis in medical imaging, this work contributes by offering a methodology for two-view mammogram synthesis.
A v-transformed copula-based simulation model for lithological classification in an Indian copper deposit

Abstract Copula functions are widely used for modeling multivariate dependence. Since the multivariate data may not necessarily be linear and Gaussian, the copula model is very often brought into the picture for modeling such multivariate phenomena. The lithological classification in spatial domain is a class of problems dealing with categorical variables. A generalized class of copula model is effective for such classification tasks. In this paper, a non-Gaussian copula (v-transformed copula) model has been used for lithotype classification of an Indian copper deposit. Coupling of Markov chain Monte Carlo (MCMC) simulation and copula discriminant function is performed for this purpose. Specifically, four lithotypes, e.g., granite, quartz, basic, and aplite are simulated in the case study deposit. The efficacy of v-transformed copula discriminant function-based simulation is compared with those of Gaussian copula, t copula, and sequential indicator simulations. Finally, the classification accuracy of all the approaches is examined with ground-truth lithological classes obtained from blast hole information. The results show that the v-transformed copula simulation has a relatively higher classification accuracy (76%) than those of Gaussian copula (70%), t copula (69%), and sequential indicator (70%) simulations.
High Order Side-Channel Security for Elliptic-Curve Implementations

Elliptic-curve implementations protected with state-of-the-art countermeasures against side-channel attacks might still be vulnerable to advanced attacks that recover secret information from a single leakage trace. The effectiveness of these attacks is boosted by the emergence of deep learning techniques for side-channel analysis which relax the control or knowledge an adversary must have on the target implementation. In this paper, we provide generic countermeasures to withstand these attacks for a wide range of regular elliptic-curve implementations. We first introduce a framework to formally model a regular algebraic program which consists of a sequence of algebraic operations indexed by key-dependent values. We then introduce a generic countermeasure to protect these types of programs against advanced single-trace side-channel attacks. Our scheme achieves provable security in the noisy leakage model under a formal assumption on the leakage of randomized variables. To demonstrate the applicability of our solution, we provide concrete examples on several widely deployed scalar multiplication algorithms and report some benchmarks for a protected implementation on a smart card.
Graph Multihead Attention Pooling with Self-Supervised Learning

Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for encoding the node representations, the graph pooling operation, which maps the set of nodes into a coarsened graph, is crucial for graph-level tasks. We argue that a well-defined graph pooling operation should avoid the information loss of the local node features and global graph structure. In this paper, we propose a hierarchical graph pooling method based on the multihead attention mechanism, namely GMAPS, which compresses both node features and graph structure into the coarsened graph. Specifically, a multihead attention mechanism is adopted to arrange nodes into a coarsened graph based on their features and structural dependencies between nodes. In addition, to enhance the expressiveness of the cluster representations, a self-supervised mechanism is introduced to maximize the mutual information between the cluster representations and the global representation of the hierarchical graph. Our experimental results show that the proposed GMAPS obtains significant and consistent performance improvements compared with state-of-the-art baselines on six benchmarks from the biological and social domains of graph classification and reconstruction tasks.
State of the Art Blockchain Enabled Smart Contract Applications in the University

A comprehensive analysis is necessary for the implementation and use of smart contracts in companies. This overview clarifies how this technology is now being used and identifies problems that are preventing its usage in contemporary organizations. This paper offers an organized assessment of prior research that includes frameworks, techniques, functional prototypes, and simulations that show how smart contracts are used in businesses. This paper's main goal is to better understand how smart-contract technology is used in organizations today. While there is limited knowledge about how smart contracts are used in companies, there is significant progress being made in the development of the technology that supports them. In this work, we describe the characteristics of smart-contract applications in many current organization fields. We further examine and group issues that hinder the adoption of smart-contract applications.
Evaluation of User Experience in Integrated Learning Information Systems Using User Experience Questionnaire (UEQ)

Information and Communication Technology (ICT) has a role in assisting in monitoring and evaluating the learning process in schools. Integrated Learning Information System is the application of an integrated information system to improve the quality of academic and learning services accessed by all school members (teachers, homeroom teachers, foundations, and students) and parents/guardians of students. The learning information system focuses on making it easier to monitor and evaluate student academic and learning outcomes. User experience measurement is carried out to determine the extent to which the success of user understanding using the system so that the purpose of using the system can be achieved. This study aims to analyze the user experience of the Integrated Learning Information System using the User Experience Questionnaire (UEQ) method. This study uses quantitative data by distributing online questionnaires to 50 students. The results of the evaluation of user experience on learning information systems using the UEQ method state that attractiveness is 2.121, perspicuity is 2.152, efficiency is 2.319, dependability is 1.505, stimulation is 1.716, and novelty is 1.020. This proves that all aspects have positive values. The benchmark results showed that the attractiveness, perspicuity, efficiency, and stimulation aspects were included in the excellent criteria, while the dependability aspect was included in the good criteria and novelty included in the above average criteria.
Generating and authoring high-variability exercises from authentic texts

Integrating adaptivity into Task-Based Language Teaching requires exercises that transmit a specific content but whose complexity is adjusted to the learner’s level. Thus, exercises of varying complexity based on the same text are needed. Revising generated exercise variants is time consuming and redundant where the same underlying linguistic annotations can be used for exercise generation. We present a fully implemented approach to generate generalized exercise specifications as an interim step before turning them into concrete exercises, as well as an interface for efficient reviewing of the specifications.