Reviewerly/exHarmony huggingface dataset:
Fast Electrons Interacting with Chiral Matter: Mirror-Symmetry Breaking of Quantum Decoherence and Lateral Momentum Transfer

Photons experience mirror asymmetry of macroscopic chiral media, as in circular dichroism and polarization rotation, since left- and right-handed circular polarizations differently couple with matter handedness. Conversely, free relativistic electrons with vanishing orbital angular momentum have no handedness so the question arises whether they can sense chirality of geometrically symmetric macroscopic samples. In this paper, we show that matter chirality breaks mirror symmetry of the scattered electrons' quantum decoherence, even when the incident electron wave function and the sample shape have a common reflection symmetry plane. This is physically possible since the wave-function transverse smearing triggers electron sensitivity to the spatial asymmetry of the electromagnetic interaction with the sample, as results from our nonperturbative analysis of the scattered electron reduced density matrix, in the framework of macroscopic quantum electrodynamics. Furthermore, we prove that mirror asymmetry also shows up in the distribution of the electron lateral momentum, orthogonal to the geometric symmetry plane, whose nonvanishing mean value reveals that the electron experiences a lateral mechanical interaction entirely produced by matter chirality.
BEPART PROJECT: SCENARIOS OF YOUTH PARTICIPATION AND CURRICULAR INNOVATION AT SCHOOLS

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 1470-1477ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0422Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
REELCHART: A TOOL FOR THE EXPLORATION OF VIDEO FOOTAGE IN THE CONTEXT OF ENGAGED RESEARCH-LED TEACHING

REELCHART: A TOOL FOR THE EXPLORATION OF VIDEO FOOTAGE IN THE CONTEXT OF ENGAGED RESEARCH-LED TEACHING
ACTION RESEARCH AND REFLEXIVE METHODOLOGIES FOR ONLINE EVENT PROCESSING

S. Dugal1O. Demoura1E. Sanford1E. Catalan1E. Costa1C. Masiello1J. Puccetti1A. Kozaritz1H. Runci1A. Lee1B. Ellis1T. Kelly1S. Sampson1K. Slawinski1F. Torres1S. Singh2M. Troiano3R. Singh4E. Eaton-Ayres1A. Hogan1L. Abelson1S. Murray1C. Lamountain1C. Rodriguez1H. Marney1S. Melia1
DEVELOPMENT OF A SYSTEM TO SUPPORT GRASPING FOSTERING STATUS OF STUDENTS’ QUALITIES AND ABILITIES FROM TEXT DATA OF REFLECTIONS ON LEARNING

DEVELOPMENT OF A SYSTEM TO SUPPORT GRASPING FOSTERING STATUS OF STUDENTS’ QUALITIES AND ABILITIES FROM TEXT DATA OF REFLECTIONS ON LEARNING
THE EFFECT OF TEACHERS’ (DE)MOTIVATING TEACHING STYLE ON ENGAGEMENT AND ACHIEVEMENT IN MATHEMATICS: LITHUANIAN CASE

THE EFFECT OF TEACHERS’ (DE)MOTIVATING TEACHING STYLE ON ENGAGEMENT AND ACHIEVEMENT IN MATHEMATICS: LITHUANIAN CASE
AREAS OF MENTOR COMPETENCE FROM THE VIEW OF MENTEES

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 2398-2404ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0673Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
ICT AS A DYNAMIZING ELEMENT OF SATISFACTION IN UNIVERSITY UNDERGRADUATE TEACHING

Appears in: INTED2023 Proceedings Publication year: 2023Page: 561 (abstract only)ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0199Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
The Impact of the Size of the Partition in the Performance of Bat Algorithm

In this article the influence of the maximum partition size on the performance of a discrete version of the Bat Algorithm (BA) is studied. The Bat Algorithm is a population-based meta-heuristic based on swarm intelligence developed for continuous problems with exceptional results. Thus, it has a set of parameters that must be studied in order to enhance the performance of the meta-heuristic. This paper aims to investigate whether the maximum size of the partitions used for the search operations throughout the algorithm should not also be considered as a parameter. First, a literature review was conducted, with special focus on the parameterization of the meta-heuristics and each of the parameters currently used in the algorithm, followed by its implementation in VBA in Microsoft Excel. After a thorough parameterization of the discrete algorithm, different maximum partition sizes were applied to 30 normally distributed instances to draw broader conclusions. In addition, they were also tested for different sizes of the problem to see if they had an influence on the results obtained. Finally, a statistical analysis was carried out, where it was possible to conclude that there was no maximum partition value for which superiority could be proven, and so the size of the partition should be considered a parameter in the bat algorithm and included in the parametrization of BA.
A Study on Satisfaction and Utilization of NCS Hair Beauty Education

In national competency standard NCS hairdressing training, on-the-job knowledge and skills were categorized by job and further specified. This study aimed to investigate specific ncs items and their utilization and examine NCS satisfaction. For this, it attempted to analyze differences in NCS hairdressing training by demographic variables and examine satisfaction and utilization of NCS hairstyling courses by general characteristics. For this, studies on the satisfaction and utilization of NCS hairdressing training courses were empirically analyzed. For reliability of variables, Cronbach’s alpha was calculated. In addition, factor analysis on measurement variables was performed. The study results found the followings: hairdressing courses had a statistically significant influence on ncssatisfaction and utilization. There was no difference in satisfaction with NCS hairstyling courses regardless of where such training was given. However, period and operating pattern had an effect on such satisfaction and utilization. Therefore, it is required to develop a plan to enhance satisfaction with NCS hairstyling courses for the instructors from vocational training institutions as a way to promote such ncs-based hairstyling courses. This study investigated the current situations and influences of ncs hairstyling courses. It is anticipated that the study results would be helpful in deriving a direction for NCS hairstyling courses and their improvement plan.
Extending the Norm Activation Model and Unpacking Laypeople's Social Media Misinformation Correction Process: The Multilayered Roles of Misinformation Awareness, Personal Norms, and Efficacy

Drawing upon the Norm Activation Model (NAM), this study joins forces with and goes beyond fact-checking to examine the layperson’s motivations in misinformation correction on social media through the lens of prosocial behavior. Factors related to the norm activation process are examined as potential predictors and/or mediators. Based on a national survey of 1,022 respondents, we explore the norm activation process triggered by the awareness of misinformation consequences, which directly and indirectly impacts social media misinformation correction intentions. Moreover, this study found that the perceived prevalence of social media misinformation, self-efficacy, and outcome efficacy pertaining to social media misinformation correction play multilayered roles in shaping such intentions in different ways. This study therefore provides a more comprehensive examination of the underlying mechanisms behind a layperson’s willingness to engage in social media misinformation correction activities.
A Bayesian Network-Gis Probabilistic Model for Addressing Human Disturbance Risk to Ecological Conservation Redline Areas

The “Ecological Conservation Redline” (ECR) has been proposed as a national policy in China to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. To spatially and quantitatively address the human disturbance risk to the ECR at the regional scale, a Bayesian network (BN)-GIS probabilistic model is proposed that integrates multiple human activities, ecological receptors of the ECR, and their exposure relationships. A case learning method is then introduced to enhance the human disturbance risk prediction precision by training the BN models based on the spatial attribute of variables integrated with geographic information systems (GIS). This method was introduced to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This can help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, this probabilistic method presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.
SPEECH &amp; TEXT TO VISUAL SIGN LANGUAGE USING ISL AND NLP TOKENS

Speech and text to visual sign language translation technology is a critical tool for breaking down communication barriers between deaf and hearing individuals.This technology uses machine learning algorithms to analyze spoken or written language and generate the corresponding sign language.By doing so, it enables deaf individuals to participate more actively in social, educational, and professional settings and allows hearing individuals to communicate more easily with deaf individuals.However, there are several challenges that developers of speech and text to visual sign language translation technology face.Sign language is a threedimensional language that varies depending on the region and culture, and there are many nuances that can be challenging to capture accurately.Despite these challenges, there have been significant advances in this technology in recent years.Applications of speech and text to visual sign language translation technology include providing deaf students with access to the same educational resources as their hearing peers, facilitating communication between healthcare professionals and deaf patients, and providing deaf customers with access to customer service representatives.Overall, speech and text to visual sign language translation technology has the potential to make communication more inclusive and accessible for deaf individuals.With continued development and improvement, this technology could have a significant impact on the lives of deaf individuals and their ability to participate fully in society.
Human activity recognition method using joint deep learning and acceleration signal

Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning (DL) algorithms have been given more attention in terms of classification of human daily activities. Deep neural networks (DNNs) compute and extract complex features on voluminous data through some hidden layers that require large memory and powerful graphics processing units (GPUs). So, this study proposes a new joint learning (JL) approach to classify human activities using inertial sensors. To this end, a large complex donor model based on a convolutional neural network (CNN) is used to transfer knowledge to a smaller model based on CNN referred to as the acceptor model. The acceptor model can be deployed on mobile devices and low-power hardware due to decreased computing costs and memory consumption. The wireless sensor data mining (WISDM) dataset is used to test the proposed model. According to the experimental results, the HAR system based on the JL algorithm outperforms than other methods.
FAKE CURRENCY DETECTION USING DEEP LEARNING

In recent years, many counterfeit banknotes have been issued, causing great damage and loss to the society.Therefore, we need to develop tools to detect fake currency.In this project, we propose an image-based approach to detect counterfeit banknotes in circulation in Japan.Our project provides not only the mobility and compatibility that most people need, but also reliable detection accuracy for counterfeit money.Detecting counterfeit currency is a serious global problem, affecting the economies of almost every country, including India.Currency duplication is a vulnerable threat to the economy.Significant advances in technology in the scanning and printing industries exacerbates the counterfeit problem.By virtue of this, counterfeit cash impacts the economic system and devalues the real currency.Therefore, detecting counterfeit money is very important.Most of the earlier methods used are based on the hardware techniques and the image processing technology.Identifying the counterfeit money using these methods are ineffectual and also time-consuming.To overcome these negativities, fake money detection using machine learning concepts has been proposed.Our work will identify the counterfeit money by examining the currency images.Using this machine learning technique, we build a counterfeit currency detection system that detects fake and real currencies.Counterfeit currency detection system, as the name suggests, check whether the banknotes someone gave you are real or counterfeit.This system is very useful as the circulation of fake money is increasing day by day.Therefore, we need a system to prevent this.This system is available to banks and the general public.This allows banks to distinguish between counterfeit and genuine banknotes.This can also be used to prevent ordinary people from becoming thugs.
Power grid fault diagnosis method based on alarm information and PMU fusion

With the rapid development of smart grid technology, more and more attention has been paid to the stability of the power grid. When the power grid fails, the dispatch center will receive various types of data, including the PMU data collected by the PMU (Power Management Unit) device used by the Wide Area Monitoring System (WAMS) and the data collected by the Supervisory Control System for Data Acquisition and Control (SCADA). Alarm information data, they describe the same grid fault from different dimensions. At present, the power grid fault diagnosis based on PMU data and alarm information texts has been fully studied, but the information source is single, and it is difficult to ensure the accuracy of the diagnosis results when complex faults and multiple faults occur in the power grid. This paper starts with the information source of power grid fault diagnosis and develops a power grid fault diagnosis method that integrates the dual data sources of PMU and alarm information: 1. effectively connect alarm message text and PMU based on fault events. 2. complete the fusion of PMU and alarm information to improve the scope and accuracy of power grid fault diagnosis. Finally, the simulation fault data is used to test. The experimental results show that the accuracy of fault diagnosis of dual data source fusion is higher than that of single data.
DJ-Agent: music theory directed a cappella accompaniment generation using deep reinforcement learning

This paper proposes a song accompaniment generation method that combines audio analysis and symbolic music generation so that human music theory can be used to build a reinforcement learning model, training an agent to create music. The key to this algorithm is to extract music theory concepts from audio and a reward model that works well in reinforcement learning. However, some music theory rules are complex and challenging to describe. It is difficult to achieve competitive results only by hardcoding the reward. Therefore, to build an effective reward model, a neural network is used to evaluate the perceptual part of composition quality, and program discrimination is used to model easy-to-describe music theory, and the two work together. Experiments show that the proposed algorithm can generate accompaniment arrangements close to human composers, is compatible with various musical styles, and outperforms the baseline algorithm in multiple evaluation metrics.
Analysis of heterogeneous data model based on federated learning

The rapid development of edge network devices has led to the explosive growth of their data, and the difficulty of dealing with heterogeneous data in edge devices has been further increased. To solve the problem of heterogeneous data fusion without interaction, this paper proposes a data heterogeneous model analysis based on federated learning. Preprocess the multi-source heterogeneous data to obtain the main features of the condensed data. Then, the multi-source heterogeneous data nodes are positioned to avoid multi-fusion results, and Spatio-temporal correlation degree of the multi-source heterogeneous data is calculated to improve the accuracy of fusion. Finally, a multi-source heterogeneous data fusion model is established based on federated learning to ensure the security of data fusion. Compared with the traditional model, the data fusion of the proposed model is more stable, and the error is smaller. The effectiveness of the proposed model is verified by the stability and accuracy of the fusion of the heterogeneous data. The multi-source heterogeneous data fusion model studied in this paper can improve the quality of Internet of Things data and promote the development of edge devices in China.
A prediction of human body key-points based on Kalman filter in transient visual masking

While extracting dynamic 3D human key-points data for human pose estimation using only one RGBD camera, some key-points may be covered by other body parts during users’ movement. In this case, some key-points’ depth data can’t successfully be fused with aligned RGB data and that will influence final estimation of movement in this period. In this article, we present a method to predict users’ movement when camera can’t read exact 3D information of human key- points. This method proposes a Kalman filter model with constraints for predicting movement while there is a short sensor failure. The experiments show that these constraints can enhance robustness of system and help the filter to have a smooth performance. During sensor failure, 60% location data is well predicted.
Electronic insulator detection based on DN-DETR

It is a challenge to detecting insulators quickly and correctly from complex and variable images of power line. An insulator detection method based on DN-DETR model is proposed in this paper. DN-DETR is an end-to-end object detection model based on the transformer model. Self-attention of transformer can obtain the global information of image quickly and correctly. Experiments show that our model converges quickly, and precision achieves 89.6% at 50 epochs.
Research on pedestrian targe detection based on deep learning

In the process of autonomous driving, there will be missed detections and false detections caused by dense crowds and occlusions during pedestrian target detection. This paper proposes a pedestrian object detection network model that combines Swin Transformer and YOLOv3. First use the lightweight Swin Transformer Tiny to replace the original Darknet53 as the backbone network of YOLOv3. The multi-scale detection is realized through the self-attention hierarchical network, which optimizes the detection effect in the case of dense pedestrians. Secondly, to deal with the occlusion in the crowd, Focal-EIoU Loss is used as a new loss function. I Introduce edge length loss and Focal L1 loss to increase the loss and gradient of IoU, thereby improving the regression accuracy. Finally, experiments are performed on the Caltech dataset. The experimental results show that the precision on the Caltech dataset reaches 95.23% and the recall rate reaches 89.57%. Compared with the original YOLOv3 algorithm, the precision is increased by 3.22%, and the recall rate is increased by 4.35%. The effectiveness of the algorithm is verified, and the performance of pedestrian detection is greatly improved.
Design and implementation of art grading line dance scoring system based on Android and SQLite

Combined with the actual needs of scoring in line dancing competition, an Android-based competition scoring system is designed. The system is mainly divided into Android terminal and PC terminal. The administrator can enter the main interface of the PC terminal by entering the correct username and password, and perform user management, parameter setting, data import, printing score table, and data export operations. On the Android side, the judges can perform user management, parameter setting, scoring and other operations after entering the correct username and password. The implementation of the system effectively improves the efficiency of scoring in the line dance competitions and ensures the correctness of the final score.
Prediction of online and offline academic performance of college students based on partial least squares regression analysis

This paper selects the online and offline learning data of ten students to calculate six component functions of the independent variable group and the dependent variable. The three principal components with large contribution rate was selected to obtain the regression equation between the standardized variable group and the three component variables, the regression equation between the standardized independent variable and the standardized dependent variable, and the regression equation between the original independent variable and the original dependent variable, and finally to obtain the regression prediction value. The MATLAB program was used to generate regression coefficient histogram and regression prediction score chart. The conclusion that the regression equation has a good prediction effect is obtained by using the regression prediction value and the regression prediction diagram. The independent variables that play an important role in explaining the regression equation among the five independent variable factors are obtained from the regression coefficient histogram.
Research on data usability measurement of scientometrics research based on keyword analysis

In this study, we firstly combine data usability with data usability and summary the framework of scientometrics research from the perspective of data usability which includes main research fields, data sources, methods, and software. We select 1539 papers published in scientometrics to summary the research content of scientometrics based on paper analysis, and then, develop the measurement scales based on the general process of research which consist of type of application, data source and structure, data quality, users’ skill requirements, and software usage which can provide reference researchers to evaluate the difficulty of research, and also can be used to measure the value of manuscripts for editors and reviewers of scientific journals.