Reviewerly/exHarmony huggingface dataset:
Intelligent course recommendation based on neural network for innovation and entrepreneurship education of college students

This paper focuses on intelligence course recommendations for college students' innovation and entrepreneurship education.Firstly, the traditional collaborative filtering algorithm was introduced, and then a new recommendation technique was designed based on an artificial neural network (ANN).The experimental data were collected through a crawler framework.The two methods were compared and analyzed.It was found that the training time of collaborative filtering and ANN was 16.78 s and 12.36 s, the testing time was 2.64 s and 2.12 s, the hit rate (HR) were 0.6078 and 0.6264, and the normalized discounted cumulative gain (NDCG) values were 0.2948 and 0.3356, respectively.The results reveal that ANN was more efficient in computation and better in recommendations.The results demonstrate the effectiveness of the ANN method for intelligent course recommendations.The method can be applied to the selection of innovation and entrepreneurship education courses for college students.
Developing BrutNet: A New Deep CNN Model with GRU for Realtime Violence Detection

Computer vision with deep learning has recently emerged for Automatic Violence Detection and Classification (AVDC) with enormous potential. This paper reports an early development of a new Deep Convolutional Neural Network (DCNN) model that we call BrutNet. Building on the Gated Recurrent Unit (GRU), the BrutNet is designed to operate on the patterns within multiple frames of a video or video clips of shape 160× 90 with a duration of at least 3 seconds. For obtaining the image-feature set and the pattern of each frame, convolutional layers were considered for each frame of the time distributed layer. The model thus encodes the data from 4D to 2D to obtain a 512-features set for each frame. The temporal nature of these frames is then extracted by the GRU layer as a 1D vector, which is processed by several dense layers. A binary classification is thereby performed denoting the content as violent and non-violent. Dropout layers with a dropping rate of 0.25 were added to avoid overfitting the model. Besides, ReLu-activation and sigmoid-activation functions were defined in the hidden and output layers, respectively. Being trained with a recent high-resolution AVDC video dataset and appropriate hyper-parameters on the NVIDIA Tesla K80 GPU of Google Colab, the initial testing and validation of the model has recorded a test accuracy of 90.00% outperforming the earlier LSTM based ResNet50 model.
Real Time Hand Gesture Based User Friendly Human Computer Interaction System

The demand for interface technologies such as hand gesture detection is growing as virtual reality technology advances. In our study, we employed the state of art hand-tracking technology to construct an accurate and robust human-computer interaction(HCI) system. Our proposed method is cross-platform, lightweight, and effectively utilizes the resources from both CPU and GPU to achieve low latency performance. The MediaPipe framework, which yields a mean precision of 95.7 percent in palm identification, is used in this research to construct a realtime human-computer interaction interface that works properly regardless of difficult conditions such as different skin color, shape of hand, lighting condition, background, etc. Additionally, the speed of the proposed system is close to 40 fps acquired in a computer with weak hardware, which is sufficient for real-time HCI applications.
Performance of Recent Tiny/Small YOLO Versions in the Context of Top-View Fisheye Images

With the spreading of the computer vision field, human detection and tracking are problems more relevant than ever. However, due to the complexity of fisheye images, current lightweight detection models show difficulty when processing them. The aim of this article is to compare the performance on fisheye images of current real time detection solutions, specifically YOLOv3-tiny, YOLOv4-tiny and YOLOv5-small. Experiments carried out using a top-view fisheye camera, show faster performance but the very poor detection quality from YOLOv4-tiny. YOLOv5-small, while being slightly slower, gives a far better detection than both other solutions. The database created for this paper is available online. In conclusion, the current review shows YOLOv5-small is the best solution out of the 3 reviewed in a fast, real time, fisheye application.
BARRIERS TO BANK LOANS AND SMEs EARNINGS: EVIDENCE FROM KOSOVO

The purpose of this paper is to investigate the impact of bank loans on financing the business activities of manufacturing SMEs (Small and Medium-sized Enterprises) in Kosovo. Increasing the level of production of these SMEs is vital for a transition economy such as Kosovo. Representatives of Kosovo's 200 SMEs were interviewed regarding loan barriers and financial support from local and EU (European Union) institutions. Kosovo has a limited number of manufacturing SMEs. The random sampling method was used as a sampling method. The data are cross-section. The collected data were analyzed through the probit model. The SPSS (Statistical Product and Service Solutions) program has also been used to derive statistical results. According to the achieved findings, Not consulting an expert and obtaining loans twice within the analyzed period are significant variables. The results confirm the hypotheses that manufacturing SMEs have obstacles in financing their activities through bank lending and collateral is important for facilitating bank lending.
Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction

Compared with traditional sentence-level relation extraction, document-level relation extraction is a more challenging task where an entity in a document may be mentioned multiple times and associated with multiple relations.However, most methods of documentlevel relation extraction do not distinguish between mention-level features and entity-level features, and just apply simple pooling operation for aggregating mention-level features into entity-level features.As a result, the distinct semantics between the different mentions of an entity are overlooked.To address this problem, we propose RSMAN in this paper which performs selective attentions over different entity mentions with respect to candidate relations.In this manner, the flexible and relation-specific representations of entities are obtained which indeed benefit relation classification.Our extensive experiments upon two benchmark datasets show that our RSMAN can bring significant improvements for some backbone models to achieve state-of-the-art performance, especially when an entity have multiple mentions in the document.
The Development of Driving Simulator Game-Based Learning in Virtual Reality

Road safety training in real-world environments is considered one of the most risky and dangerous tasks and associated with various difficulties. In the past decade, the virtual reality community has based its development on early work synthesis in interactive 3D graphics, user interfaces and visual simulations. Currently, the field of Virtual Reality is moving to the workplace influenced by gaming industry. Further, simulation in the form of Virtual Reality has been developed and has the potential to impact a larger audience. Therefore, this research is aimed to develop a driving simulator game-based learning system that assist people to practice with good driving behavior. This Driving simulator game-based learning research expected to reduce failure rates during driving license test.
A study on the possibility of a change in culture and arts education curriculum by shooting “METACLASSROOM” in the COIVD19 pandemic era

This study was intended to hear from experts. Meta-classroom is a university curriculum in the field of culture and arts. The research area is a Korean university lecture room. The target audience is the master's program, doctoral program and related experts, and the departments are performing arts, media communication, and film department. Outside were educators and experts. Due to the COVID-19 environment, meta-classrooms were provided to the educational environment. The purpose is to find the Meta-classroom and the possibility of curriculum progress. The content of the curriculum consists of knowledge VR, AR, AI and big data. The research tool did a regression analysis, found to have an impact on, they are online education is 0.28 (%), metaverse classroom is 20.6 (%), knowledge system is 12.5 (%), Meta-class is overall influence 33.38 (%). It had a positive impact on a new educational curriculum in the field of culture and art.&#x0D; &#x0D; Keywords: Metaverse classroom; Knowledge system; Interdisciplinary; COIVD19 Condition; Culture &amp; Arts Curriculum
PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.
Cyberbullying Detection using Natural Language Processing

Abstract: Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the Internet and social media. But with social networking sites becoming more popular, cyberbullying is on the rise. Using technology as a medium to bully someone is known as Cyberbullying. The Internet can be a source of abusive and harmful content and cause harm to others. Social networking sites provide a great medium for harassment, bullies, and youngsters who use these sites are vulnerable to attacks. Bullying can have long-term effects on adolescents’ ability to socialize and build lasting friendships Victims of cyberbullying often feel humiliated. social media users often can hide their identity, which helps misuse the available features. The use of offensive language has become one of the most popular issues on social networking. Text containing any form of abusive conduct that displays acts intended to hurt others is offensive language. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and sometimes forces them to commit suicide. The purpose of this project is to develop a technique that is effective to detect and avoid cyberbullying on social networking sites we are using Natural Language Processing and other machine learning algorithms. The dataset that we used for this project was collected from Kaggle, it contains data from Twitter that is then labeled to train the algorithm. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed Model for cyberbullying dataset shows that Logistic Regression performs better and achieves good accuracy than SVM, Ransom forest, Naive-Bayes, and Xgboost algorithm. Keywords: Cyberbullying, Machine learning, Natural language processing, Social media, Kaggle, Dataset.
An Implementation of an optimized dual-axis solar tracking algorithm for concentrating solar power plants deployment

A recent assessment of a pilot concentrating solar power plant in Ghana noticed the failure of the tracking system to accurately align the multifaceted heliostats toward the sun and focus the sunbeams onto the target. A further review of solar tracking systems revealed the limitation of concentrated solar power tracking systems to simultaneously improve accuracy and reduce operational cost. In this paper, a computer vision-based solar tracking system, comprising a vision controller, webcam, light sensor, Arduino microcontroller, two stepper motors, a stick, and a transparent plate, is proposed and implemented. The light sensor indicates the presence or absence of sunlight, the webcam captures the image of the stick's shadow on the plate and the vision controller reads this data from the webcam and processes it to extract the solar angles. The angles are sent to the microcontroller which uses them to orient the heliostat appropriately through the stepper motors. The proposed system is location independent and can be installed in each heliostat or a group of heliostats. the preliminary tests show it tracks the sun accurately and exhibits minimal operation cost.
The Draft Online Safety Bill and the regulation of hate speech: have we opened Pandora’s box?

In thinking about the developing online harms regime (in the UK and elsewhere1) it is forgivable to think only of how laws placing responsibility on social media platforms to prevent hate speech may benefit society. Yet these laws could have insidious implications for free speech. By drawing on Germany’s Network Enforcement Act I investigate whether the increased prospect of liability, and the fines that may result from breaching the duty of care in the UK’s Online Safety Act - once it is in force - could result in platforms censoring more speech, but not necessarily hate speech, and using the imposed ‘responsibility’ as an excuse to censor speech that does not conform to their objectives. Thus, in drafting a Bill to protect the public from hate speech we may unintentionally open Pandora’s Box by giving platforms a statutory justification to take more ‘control of the message’.
The Development of Prospective Primary School Science Teachers’ TPaCK Fostered by Innovative Science-Teacher Education

The EEdnaS study “Development and testing of digitally enriched science-related subject matter teaching in digital teaching-learning labs and university classrooms” aims to promote the professional competencies of prospective teachers that are needed for teaching science content in a world shaped by digitalization. To achieve this goal, university teaching units (seminars) that directly address cognitive components of a teacher’s professional competencies, which are important to teaching science content in primary school education, were developed. In addition, prospective teachers were asked to plan, implement, and evaluate primary school science education with a particular focus on digitization, as well as sharing the developed units as open-educational resources. This article reports on the impact of the first part of the seminar concept, in which the promotion of digitization-related, subject-specific teaching methodology, as well as content-related knowledge (TPaCK) was systematically promoted. In a standardized survey, it could be shown that the prospective teachers demonstrated positive developments, particularly in the components PCK, TCK, TPK, as well as TPaCK, regarding the self-efficacy in cognitive characteristics about one’s own ability within the reference frame of self. Furthermore, the development of knowledge, especially in the areas of TK, PCK, TCK, and TPK, could also be determined, but not in relation to TPaCK itself.
Pyramid Tokens-to-Token Vision Transformer for Thyroid Pathology Image Classification

Histopathological image contains rich phenotypic information, which is beneficial to classifying tumor subtypes and predicting the development of diseases. The vast size of pathological slides makes it impossible to directly train whole slide images (WSI) on convolutional neural networks (CNNs). Most of the previous weakly supervision works divide high-resolution WSIs into small image patches and separately input them into the CNN to classify them as tumors or normal areas. The first difficulty is that although the method based on the CNN framework achieves a high accuracy rate, it increases the model parameters and computational complexity. The second difficulty is balancing the relationship between accuracy and model compu-tation. It makes the model maintain and improve the classification accuracy as much as possible based on the lightweight. In this paper, we propose a new lightweight architecture called Pyramid Tokens-to-Token VIsion Transformer (PyT2T-ViT) with multiple instance learning based on Vision Transformer. We introduce the feature extractor of the model with Token-to-Token ViT (T2T-ViT) to reduce the model parameters. The performance of the model is improved by combining the image pyramid of multiple receptive fields so that it can take into account the local and global features of the cell structure at a single scale. We applied the method to our collection of 560 thyroid pathology images from the same institution, model parameters and computation were greatly reduced. The classification effect is significantly better than the CNN-based method.
Flexible protein database based on amino acid k-mers

Abstract Identification of proteins is one of the most computationally intensive steps in genomics studies. It usually relies on aligners that do not accommodate rich information on proteins and require additional pipelining steps for protein identification. We introduce kAAmer, a protein database engine based on amino-acid k-mers that provides efficient identification of proteins while supporting the incorporation of flexible annotations on these proteins. Moreover, the database is built to be used as a microservice, to be hosted and queried remotely.
Interactive Augmented Reality Technology via Blended Instruction Lesson on Cloud

The study aims to study the processes of the interactive augmented reality technology via blended instruction lesson on cloud, to design the interactive augmented reality technology via blended instruction lesson on cloud, to develop the interactive augmented reality technology via blended instruction lesson on cloud and to study the results of the development of interactive augmented reality technology via blended instruction lesson on cloud. The study&amp;rsquo;s sample was selected using purposive sampling, i.e., 5 people from various higher education institutions and 30 people from undergraduate students in the first year of social sciences division in Pakse Teacher Training College, Lao PDR. The results showed the following: 1) the results of the quality assessment of developed blended instruction lesson on cloud had an overall very high level (mean = 4.51, SD = 0.57); 2) the results of the developed blended instruction lesson on cloud summarised that the results of the post-test achieved a score higher than the pre-test, which is statistically significant at 0.01; 3) the results of the assessment of the digital literacy of students after studying had a good level (mean = 40.73, SD = 2.18); and 4) the results of the student satisfaction to study with the developed blended instruction lesson on cloud were found to have a high level of satisfaction (mean = 4.31, SD = 0.55).
Security by Design: Aspirations and Realities in a Regulatory Context

Recently, 'security by design' has surfaced as an aspirational mantra in cybersecurity regulation and policy.It urges those involved in building information systems to consider the systems' security needs before they are built and integrate these needs in the systems' subsequent design and construction.The mantra joins a design-focused discourse on the integration of various values into technology development processes.However, critical scholarship on the roots, meaning(s) and regulatory dimensions of 'security by design' is scarce.This article aims to fill this gap.It examines the nascent discourse on 'security by design' with a view to assessing the mantra's utility as a regulatory principle in the context of information systems development.An argument advanced in the article is that while the mantra is a valuable addition to cybersecurity law and policy, realising its aspirations is likely to be hindered by its nebulous semantics and particular characteristics of computer engineering culture.The article warns that the legitimacy of 'security by design' as a regulatory principle could be weakened if it is used to further authoritarian or corporate interests at the expense of civil liberties or consumer protection.
An Intelligent Search Engine Based on Knowledge Graph for Power Equipment Management

The intelligent search engine (ISE) can accurately identify users&#x2019; search intention and interests automatically by parsing the natural language questions, can provide fast retrieval and relevance ranking of direct and associate search results, and can filter and push information to the user intelligently. Since ISE can save inestimable time and workforce in data search and correlation analysis, it has been widely used in the Internet field. This article proposed an ISE based on a constructed electrical power knowledge graph to extract the target and conditions of natural interrogation based on semantic analysis, to generate index network via vertex-to-edge mapping and ontology network path analysis, and to perform the direct and associate knowledge searches to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop data mining and correlation analysis in a domain knowledge graph. The proposed work can also provide a basis for automatic identification of user interests, intelligent information filtering and push.
The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning

To improve the accuracy of English pronunciation level evaluation, we study the modularization of the English pronunciation level evaluation system unfolding based on machine learning. The S3C2440A chip is used as the main processor of the system, and the spoken English recordings are sent to the evaluation module through the speech upload module. In the evaluation module, the pronunciation signal is filtered by the multilayer wavelet feature scale transformation method, and the intonation, speed, pitch, rhythm, and emotion features are decomposed and extracted. The test results show that the misjudgment rate of different mispronunciations is less than 1% when the system is used to evaluate the English pronunciation level, which proves that it has high evaluation accuracy. In-depth study of speech recognition related theories, speech scoring, and pronunciation correction algorithms are discussed, and an assisted learning system based on AP scoring method and pronunciation resonance peak comparison technology is proposed for the problem of inaccurate pronunciation scoring and lack of effective feedback of speech recognition technology applied to oral learning. The English pronunciation training system has achieved the expected pronunciation following of English phonetic symbols and words, real-time pronunciation. The English pronunciation training system has achieved all basic functions such as pronunciation following, real-time pronunciation evaluation, and pronunciation correction of English phonemes and works as expected. After testing, the system has achieved high accuracy in pronunciation scoring, and the similarity with experts’ scoring is over 90% for vowel and word pronunciation; the efficiency of pronunciation correction reaches 80%, which can improve learners’ pronunciation level to a certain extent.