Reviewerly/exHarmony huggingface dataset:
Research on a random sampling method for bulk grain based on the M-Unet and SGBM algorithms

In response to the challenge of limited real-time capability in identifying and positioning sampling points during random sampling of bulk grains in industrial settings, this paper introduces a novel approach. It combines the utilization of a lightweight neural network model, known as M-Unet, with the Semi-Global Block Matching (SGBM) algorithm to achieve the random sampling of bulk grains. The process commences with the deployment of a binocular camera to capture image data from the grain loading area, followed by binocular correction. Recognizing the constraints associated with the traditional U-Net network model, characterized by large parameters and suboptimal real-time performance, we introduce the M-Unet network, which is a more efficient alternative. M-Unet incorporates MobileNet as a substitute for the U-Net network encoder, resulting in a significant improvement in real-time performance and segmentation quality. Subsequently, the M-Unet network is employed to partition the corrected left image into multiple regions based on predefined sampling principles, and random sampling points are generated within each region. To determine the three-dimensional spatial positioning of these sampling points, we employ the SGBM algorithm, a semi-global stereo matching technique, on the left and right images of the corrected grain loading area. Experimental results demonstrate that the proposed method exhibits robust real-time performance and effectively recognizes grain loading areas. Furthermore, this method is suitable for deployment on portable devices, offering novel insights and methodologies for automating and advancing the intelligence of grain sampling processes. This innovation effectively addresses the challenges associated with low efficiency and human-related factors in traditional grain sampling. The findings presented in this paper carry substantial significance in safeguarding the interests of grain farmers, reducing food losses, minimizing waste, and enhancing the intelligence of sampling processes.
Analisis Penerapan Sistem Kredit Semester (SKS) Bagi Peserta Didik yang Memiliki Potensi Cerdas Istimewa dan Bakat Istimewa

The government accommodates students who have different potentials of intelligence, talent and the demands of the times by issuing Law number 20 of 2003 which regulates the National Education System. This study aims to analyze the application of the Semester Credit System (SKS) for students who have special intelligent potential and special talents which is a case study at SMA Negeri 1 Lhokseumawe. The research method is based on a survey. Based on the results of the research, the results of the implementation of the Semester Credit System (SKS) program policies are managed starting from the application for operator permits, recruitment of new students, learning load, curriculum, assessment, graduation, supervision and evaluation. So it can be concluded that the implementation of the Semester Credit System (SKS) program policy on students who have special intelligent potential and special talents if managed properly can accommodate services for these unique students.
How Can Transformer Models Shape Future Healthcare: A Qualitative Study

Transformer models have been successfully applied to various natural language processing and machine translation tasks in recent years, e.g. automatic language understanding. With the advent of more efficient and reliable models (e.g. GPT-3), there is a growing potential for automating time-consuming tasks that could be of particular benefit in healthcare to improve clinical outcomes. This paper aims at summarizing potential use cases of transformer models for future healthcare applications. Precisely, we conducted a survey asking experts on their ideas and reflections for future use cases. We received 28 responses, analyzed using an adapted thematic analysis. Overall, 8 use case categories were identified including documentation and clinical coding, workflow and healthcare services, decision support, knowledge management, interaction support, patient education, health management, and public health monitoring. Future research should consider developing and testing the application of transformer models for such use cases.
Critically reflecting on the use of immersive virtual reality in educational settings: What is known and what has yet to be shown?

Interest in the educational applications of immersive virtual reality (IVR) has continued to grow worldwide, particularly in recent years. With the ever-increasing literature base on IVR in educational contexts, two patterns of data have emerged: one focused on the affective component and one focused on the cognitive component of IVR. Research focused on the affective component of this technology has consistently found that it is beneficial in increasing students’ motivation to learn. However, there is less of a consensus in the literature on the cognitive benefits of IVR, with results sometimes indicating it (a) is an effective tool for learning, (b) is not an effective tool for learning, and (c) is similar to other instructional media in its impact on learning outcomes. As suggested by these inconsistent findings, there is a great deal left to be understood about when and how IVR can be effective for learning. Therefore, the goal of this reflection article is to draw attention to important research gaps that, if filled, may help to explain the inconsistent effects of this technology in the research literature. Additionally, this article highlights areas in need of further research, which we hope will aid in the advancement of knowledge surrounding the effective implementation of IVR in education.
DESCRIBING THE KNOWLEDGE ABOUT THE SOURCE CODE USING AN ONTOLOGY

This research focuses on the use of ontologies as formal knowledge bases for representing source code in automated systems.By creating an ontology that represents the concepts, relationships, and properties of source code elements, it becomes possible to capture and organize knowledge about the codebase. This enables various functional abilities such as automated code analysis and reasoning about code structure and behavior. The aim of this research is to verify the ability to use ontology as a formal knowledge base about source code in automated systems. A review of the recent articles showed that existing tools to work with ontologies does not provide direct functionality to convert the code into a knowledge, so the idea proposed in the article has novelty. The research explores the use of the Web Ontology Language (OWL) as the primary ontology representation language due to its expressiveness, reasoning capabilities, and compatibility with semantic web standards. The research proposes the creation of ontology representations for various code entities, with classes representing main entity types and individuals representing specific instances within the codebase. To validate the concept and demonstrate the expected results, a proof-of-concept system is built, consisting of a parsing client, a RESTful API for communication, and an ontology manager. The system utilizes libraries such as SwiftSyntax for parsing Swift code, Alamofire for HTTP communication, and OWL manipulation libraries (such as OWL-API and ONT-API) for working with ontologies. The system aims to extract knowledge from the source code, convert it into ontology entities and axioms, and reason over the ontology using a reasoner (such as HermiT). The research concludes that ontologies can effectively store knowledge about source code and enable various further manipulations on that knowledge. However, further investigation is needed to determine the optimal set of properties and entities that should be supported by the code ontology extraction system to infer more knowledge about the source code.
Professional Development of Future Primary School Teachers: Enhancement of Scientific Research Activities

The article outlines the theoretical essence of the “students’ research activity” concept, detailing its content and fundamental methodological principles for application in a higher education institution. Innovative approaches to engaging students in research projects are introduced. The author substantiates the distinctive features and forms of cultivating future primary school teachers’ professional skills through research activities (students’ work as part of national or international grants; scientific societies, workshops, seminars (webinars), leading scientific schools; participation in scientific conferences – online or offline, research competitions, Olympiads, exhibitions, forums; participation in student scientific mobility programs, non-formal education). The importance of involving future professionals in research in the community of a city or region is emphasized, as well as stimulating them with scholarship funds and programs. The findings of the study of the state of this problem in the practice of professional training of students of the Faculty of Pedagogy are presented, including, the outcomes from the survey aimed at identifying the dynamics of the level of interest of future educators in research engagement during their 4 years of study at the university. The emphasis is placed on the importance of individualizing students' research work, increasing their motivation, intellectual culture, critical thinking, and creativity, as well as actively engaging them in national and international scientific events, and focusing on interdisciplinarity in scientific project work.
CYBERBULLYING AMONG ADOLESCENTS AND ONLINE INFORMATION SEEKING ABOUT MENTAL HEALTH

Cyberbullying is a serious problem among adolescents and has an impact on mental health. The purpose of this study was to: 1) translate and validate the Cyberbullying and Online Aggression Survey in the Serbian language; 2) explore factors associated with more intense cyber victimization and cyber offending and 3) examine whether more intense cyber victimization and cyber offending are associated with a higher likelihood of online search for topics on mental health in a sample of high school students.A total of 702 students from 4 public high schools participated in this cross-sectional study. Data were collected using a general questionnaire and the Cyberbullying and Online Aggression Survey. Internal consistency of the questionnaire was examined and confirmatory factor analysis was conducted to assess construct validity. The multiple linear regression model examined factors associated with higher levels of cyber offending and cyber victimization. The multiple logistic regression models examined whether cyber offending and cyber victimizations were associated with searching for mental health topics on the Internet.Cronbach's alpha for the Cyberbullying Victimization Scale was 0.813 and for the Cyberbullying Offending Scale was 0.789. Both scales had a one-factorial structure and parameters on the confirmatory factor analysis were appropriate. More than one-half of students (56.2%) reported ever being cyber victimized and 39.9% of students reported ever being cyber offenders. Having lower grades, being younger at first Internet use and more frequent use of the internet were associated with both higher Cyberbullying Victimization and Offending scores. Having higher scores on both Cyberbullying Victimization and Offending scales was associated with higher odds of searching for mental health topics online.It is necessary to address cyberbullying in schools to help recognize and modify the behavior of cyber offenders and provide means of support and empowerment to cyber victims.
Image Encryption using Half-Inverted Cascading Chaos Cipheration

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
DC-Offset Strategy for Controlling Hidden and Multistable Behaviors in Physical Circuits

Analog circuit detection of hidden and multistable dynamics is an intractable problem as their attraction basins are hard to locate rigorously in analog circuits. This article proposes a dc-offset strategy to achieve flexible control of hidden and multistable dynamics. A modified Chua's circuit with multiple dc control signals is constructed to demonstrate the feasibility of this dc-offset strategy. The kinetic effects of these dc control signals are systematically demonstrated. Then, taking the series dc voltage signal in the inductor branch as an example, the induced hidden and coexisting behaviors are detailed via theoretical analyses and numerical simulations. By introducing two dc voltage signals into the capacitor branches, the initial voltages of the two capacitors are accurately configured via predischarging the dynamic elements. The physical circuit is linearly reconstituted to make the attraction basins of desired coexisting attractors intersect with the easily implemented origin point. This improves the controllability of the hardware circuit and facilitates the analog circuit detection of hidden and coexisting dynamics. Hardware experiments are finally carried out to verify the feasibility of the dc-offset strategy.
GhostViT: Expediting Vision Transformers Via Cheap Operations

Vision Transformers (ViTs) have recently achieved promising results in various computer vision tasks. However, ViTs have high computation costs and a large number of parameters due to the stacked multi-head self-attention (MHSA) and expanded feed-forward network (FFN) modules. Since the complexity of Transformer-based models is quadratic with the length of the input tokens, most current efforts focus on reducing the number of tokens in ViTs to improve the model efficiency. Unlike previous studies, we argue that diverse redundant features help ViTs understand the data comprehensively. In this paper, we propose GhostViT, which achieves both computation and storage efficiency. The key concept of GhostViT is to generate more diverse features using cheap operations in the MHSA and FFN modules. We experimentally demonstrate that our GhostViT can significantly reduce both the parameters and FLOPs of ViTs while achieving the similar or better accuracy. For example, about 14% of parameters and 17% of FLOPs of the DeiT-tiny model are reduced without any accuracy loss on the ImageNet-1 K dataset. The codes and trained models can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HuCaoFighting/GhostViT</uri> .
Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition

Sign languages are complex, but there are ongoing research efforts in engineering and data science to recognize, understand, and utilize them in real-time applications. Arabic sign language recognition (ArSL) has been examined and applied using various traditional and intelligent methods. However, there have been limited attempts to enhance this process by utilizing pretrained models and large-sized vision transformers designed for image classification tasks. This study aimed to create robust transfer learning models trained on a dataset of 54,049 images depicting 32 alphabets from an ArSL dataset. The goal was to accurately classify these images into their corresponding Arabic alphabets. This study included two methodological parts. The first one was the transfer learning approach, wherein we utilized various pretrained models namely MobileNet, Xception, Inception, InceptionResNet, DenseNet, and BiT, and two vision transformers namely ViT, and Swin. We evaluated different variants from base-sized to large-sized pretrained models and vision transformers with weights initialized from the ImageNet dataset or otherwise randomly. The second part was the deep learning approach using convolutional neural networks (CNNs), wherein several CNN architectures were trained from scratch to be compared with the transfer learning approach. The proposed methods were evaluated using the accuracy, AUC, precision, recall, F1 and loss metrics. The transfer learning approach consistently performed well on the ArSL dataset and outperformed other CNN models. ResNet and InceptionResNet obtained a comparably high performance of 98%. By combining the concepts of transformer-based architecture and pretraining, ViT and Swin leveraged the strengths of both architectures and reduced the number of parameters required for training, making them more efficient and stable than other models and existing studies for ArSL classification. This demonstrates the effectiveness and robustness of using transfer learning with vision transformers for sign language recognition for other low-resourced languages.
A Two-Stages Unsupervised/Supervised Statistical Learning Approach for Drone Behaviour Prediction

Drones are prone to abuse due to their low cost and their pool of potential illegal applications that can compromise safety of national infrastructures and facilities. Hence, drone detection and predict its behaviour is crucial to ensure smooth operation of services. In this paper, an unsupervised/supervised statistical learning algorithm for drone behaviour prediction is proposed. The algorithm is based on drone detection data collected from any radar or RF- sensor. The architecture of the approach is comprised of two stages: i) the first stage attempts to study the drone detection data using either unsupervised or supervised learning methods to model low dimensional expert's features, and ii) in the second stage a real time drone behaviour predictor model is proposed based on the Kolmogorov-Smirnov and Wasserstein distances. Simulation studies using synthetic data obtained from the AirSim simulator are given to provide the evidence-base for future improvements in the field of drone behaviour prediction.
Quantum physics cannot be captured by classical linear hidden variable theories even in the absence of entanglement

Recent experimental tests of Bell inequalities confirm that entangled quantum systems cannot be described by local classical theories but still do not answer the question whether or not quantum systems could in principle be modelled by linear hidden variable theories. In this paper, we study the quantum trajectories of a single qubit that experiences a sequence of repeated generalised measurements. It is shown that this system, which constitutes a Hidden Quantum Markov Model, is more likely to produce complex time correlations than any classical Hidden Markov Model with two output symbols. From this, we conclude that quantum physics cannot be replaced by linear hidden variable theories. Indeed, it has already been recognised that not only entanglement but also non-classical time correlations of quantum systems with quantum feedback are a valuable resource for quantum technology applications.
Optimal quantum teleportation protocols for fixed average fidelity

We demonstrate that, among all quantum teleportation protocols giving rise to the same average fidelity, those with aligned Bloch vectors between the input and output states exhibit the minimum average trace distance. This defines optimal protocols. Furthermore, we show that optimal protocols can be interpreted as the perfect quantum teleportation protocol under the action of correlated one-qubit channels. In particular, we focus on the deterministic case for which the final Bloch vector length is equal for all measurement outcomes. Within these protocols, there exists one type that corresponds to the action of uncorrelated channels: these are depolarizing channels. Thus, we established the optimal quantum teleportation protocol under a very common experimental noise.
TIVA-KG: A Multimodal Knowledge Graph with Text, Image, Video and Audio

Knowledge graphs serve as a powerful tool to boost model performances for various applications covering computer vision, natural language processing, multimedia data mining, etc. The process of knowledge acquisition for human is multimodal in essence, covering text, image, video and audio modalities. However, existing multimodal knowledge graphs fail to cover all these four elements simultaneously, severely limiting their expressive powers in performance improvement for downstream tasks. In this paper, we propose TIVA-KG, a multimodal Knowledge Graph covering Text, Image, Video and Audio, which can benefit various downstream tasks. Our proposed TIVA-KG has two significant advantages over existing knowledge graphs in i) coverage of up to four modalities including text, image, video, audio, and ii) capability of triplet grounding which grounds multimodal relations to triples instead of entities. We further design a Quadruple Embedding Baseline (QEB) model to validate the necessity and efficacy of considering four modalities in KG. We conduct extensive experiments to test the proposed TIVA-KG with various knowledge graph representation approaches over link prediction task, demonstrating the benefits and necessity of introducing multiple modalities and triplet grounding. TIVA-KG is expected to promote further research on mining multimodal knowledge graph as well as the relevant downstream tasks in the community. TIVA-KG is now available at our website: http://mn.cs.tsinghua.edu.cn/tivakg.
Detecting Pretraining Data from Large Language Models

Although large language models (LLMs) are widely deployed, the data used to train them is rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but certain that it includes potentially problematic text such as copyrighted materials, personally identifiable information, and test data for widely reported reference benchmarks. However, we currently have no way to know which data of these types is included or in what proportions. In this paper, we study the pretraining data detection problem: given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text? To facilitate this study, we introduce a dynamic benchmark WIKIMIA that uses data created before and after model training to support gold truth detection. We also introduce a new detection method Min-K% Prob based on a simple hypothesis: an unseen example is likely to contain a few outlier words with low probabilities under the LLM, while a seen example is less likely to have words with such low probabilities. Min-K% Prob can be applied without any knowledge about the pretraining corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data. Moreover, our experiments demonstrate that Min-K% Prob achieves a 7.4% improvement on WIKIMIA over these previous methods. We apply Min-K% Prob to three real-world scenarios, copyrighted book detection, contaminated downstream example detection and privacy auditing of machine unlearning, and find it a consistently effective solution.