Reviewerly/exHarmony huggingface dataset:
FORMATION OF STUDENTS' ENVIRONMENTAL AWARENESS THROUGH SOCIAL MEDIA

Implementation of students' environmental education in the context of rapid economic and social development is one of the priority tasks of forming a society, which is impossible without the active participation of higher education institutions, as well as awareness of the need for environmentally friendly use of natural resources and a frugal attitude towards nature. Furthermore, students are the driving force that can influence the state of the environment in the future, so it is extremely important today to organize the educational process to familiarize children and youth with environmental issues. The basis for the implementation of an effective environmental education strategy is the maximum unification of efforts for an effective result based on the definition of reference positions in formal and non-formal environmental education, the formation of the child's ability to purposefully observe, study and correctly evaluate objects and phenomena in the process of observation from the point of view of environmental thinking. This article aims to identify the key features of forming environmental awareness in students through social media. Methodology. During this study, analytical and bibliographic methods were used to study the scientific literature on the development of environmental awareness, as well as synthesis, analysis of information, system-structural, comparative, logical, and linguistic methods, abstraction, idealization, induction, a deduction for studying and processing data, and a questionnaire survey conducted by the authors of the study online to practically clarify the most critical issues related to the formation of environmental awareness in students using social media. Results. The study identified the leading, most important theoretical aspects of the issue of environmental awareness, as well as the point of view of scientists and university professors on the critical aspects of this issue.
The design of lightweight vehicle detection model based on improved YOLOv5

Convolutional neural network-based target detection algorithms are widely used in vehicle detection due to their high speed and accuracy. However, existing algorithms are characterized by large computational volumes, complex network structures, and severe resource constraints. They make them difficult to be ported to mobile platforms and embedded devices. Therefore, the structure of the relevant target detection algorithm needs to be optimized to enable wider deployment of the algorithm. To address the problems mentioned earlier, a YOLOv5SCB lightweight target detection network model is proposed. In the presented model, Shufflenetv2 and CA module are introduced into the backbone network to reduce the complexity of the network model and improve the detection accuracy of the model. Furthermore, BiFPN is integrated into the neck network to improve the efficiency of network feature fusion and enhance the ability of network feature expression. The experimental data show that compared with the original YOLOv5, the model parameters of the proposed YOLOv5SCB are reduced by 62.4% and the overall detection accuracy is improved by 1.1%.
Supplemental Material: Role of volatiles in intrusion emplacement and sulfide deposition in the supergiant Norilsk-Talnakh Ni-Cu-PGE ore deposits

&lt;p&gt;Geological map (regional and local geology) and stratigraphic column of Paleozoic sedimentary sequence and of the individual ore-hosting intrusions. &lt;/p&gt;
Open quantum behaviour manifested in the double-slit experiment

Abstract In certain analyses of quantum systems, it is common to disregard the dissipation effect and focus solely on decoherence. This is because decoherence typically occurs on a much shorter timescale compared to dissipation. However, in order to gain insight into the role of dissipation, we examine the interference pattern in a double-slit experiment while considering both effects. To do so, we establish time scales for decoherence and dissipation and acknowledge the possibility of these effects occurring in close proximity. By comparing the relative intensity and fringe visibility between a purely decoherent model and a model that incorporates both decoherence and dissipation, we observe that when the time scales of decoherence and dissipation are similar, there exists a propagation time interval where dissipation contributes to a faster degradation of quantum features compared to the sole influence of decoherence. Hence, it is crucial to consider both decoherence and dissipation effects, especially when their time scales are closely aligned, to gain a comprehensive understanding of quantum behavior.
White light phase-shifting interferometry using deep learning-based phase-shifter

White light phase shifting is an important technique in interferometry to extract the high-resolution quantitative phase images with high spatial phase sensitivity i.e., of the order of sub nanometer. The dynamical information about a biological sample is limited in white light phase-shifting interferometry (WL-PSI) due to multiple frame requirement. The multiple frame requirement with controlled phase-shift is the key limitation for the high-resolution phase information extraction about the sample. A high-cost piezoelectric transducer (PZT) is required to introduce equal phase-shifts between the frames in WL-PSI. Here, we introduce a deep learning (DL)-based phase-shifter instead of PZT to introduce equal phase-shift in WL-PSI. We use deep neural network to introduce the equal phase-shift between the data frames. The idea is to train the network with multiple equal phase-shifted frames for training and learn the basic application of a PZT. After sufficient training of the network, it will generate multiple phase-shifted frames. Our study is validated by simulating step-like object with equal phase-shifts for training and testing of the network. The network is trained for a total 4 phase-shifted frame generation from a single interferogram. Further, the line profile of DL-based phase-shifter generated data frames are compared with the line profile of simulated data frames. Finally, generated phase-shifted data frames are used for the final phase reconstruction of the sample and compared with the phase reconstruction from simulated data frames.
Design and Research of Dynamic Knowledge Graph Construction and Visual Retrieval platform for the Medical Field

Health care is one of the hottest topics at present, the information about the medical field on the network is characterized by complexity and huge volume. People normally use the Internet search engine to obtain medical relevant information, Due to the large amount of retrieval results, it is difficult for information filtering. At present, knowledge graph develops rapidly in various fields with its advantages in understanding semantic relations. In this paper, it is applied to the medical field. Based on OpenKG community medical field data, an effective medical knowledge system is established, and then a dynamic knowledge graph for the medical field is created, and based on which a visual retrieval platform is constructed.
Using PGAN to Create Synthetic Face Images to Reduce Bias in Biometric Systems

This work does not aim to advance the state of the art for face demographic classification systems, but rather to show how synthetic images can help tackle demographic unbalance in training them. The problem of demographic bias in both face recognition and face analysis has often been underlined in recent literature, with controversial experimental results. The outcomes presented here both confirm the advantage of using synthetic face images to add samples to under-represented classes and suggest that the achieved performance increase is proportional to the starting unbalance.
An Applied Image Cryptosystem on Moore’s Automaton Operating on δ ( <i> q <sub>k</sub> </i> )/𝔽 <sub>2</sub>

The volume of multimedia-based image data or video frames in Web 3.0 is constantly increasing, owing to the advancement of real-time data transmission. However, security vulnerabilities frequently impair the performance of real-time applications. Many researchers have recently proposed image encryption schemes based on a high-dimensional chaotic system due to properties such as ergodicity and initial state sensitivity. Nonetheless, most schemes have suffered from excessive computational complexity, low security, and the generation of cryptographically secure random numbers. To overcome these challenges, an efficient and highly secure cryptosystem is necessary for safe multimedia transmission in Web 3.0. This article proposes a novel work on the image cryptosystem based on the Escalation function with a one-time key-oriented Moore’s Automaton over a finite field 𝔽 2 . The Escalation function is a nonlinear scrambling technique for plaintext images that goes through the confusion phase and plays an essential role in row-column permutation. To make the algorithm more secure and robust in the diffusion phase, the proposed Moore’s Automaton produced ciphertext images through a highly random key stream generated by the combination of a logistic map and cyclic group. Specifically, the proposed Moore’s Automaton operates on δ ( q k )/𝔽 2 to render random binary bits into unpredictable sequences to construct ciphertext images. Our new finding quickens the speed and provides adequate key space, and pixel distributions are more uniform, have high entropy value, and are secure against differential and statistical attacks.
Behavior-Driven Development

Behavior-Driven Development (BDD) methodology was coined by Dan North. His goal was to improve communication between business and technical teams to aid in the creation of software with business value. Miscommunication between business and technical teams is often the biggest bottleneck in the delivery of software projects, and developers often misunderstand the business goals, and business teams fail to grasp the capabilities of the technical team.
Impact of Communication, Motivation, Work Discipline, and Environment on Employee Performance: A Study at a Manufacturing Firm

This study examines the influence of communication, motivation, work discipline, and work environment on employee performance within a manufacturing context. With a sample size of 90 employees, a structured questionnaire was employed for data collection, followed by validity and reliability assessments, classic assumption tests, multiple linear regression analysis, and hypothesis testing using SPSS V25. Findings revealed that communication style significantly impacted employee performance, while motivation, work discipline, and work environment did not exhibit significant effects. However, when considered collectively, the variables of communication, motivation, work discipline, and work environment exhibited a significant influence on employee performance, offering valuable insights for optimizing performance enhancement strategies in similar organizational settings. Highlight: Holistic Examination: Investigating the impact of communication, motivation, work discipline, and work environment on employee performance in a manufacturing context. Methodological Rigor: Employing a structured questionnaire and advanced statistical analyses, including validity and reliability assessments, classic assumption tests, multiple linear regression, and hypothesis testing. Integrated Insights: Revealing a significant influence of communication style on employee performance, while motivation, work discipline, and work environment individually did not exhibit significant effects. However, their combined effect underscores their collective importance for optimizing performance enhancement strategies in similar organizational settings. Keyword: Communication, Motivation, Work Discipline, Work Environment, Employee Performance
Pengelompokan Seleksi Siswa Baru di Lembaga Pendidikan Non Formal Kabupaten Gresik Menggunakan Clustering K-Medoids

Non-formal education has different programs according to the current and future needs of society. The course institutions in Gresik Regency are the unofficial educational institutions of the Gresik Regency Education Office which organize programs, namely the SMA Package C Equivalency Education Program, the SMP Package B Equality Education Program and the Sewing Course. The current grouping of new student selection is felt to be less effective because the grouping is based on certificates obtained more than two years ago. This is because most non-formal students drop out of school due to financial constraints. So that the value given to the certificate is not in accordance with the qualifications of students while taking informal education. This study uses the K-Medoids cluster using datasets, namely practical exam scores, final exam scores and interview scores. The data used are students enrolled in package C as high school equivalents as many as 28 students and 32 students enrolled in package B as high school equivalents. The results obtained in this study are educational institutions that can place prospective students at a more accurate value. The Medoid-K clustering procedure for new student clustering requires two iterations to get a total S-distance of 54.594255.
Framework for privacy preserving credential issuance and verification system using soulbound token

This paper proposes a framework for privacy-preserving credential issuance and verification over the public blockchain. The credential used in this framework is a soulbound token (SBT), a non-transferrable non-fungible token (NFT) verifiable on the blockchain. Once the issuing organization issues the credential, this framework gives the holder complete control of the credential. This privacy-preserving property allows the holder to selectively disclose the credential attributes in the verification process. The framework proposed suggests a decentralized credential recovery mechanism if the credential holder loses their private key. This paper compares this framework’s efficiency with different schemes based on privacy-preserving, selective disclosure, and decentralized credential recovery. This paper also compares the overhead for credential issuance and verification with Merkle trees. This paper also discusses the real-world use cases where this framework can be applied.
The Effect of the Blended Learning Model on Student Critical Thinking Skill: Meta-analysis

This study aims to investigate the effect of blended learning learning models on students' critical thinking skills: meta-analysis. This research is a meta-analysis research. The source of the data in this study came from an analysis of 10 national and international journals published from 2015-2023. Data source tracking through Google Scholar, ScienceDirect, Wiley, ProQuest and Sage. The inclusion criteria in the study are 1) published articles must be indexed by SINTA and Scopus; 2) Research must be experimental or quasi-experimental; 3) the article has data that can be calculated by the value of the effect size (ES). Data analysis in this research is a quantitative analysis by calculating the effect size value of each article with the help of the JSAP application. The results showed that the average effect size was 0.79 with moderate criteria. These findings indicate that the blended learning learning model has a significant influence on students' critical thinking skills. The blended learning model helps students grow their critical thinking skills when studying.
A Contrastive Analysis Of Prefix In English And Batak

Indonesia is one of the big countries in the world consists of many islands which are separated by oceans and islands that has many tribes as well as their languages. One of the tribes in Indonesia is Bataknese. Batak are an ethnic group from North Sumatra. Most of Bataknese are living in North Sumatra. In this contrastive analysis, the writer chooses two languages, first, English as the source language and Batak Toba language as the target language. objectives of this study is restricted to investigating and describing the differences of the two languages about suffix in English and Toba Batak Language, that focus in prefix in meaning.This study was applied by descriptive qualitative design. The data was taken a news text with the titlle Ridwan Kamil is popular among female voters, Sandiaga Uno among male voters from kompas website.The findings describied that prefix meaning in english and batak language is different .Both the languages, have the different prefix with the different meaning for a word. In conclusion, that prefix in English and Batak language are different.
Efficient de-noising brain MRI images using various filtering techniques

Brain cancer affects millions today. MRI images must be separated, identified, and extracted to locate a brain tumour. This process is complicated and error prone. In order to minimise the limitations, segmentation and categorisation are currently done utilising automatic and semiautomatic techniques. The initial step of image processing is de-noising. The image may become hazy if the noise-reduction technique is not carefully followed. The image may become hazy if the noise-reduction technique is not carefully followed by salt and pepper sounds. Gaussian and speckle noise alter the MRI image. Therefore, getting exact photographs of the brain is a tough undertaking. Different de-noising techniques are performed on MRI scans; each has unique properties. The noise from the provided images is removed in this research effort using a variety of filters, including mean filter (MF), Gaussian filter (GF), Kalman filter (KF) and alpha-trimmed mean filter (ATMF). The outcomes of these techniques are evaluated based on various criteria, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). The outcomes demonstrate how the suggested alpha-trimmed mean filter (ATMF) works better and used for MATLAB execution.
Sentiment Analysis of Potential Presidential Candidates 2024: A Twitter-Based Study

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study aims to analyze the sentiment towards potential presidential candidates for the 2024 election in Indonesia based on Twitter users' opinions. Three prominent figures, Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto, were surveyed to gauge their electability. Using machine learning classification methods, Support Vector Machine, Bernoulli Naïve Bayes, and Logistic Regression, sentiment classification was performed. The findings indicate that Twitter users expressed predominantly positive sentiments towards each potential candidate. The evaluation of the classification algorithms showed SVM with 84% accuracy, Bernoulli Naïve Bayes with 77%, and Logistic Regression with 84%. This research sheds light on public sentiment towards potential leaders, offering valuable insights for political strategists and decision-makers in shaping effective election campaigns.&#x0D; Highlight:&#x0D; &#x0D; Sentiment Analysis: The study employs machine learning techniques to analyze the sentiments expressed by Twitter users towards potential presidential candidates for the 2024 election in Indonesia.&#x0D; Positive Sentiments: The findings reveal that Twitter users predominantly exhibit positive sentiments towards all three potential candidates, Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto.&#x0D; Election Insights: This research provides valuable insights into public sentiment, offering valuable information for political strategists and decision-makers in devising effective election campaigns for the upcoming presidential election.&#x0D; &#x0D; Keyword: Sentiment Analysis, Twitter Users, Potential Presidential Candidates, Machine Learning, Election 2024&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
ShuffleTrans: Patch-wise weight shuffle for transparent object segmentation

Transparent objects widely exist in the world. The task of transparent object segmentation is challenging as the object lacks its own texture. The cue of shape information therefore gets more critical. Most existing methods, however, rely on the mechanism of simple convolution, which is good at local cues and performs weakly on global cues like shape. To solve this problem, an operation named Patch-wise Weight Shuffle is proposed to bring in the global context cue by being combined with the dynamic convolution. A network ShuffleTrans that recognizes shape better is then designed based on this operation. Besides, fitter for this task, two auxiliary modules are presented in ShuffleTrans: a Boundary and Direction Refinement Module which collects two additional information, and a Channel Attention Enhancement Module that assists the above operation. Experiments on four texture-less object segmentation datasets and two normal datasets verify the effectiveness and generality of the method. Especially, the ShuffleTrans achieved 74.93% mIoU on the Trans10k v2 test set, which is more accurate than existing methods.
Quantum codes from $ \sigma $-dual-containing constacyclic codes over $ \mathfrak{R}_{l, k} $

&lt;abstract&gt;&lt;p&gt;Let $ \mathfrak{R}_{l, k} = {\mathbb F}_{p^m}[u_1, u_2, \cdots, u_k]/ \langle u_{i}^{l} = u_{i}, u_iu_j = u_ju_i = 0 \rangle $, where $ p $ is a prime, $ l $ is a positive integer, $ (l-1)\mid(p-1) $ and $ 1\leq i, j\leq k $. First, we define a Gray map $ \phi_{l, k} $ from $ \mathfrak{R}_{l, k}^n $ to $ {\mathbb F}_{p^m}^{((l-1)k+1)n} $, and study its Gray image. Further, we study the algebraic structure of $ \sigma $-self-orthogonal and $ \sigma $-dual-containing constacyclic codes over $ \mathfrak{R}_{l, k} $, and give the necessary and sufficient conditions for $ \lambda $-constacyclic codes over $ \mathfrak{R}_{l, k} $ to satisfy $ \sigma $-self-orthogonal and $ \sigma $-dual-containing. Finally, we construct quantum codes from $ \sigma $-dual-containing constacyclic codes over $ \mathfrak{R}_{l, k} $ using the CSS construction or Hermitian construction and compare new codes our obtained better than the existing codes in some recent references.&lt;/p&gt;&lt;/abstract&gt;
INNOVATIVE MOTORLESS SOLAR TRACKING SYSTEM USING SHAPE MEMORY ALLOY

This paper presents the design and fabrication of a prototype model of a novel solar tracker that exploits the unique properties of shape memory alloy for its actuator. As against the conventional solar trackers, the proposed tracker does not use a motor for changing the position of the PV cell. The shape memory alloy is in the form of a spring and is used for actuating. SMA spring moves the solar panel to track the Sun during day time and back to its original position at the end of the day. The proposed structure of the tracker not only increases the efficiency but also proves to be compact, light weight and robust.
Explaining Classifications to Non-experts: An XAI User Study of Post-Hoc Explanations for a Classifier When People Lack Expertise

Very few eXplainable AI (XAI) studies consider how users’ understanding of explanations might change depending on whether they know more/less about the to-be-explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high-stakes, human decision-making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N = 96) on how people’s expertise in a domain affects their understanding of post-hoc explanations-by-example for a deep-learning, black-box classifier. The results show that people’s understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada-MNIST). The wider implications of these new findings for XAI strategies are discussed.