Reviewerly/exHarmony huggingface dataset:
The application of a lightweight model FA-YOLOv5 with fused attention mechanism in insulator defect detection

Insulators are important components of transmission lines, serving as support for conductors and preventing current backflow. However, insulators exposed to natural environments for a long time are prone to failure and can cause huge economic losses. This article proposes a fast and accurate lightweight Fast and Accurate YOLOv5s (FA-YOLO) model based on YOLOv5s model. Firstly, attention mechanisms are integrated into the network module, improving the model’s ability to extract and fuse target features. Secondly, the backbone part of the network is lightweightened to reduce the number of parameters and computations at the cost of slightly reducing the accuracy of detecting a few objects. Finally, the loss function of the model is improved to accelerate the convergence of the network and improve detection accuracy. At the same time, a visual insulator detection interface is designed using PyQt5. The experimental results show that the algorithm in this paper reduces the number of parameters by 28.6%, the computational effort by 35.7%, and the mAP value by 1.7% compared with the original algorithm, and is able to identify defective insulators quickly and accurately in complex backgrounds.
Students’ Perceptions on The Use of Numbered Head Together Technique in Teaching Reading Comprehension

The aim of this study was to find out students’ perceptions on the use of Numbered Head Together technique in teaching reading comprehension. The participants of the study were 30 students of Grade VIII SMP N 35 Medan. The data were analyzed by using descriptive qualitative. The data were collected by conducting observation, questionnaire, and interview. The questionnaire was designed based on Likert scale. The result of this study showed that most students gave positive response on the use of Numbered Head Together technique in teaching and learning reading comprehension process. There were only 9 students who gave negative response to the used of Numbered Head Together technique in teaching reading comprehension which could not help them in learning many reasons namely; they felt uncomfortable to learn in group discussion and they felt boring during the teaching and learning process. In short, the used of Numbered Head Together technique in teaching reading comprehension can motivate and attract them to be more enthusiastic in learning reading comprehension.
Applications of Educational Technology in Solving Learning Problems

Educational technology in its contribution to the management and learning of education is opening up knowledge horizons about changing strategic environments. We need the growth of science and technology caused by learning and learning activities of innovation. Speaking in this case is inadequate because the concept of technological studies provides a formula for how to bring teaching and learning activities. Then, the application of educational technology, especially ICT (Information and Communication Technology) in education to overcome learning problems has a number of requirements. For example, the need for large investment costs and energy that are skilled in managing and developing education and utilizing technological advancements that will support the process of learning. It is noteworthy that education is a long-term investment. Therefore, investment in basic education facilities must be calculated with future educational outcomes
High-Fidelity Qubit Transfer Between Leaky Memory Blocks.

and a waveguide channel connecting them can be used to achieve a quantum state transfer fidelity of 96% with realistic parameters using our optimal control solution. The effects of the intrinsic losses and the asymmetries in the physical memory parameters are discussed quantitatively.
Exploring the direction of development of music textbooks for music education in the New Normal Era

The change in society as a whole after COVID-19 has led to the creation of a new word, new normal. It also influenced school education, and the need to reflect the changes of the new era emerged in education. School music education has also changed a lot compared to the past, but music textbooks have not reflected various changes. The purpose of this study is to explore the direction of the development of music textbooks according to the 2022 revised music curriculum in the new normal era. To this end, first, the meaning of the new normal was grasped and implications for music education were derived. Second, the characteristics of the new normal era reflected in the 2022 revised curriculum were analyzed. Third, the direction of music textbook development was explored for music education in the new normal era. As a result of the study, the implications of the new normal for music education were as follows: strengthening teacher competency, support for students, teaching and learning methods using digital technology, and the necessity of discussion to harmonize with the nature of music subjects. In addition, the characteristics of the new normal era were generally reflected in the curriculum amid the changed social background and needs. Music textbooks in the new normal era should be developed as textbooks for customized learning for learners, textbooks that embrace various aspects of music in the present and future, textbooks that seek to cultivate digital basic knowledge, and textbooks that encompass collaborative music activities.
Photo-crosslinking speckle patterns for large deformation measurement of hydrogels using digital image correlation

Hydrogels often undergo large or inhomogeneous deformation when they are used in soft electronic devices, adhesives, or biological implants. To avoid the potential risk of damage and failure in service, the mechanical response of hydrogels, especially subjected to large deformation, requires meticulous evaluation. Digital image correlation (DIC) has been increasingly employed in the mechanical tests of hydrogels due to non-contact measuring the deformation field by tracking speckle patterns motion on the specimen. However, measuring large deformation of hydrogels using DIC is challenging because the speckle patterns painted on the wet surface suffer various issues, such as bleeding when water is squeezed out, fragmentation or debonding if the stress transferred from hydrogel exceeds the strength or adhesion of painting. In this work, we developed a UV lithography-based speckle pattern preparation method to overcome these difficulties. Speckle patterns are generated by curing a polymer on the surface of hydrogels through chemical-crosslinked bonds, making them an integral part of the hydrogel surface. Experiments indicate that the speckle patterns work as reliable information carrier for DIC to measure large deformation up to strain of 580% and highly concentrated localized strain field within specimen. The speckle patterns show good durability in cyclic loading tests with peak strain up to 150%, achieving low relative deviation (&amp;lt;6%) of the measured deformation field in different cycles. Furthermore, our method allows the optimization of speckle patterns by controlling the shape, size, and coverage of speckles through well designed masks, which guarantees the accuracy and robustness of DIC measurement.
Digital Literacy, Cognitive Control and Student Use of Digital Devices

&lt;p&gt;There is an idea that modern young people who grew up surrounded by digital devices spontaneously master digital skills, and their formation does not require special attention from the school. Teachers' observations and research results show that this is not the case. Most schoolchildren are not able to effectively solve problems in the digital environment, for example, correctly construct a search query, or ensure their information security. The purpose of the study presented in the article is to assess the relationship of digital literacy, including its individual components (for example, the ability to work with information in a digital environment), with some cognitive characteristics of students. In particular, the relationship of digital literacy with the features of cognitive control of students is considered, taking into account the frequency and specifics of the activity of using digital devices. The study is based on data from the assessment of the level of digital literacy by the developed measurement tool on a sample of 4,700 students in grades 7 and 8 of schools in 5 regions of Russia in the fall of 2022.&lt;/p&gt;
Gender and Politeness of Request Expression on Whatsapp

The research discussed about politeness strategy used by male and female students in requesting on WhatsApp message. The aims of this study were: to find out the types of politeness strategy used by male and female students by using Brown and Levinson (1987) politeness theory, to find out the differences and reason of politeness strategy used by male and female students in requesting on WhatsApp. The descriptive qualitative method were used in this research, and the data were derived from the WhatsApp message script. The findings of the study showed that male students applied 3 politeness strategies proposed by Brown and Levinson (1987): bald-on record (21 sentences), positive politeness (8 sentences), negative politeness (3 sentences). The researcher also found that female students applied 4 politeness strategies proposed by Brown and Levinson (1987): bald-on record strategy (5 sentences), positive politeness (7 sentences), negative politeness (17 sentences), and off record (5 sentences). The significant difference in politeness strategies used by male and female students are that there is no off record strategy used by male students, they apply the bald-on record strategy more often, while female students are more varied in using of politeness strategies and mostly applied the negative politeness. The difference of male and female students politeness strategy in requesting on WhatsApp because each gender has own way in conveying their want. Male students were more direct when requesting to others, while female students were more indirect when requesting to others.
Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.
Resilient adjudication in non-intrusive inspection with hierarchical object and anomaly detection.

trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.
Students’ perception of digital transformation in higher education: A case study at Tra Vinh University

Amid the remarkable development of information technology and scientific and technical advancements, combined with the trend of international economic integration, particularly in education, as well as the post-COVID-19 cultural shift, managing changes in university education has become crucial. Consequently, educational institutions are not immune to changes in teaching activities (methods, tools, and techniques). The purpose of the study is to identify and measure the influence of factors on students’ perceptions of the digital transformation in higher education. Implications are then proposed for the university and relevant stakeholders to prepare resources for digital transformation in higher education and enhance students’ self-learning spirit. The article reviews relevant studies and utilizes an expert method to formulate survey questions. The survey is conducted on 220 students at Tra Vinh University, and the data is processed using descriptive statistics and mean value statistics. The results identify six factors that students believe have an impact on the digital transformation in university education: the lecturer, the educational institution, the family, the trend of the times, the perception of the benefits of digital transformation, and personal competence.
Human activity tracking using wearable sensors in loose-fitting clothes: survey

Human activity tracking using wearable technology is an important research field of study and is employed in many applications such as healthcare, aged care, fitness, and tracking activity. Although this field has been well addressed by researchers in the last decade that most of the research is done with tightly fixed sensors (either to the body or to the tight garment), while in contrast, the majority of our daily clothes are loosely fitting so we must be interested in this fields but the main challenge in tracking/recognizing activities in loose clothing is the higher level of noise generated by the movement of clothing compared fixed sensor. This paper reviews recent research on human activity tracking techniques that use sensors that are attached or embedded in loose-fitted clothes. It is predicted from recent studies that many low-cost, reliable, and socially acceptable wearable devices will be available to aid activity tracking. Survey also predicts an increase in the interest for wearables in loose-fitting clothes due to their wide real-time applications. Most of the reviewed work based heavily on off-shelf microcontroller board and sensor, which are characterized by their moderate performance. Hence most of the reviewed work used machine learning to enhance results classification.
BackTrace: A Human-AI Collaborative Approach to Discovering Studio Backdrops in Historical Photographs

In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and events surrounding certain photographs. However, there are few dedicated tools or resources available to aid researchers in this largely manual task. In this paper, we propose BackTrace, a human-AI collaboration system that employs a three-step workflow to retrieve and organize historical photos with similar backdrops. BackTrace is a content-based image retrieval (CBIR) system powered by deep learning that allows for the iterative refinement of search results via user feedback. We evaluated BackTrace with mixed-methods evaluation and found that it successfully aided users in finding photos with similar backdrops and grouping them into collections. Finally, we discuss how our findings can be applied to other domains, as well as implications of deploying BackTrace as a crowdsourcing system.
Typical Contract Graph Feature Enhanced Smart Contract Vulnerability Detection

Due to the significant losses caused by vulnerabilities, the security of smart contracts has attracted widespread attention and research. Existing methods for detecting smart contract vulnerabilities can be classified into traditional detection methods and machine learning-based detection methods. Traditional detection methods rely on fixed expert rules, which result in low robustness and inability to identify complex vulnerability patterns. Machine learning-based detection methods have shown better performance than traditional detection methods. However, some mainstream methods have not fully explored the relationship between contract types and vulnerabilities. In this paper, we attempt to construct typical contract graphs using clustering methods to further extract the type features of smart contracts. We concatenate the type features with the overall semantic syntax features of smart contracts, achieving data enhancement of smart contract features. We evaluate our method on an Ethereum smart contract dataset, and our method achieves an accuracy of 89.28% and a recall rate of 89.08% for detecting reentrancy and timestamp vulnerabilities.
MMID: Combining Maximized the Mutual Information and Diffusion Model for Image Super-Resolution

The Denoising Diffusion Probabilistic Models (DDPM) [11] have shown promise in recovering realistic details for single image super-resolution (SISR). However, the diffusion model’s recovery results often suffer from unpleasant artifacts due to the optimization objective of DDPM, which relies on the $$L_{p}$$ norm distance and is sensitive to data uncertainty. To address this issue, we propose a novel method named MMID (Maximize the Mutual Information for Diffusion model). MMID enhances the relationship between the input and output by maximizing their mutual information. This helps to alleviate the problem of instability arising from the $$L_{p}$$ norm optimization, where the same input may yield multiple output possibilities. Moreover, this paper incorporates the Convolutional visual Transformer (CvT) network structure into the conventional U-Net to form the CTU-Net model, which improves the quality of image restoration. The proposed MMID approach offers a novel and powerful solution, enabling the diffusion model to achieve significantly better performance in super-resolution tasks.
21st-century skill-based literacy learning guide for elementary schools

Reading literacy is a basic literacy that must be learned early on. Many studies have studied solutions for improving student writing skills, but most of these studies only examine how to create learning by collaborating learning with literacy programs, making reading corners and developing books that can be used to improve basic literacy. However, the solution has not been effective in optimizing literacy learning. Therefore, this study aims to produce a Literacy Learning Guide to Skills Based on 21st Century Skills in Elementary Schools. This study uses the Four-D development model consisting of 4 stages, namely, define, design, develop, and disseminate. The subjects in this study are content experts, learning design experts, and teachers. Sampling in this study was carried out using saturated sampling techniques with samples obtained from all SD Piloting K13 in Buleleng District. The number of samples in this study was grade 3 elementary school teachers with 9 people and class III students totaling 61 students. Data collection is carried out by the method of observation and distribution of questionnaires. The data analysis technique used is qualitative and quantitative analysis techniques. The results of this study indicate that the literacy condition of reading students is still said to have not been maximized because of their ability to read and write. The results of this study also show that the Literacy Literacy Learning Guide based on Abab-21 skills is needed in elementary schools, and the effectiveness of the 21st Century Ib-Based Literacy Literacy Learning Guidelines for elementary school students is declared effective.
A Privacy Preserving Method for Trajectory Data Publishing Based on Geo-Indistinguishability

Trajectory data contains rich space-time information. Analysis and research on it can provide important information for location-based services. However, these data are usually private to users, and there will be serious privacy leakage if they are directly published to a third party. Therefore, privacy preserving should be carried out before the data is published. Aiming at the problems of existing research in terms of low privacy preserving degree and data utility, a privacy preserving algorithm for trajectory data publishing based on Geo-indistinguishability is proposed. The algorithm mainly includes two parts of constructing a noise trajectory set and selecting the optimal noise trajectory. n constructing a set of noise trajectories, the total noise value of each trajectory is first calculated using a planar Laplace function, so that the algorithm satisfies geographical indistinguishability as a whole. Then, the total noise value is randomly decomposed and assigned to the position points of the trajectory to generate noise trajectories. Then, based on the trajectory uncertainty model, calculate the Manhattan distance between each noise trajectory at each time, obtain the number of indistinguishable trajectories for each noise trajectory, and preferentially select a noise trajectory with the largest number of indistinguishable trajectories to replace the real trajectory for publishing. The experimental results show that compared with other methods, the algorithm proposed in this paper has a higher degree of privacy preserving and better data utility.
CNGT: Co-attention Networks with Graph Transformer for Fact Verification

Fact verification is a challenging task that requires retrieving evidence from a corpus and verifying claims. This paper proposes Co-attention Networks with Graph Transformer (CNGT), a novel end-to-end reasoning framework for fact verification. CNGT constructs an evidence graph given a claim and retrieved evidence, uses a graph transformer to capture semantic interactions among the claim and evidence, and learns global node representations of the evidence graph via self-attention mechanisms and block networks. Deep co-attention networks integrate and reason on the evidence and claim simultaneously. Experiments on FEVER, a public large-scale benchmark dataset, demonstrate that CNGT achieves a 72.84% FEVER score and a 76.93% label accuracy score, outperforming state-of-the-art baselines. CNGT has de-noising and integrated reasoning abilities and case studies show that it can explain reasoning at the evidence level.