Reviewerly/exHarmony huggingface dataset:
Research on the Training Path for Enhancing the Abilities of Business English Major Students under the Background of Industry-Education Integration

Integration of industry and education is not only an educational concept, but also one of the means of school-enterprise cooperation. Based on the current background of integration of industry and education, there are still many shortcomings in the implementation of teaching aimed at enhancing the comprehensive abilities of business English major students, such as the lack of rationality in teaching design, insufficient foresight in professional planning, and inadequate teacher allocation. If these problems cannot be solved as soon as possible, it will inevitably have a great impact on the quality of talent training in the field of business English, and also restrict the improvement of students’ core competitiveness. This paper first analyzes the connotation of integration of industry and education and the existing problems in teaching business English major under the background of integration of industry, and then proposes corresponding improvements for enhancing students’ abilities, including planning the development of business English major, creating a new curriculum system, optimizing the teacher training path, and establishing a diversified evaluation mechanism.
Perspektif Para Peneliti Senior Tentang Penyusunan Latar Belakang Penelitian yang Baik

Some final year students experience difficulties in writing scientific papers which lie in the preparation of research backgrounds. The purpose of this research is to describe the steps and strategies in writing the research background. The research methodology used was descriptive qualitative by interviewing senior researchers from the Jambi University Postgraduate Program. The results of the study show that to start conducting research, the first step is to find and determine the problem to be studied. Second, supported by supporting theory. And third, determine the purpose and usefulness of the research . The problems encountered by the authors are the beginning of a series of scientific paper writing. The problem is a phenomenon that occurs in the environment to be studied and is a reality in the field that looks different from what is expected. The background is supported by supporting theories and the aims and benefits of the research. In writing scientific papers contained in the research background.
Pengaruh Media Buku Cerita Bergambar Terhadap Kemampuan Membaca Nyaring Siswa Kelas 2 Sekolah Dasar Islam Al-Fajri Kota Bekasi Tahun Ajaran 2022/2023

The research was conducted because it was suspected that there was an effect of picture storybooks on the ability to read aloud of students of Al-Fajri Islamic Elementary School, Bekasi City in the academic year 2022/2023. Quantitative research method One Group Pretest-Posttest Desig and Purposive Sampling technique chosen for the sample. Posttes minimum 71, maximum 92, average 81.68 very good category. While the standard deviation value is 5.47 which is still smaller than the average value (mean) of 81.68. This shows that the Posttest data deviation is good. The result is that storybooks with pictures show improvement in reading ability.
Exploring the meaning of Grit experienced by Taekwondo Poomsae leader and how to use it in training

This study began with the aim of exploring the meaning of grit in games and training through five male and female leaders registered as Poomsae leaders of the Korea Taekwondo Association in 2023. Leaders were selected through regional arrangements from their 40s to 60s, and three results were finally drawn after a period of data collection during the three-month competition. First, it was the belief of Grit and the leaders that showed up with the passion, sincerity, and effort of the players. Second, it was found that grit was used as a method of psychological and functional training experienced by leaders. Third, it was the use, reality, and complementarity of Grit in training that appeared as experience.
A study on the mediating effect of organization-based pride in the relationship between Taekwondo gym director-instructor exchange relationship and organizational commitment

This study investigated the relationship between Taekwondo gym director-instructor exchange relationship and organization-based pride and organizational commitment. First, the exchange relationship between Taekwondo gym directors and instructors affects organization-based pride. In particular, the higher the desire to contribute, emotional attachment, and loyalty, excluding intellectual respect, the higher the instructor's organization-based pride. Second, the exchange relationship between Taekwondo gym directors and instructors affects organizational commitment. In particular, the higher the desire to contribute, loyalty, and intellectual respect, excluding emotional attachment, the higher the instructor's organizational commitment. Third, the organization-based pride of Taekwondo gym instructors was found to have an effect on organizational commitment.
The Impact of Compensation, Organizational Commitment, and Job Satisfaction on Turnover Intention among Young Female Employees

This study meticulously examined the impact of compensation and organizational commitment on turnover intentions among young female employees, with job satisfaction as a mediating factor. The study's subject pool comprises young female employees aged between 19 and 25 in private healthcare institutions. Data collation entailed employing a Likert scale questionnaire, while the ensuing analysis adopted the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The conclusions study proffered that compensation exerted a non-significant negative impact on turnover intentions, concurrently exerting a significant positive influence on job satisfaction. Organizational commitment yielded a notable and negative impact on turnover intentions, simultaneously engendering a marked positive impact on job satisfaction. Curiously, job satisfaction evinced a non-significant negative impact on turnover intentions. Further nuanced analyses revealed that compensation yielded a non-significant negative impact on turnover intentions via the intermediary of job satisfaction. Similarly, organizational commitment's influence on turnover intentions was insignificant when mediated through job satisfaction. This study's seminal contribution discovered that contrary to prevalent assumptions, especially for young female employees, when compensation could provide job satisfaction and is combined with organizational commitment, it will not affect turnover intentions directly or indirectly.
Implementation of Data Mining at Laboratory Vocational High School Using The C4.5 Algorithm to Predict Students Major Preferences

Education or the learning process is the primary thing for human life. Therefore, a place for acquiring knowledge is established, which is called a school. Schools have their own levels, ranging from early childhood education to higher education institutions. When students enter high school, they are required to make decisions in choosing their majors. Accompanied by technological advancements, the issues in high school major selection can be effectively and efficiently addressed using data mining. Common issues that usually arise include lack of accuracy, precision, and requiring a significant amount of time. Hence, the issues within major selection necessitate the use of data mining, employing the C4.5 algorithm method, to determine the accuracy and precision of large datasets. This research achieved with RapidMiner the result is accuracy score of 94.44%, precision of 81.37%, and sensitivity of 74.00%. Additionally, it also generated a decision tree and with Python has an accuracy of 93% because it automatically rounds the values, so there is no significant difference between the two tools. This proves that the C4.5 algorithm produces fairly accurate performance.
Application of Neural Machine Translation with Attention Mechanism for Translation of Indonesian to Seram Language (Geser)

The Seram language (Geser) is one of the regional languages in Kabupaten Seram Bagian Timur of Maluku Province which has been classified by the Language Office as an endangered language. This study uses the Neural Machine Translation (NMT) method in an effort to preserve the Seram (Geser) language. The NMT method has proven to be effective compared to SMT in overcoming the challenges of language translation by using the attention mechanism to improve translation accuracy. The data used in this study were obtained through interviews of 3538 parallel corpus, 255 Indonesian vocabularies and 269 Seram (Geser) vocabularies. The result showed that using 708 test data without Out-of Vocabulary (OOV) the BLUE Score was 0.90518992895191 or 90.518%.
Klasifikasi Rentang Usia Dan Gender Dengan Deteksi Suara Menggunakan Metode Deep Learning Algoritma Cnn (Convolutional Neural Network)

The research discusses the identification of human voices based on gender by utilizing the differences in voice characteristics between males and females. In addition to differences in vocal tract size, factors such as length, thickness, and stiffness of the vocal cords also play a role in producing the differences in fundamental frequency between the two genders. Fundamental frequency serves as an indicator used in acoustic analysis to classify gender based on voice. In the automatic classification of voices, sound processing techniques and machine learning are key in system development. Gender recognition methods based on voice involve acoustic analysis using voice features such as fundamental frequency, formants, duration, intensity, and intonation patterns. Diverse voice datasets containing recordings of both male and female voices are used to train gender recognition models. From the results of research from modeling using CNN on audio to get 92% accuracy and for testing results it is good enough in classifying.&#x0D; Keywords – Deep Learning, Voice Recognition, Audio Classification, CNN, Gender
AKTUALISASI PENDIDIKAN NILAI DAN MORAL PADA SISWA SEKOLAH DASAR MELALUI MEDIA PEMBELAJARAN BERBASIS PERMAINAN WORDWALL

The purpose of this study was to find out how the model of inculcating value and moral education in elementary school students through game-based learning media called wordwall. The research method used is qualitative research with a case study approach, using interview and observation instruments. The results of this study describe the moral values of elementary school children that need to be improved again. At any level of education, especially basic education. Because elementary school education is the main foundation for Indonesia's young generation. Students are identical with the world of play, so the suitable media is media that contains elements of the game. In line with that, the media that can be used for interactive learning is Wordwall. This website-based application can be used to create learning media such as quizzes, match, pair, word random, word search, grouping. This Wordwall learning media was created with the aim of providing knowledge and instilling what and how these moral values are in elementary school children in everyday life. The method used in this research is observation or survey. The results of this study describe the moral values of elementary school children that need to be improved again.
PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition

The main goal of a Data Service Composition is combining multiple data services to provide for a user’s query a new service which uses data from multiple service providers that are incorporated in the composition. In this situation, the data privacy and especially of the service providers can be breached when their critical data can be seen by another party. Therefore, keeping the data privacy during the composition process is crucial by every work in the context of the service composition. Recent approaches rely on a central mediator that can be trusted or not to ensuring the privacy of the service providers during the query execution. The most recent approaches found problems in case of untrusted mediator where they enforce restrictions like k-protection that can affect the efficiency of their works. Therefore, we propose PrSChain which preserves the privacy of all service providers during service composition and execution using BlockChain technology. We used a permissioned BlockChain that acts as trusted mediator where it enables users to access to the BC if a valid certificate is given. We use Hyperledger Fabric to implement our solution where it stores sensitive data about the composition plan. In addition, the intermediate query results are saved in IPFS that acts as offchain storage. As a proof of concept, we have tested PrSChain on a real-world medical dataset to show its feasibility and efficiency for maintaining privacy in a secure and trusted manner.
Temporal Modeling Approach for Video Action Recognition Based on Vision-language Models

The usage of large-scale vision-language pre-training models plays an important role in reducing computational consumption and improving the accuracy of the video action recognition task. However, pre-training models trained by image data may ignore temporal information which is significant for video tasks. In this paper, we introduce a temporal modeling approach for the action recognition task based on large-scale pre-training models. We make the model capture the temporal information contained in frames by modeling the short-time local temporal information and the long-time global temporal information in videos separately. We introduce a multi-scale difference approach to getting the difference between adjacent frames, and employ a cross-frame attention approach to capturing semantic differences and details of temporal changes. In addition, we use residual attention blocks to implement the temporal Transformer and assign individual importance scores to each frame by computing the similarity of the frame to the clustering center, to obtain the overall temporal information of the video. Our model achieves 82.3% accuracy on the Kinetics400 dataset with just eight frames. Furthermore, zero-shot results on the HMDB51 dataset and UCF101 dataset demonstrate the strong transferability of our model.
Rethinking the Exploitation of Monolingual Data for Low-Resource Neural Machine Translation

Abstract The utilization of monolingual data has been shown to be a promising strategy for addressing low-resource machine translation problems. Previous studies have demonstrated the effectiveness of techniques such as Back-Translation and self-supervised objectives, including Masked Language Modeling, Causal Language Modeling, and Denoise Autoencoding, in improving the performance of machine translation models. However, the manner in which these methods contribute to the success of machine translation tasks and how they can be effectively combined remains an under-researched area. In this study, we carry out a systematic investigation of the effects of these techniques on linguistic properties through the use of probing tasks, including source language comprehension, bilingual word alignment, and translation fluency. We further evaluate the impact of Pre-Training, Back-Translation, and Multi-Task Learning on bitexts of varying sizes. Our findings inform the design of more effective pipelines for leveraging monolingual data in extremely low-resource and low-resource machine translation tasks. Experiment results show consistent performance gains in seven translation directions, which provide further support for our conclusions and understanding of the role of monolingual data in machine translation.
Utility-Aware Time Series Data Release with Anomalies under TLDP

With the prevalence of mobile computing, mobile devices have been generating numerous sensor data, a.k.a., time series. Since these time series may include sensitive information, users are posed with severe privacy risks. To protect individuals' privacy, local differential privacy (LDP) is proposed. However, the added noise satisfying LDP typically degrades the utility of released data, especially for anomaly detection such as healthcare monitoring and hazard alarming. In this paper, we study privacy-preserving time series release with anomalies. Recently, local differential privacy in the temporal setting (TLDP) is proposed to perturb the temporal order rather than the values. While it improves the utility for releasing value-critical data, it still suffers from low utility for anomaly detection, because of the inevitable missing and delayed values incurred by TLDP perturbation. We propose to improve its utility from two aspects. To reduce the missing values, we utilize selective substitution according to items' anomaly scores. To decrease the delayed values, we define metric-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\alpha , \delta )$</tex-math></inline-formula> -TLDP and propose a mechanism that can prioritize anomaly release at a close timestamp while still guaranteeing the same TLDP privacy. Through theoretical and empirical evaluation, we show superior performance gain over existing TLDP-based mechanisms on both synthetic and real-world datasets.
Feature Weighted Multi-View Graph Clustering

Graph-based multi-view clustering aims to learn an affinity graph by exploiting consistent and complementary information from multiple views. However, most existing methods suffer the following problems: (1) they treat all features equally for each view during learning, but the redundant and corrupted features should not be treated equally without discrimination; (2) they sometimes employ low-rank constraint to capture the latent global manifold structure of views, this inevitably introduces a parameter that is difficult to tune, and aggravates the computational complexity; and (3) they usually fed the raw multi-view data into graph learning model directly, but the unclean data impair the quality of affinity graph greatly. In this work, we propose a unified Feature Weighted Multi-view Graph Clustering (FWMGC) method to tackle the above problems. For each view, a latent low-rank weighting matrix with mandatory row-sparsity property is efficiently learned to assign weight values to different feature relations, so as to select important features and ensure the robustness to noise. Furthermore, the complementary and consistent information of the clean weighted multi-view data are integrated to learn a consensus affinity graph. Comprehensive experiments on some widely used benchmark datasets demonstrate that the proposed FWMGC yields significant improvements of clustering performance and running time.
The Design of a High-Throughput Hardware Architecture for the AES-GCM Algorithm

This paper proposes the hardware architecture capable of processing the AES-GCM algorithm in parallel and pipeline. To boost the hardware efficiency, two parallel AES processing units, each consisting of a mixed inner-and outer-round pipelining architecture, are employed in the encryption/decryption module. These two AES units share a key expansion unit to save the hardware resource. To optimize the architecture of the AES algorithm, the S-box function is directly realized with the built-in BRAM in the FPGA technique to decrease the number of LUTs used, and implemented with the composite field operation in the ASIC technique in order to save up to 25% logic gates. In the GHASH module design, two pipelining finite field GF(2128) multipliers with appropriate data scheduling are utilized. In addition, realizing the finite field multiplication with the Karatsuba algorithm can further save 50.7% of hardware resource. The resulting AES-GCM IP has been implemented and verified with a Xilinx Virtex 5 series device (XC5VLX220), and a tsmc 0.18-μm cell library. In FPGA verification, the working frequency can achieve 223.314 MHz, yielding the maximum throughputs of 57.17 Gbps. The number of slices/BRAMs used is 5,944/109. The resulting ASIC can operate at 197.239 MHz in simulation and achieve a high throughput of 50.49 Gbps. The core area of the chip is 6,552 μm × 6,516 μm. The power consumption is 1,466.5 mW.
Use of Language Models based on Deep Learning to improve reading comprehension

Reading comprehension is a key process in academic and personal development. Different instruments are systematically applied to analyze the behavior of this indicator, such as the PISA tests, which reflect that the situation in OECD countries such as Chile is far from good. This paper presents a proposal that, through Language Models based on Deep Learning, can be used to improve reading comprehension. Using pre-trained models, together with an additional dataset, fine-tuning has been made to obtain pairs of questions and answers from documents supplied by users. These models are used for the automated preparation of questionnaires with which reading comprehension can be evaluated. A set of experiments was carried out to determine the best adjustment parameters of these models, and these were validated in books of the Chilean National Reading Curriculum, specifically in Readings present in basic education. In all cases, good results were obtained.
The false turn of digital technology

This chapter considers the development of cultural biological works under existing legal regulation, which is focused on the characteristics of digital technology. Digital computers are interacted with in a machinic manner, mirroring proprietary protections given under intellectual property law. It is submitted that this is a ‘false turn’ in legal regulation. The primary form of legal protection is copyright law. It is the contention of this book that as the world has become more informatised, we have an increasing expanse of copyright regulation. Indeed, copyright regulation has considerably expanded as a consequence of the expansion of information technologies. This is why we increasingly see the use of copyright and watermarking technologies in the use of information technologies. It is argued that these are machinic in nature, which conflicts with the notion of cultural biological works and biological technologies. Nonetheless, this book argues that we will increasingly see the use of these copyright and watermarking technologies in the biological context (for example, the use of genetic use restriction technologies (GURTS) which are akin to digital rights management (DRM)). Copyright and watermarking technologies are one of the foremost means by which biological entities are likely to be regulated in in the future, if not already so in the present. The chapter outlines why this is likely to become more significant than, say, existing patent law. The consequences are considered, in terms of whether existing legal protection should require a copyright element or not and whether or not other regulations, such as those governing bioprinting, will lead to favoured forms of biological cultural works.