Reviewerly/exHarmony huggingface dataset:
PriRepVGG: Privacy-Preserving 3-Party Inference Framework for Image-Based Defect Detection

Image classification is widely used in industrial defect detection, medical diagnosis, social welfare, and other fields, in which privacy and security of models and data must be involved. For example, in diamond synthesis, the diamond substrate image annotation data and the defect detection model are of value for conservation. Based on ensuring inference efficiency and the security of these private data intellectual property, the 3-party secure inference based on secure multi-party computation (MPC) can be adopted. MPC allows parties to use neural networks while preserving their input privacy for collaborative computing, but it will lead to huge communication and memory consumption. This paper propose PriRepVGG, a lightweight privacy-preserving image-based defect detection framework for 3-party. In this work, firstly, This work optimized the division and added an AdaptiveAvgpool layer in MPC framework FALCON; then, This work ported the inference architecture of the RegVGG network into FALCON creatively. Our work applied PriRepVGG to the secure inference of the diamond substrates defect detection under the data server, model server, and compute server settings, which can be carried out in batches with a low misjudgment rate and verify the feasibility of image-based secure inference with a lightweight network in an industrial case under MPC.
Empirical study of the differences in the effect of oral corrective feedback from classroom teachers

In the process of classroom teaching, English teachers need to correct students' English &#x0D; pronunciation, vocabulary and other problems orally, so as to effectively improve students' &#x0D; English level. English teacher in order to improve the students' English level, through to the &#x0D; different teaching methods, teaching period, research and analyze the teaching feedback, after &#x0D; long time of study, can analyze the classroom oral English teachers correct teaching methods &#x0D; obviously better than the effect of repeat, especially English good students teaching effect is &#x0D; more obvious. It can be seen that oral correction by English teachers in the process of classroom &#x0D; teaching has obvious effects, which can effectively mobilize the enthusiasm of students to learn &#x0D; English in class and arouse their interest in Learning English, so that students can obtain &#x0D; excellent results.
PENERAPAN ALGORITMA K-MEANS CLUSTERING PADA PENGELOMPOKKAN PASANGAN USIA SUBUR PESERTA KB DI KABUPATEN CIREBON

One of the problems faced by newly married couples is planning the birth of children in the family. The problem of regulating the birth spacing of children in the family can be regulated by the family planning program that has been launched by the Government. One way to suppress the problem of population growth rate in Indonesia is through the KB (Family Planning) program. The success of the family planning program can be seen from the contraceptive methods used by couples of childbearing age (PUS) in each region. The main purpose of this study was to determine the best group of couples of childbearing age for family planning participants based on the contraceptives used in Cirebon Regency using the K-means algorithm, where this algorithm can solve the clustering problem in determining the best group of couples of childbearing age family planning participants. The results of the clustering process to find the best group of couples of childbearing age for family planning participants by looking for the smallest DBI value and obtained K-2 as the best group with a Davies Bouldin Index value of 0.071. The best number of clusters from the grouping of fertile age couples for family planning participants is 2 clusters, cluster 0 has 20 items members and cluster 1 has 20 items members.&#x0D; &#x0D; Keywords: Clustering, K-Means, Family planning(KB), Couples of childbearing age(PUS)
BERT-based Sentiment Analysis of Chinese Online Social Movements

Online social movements are a group of netizens with the same or similar purpose, spontaneously discussing and disseminating certain information, trying to attract more people to participate, and creating a public opinion on the Internet or even a social atmosphere of anxiety. Online social movements analysis can reveal the evolution of sentiment during the movements and therefore prevent the relevant social disasters from happening. The literature shows the lack of public sentiment analysis text datasets and effective analysis methods for Chinese online social movements. In this paper, we classify sentiment into four categories: positive, anger, anxiety, and weak negative. We believe anger and anxiety are the two most important sentiments in forming an online social movement. Afterwards, we first time create a public sentiment analysis dataset about Chinese online social movements, and then propose a bidirectional encoder representation from transformers (BERT)-based model to classify the sentiment. Moreover, we use the focal loss in the BERT model rather than cross entropy loss to enhance the contribution of minority classes to the total loss to address the imbalance issue in the dataset. The proposed BERT-based model is compared with six baseline methods. The results show that our method outperforms those baseline models by achieving higher macroF1 scores.
ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot Analysis

We propose a fine-grained basketball shot analysis system called ScoreActuary to analyze the players' shot events, which can be applied to game analysis, player training, and highlight generation. Given a basketball video as input, our system first detects/segments shot candidates and then analyzes "Shot Type", "Shot Result", and "Ball Status" of each shot candidate in real-time. Our approach is composed of a customized object detector and a trajectory-aware network to learn the information of ball trajectory. Compared to the existing methods that analyze basketball shots, our algorithm can better handle videos with arbitrary camera movements while improving the accuracy. To the best of our knowledge, this work is the first system that can analyze fine-grained shot events accurately in real basketball games with arbitrary camera movements.
Efforts to Improve Japanese Language Skills of Prospective Workers at Mirai Jaya Indralaya Job Training Institute

ABSTRACTThis study aims to determine the efforts to improve the Japanese language skills of prospective workers at LPK Mirai Jaya Indralaya, with a qualitative descriptive type of research. Data was collected using interview, observation and documentation methods with research subjects LPK Mirai Jaya managers, instructors, learning residents and alumni of LPK Mirai Jaya. The results obtained in this study are: 1) Internal efforts made by LPK Mirai Jaya in improving the Japanese language skills of prospective workers are going well. These efforts include education and training, as well as a learning community ability test which is carried out in accordance with the provisions of the Japan Foundation. 2) External efforts made by students at LPK Mirai Jaya to support the efforts made by LPK Mirai Jaya managers, namely the competence of learning citizens, active learning of citizens, and Japanese language skills. Keywords: Effort, Japanese language, skill
LINEAR REGRESSION FOR DISCOUNTING PRESENTATION RECOMMENDATIONS (Kaggle Dataset)

In the business of selling goods, there must be goods that do not sell well and sell well. How to make unsold items sell well by giving customers a discount or discount strategy. The goal is to provide discounted prices to attract customers' attention and increase sales turnover. Prediction to give the right discount presentation is needed in the discount strategy. How to determine discount prediction using linear regression method, looking for line equations by training data taken from Kaggle.com. The data were trained to find the constants and coefficients of the independent variables. The resulting line equation will be tested to determine the discount prediction that will be given and calculate the error value. The data used is painting sales data taken from Kaggle.com, a total dataset of 1525, and a prediction calculation is made with 225 lines of test data. The input variable used is the price of the painting, and the output variable is the discount presentation. The process of evaluating the prediction results by calculating the MAE value, the difference between the actual data and the predicted data, the MAE value from the trial is 0.108.
UTILIZING GOOGLE DRIVE AS A PERSONAL DIGITAL LIBRARY

Libraries have another form called digital libraries. In line with the development of information technology that has entered human life, it has brought changes to the library system. The desire to have library resources that can be accessed anywhere and anytime also encourages the existence of digital libraries. By utilizing a digital library-based information system, everyone can obtain appropriate and useful reading sources. The impracticality of the conventional library system is that one has to go to the location first, which is very impractical. This situation makes digital libraries one of the solutions that can be built to bridge these weaknesses. The method used in this study is a qualitative descriptive method. From the results and discussion obtained the steps for creating an account, digital library design, how the system works, and the appearance of digital libraries. The hope of this research is that the library system created can be used by anyone to support an easy, inexpensive, and paperless information system.
Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles

Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models.
Developing Business English Learners’ Speaking Skills Using Framework Materials

One of the major consequences of globalization is the increased use of English as a means of business communication all over the world. Hence, the necessity of developing business English speaking skills using efficient materials has become a core necessity. Therefore, the aim of the research was to ascertain the effectiveness of framework materials in business English learners’ speaking skills development. In order to achieve it, the case study used observation as a research tool to follow the students’ task completion process and interaction. The results revealed both advantages and difficulties of framework materials application. Nevertheless, it was concluded that these materials can be utilized as an effective means of spoken fluency and accuracy development. It is recommended that framework materials should be used as a supplement to the course.
Encryption Modes Identification of Block Ciphers based on Machine Learning

Encryption modes affect the security of block ciphers. This paper proposes a new approach for identifying encryption modes based on machine learning and feature engineering. In the conditions of random keys and initialization vectors, five encryption modes are used for identification. Each mode is encrypted by several block ciphers. By comparing with previous work, we have overcome the shortcomings and improved the accuracy of identification by about 30% to 40%. The experiments improve the existing results and can effectively help cryptanalysts recover the keys.
An Enhanced Adaptative System based on Machine Learning for Predicting the Evolution of Islamic Stock Prices

This paper suggests an enhanced machine-learning-based system to guide future stock price decisions. In reality, most existing machine learning systems, such as SEA (Stream Ensemble Algorithm), VFDT (Very Fast Decision Tree ), and online bagging and boosting, keep models updated with only new data and reduce training timeframes to allow working rapidly with the most recent model. However, limited learning times and the exclusion of essential information from previous data may result in a bad performance. When it comes to learning models, our system takes a different approach. It builds several models based on random selections of historical data from the main stock as well as related stocks. The best models are then combined to generate a final, performant model. We performed an empirical study on five Islamic stock market indices. We can say from the results that our system outperforms the existing published algorithms. This framework can contribute then to having an enhanced system that will enable different stakeholders to make rapid decisions based on the forecasted trend of indices.
Basic Spanish for Malaysian Students: Learning Difficulties

: The present study identifies the difficulties in learning Spanish as a foreign language among Malaysian students. The data presented in this study was collected through; 1) interviews with ten (10) students between 20 to 23 years old enrolled in the Basic Spanish Language course at the Universiti Putra Malaysia and 2) in-class activities and assessments answered by these students. The data collected were analysed using a qualitative-descriptive approach. The results show that Malaysian students face a series of difficulties that hinder them from acquiring adequate competence in Spanish, namely the lack of lexis, pronunciation ability and grammar, particularly verbal conjugation. The findings of this study have implications for the teaching and learning of Spanish as a foreign language in Malaysia, whereby it can be used as guidance for Spanish language educators to design suitable teaching and learning activities to facilitate Malaysian students' understanding and mastery of Spanish as a foreign language.
The CLARIN Resource and Tool Families

This chapter presents the CLARIN Resource and Tool Families initiative, whose aim is to offer researchers from Digital Humanities, Social Sciences, and Human Language Technologies aggregated, user-friendly overviews of the tools and resources available through the CLARIN infrastructure, including a unified, human-readable presentation of their metadata.The initiative also raises awareness of the importance of good and harmonzed metadata documentation, thus supporting other core CLARIN services like the Virtual Language Observatory and the CLARIN Language Resource Switchboard in relation to findability and (re)usability.
Predictive Maintenance of ATM Machines by Modelling Remaining Useful Life with Machine Learning Techniques

One of the main relevant topics of Industry 4.0 is related to the prediction of Remaining Useful Life (RUL) of machines. In this context, the “Smart Manufacturing Machine with Predictive Lifetime Electronic maintenance” (SIMPLE) project aims to promote collaborations among different companies in the scenario of predictive maintenance. One of the topics of the SIMPLE project is related to the prediction of RUL of automated teller machines (ATMs). This represents a key task as these machines are subject to different types of failure. However the main challenges in this field lie in: i) collecting a representative dataset, ii) correctly annotating the observations and iii) handling the imbalanced nature of the dataset. To overcome this problem, in this work we present a feature extraction strategy and a machine learning (ML) based solution for solving RUL estimation for ATM devices. We prove the effectiveness of our approach with respect to other state-of-the-art ML approaches widely employed for solving the RUL task. In addition, we propose the design of a predictive maintenance platform to integrate our ML model for the SIMPLE project.
STRATEGI GURU DALAM PENINGKATAN KEMAMPUAN BACA Al-QUR’AN SISWA DI MA AT-TAUFIQ GROGOL DIWEK JOMBANG

Reading the Qur'an is a basic ability that must be possessed by humans, because the process of learning Islam itself is closely related to the arguments and sources taken from the Qur'an. Allah's mercy towards humans is that He not only gives them clean qualities that can guide and guide them towards goodness, but also from time to time He sends a messenger to mankind with a book from Allah and orders them to worship only Allah. only, giving glad tidings and giving warnings. The aims of this research are 1). To describe the Strategy of Qiroatul Quran Teachers in Improving Students' Quran Reading Ability at MA at-Taufiq Grogol Diwek Jombang. 2). To describe Students' Quran Reading Ability at MA At-Taufiq Grogol Diwek Jombang. 3). To describe the supporting and inhibiting factors of Qiroatul Quran Teachers in Improving Students' Quran Reading Ability at MA at-Taufiq Grogol Diwek Jombang. The method used in this research is a descriptive qualitative research method with a field research approach. The research design used in this study is a qualitative descriptive design. With in-depth interview data collection techniques, observation, documentation and qualitative descriptive data analysis techniques. Based on the results of research conducted by researchers to answer the focus of the research above, it can be concluded as follows: Strategies in Improving Students' Quran Reading Ability, among others: 1). The teacher's ability to open lessons. 2). The ability of teachers to carry out the core of learning. 3). The ability of teachers in assessment and evaluation. 4). The teacher's ability to close the lesson. The factor that affects the students' ability to read the Koran at MA At-Taufiq itself is from the madrasa environment. The inhibiting factor experienced is the lack of awareness of the importance of learning the koran.
Security Enhancements for Data-Driven Systems: A Blockchain-Based Trustworthy Data Sharing Scheme

With the increasingly prominent value of big data, data sharing within enterprises and organizations has become increasingly popular, and many institutions have established data centers to achieve effective data storage and sharing. Meanwhile, cyberspace data security and privacy have become the most critical issue that people are concerned about since shared data often involves commercial secrets and sensitive information. At present, data encryption techniques have been applied to protect the security of the sensitive data stored in and shared by the data centers. However, the challenges of efficient data sharing, secure management of decryption keys, deduplication of the plaintext, and transparency and auditability of the data access arise. These challenges may obstruct the development of data sharing in data-driven systems. To meet these challenges, we propose a secure and trustworthy data sharing scheme and introduce blockchain, proxy re-encryption (PRE), and trusted execution environments (TEEs) into the data-driven systems. Our scheme mainly enables (1) automatic distribution and management of the decryption keys, (2) reduction of the reduplicative data, and (3) trustworthy data sharing and recording. Finally, we implement the proposed scheme and compare it with other existing schemes. It is demonstrated that our scheme reduces the computation and communication overhead.
BOARD-AI: A goal-aware modeling interface for systems engineering, combining machine learning and plan recognition

Paper and pens remain the most commonly used tools by systems engineers to capture system models. They improve productivity and foster collaboration and creativity as the users do not need to conform to formal notations commonly present in Computer-Aided Systems Engineering (CASE) tools for system modeling. However, digitizing models sketched on a whiteboard into CASE tools remains a difficult and error-prone activity that requires the knowledge of tool experts. Over the past decade, switching from symbolic reasoning to machine learning has been the natural choice in many domains to improve the performance of software applications. The field of natural sketching and online recognition is no exception to the rule and most of the existing sketch recognizers rely on pre-trained sets of symbols to increase the confidence in the outcome of the recognizers. However, that performance improvement comes at the cost of trust. The lack of trust directly stems from the lack of explainability of the outcomes of the neural networks, which hinders its acceptance by systems engineering teams. A solution shall not only combine the performance and robustness but shall also earn unreserved support and trust from human users. While most of the works in the literature tip the scale in favor of performance, there is a need to better include studies on human perception into the equation to restore balance. This study presents an approach and a Human-machine interface for natural sketching that allows engineers to capture system models using interactive whiteboards. The approach combines techniques from symbolic AI and machine learning to improve performance while not compromising explainability. The key concept of the approach is to use a trained neural network to separate, upstream from the global recognition process, handwritten text from geometrical symbols, and to use the suitable technique (OCR or automated planning) to recognize text and symbols individually. Key advantages of the approach are that it does not resort to any other interaction modalities (e.g., virtual keyboards) to annotate model elements with textual properties and that the explainability of the outcomes of the modeling assistant is preserved. A user experiment validates the usability of the interface.