Reviewerly/exHarmony huggingface dataset:
TINGKAT PENCAPAIAN KEMAMPUAN GURU SD DALAM MENGGUNAKAN APLIKASI ANATES MELALUI PELATIHAN

The partnership of Program Kemitraan (PKM) are the elementary school teachers in Bone Regency who are UNM partners through the MOU. The problem of this PKM is that the UNM partner elementary school teachers have not been able to analyze the question items using the Anates application. To overcome this problem, face-to-face training and direct guidance were carried out. The methods used in this PKM are (1) observation, (2) lecture, (3) demonstration, (4) question and answer, and (5) training. Activities are carried out in three stages, namely; (1) planning, (2) implementation, and (3) evaluation of the activities. The program planning stage includes activities: (1) preparing a guide on how to analyze the test items using Anates and (2) preparing the activity evaluation instruments. The implementation stage includes activities: (1) conducting questions and answers with teachers to find out their initial knowledge about how to analyze the question items using Anates, (2) providing the explanations to the teachers on how to analyze the question items using Anates, and (3) training the teachers on how to analyze the test items using Anates. The evaluation stage includes activities: evaluating the teacher's ability to analyze the questions using Anates. The results and conclusions of the training show that: the training activities were carried out smoothly without significant obstacles and were enthusiastically received by all participants, the participants' ability to use the Anates application was at the excellent category consists of: the ability to open applications, import the raw data, process the data, save the files, save the data, and interpret the data processing results
Analisa Perhitungan Timbunan Maindam Pada Proyek Bendungan Cipanas Kab. Sumedang Paket 3 Menggunakan Metode Cross Section

The purpose of this research is to know the calculation of the main dam volume in the Cipanas Dam Development Project, Kab. Sumedang Package 3 uses the Cross-Section Method, knowing the calculation of the productivity of the heavy equipment used, and knowing the stages of carrying out the main dam embankment work.The method of calculating the volume of main dam embankment material uses the coordinate point method of the cross-section method to calculate the cross-sectional area at each station point where the coordinate point data is X as distance and Y as elevation. At each of the two station points the cross-sectional area results at each station point will be calculated for the average cross-sectional area and then multiplied by the distance between the two station points to get the main dam embankment volume calculation results.The results of the calculation of the volume of the main dam embankment in the Cipanas Dam Development Project, Kab. Sumedang Package 3 uses the cross-section method, namely 133,967.48 m3 for main dam embankments zone 1, 71,834.86 m3 for maindam embankments zone 2a, 54,229.91 m3 for main dam embankments zone 2b, 805,398.12 m3 for main dam embankments zone 3, 60,161 .47 m3 for zone 4 main dam embankment.
Real-Time Moving Horizon Estimation for Nonlinear Systems With Unknown Inputs

Moving horizon estimation (MHE) is a well-known alternative to Kalman-like filtering due to its superior performance in terms of estimation accuracy, convergence speed, and robustness to poor initial state guesses. However, most MHE methods cope with unknown inputs by introducing a random walk model, which is unsuitable for fast-varying unknown inputs. To address this issue, we propose an unknown input moving horizon estimator (MHE-UI) that does not require any prior knowledge of the dynamic behavior of unknown inputs. To solve the associated nonlinear least squares problem in real time, we develop a computationally efficient Gauss-Newton iterative algorithm by exploiting the block pentadiagonal structure of the resulting Karush-Kuhn-Tucker (KKT) matrix. A numerical case study demonstrates that the proposed real-time MHE-UI algorithm achieves higher estimation accuracy than the unknown input unscented Kalman filter (UKF-UI) and significantly reduces the computational cost compared to the exact MHE-UI algorithm.
Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.
E2LNet: An Efficient and Effective Lightweight Network for Panoramic Depth Estimation

Monocular panoramic depth estimation has various applications in robotics and autonomous driving due to its ability to perceive the entire field of view. However, panoramic depth estimation faces two significant challenges: global context capturing and distortion awareness. In this paper, we propose a new framework for panoramic depth estimation that can simultaneously address panoramic distortion and extract global context information, thereby improving the performance of panoramic depth estimation. Specifically, we introduce an attention mechanism into the multi-scale dilated convolution and adaptively adjust the receptive field size between different spatial positions, designing the adaptive attention dilated convolution module, which effectively perceives distortion. At the same time, we design the global scene understanding module to integrate global context information into the feature maps generated using the feature extractor. Finally, we trained and evaluated our model on three benchmark datasets which contains the virtual and real-world RGB-D panorama datasets. The experimental results show that the proposed method achieves competitive performance, comparable to existing techniques in both quantitative and qualitative evaluations. Furthermore, our method has fewer parameters and more flexibility, making it a scalable solution in mobile AR.
Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network

Quantum secure direct communication (QSDC) offers a practical way to realize a quantum network which can transmit information securely and reliably. Practical quantum networks are hindered by the unavailability of quantum relays. To overcome this limitation, a proposal has been made to transmit the messages encrypted with classical cryptography, such as post-quantum algorithms, between intermediate nodes of the network, where encrypted messages in quantum states are read out in classical bits, and sent to the next node using QSDC. In this paper, we report a real-time demonstration of a computationally secure relay for a quantum secure direct communication network. We have chosen CRYSTALS-KYBER which has been standardized by the National Institute of Standards and Technology to encrypt the messages for transmission of the QSDC system. The quantum bit error rate of the relay system is typically below the security threshold. Our relay can support a QSDC communication rate of 2.5 kb/s within a 4 ms time delay. The experimental demonstration shows the feasibility of constructing a large-scale quantum network in the near future.
Deep Collaborative Filtering Recommendation Algorithm Based on Sentiment Analysis

Data sparsity is the main challenge that recommendation algorithms have been facing. E-commerce recommendation mainly focus on the explicit interaction between users and items to alleviate this problem, while ignoring the implicit feature of the hidden sentiment information in review texts. To address the above issues, a deep collaborative filtering recommendation model based on sentiment analysis of reviews (SA-NCF) is proposed. First, the sentiment information in user reviews is extracted for quantitative processing. Then the implicit feature matrix of users and items is constructed by combining with the explicit feature of user ratings. Finally, the whole model is nonlinear processed by Matrix Factorization and Multi-layer Perceptron to improve the performance of the recommender system. Experiments are conducted on the JD public dataset and compared with several classical models. The results show that the effectiveness and accuracy of the recommendation algorithm with sentiment factors are better than the comparison model.
A Short Review: Semantic Segmentation for Breast Cancer Detection in MRI Images

Automated breast cancer detection is of paramount importance as it could potentially reduce false-negative examinations and allow for earlier treatments, which would have a significant impact on patients and healthcare system costs. However, insufficient data, imbalanced classes, the detection of cancer in dense breasts, and the difficulty in distinguishing breast lymph nodes hinder the application of automated methods. To address these problems, in this paper, we presented an overview of deep learning models for the segmentation of breast cancer at an early stage by examining the contrast-enhanced MRI images. We examined the effectiveness of supervised machine learning models (UNet, VNet, SegResNet, and HighResNet) in the detection of breast cancer. We used 3D and 2.5D semantic segmentation models for pixel-level segmentation, which overcame the drawbacks of region-based segmentation. All the models are trained, validated, and tested on a private dataset collected from the Champalimaud Foundation, Portugal, and evaluated quantitatively and qualitatively.
Experiences of implementing digital transformation at higher education institutions in the world and lessons for Vietnam

Digital transformation in education in general and at higher education institutions in particular has been taking place strongly in recent years, bringing positive results for innovation in teaching and learning as well as training and scientific research governance. This study presents theoretical frameword of digital transfer in higher education institutions, accordingly analyzes the experience of implementing digital transformation at some leading universities in Asia and Europe in terms of digital infrastructure transformation, training programs and digital platform operations, from which some lessons can be drawn for Vietnam.
Addressing Information Consumer Experience through a User-Centered Information Management System in a Chilean University

Prior research on the successful design and construction of data visualization systems or information management systems has not fully taken into account the holistic experience of employees working with information within the organization but has centered on specific aspects, such as user experience or data quality, attempting to go against information management quality, as those approaches can significantly influence users’ perceptions and their motivation to effectively use such tools for decision making. This study addresses the information consumer experience (ICX) in a Chilean Higher Education institution through the design and implementation of an user-centric centralized information management system. This system was created using an adapted design thinking methodology with an ICX perspective to identify and integrate the information consumers’ demands and other factors correlated with ICX into the system’s design. The proposed system is a technological extension of an information resource validation process that involves senior data analysts from the Analytics Department and external data analysts from other departments across the organization. This process helps to address data quality and information management quality (IMQ) problems of the organization, representing a centralized data source for all information consumers into the organization, offering consistent, accessible, and good quality data to address daily work and enhance information consumers experience, and managerial work.
Gaming Technologies in the Formation of Legal Students’ Professional Competencies: Moot Courts

The modern labor market requires specialists with practical skills, as a result of which traditional educational approaches that emphasize the deep theoretical knowledge of the graduate give way to innovative teaching methods that allow students to develop practical skills in the field of professional activity. In legal education, various active teaching methods are used, including a legal clinic, case study, quest. Of significant interest is such a game learning model as a simulated trial, which allows us to combine theoretical knowledge with the practical possibilities of their implementation within a certain model of legal proceedings, as well as to form interdisciplinary competencies necessary for a specialist in the field of jurisprudence. The study is aimed at determining the role of game approaches in the formation of practice-oriented competencies of students of law schools and establishing the conditions under which a business game can be effectively implemented in the educational process. The achievement of the research goal is based on general scientific methods, such as analysis and synthesis, as well as methods of questioning, observation, the use of pedagogical experience and legal statistics. As a result, the necessity of using game trials in legal education, based on well-defined criteria for their conduct, was established to form a number of students’ competencies: teamwork skills, professional communication, stress resistance, ability to apply substantive law to specific legal situations.
Smart Education Empowers Primary Schools to Practice Cooperative Education

Kang Youwei Memorial Primary School is the virtual experimental school of the National Center for Educational Technology (NCET), Guangzhou Artificial Intelligence Experimental School, Guangzhou Science and Technology Education Characteristic School. The school fully promotes information technology-enabled education in the campus from the aspects of smart campus application construction, curriculum promotion, resource accumulation, etc. The school promotes learning through interests, research, and competition.
Digital Resource Supply Solution: Create a Regional “New Learning Engine”

Suzhou Industrial Park (hereinafter referred to as the “Park”) is based on the “Easy Plus” platform. From the perspective of resource services, it focuses on systematic construction, structured presentation and scene application, providing effective support for teachers’ teaching and students’ learning, improving teaching quality and efficiency, and igniting a “new engine” of intelligent learning.
Water Meter Reading Recognition Based on Deep Learning

The widespread and large-scale distribution of water meter users in China poses a significant challenge to the traditional manual meter reading method in most regions of the country. Rapid and accurate meter reading for a large number of water meters distributed in different areas has become an urgent problem for various water supply companies. With the rapid development of deep learning, computer vision and other fields, more and more industrial and commercial projects are starting to utilize machine learning-based computer vision technology to process water meter readings, which previously required manual identification by human eyes.This paper mainly studies the problems of text area extraction and reading recognition of the dial-type water meter in real scenarios. Based on deep learning methods, DBnet semantic segmentation algorithm is first used for text detection, followed by CRNN convolutional recurrent neural network for text recognition. Regarding the difficulty of recognizing half characters, this paper proposes an agent character scheme by readjusting the format of the training set, which significantly improves the accuracy of half character recognition. The model is trained and tested on a dataset of real-scenario dial-type water meter images, and the test results show that the algorithm network still has good accuracy and fast recognition speed in complex scenarios such as low light, reflection, partial occlusion, angle tilt, and half-characters.
Spin squeezing in internal bosonic Josephson junctions via enhanced shortcuts to adiabaticity

We investigate a time-efficient and robust preparation of spin-squeezed states---a class of states of interest for quantum enhanced metrology---in internal bosonic Josephson junctions with a time-dependent nonlinear coupling strength between atoms in two different hyperfine states. We treat this state-preparation problem, which had previously been addressed using shortcuts to adiabaticity (STA), using the recently proposed analytical modification of this class of quantum control protocols that has become known as the enhanced STA (eSTA) method. We characterize the state-preparation process by evaluating the time dependence of the coherent spin-squeezing and number-squeezing parameters and the target-state fidelity. We show that the state-preparation times obtained using the eSTA method compare favorably to those found in previously proposed approaches. We also demonstrate that the increased robustness of the eSTA approach---compared to its STA counterpart---leads to additional advantages for potential experimental realizations of strongly spin-squeezed states in internal bosonic Josephson junctions.
Contact tracing for COVID-19 in a healthcare institution: Our experience and lessons learned

During the initial phases of the COVID-19 pandemic contact tracing was used to control spread of the disease. It played a key role in health care institute which continued to work even during lockdown. In this piece of work, we share the lessons learnt from the contact tracing activity done in the health care institution during April to July 2020. The training needs of persons involved in contact tracing, the follow of activities, use of technology, methods to fill the missing gaps were the key lessons learnt. Its documentation supports in setting up contact tracing activity for any emerging infectious disease outbreaks in future.
Review on a rule based approach to align natural language with query language

Information is crucial to our daily life in the globe. Databases are one of the main sources of information. Massive amounts of data are stored in databases. For access to that data, knowledge of structured query language is necessary. The requirement for a system that converts text to a SQL query itself rather than the user arises from the fact that not all non-technical people can do that. Due to its importance in industry and commerce, the text to SQL task is also essential. The system that supports the text-to-SQL job is called Natural Language Interface to Database (NLIDB). The creation of the NLIDB system has long been a challenge. In this paper, we review the current work and summarize the research trends of NLIDB.
Securing Healthcare Data in Blockchain Using TSE Algorithm

Objective: The main objective of this work is to create a novel patient-centered management system that uses the two-stage encryption TSE algorithm to secure a private blockchain-based healthcare system while providing users with the highest privacy, complete control, and security over their sensitive data. Methods: TSE Encryption Process: The user or the patient asymmetrically encrypts the data\'s symmetric key using the public key of the party with whom the data is to be shared. TSE Decryption Process: The party wishing to access the health data initially selects the user (patient), then sends the private key to decrypt the data present in the network. The public key of the user is used to verify the signature, and finally, the confidential data is retrieved back to be accessed by the party intended to see it. The TSE algorithm is compared to the DES (Data Encryption Standard) and MD5 (Message Digest Method 5) algorithms, and the results show that the proposed algorithm performed significantly better than the other two. Finding: In this work, the asset is encrypted and indexed in a database, and the asset\'s value or data is encrypted and stored on a private blockchain to provide the best possible privacy and security. The concept makes use of the immutability, cryptography, and distribution aspects of the blockchain but overlooks the blockchain\'s public nature and the conventional mining process. Novelty: Most existing approaches store all of the data in the blockchain, which reduces user privacy. The proposed algorithm uses the blockchain to store the index of the health care asset. Keywords: Blockchain, Healthcare, Privacy, cryptography, and Two Stage Encryption (TSE) algorithm
LAE-GAN: a novel cloud-based Low-light Attention Enhancement Generative Adversarial Network for unpaired text images

Abstract With the widespread adoption of mobile multimedia devices, the deployment of compute-intensive inference tasks on edge and resource-constrained devices, particularly in the context of low-light text detection, remains a formidable challenge. Existing deep learning approaches have shown limited effectiveness in restoring images for extremely dark scenes. To address these limitations, this paper presents a novel cloud-based L ow-light A ttention E nhancement G enerative A dversarial N etwork for unpaired text images (LAE-GAN) for the non-paired text image enhancement task in extremely low-light conditions. In the first stage, compressed low-light images are transmitted from edge devices to a cloud server for image enhancement. The LAE-GAN, an end-to-end network comprising a Zero-DCE and AGM-net generator, is designed with a global and local discriminator structure. The initial illumination restoration of extremely low-light images is accomplished using the Zero-DCE network. To enhance text details, we propose an Enhanced Text Attention Mechanism (ETAM) that transforms text information into a comprehensive text attention mechanism across the entire network. The Sobel operator is employed to extract text edge information, while attention is focused on text region details through constraints imposed on the attention map and edge map. Additionally, an AGM-Net module is integrated to reduce noise and fine-tune illumination. In the second stage, the cloud server makes decisions based on user requirements and processes requests in parallel, scaling with the quantity of requests. In the third stage, the enhanced results are transmitted back to edge devices for text detection. Experimental results on widely used LOL and SID low-light datasets demonstrate significant improvements in both quantitative and qualitative analysis, surpassing state-of-the-art enhancement methods in terms of image restoration and text detection.