Reviewerly/exHarmony huggingface dataset:
DIGITALIZATION OF MIDDLE ENVIRONMENT AS A STRATEGIC ORIENTATION FOR THE DEVELOPMENT OF THE COUNTRY

The article is devoted to the study of the process of digitalization of secondary education as a strategic reference point for the development of countries. The following key directions of the digital transformation of education are highlighted – the development of the digital infrastructure of education, the development of digital teaching and methodical materials, digital assessment and certification, the transition to a personalized organization of the educational process. The study analyzes the process of digitalization of secondary education in countries such as the Republic of Korea, Germany, the USA, and Ukraine. In the Republic of Korea, EDUNET can be mentioned among the information approaches, the application of which allowed to really improve the results of students. Also, the Republic of Korea pays special attention to the development and widespread distribution of digital textbooks, the use of which allows schoolchildren, using digital technologies, to learn the correctness of digital decision-making, independence. In Germany, the introduction of the «Digital School Pact» made it possible to promote the division of responsibilities between the federal government and the federal states and municipalities in the issue of the spread of information technologies in secondary education institutions. In the United States of America, digital library distribution strategies such as LearnSmart and Language Arts &amp; Writing Redbird play an important role in making information more accessible to students and teachers. In Ukraine, the Plan for the Restoration of Education and Science plays an important role in the process of digitalization of secondary education. Among the strategic priorities of this plan: the introduction of electronic record-keeping in all educational institutions by the end of 2025, the formation of school reports automatically from the level of individual data and submission in electronic form by the beginning of 2026. Thus, it should be understood that global digitization forms a new digital culture of modern society, which requires a change in the education system in order to be ready to use the opportunities of all technological innovations.
Reconfiguring Surveillance Futures for Higher Education Using Speculative Data Stories

Surveillance in many higher education settings has become increasingly pervasive and fine-grained. Concerns are growing about negative impacts on learning relationships, exploitative commercial uses of collected student data, discriminatory practices, and even political, social, or physical harm inflicted because of surveillance and monitoring. At the same time, complex surveillance cultures in higher education make it difficult to disentangle personal and collective responsibility, understand the gap between intentions and impacts, or navigate the risks that can come with addressing these matters. In 2020, the Data Stories research project used speculative and co-design approaches to develop a ‘data stories’ storytelling tool. The project supported people working and studying in higher education to create and share anonymous stories about what the future of surveillance in higher education might look like. This chapter explores the context for this work and draws out some themes from these stories, exploring a range of responses to surveillance that are expressed in them. We describe how the data stories tool was mobilised to produce a space for people to make and share new meanings around surveillance, in the form of Participatory Speculative Fictions, which have potential for working with and possibly reconfiguring a range of networked learning futures.
Sentiment Analysis with Supervised Topic Modelling on Twitter Data Related to Indonesian Election 2024

Generally, The topic model is an unsupervised learning approach to find structures between topics and terms and relationships between documents and topics. In this research, we evaluated topics in the 2024 presidential election dataset related to their output i.e., sentiment polarity. This paper proposes a Supervised Latent Dirichlet Allocation (sLDA) approach to predict the observed sentiment outcome and adjust the topic model concurrently. We used the Twitter dataset related to the 2024 Indonesian election and the other tweets datasets as a comparison we got from the Kaggle website about the 2017 Jakarta regional head election. The Sentiment annotation process for each dataset uses the InSet Lexicon. This research contributes to exploring topics from Indonesian Twitter data and predicting the labels appearing from the resulting topics. The sLDA package from R is used. It is worth noting that in this study, we handled sentiment analysis as a regression task then the evaluation result of the model is in the form of a correlation and R2 between the prediction and the actual score sentiment. The results show that the correlation and R2 value increase as the number of topics increases. The highest correlation for the 2024 election dataset is 0.599, and R2 is 0.352 with 50 topics, and the highest correlation for the Jakarta regional election 2017 dataset is 0.574, and R2 is 0.307 with 25 topics.
Study of the C-band dynamical response of an injection locked LA-EEL for fully integrated telecommunication data processing

A high-performance photonic reservoir, which utilizes the injection locking effect in a highly multimodal semiconductor laser, has been developed. This innovative design allows for fully parallel and high-bandwidth operation. The output of this system is projected in space and imaged onto a digital micromirror device, which provides a readout and facilitates the hardware integration of programmable output weights. By using a highly multimodal semiconductor laser, the injection locking effect enables a large number of modes to be simultaneously locked to the injected signal, resulting in high dimensionality of the reservoir, reducing the computational time and complexity. The use of a digital micromirror device provides a flexible readout, allowing the output to be programmed to suit a range of applications. The hardware integration of programmable output weights enables the system to be optimized for specific tasks, improving performance and reducing power consumption.
Student Perceptions about Online Learning Media Through Zoom Cloud Meeting and Google Meet Applications

This research aim to identifying student perceptions of online learning media through the zoom cloud meeting and Google meet applications during the Covid-19 pandemic which was carried out for Tadris Biology students class of 2020. This type of research uses descriptive quantitative research. Data retrieval via questionnaires and interviews with sample used _ as many as 41 students. The research results obtained from the responses of each student were included in the good category with a percentage score of 61-80% for 36 students and in the very good category with a percentage score of 81-100% for 5 students. This shows that student perceptions are considered positive about online learning through the zoom meeting application and Google meet for Tadris Biology students at UIN Syarif Hidayatullah Jakarta as supporting media during distance learning. It was proven based on the instruments distributed to Tadris Biology students class of 2020 by giving a good response to use zoom cloud meeting and google meet applications , which are expected to help students in carrying out the lecture process during a pandemic as a transition from face-to-face learning to distance learning far
N-Accesses: A Blockchain-Based Access Control Framework for Secure IoT Data Management

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.
Machine Learning Techniques for Breast Cancer Prediction: A Concise Review

Breast cancer is the most trending type of cancer globally with close to two and half million cases recorded based on research by the World Health Organization in 2021 and it is also the most common cancer among women in all countries, posing a major cause for public health concern. In Nigeria and the world at large, over a hundred thousand new cases of cancer occur every year with high death among women. It has been researched that early and accurate detection of breast cancer can aid in the diagnosis of the disease for women and it may also reduce the risk of death rate among women. Literature shows that several machine learning techniques have been carried out on breast cancer diagnosis to help provide accurate technology solutions to early detection. The machine learning techniques used have different accuracy rate which varies for dissimilarity conditions. In this study, we compared different methods with many existing machine-learning techniques commonly used for breast cancer detection and diagnosis. Also, the aim of this review method will show an improvement in accuracy performances by implementing different methods and analysis in existing machine learning techniques to proficiently assist doctors in decision-making on an accurate detection and diagnosis of breast cancer and classifying tumors as benign or malignant thereby reducing the risk of death rate among women.
New Algorithm for Digital Video Encryption

This research aims to propose a safe digital video data alternative employing symmetric cryptography and an encryption technique using Partition. The Hill Cipher method requires a square key matrix with an inverse modulo the unimodular matrix is one of the unique matrices with an inverse. The encryption mechanism is modulo matrix multiplication, with shift cipher encryption employed to encrypt defective partitions. The results reveal that the videos are well encrypted and difficult for third parties to read the partition encryption technology assures that the encrypted and decrypted file sizes are the same. The background of this topic is that digital data security is increasingly important due to the increasing use of digital technology. This research aims to develop a more secure and effective video encryption algorithm by combining several cryptographic methods such as Hill cipher, partition, the shift cipher, unimodular matrix, and binary file concept logistic function. The method used in this study is experimental by using the Python programming language to implement the encryption algorithm. The trials were carried out by comparing the performance of the algorithms developed using different key sizes and variations of the combination methods used. The results show that the developed algorithm can provide a high level of security in the video encryption process with good effectiveness. The use of a combination of different cryptographic methods also has a positive impact on the resulting level of security. Therefore, the developed encryption algorithm can be a good alternative for use in securing sensitive video data.
Scalable Programmable Photonic Integrated Circuits by III-V/Si Hybrid Integration

We discuss efficient optical phase shifter, in-line optical power monitor, and low-power photodetector based on III-V/Si hybrid integration technologies, which enable scalable Si programmable photonic integrated circuits.
Multi-Branch GAN-based Abnormal Events Detection via Context Learning in Surveillance Videos

Video anomaly detection is an important task in the field of intelligent security. However, existing methods mainly detect and analyze videos from a single time direction, ignoring the semantic information of the video context, which adversely affects the detection accuracy. To address this issue, we design a multi-branch generative adversarial network with context learning (MGAN-CL) to detect abnormal events. In particular, we combine video context information to generate predicted frames, and determine whether an anomaly occurs by comparing the predicted frame with the actual frame. Different from the existing GAN-based methods, in the anomaly event detection stage, we use the discriminator to judge the video frames generated by the generator, which improves the accuracy of anomaly detection. In order to improve the ability of the discriminator, a pseudo-anomaly module is added to the discriminator for data augmentation to improve the robustness of the model. An extensive set of experiments performed on public datasets demonstrate the method’s superior performance.
QARMA-FL: Quality-Aware Robust Model Aggregation for Mobile Crowdsourcing

Over the past few years, the improved detection and processing features of Internet-of-Things (IoT) devices have opened the doors to several mobile crowdsourcing applications. Federated Learning (FL) is being seen as an attractive framework to address the data privacy concerns of mobile users in the context of crowdsourcing. In FL on a crowdsourcing platform, constructing an effective deep neural network (DNN) is challenging. This is primarily because the quality of the global model depends on the local model quality, which can vary greatly due to differences in the computational resources, data quantity, and data quality provided by each worker. To address these challenges, we propose QARMA-FL: Quality-aware robust model aggregation for federated learning in crowdsourcing applications, where we select the local model for aggregation based on its quality and performance. We also propose a model-quality-aware incentive mechanism to reward workers, based on their contribution to model training. Our model selection and incentive mechanism is capable of detecting Free Rider attacks, identifying workers who benefit from others contributions without contributing themselves. Most existing evaluations of FL in mobile crowdsourcing studies are not based on the real-world FL scenarios. Therefore, we evaluate QARMA-FL alongside a baseline FL model in a quantity-skew, non-IID data setup where different workers contribute varying amounts of data for model training. Our diverse experiments validated QARMA-FLs performance, demonstrating its ability to efficiently aggregate models in mobile crowdsourcing scenarios, reaching baseline results with a reduced worker participation by 40% to 60%.
Semi-supervised depth completion with fusion of LiDAR sequences and stereo imagery

The depth completion task aims to fill in the missing depth values of a sparse depth map to obtain a dense depth map, which is crucial for application of computer vision especially autonomous driving. Since acquiring a dense or even semidense ground-truth depth map for supervised training is laborious and difficult, sparse depth map is used to achieve semi-supervised learning. But the sparse depth map has not enough valid values, which means it cannot provide effective constraint. Therefore, we merge multi-frame point clouds from LiDAR sequences into a same frame to improve the density of sparse depth map and enhance the constraints of depth labels in semi-supervised learning. So a semisupervised multimodal multitask framework is proposed which includes two sub-networks: LiDAR odometry and depth completion network. The LiDAR odometry sub-network takes LiDAR sequences as input and achieves self-supervised learning based on geometric consistency between sequences. Based on the pose estimated by odometry network, we use differential projection module (DPM) to obtain a denser merged depth map. The depth completion sub-network takes binocular image and sparse depth map as input, and realizes semi-supervised learning with supervision of the stereo view synthesis and the merged depth map from LiDAR odometry branch. These two sub-networks can be trained in a multitask fashion with the help of DPM. The experiments conducted on the KITTI dataset show that the proposed method outperforms other state-of-the-art methods.
Survey of Code Search Based on Deep Learning

Code writing is repetitive and predictable, inspiring us to develop various code intelligence techniques. This survey focuses on code search, that is, to retrieve code that matches a given natural language query by effectively capturing the semantic similarity between the query and code. Deep learning, being able to extract complex semantics information, has achieved great success in this field. Recently, various deep learning methods, such as graph neural networks and pretraining models, have been applied to code search with significant progress. Deep learning is now the leading paradigm for code search. In this survey, we provide a comprehensive overview of deep learning-based code search. We review the existing deep learning-based code search framework that maps query/code to vectors and measures their similarity. Furthermore, we propose a new taxonomy to illustrate the state-of-the-art deep learning-based code search in a three-step process: query semantics modeling, code semantics modeling, and matching modeling, which involves the deep learning model training. Finally, we suggest potential avenues for future research in this promising field.
Optimal Operation of Domestic and Industrial Sewage Treatment Plants Using Machine Learning Methods

Purpose: This study aims to determine the economic and technical feasibility of operating and leasing sewage treatment plants through an application that uses mathematical modeling and Machine Learning techniques for process optimization. Theoretical Framework: Efficient operation of sewage treatment plants (STPs) is crucial to ensure water quality, minimize environmental impacts, and optimize costs. This study explores how Machine Learning (ML) can model and optimize sewage treatment processes, adapting to real-time conditions. Method/Design/Approach: The BSM1 model is combined with Machine Learning techniques to create simplified metamodels, enabling optimized results and the development of an application for evaluating economic and technical outcomes. Results and Conclusion: The reduced metamodel successfully reproduced the Simulink model, achieving satisfactory optimization. Research Implications: This research benefits water quality improvement, cost reduction, sustainability, innovation, water resource management, awareness, and resilience to extreme weather events, as well as influencing informed policies. Originality/Value: Efficiency, sustainability, economy, and quality of life are core values in this research, benefiting society, the environment, and the economy.
Unleashing the Power of Visuals: A Captivating Exploration of Scientific Data Visualization Methods and Techniques

Scientific visualizations are the building blocks for conveying results and ideas to a wide range of audiences. It is one of the steps in the data science process, and it should be done before the data presentation phase. Because some data is not easy to read for humans, visualization can help transform the data into visual content such as graphs or plots, as the requirement for scientific visualization techniques and methods is increasing greatly. Reviewing various visualization methods is necessary to increase knowledge about it. In this paper, an overview of scientific data visualization methods and techniques has been reviewed. To conduct the experiment, a total of five peer-reviewed articles relating to scientific visualization techniques were chosen. The problem with this study is that people still do not have a clear idea of how to use or what is the importance of scientific visualization. The findings of this paper show that the occurrence of visualization techniques improves the effectiveness of reading data and helps people to understand data in a more vivid way. A systematic literature review has been adopted in this study to assist the author in doing a study without bias. Finally, this paper can help readers increase their knowledge of the scientific visualization technique.
Improving Student's Vocabulary Mastery Through Hello English Application at Tenth-Grade Students of High School

This study aims to determine 1) the effect of using the Hello English application in English teaching on improving students' ability in vocabulary mastery and 2) determine students' perceptions of the application of Hello English application in teaching English subjects at SMAN 1 Mayong, Jepara. This study used a type of quantitative research with a pseudo-experimental research design. This study used test data collection techniques and questionnaires to measure students' vocabulary mastery. The population of this study was grade X students of SMAN 1 Mayong, with a total of 72 students consisting of grades X 7 and X 9. The test result data in this study was calculated and analyzed automatically using SPSS. Based on the data analysis using the t-test, the test data shows that the result of t count (df = 70) = 9.881 and p-value or significant level of 0.000, which is smaller than 0.05. Researchers use α = 0.05 (5%) as a significant standard, so the null hypothesis (Ho) is rejected, and the alternative hypothesis (Ha) is accepted because the p or sig (2-tailed) value of 0.000 is lower than α = 0.05 (5%). This means a significant difference exists between the results of using the Hello English Application in teaching vocabulary to grade X students of SMA Negeri 1 Mayong for the 2022/2023 school year. The results showed that the Hello English application significantly increased students' vocabulary mastery after treatment. The average score of experimental class students increased from 73 on the pre-test to 91 on the post-test.Moreover, the control class's average score was 74 on the pre-test to 79 on the post-test. This means that the average score of the students in the experimental class is higher than the average score in the control class. Researchers concluded that the Hello English Application is effective through mastery of student vocabulary, especially for tenth-grade students of SMA Negeri 1 Mayong for the 2022/2023 academic year. Based on the questionnaire, the results showed that using the Hello English application in teaching English has many positive impacts on students, including students being more motivated in learning and making it easier for students to understand the material.