Reviewerly/exHarmony huggingface dataset:
Research on IoT Data Collection Middleware Based on Microservice Architecture

Given that there is no unified standard for the communication protocol of IoT sensing devices and the single program has poor scalability, this paper proposed a data IoT collection middleware based on microservice architecture. The middleware used microservice technology to reduce the coupling degree of each module of the program and established a suitable communication mechanism to support the heterogeneous IoT communication protocol. Because of the gradual increase in the number of Internet of Things sensing devices, the number of microservice instances and resources needs to be dynamically adjusted. The middleware introduces the ability evaluation model based on extreme learning machine, and it is an essential basis for changing the number of resources and microservices based on the actual number of connected IoT sensing devices. Experimental results show that the middleware has good scalability and high availability.
Project Technology in Education as a Promising Form of University and Business Cooperation

The article presents the experience of partnership between universities and business partners in the development and implementation of joint projects. The article reflects the features and methods of introducing project-based education into the education and training system. The authors identified pedagogical conditions as the main condition for the effective implementation of the project. Also, a complex of risks limiting the effectiveness of design technology in the educational process has been identified.
A Feasibility Study of GCPV Solar Panels for Commercial Buildings

Grid-connected photovoltaic (GCPV) solar panels are an option for integrating sustainable approaches in energy production. The practicality of using renewable energy sources in commercial buildings is a critical factor in the willingness of large corporations in adopting these systems. This study begins with assessing the natural solar potential of Turkey in terms of solar radiation and sunshine duration and is followed by modeling the energy requirements of a shopping mall with a surface area of 20,000 m2 in Istanbul with consideration of the current feed-in tariff (FiT) rate and renewable policies via HOMER Grid software for simulation and calculation of most efficient solution. Three economical indices, discounted payback period (DPBP), simple payback period (SPBP), and internal rate of return (IRR), are measured to evaluate the feasibility of the proposed system. The sensitivity analysis is also conducted among four different possible situations to investigate the effects of changes in the current FiT rate as well as the capital cost of GCPV systems.
Ensuring Long-Term Trustworthy Collaboration in IoT Networks using Contract Theory and Reputation Mechanism on Blockchain

Internet of Things (IoT) devices operate in an untrusted environment, and blockchain technology can provide a secure distributed collaboration method. However, the widespread distribution of heterogeneous devices in the IoT creates complex information asymmetry issues, leading to inefficient resource allocation. This is especially true when information asymmetry occurs in long-term cooperative relationships, where contract design often faces more complex incentive problems. This article focuses on studying long-term cooperation between IoT devices based on blockchain technology and proposes a solution to information asymmetry in long-term cooperation based on contract theory and reputation mechanism. 1) We design more flexible pricing strategies based on contract theory to attract more devices to become validators and provide different long-term job contracts to overcome information asymmetry issues in the cooperative process. 2) We propose a subjective reliability-linked reputation evaluation mechanism to construct the career of validators, which can constrain unethical behavior and incentivize validators to comply with rules and protect data security in long-term cooperation. Experimental results show that our scheme can improve device willingness to cooperate, overcome information asymmetry in long-term cooperation, improve the efficiency of computing resource allocation, reduce network security risks, achieve long-term trusted cooperation between devices, and provide effective security for the development of the IoT.
Non-Euclidean Data Exploration using Turiyam Set and its Complement

Recently, several researchers paid attention towards dealing the data sets beyond Non-Euclidean geometry. To achieve this goal, Turiyam set and its properties is introduced for precise measurement of uncertainty in data sets beyond acceptation, rejection and uncertain parts. However the characterization of uncertainty requires a new operator and method. To resolve this issue, the current paper introduces a method for precise characterization of fourth dimensional data based on Turiyam operator and its complement with an illustrative example. The proposed method also compared the given method with Euclidean, Non-Euclidean, and NeutroGeometry data characterization.
Reversible Data Hiding with Contrast Enhancement Using Bi-histogram Shifting and Image Adjustment for Color Images

Prior versions of reversible data hiding with contrast enhancement (RDHCE) algorithms strongly focused on enhancing the contrast of grayscale images. However, RDHCE has recently witnessed a rise in contrast enhancement algorithms concentrating on color images. This paper implies a method for color images that uses the RGB (red, green, and blue) color model and is based on bi-histogram shifting and image adjustment. Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image. Images are first divided into three channels—R, G, and B—and the Max, Med, and Min channels are then determined from these. Before histogram shifting, some calculations are done to determine how many iterations there will be for each channel. The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel. The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods, and their quality was assessed using PSNR, SSIM, RCE, RMBE, and CIEDE2000. The embedding rate obtained by the suggested method is acceptable.
Research on Improving Teaching Quality and Optimizing Teaching Scheme Based on Deep Learning in Chinese Literature Scene

With the rapid development of society nowadays, this paper begins to study the teaching strategies of promoting students’ deep learning in the Chinese literature scene, and the attitudes and teaching quality of students and teachers when learning Chinese literature. The investigation and analysis show that: (1) For example, the relationship between literary scenes and characters in the famous literary work “Three Kingdoms” is analyzed. The complex character relationships in literature are important to literary scenes and learning. (2) It explains that the suggestions when writing Chinese literary scenes need to be pragmatic, pay attention to modern people’s livelihood, and the author should integrate into the literary scene to create good works. (3) The experiment in this paper investigates the depth of students’ learning of Chinese literature and their learning methods, and discovers methods that can make students learn more deeply.
Software and software metadata

As I’ve been involved in the new FAIR for Research Software (FAIR4RS) activity (jointly run under FORCE11, RDA, and ReSA), I’ve been leading a subgroup looking at the FAIR (data) principles and thinking about how they can be applied to software. One of the things that has become clear to me is how fundamentally different software and data are, and the consequences this has on how software can be made FAIR. The gaps that we have in practices and technology are really the same gaps...
The History of English through SparkNotes

Language change has been the focus of my research for over twenty years now, so when I taught second semester linguistics at Saint John’s University, I was very much looking forward to teaching a unit focused on change. I had been working to replace constructed examples with real data, so I took a tip from my natural language processing colleague Dr. Wei Xu and turned to SparkNotes. I first encountered SparkNotes when I was teaching French Language and Culture, and I assigned...
Model of civic consciousness formation in students with project activity

The relevance of the research is related to the important task of the higher education system – increasing the level of civic consciousness of students, developing their civic and social competencies. The research analyzes the theoretical and practical aspects of the development of civic consciousness in the context of classical and modern psychological and pedagogical theories that describe the structure (model) of personality. The use of these theories allows us to consider the most important components of personality involved in civic consciousness formation. The research purpose is to develop and implement a model for the formation of students' citizenship in the conditions of project activities. The scientific novelty is in developing what contributes to the effective formation of students' civic consciousness in project activities. The research is theoretically significant as its results enrich the theory and methodology of vocational education in the field of civic consciousness formation in project activities; complement scientific ideas about models and the concept of civic consciousness of the individual. The research is practically significant as the developed model is included in the practical activities of the educational process of the university and ensures the effective formation of students' civic consciousness in the conditions of project activities. The experimental base of the study was the budgetary institution of higher education “Surgut State University”. Methods: generalization, analysis and synthesis of information, comparison, analysis of students' project activities.
Preparation of educational and methodological materials in local history for general education organizations by students of pedagogical specialties

The research is devoted to the issue of developing educational and methodological materials on History and Local studies by students of pedagogical specialties. Today, teachers face a difficult task related to improving the effectiveness of teaching History and Local studies in primary school. To organize teaching with the requirements of the Federal State Educational Standard, teachers need to use new universal methods and technologies. In this matter, teaching materials prepared by students – future history teachers – will be an excellent assistance for teachers. The project activity of students of pedagogical specialties 44.03.05 Pedagogical education (with two training profiles) (profiles «History and culture of confessions», «History and social studies») is analyzed. Considered various educational and methodological materials in local history for educational organizations created by students at project activities are: lesson-game, lesson-excursion, reproduction of archival graphic materials, interactive video tour. The main attention is paid to the workbook on local history, which will allow integrating the local history module into the academic subject «History». The concluded analyzed role of the workbook in the educational process is that the workbook is one of the leading tools that allow independent study of the subject and control of students' knowledge at all stages of learning, which provides an opportunity for the teacher to eliminate gaps in knowledge in time. Designed its own methodological development on the history of the cities of Tambov province in the 19th century, which is a workbook with assignments for students, with which you can study not only historical urbanism, Tambov local history, but also the history of Russia.
Python API Misuse Mining and Classification Based on Hybrid Analysis and Attention Mechanism

APIs play a crucial role in contemporary software development, streamlining implementation and maintenance processes. However, improper API usage can result in significant issues such as unexpected outcomes, security vulnerabilities and system crashes. To detect API misuses, current methods primarily rely on comparing established API usage patterns with target points for automated detection, mainly based on pre-validated datasets. Nonetheless, there is a scarcity of publicly available datasets on API misuses and their corresponding fixes, which hinders data-driven research. Moreover, most existing techniques concentrate on statically typed languages, such as Java and C, with only a few addressing dynamic languages like Python effectively, due to difficulties in handling dynamic features. Therefore, it is essential to identify Python API misuses and their fixes automatically and promptly. In this paper, we introduce HatPAM, a Hybrid Analysis and Attention-based Python API-Misuse Miner, which (a) provides a method for automatically mining true-positive commits related to Python API-misuse fixes from GitHub and (b) presents the subsequent processing for classifying Python API misuses in true-positive cases. Particularly, HatPAM applies hybrid static analysis and introduces a structure-based attention mechanism to examine syntax, semantics and structural features in Python code context, and considers the consistency between code and developers’ natural intent to significantly reduce false-positive cases. Evaluation on six popular Python projects reveals that HatPAM outperforms various state-of-the-art baselines, achieving up to 92.2% Precision, 86.7% Recall and 89.3% F1-score, indicating its capability to identify and classify Python API-misuse commits.
A self-validation Noise2Noise training framework for image denoising

Image denoising is a crucial algorithm in image processing that aims to enhance image quality. Deep learning-based image denoising methods can be categorized into supervised and unsupervised approaches. Supervised learning requires pairs of noisy and noise-free training data, which is impractical in real-world scenarios. Unsupervised learning uses pairs of noisy images for training, but it may yield lower accuracy. Additionally, deep learning-based methods often require a large amount of training data. To overcome these challenges, this research proposes a self-validation Noise2Noise (SV-N2N) framework that generates validation sets using only noisy images without requiring noise-free pairs. The proposed SV-N2N method effectively reduces noise, comparable to supervised and unsupervised methods, without requiring a noise-free ground truth, which is efficient for solving real-world scenarios.
Adjusted Simultaneous Importance Performance Analysis in E-government Implementation

The Importance Performance Analysis (IPA) method assists managers in making decisions, but service performance attributes from other institutions are often ignored.Furthermore, considering the competitor aspect, the analysis will be more comprehensive because it looks at internal and external factors.The IPA method was then improved by focusing on the competitor's side of the competitor's position to plan a management strategy, namely Simultaneous IPA (SIPA).The traditional SIPA does not discuss item weights between criteria, so the criteria for the most significant improvement often cannot be seen.Therefore, the adjusted SIPA calculates the weight of each indicator multiplied by performance and importance score to gain the perfect result.Based on the results, the adjusted SIPA model has been implemented on E-government problems in Indonesia.The importance of quadrant II requires local governments to improve the existing problems.Performance matrix items are neglected opportunities and competitive disadvantages.In the first quadrant, the importance and performance of both local governments are equally high, but it needs to maintain its performance in direct competition.The low priority quadrant III position has no opportunity and is full of false alarms.According to the experimental results, the item in the second quadrant is the part that the Meranti Islands Regency needs to improve mainly in the service domain, Q32-Q39, Q46, Q47, especially in the governance domain (Q13).These items are considerably harder than those of high importance but with low power.The gap analysis results show that the central government's rating score is lower than the self-assessment rate.This phenomenon indicates that expectations are higher than performance, and governments need to improve performance to match their value with profits.The gap analysis results between the two districts show that the gap between the two municipalities is negative overall and that the Meranti Island needs to improve all aspects of the E-government maturity assessment.A comparison of the SIPA and adjusted SIPA methods using the Wilcoxon method shows that there is an effect between changes in the two approaches, or in other words, the SIPA method was successfully improved using adjusted SIPA.
Cluster analysis of section properties with the aim of searching areas for drilling exploration geothermal wells

Basing on the results of magnetotelluric sounding, carried out earlier in the geothermal area under examination and developed 2D-models of electrical resistivity, porosity, permeability, temperature, thermal conductivity and specific heat capacity, an integrated cluster analysis of sections of enumerated parameters is fulfilled . &#x0D; Combined cluster analysis enabled to detect two subsurface areas promising for drilling exploratory boreholes. One of them coincides with already developed geothermal reservoir, while another one is located at depths 2-3km in another part of the section and may be of great interest for future exploration.
Semantic Pre-training Methodology for Improving Text Summarization Quality

Recently, automatic text summarization, which automatically summarizes only meaningful information for users, is being studied steadily. Especially, research on text summarization using Transformer, an artificial neural network model, has been mainly conducted. Among various studies, the GSG method, which trains a model through sentence-by-sentence masking, has received the most attention. However, the traditional GSG has limitations in selecting a sentence to be masked based on the degree of overlap of tokens, not the meaning of a sentence. Therefore, in this study, in order to improve the quality of text summarization, we propose SbGSG (Semantic-based GSG) methodology that selects sentences to be masked by GSG considering the meaning of sentences. As a result of conducting an experiment using 370,000 news articles and 21,600 summaries and reports, it was confirmed that the proposed methodology, SbGSG, showed superior performance compared to the traditional GSG in terms of ROUGE and BERT Score.
MENINGKATKAN KESADARAN LITERASI MEDIA PADA ANAK DI DESA PAJAGAN KECAMATAN SAJIRA KABUPATEN LEBAK BANTEN

The literacy level of the Indonesian people is quite low. This is based on the Indonesian Digital Literacy Index organized by the Ministry of Communication and Informatics and Katadata Insight Center (KIC) in 2021, Indonesia's digital literacy index is at 3.49 and Digital Safety gets an index score of 3.12. In this low literacy culture, it is hoped that it will not have bad implications for children. Because according to data from the Ministry of Communication and Information, generation Z and Y internet access lasts more than 6 hours. These data show that digital media exposure in children is much greater and of higher intensity. In the development of literacy in several major cities in Indonesia, Banten occupies the 9th position for Literacy but in terms of the fulfillment of infrastructure, it is ranked 5th at the bottom. Children as the next generation to get appropriate exposure and awareness of the importance of literacy. In this study FGD and interview techniques were used as well as observation in collecting data packaged in descriptive qualitative data. From this data it is known that the urgency of literacy in children cannot be carried out without support from various parties. Literacy suggestions must be raised at the nearest scale to provide significant awareness. Data shows that parents are the main center of children's activities and a trusted source for children, besides that friends and education are the main supporting parts. However, the existence of the media is an important variable in children's awareness of literacy.
Exploring Feature Combinations for Efficient Detection of SNS Content Polluters

트위터, 페이스북과 같은 SNS 사용자가 증가함에 따라서, 상품 광고, 비방 및 성인 콘텐츠 등을 게재하여 SNS를 오염시키는 콘텐츠 오염자 혹은 스패머 문제가 점차 커지고 있기 때문에 콘텐츠 오염자 탐지 방법에 대한 연구가 필요한 실정이다. 과거 랜덤포레스트, 나이브베이즈와 같은 기계학습의 지도학습 기술을 이용한 탐지 방법은 98% 이상의 비교적 높은 분류 정확도를 나타내는 반면, 학습을 수행하기위해서 원본 데이터로부터 특징을 식별하고 특징 값을 추출하여 훈련데이터를 생성하는 과정이 필요하다. 식별한 특징이 분류 그룹의 성격을 잘 나타내고 특징의 수가 많을수록 분류 정확도를 높이는 특성이 있다. 그러나, 식별한 특징의 개수가 많다는 것은 특징 값 추출에 소요되는 시간을 고려했을 때, 학습 모델 생성의 효율성을 저하시키는 부분이다. 일정 수준의 정확도 성능은 보장되고 효율성 제고가 가능한 최소 집합의 특징 조합을 탐색하기 위해서 본 논문은 특징 중요도, 카이제곱 분포 및 PCA 분석 등을 이용하여 최소 규모 특징 조합 탐색 방법을 기술하고, 이를 랜덤포레스트 및 심층신경망 알고리즘을 이용한 실험으로 비교 평가한다. As SNS users such as Twitter and Facebook increase, the problem of content polluters or spammers polluting SNS by posting product advertisements, slander and adult content is gradually increasing. Therefore, there is a continuous need for research on methods for detecting content polluters. Detection methods using supervised learning techniques of machine learning such as Random Forest and Naive Bayes in the past show relatively high classification accuracy of 98% or more. However, in order to create a learning model, a process of generating training data by identifying features from original data and extracting feature values is required. The identified features represent the characteristics of the classification group well, and the greater the number of features, the higher the classification accuracy. However, the large number of identified features reduces the efficiency of generating a learning model when considering the time required to extract feature values. In order to search for the minimum set of feature combinations that guarantees a certain level of accuracy performance and can improve efficiency, this paper describes a method for searching for minimum scale feature combinations using feature importance, chi-square distribution, and PCA analysis. And, this is compared and evaluated through experiments using Random Forest and deep neural network algorithms.