Reviewerly/exHarmony huggingface dataset:
SPECIFIC FEATURES OF USING WEB 2.0 TECHNOLOGY IN THE EDUCATIONAL PROCESS

Introduction. The article examines the peculiarities of implementing Web 2.0 technology into the educational process, covers the advantages of certain web-services and electronic resources. It is noted that in the conditions of the information society there is a rapid development of software and hardware. Using services and electronic resources that support Web 2.0 technology, allows to diversify the educational process, make it more qualitative and interactive. The purpose of the article is to reveal specific features and benefits of online services and Web 2.0 electronic resources for organizing the educational process of a higher school. Results. It has been found that capabilities of Web 2.0 make it possible to create a modern educational environment, provide mixed and distance learning, and ensure creative application of acquired knowledge. This can be achieved thanks to various online services and electronic resources, which can be divided into several groups. It has been exposed that Web 2.0 technology is considered to be more a social innovation than a technological one. This is due to the possibility of creating thematic communities. It has also been revealed that virtual social networks, Google Classroom, Internet-blog, webinars, etc. are used to organize educational activities and communication between students and the teacher. It has been discovered that today's youth often stay in the virtual space of social networks, communicate, do educational tasks, etc. With that in view, there have been established the conditions that affect the effectiveness of the application of Web 2.0 technology in the educational process. Originality. The possibilities of virtual social networks, Google Classroom, Internet-blog, webinars and other services to organize the educational process in higher education have been identified. Conclusion. The implementation of Web 2.0 online resources and services allows to diversify the educational process, make it more interactive and interesting. The possibilities of Web 2.0 allow users to learn, exchange multimedia, increase social and informational competences. Working with Web 2.0 technology does not require special computer skills. The authors consider the usage of Web 3.0 technology in the educational process to be a promising direction of research.
Depth Separable Convolution Lightweight Blind Motion Deblurring Algorithm

Aiming at the motion blur problem caused by handheld camera shake and object movement in the process of photo acquisition, an image deblur algorithm based on depth separable convolution lightweight generative adversarial network was proposed.Firstly, a depth separable convolution residual block with selfattention mechanism is introduced in the generator.Secondly, Transpose convolution module is replaced by the Pixel-Shuffle up-sampling module.Finally, The combination of perceptual loss and relativistic generative adversarial loss can effectively alleviates the mode collapse problem of traditional GAN and improves the stability of model training.Experimental results show that compared with Deblur-GAN the number of parameters of the proposed algorithm is significantly reduced, the deblurring time of a single image is reduced by 50%, the deblurring image is clearer in subjective vision, and the objective evaluation indexes of image quality such as peak signal-to-noise ratio and structural similarity are also significantly improved.
Medical Relation Extraction from Electronic Medical Records

In the field of natural language processing, relation extraction (RE) aims to identify the relationship between entities from text and is a vital step for subsequent tasks such as question answering and Knowledge Graph (KG).Comparing to the general domain text the relationship extraction from electronic medical records (EMRs) is more challenging due to the high-density distribution of entities of EMRs.In addition, relationship extraction usually suffers from the long-tail problem.The training data mainly focuses on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations, which is more severe in electronic medical records.To address the above problems, this paper proposes a segmented attentional Convolutional Neural Network (CNN+SegAtt) for medical relationship extraction and combines it with modified typed entity representations, which significantly improve the semantic learning ability from electronic medical records.A loss function with category weights is also proposed to facilitate the extraction of long-tail relationships.Experiments are conducted on the publicly available medical corpus 2010 i2b2/VA.The results show that the value of F1 reached 73.13%, better than that of existing methods, and the values of F1 for the small category samples are also significantly improved.
Gesture Recognition using Graph Method

Recognition of gestures is a challenging task that often plays an important role in enabling smooth interaction among people and between people and new age devices.Inclusion of gesture-based device interactions allows intuitive usage of technology and ease of communication.The paper proposes a new and under-explored approach that involves graph based methods for seed marking in image segmentation in the pre-processing phase to separate the hand from it's background, further it is fed as the input to ensemble models to predict and classify the gesture being performed, while allowing the users to be in their natural conditions.
Event Detection via Graph-based Multi-hop Neighbors’ information Fusion

Event detection (ED) proves to be a crucial subtask of event extraction.There is a close connection between event trigger word and its related neighboring words in dependency parse tree, neighbors' information has been widely used in event detection task.However, only neighbors' information with a direct arc (i.e., only one hop) to the trigger candidate in the dependency parse tree is used in many existing graph convolutional network (GCN)-based methods.As a result, the multi-hop neighbors' information is not fully utilized, so that it is difficult to further improve the performance of ED.Therefore, this paper proposes an ED model of graph convolutional network based on dependency parse tree.This model introduces graph attention network (GAT) to learn the multi-hop neighbors' information of each adjacent node in the syntactic graph, and uses a multi-label attention fusion mechanism to fuse the extracted multi-hop semantic information and reduce the complexity of the model.The results of experiments on the ACE2005 dataset show that compared with other methods based on graph convolutional neural networks, the F1-socre of this method reaches 75.6%, and the experimental results reflect the effectiveness of the method proposed in this paper.
Object Detection of Roadside Pedestrians and Vehicles Based on Improved YOLO v5

Target detection algorithms based on deep learning are more and more widely used in the field of autonomous driving.At present, the target detection of roadside pedestrians and vehicles in the field of autonomous driving mainly faces the problems of too many types of vehicles, complex detection target backgrounds, overlapping of people and vehicles, and too many small targets.In view of the existing problems, this paper improves the YOLO v5 algorithm, optimizes the feature extraction network of YOLO v5, adds a small target detection head, improves the ability of the backbone network to extract target features, and strengthens the detection of small targets; adds the Selayer attention mechanism to improve the model sensitivity to channel features and enhances the network's ability to detect occluded targets.The experimental results show that the improved YOLO v5 model can achieve an average P of 91.3% for the detection of 10 kinds of roadside pedestrians and vehicles, and the mAP@0.5 of 80.1%.Compared with the original YOLO v5, the P is increased by 8.4%, and the mAP@0.5 is increased by 5.2%.
Supply Chain Perspective Oriented Business Mode of Renewable Energy Cloud and Its Data Factors Application

In the future, renewable energy source (RES) plays a main role in energy supply and demand system, which has become the consensus.The Energy Internet (EI) with deep integration of information is an important way to realize the transformation of energy operation structure.Based on the analysis theory of inventory, logistics, price and user demand of supply chain management, this paper analyzes the data factors of renewable energy cloud (REC) platform and established renewable energy cloud supply chain.Combined with data elements involved in the economy operation of REC, the value chain of renewable energy cloud is established and its business application mode is analyzed.Different market roles of supplier, operator and distributor were considered carefully to analyze the causality of data elements of REC.After the energy supply and demand system dynamics model was constructed.The regional load power data was tested in the model, and the anti-Bullwhip effect under the steady-state economic operation mode was verified and its suppression strategy and application value were discussed.
Hierarchical Bayesian Recommendation Model for Inter-Company Collaboration Using Cross-Industry Questionnaire

In recent years, companies have participated in a lot of brand collaboration, which is expected to improve consumer attitudes toward both brands.However, choosing the appropriate collaborator is a very difficult task.Therefore, we propose a hierarchical Bayesian model for recommending a company for collaboration using questionnaire data rating consumer satisfaction with the company.This model allows us to account for uncertainty, bias, and hierarchical relationships in the questionnaire data.Our experiments showed that the model can estimate the level of satisfaction with the companies, and also confirmed the validity of the company recommendations for collaboration.
DeepLabv3+ Semantic Segmentation Network Optimized by Double Weighted Feature Fusion

Intelligent identificat ion to prohibited items in lug-gage is of great significance to ensure the safety of passengers.In this paper, we propose CWF-Deep Labv3+ semantic segmentation network to improve the segmentation accuracy to small size objects in X-Ray security images, and reduce the object pixel loss caused by complex background.The CWF-Deep Labv3+ network is based on the DeepLabv3+ network, which improves both the Xception backbone and the Atrous Spatial Pyramid Pooling (ASPP) module.Firstly, we co mbine the Cross Stage Partial Net work (CSPNet) structure to propose a new CSP-Xception backbone network.The CSPNet network is capable of imp roving the learning ability of the network by using gradient split to build rich gradient co mbinations.Secondly, we design the Double Weighted Feature Fusion (DWFF) in the ASPP module.Weighted feature fusion by learning the importance of input features, and in this way, the segmentation accuracy of images with small size objects and complex background will be improved.The experimental results of training and testing on X-Ray security images show that the mPA value and mIoU value of CWF-DeepLabv3+ network reach to 90.85% and 81.64% respectively.Furthermore, co mpared with DeepLabv3+, Unet, and Bisenetv2 semantic segmentation network, the mean pixel segmentation accuracy of our proposed network is improved by at least 3.13%.
Image Tampering Localization based on Pyramid Multi-Scale Pooling Module

In this paper, we proposed a new two-stream image forgery localization method based on pyramid multi-scale pooling module.One stream learns the differences in JPEG compression rate between original regions and tampered regions by processed through ELA.In the second stream, the network learns the global image manipulation features from the input image RGB values.Next the fusion stage fuses multiple tampering features from the dual-channel feature extraction network to generate the shallow feature map, Coordinate Attention is applied to refine the predicted mask and further improve the localization accuracy.And then through the multi-scale pyramid pooling module, so the secondary fusion is carried out.Finally, the fused feature map restores the original image size by using UpSampling for Pixel level classification prediction.The proposed net can effectively decrease incorrect prediction since it makes better use of the contextual spatial information in images.And most of all, from two stages extraction fuses lowlevel and high-level information to refine the global manipulation features.The experimental results on several public datasets show that the proposed model outperforms some state-of-the-art methods.
Development of Android-Based Construction Alphabet Learning Media to Improve Reading Literacy and Digital Literacy at SDN 23 Batara, Palopo City

The development of information and communication technology in the revolutionary era 4.0 has resulted in people being accustomed to using Android as a product of information and communication technology developments. Developments in technology and communication have had an impact on all fields, especially in the field of education. The formulation of the problem in this study is to test the feasibility of the media being developed. The purpose of this study is to carry out feasibility tests through validity tests and practicality tests of the media being developed. The research method used is the Research And Development (R ND) research method with the 4D model (Define, Design, Development and Dissemination). The analysis technique used is validity test, practicality test. The results of the validity test of the construction alphabet media obtained from the three validators were 97.5% with a very high validity category. The results of the practicality test of construction alphabet media through a student response questionnaire obtained an average class value of 3.7 in the very high category. Based on the results of the validity test and media practicality test, it shows that the construction alphabet media is suitable for use in elementary school students at SDN 23 Batara, Palopo City. Keywords: [Construction Alphabet Learning Media, Reading Literacy, and Digital Literacy]
Dark Side of Social Media

The paper focuses on the mechanisms, offences and practices connected to unchartered (underdeveloped) crimes in the information society. The virtual world represents a central part of modern (modem) life, especially during the COVID-19 pandemic. Omnipresent social media, media sharing platforms, chat sites, web forums, and blogs radically change the way current societies operate. These instruments attract more and more attention from public security planners. This type of research is a normative legal study. The approach used is the law approach and conceptual approach. The legal material consists of primary and secondary legal materials. Basically, the Criminal law system has to adapt to social media to emphasise the legality principle (structure of cases in point) and guarantee measures (prohibition of broadening liability) in the post-modern world. This topic shall look at the role and the problems of criminal law related to multiple profiles, analysing the criminological aspects proposed below.
ELABORATION AND RESEARCH OF A MODEL OF OPTIMAL PRODUCTION AND DEVELOPMENT OF INDUSTRIAL SYSTEMS TAKING INTO ACCOUNT THE USE OF THE EXTERNAL RESOURCES

The problem of optimization of investment projects related to the development of modern production systems is considered. The tasks of managing of operation and development of production systems considering external resources – the synthesis and analysis of optimal credit strategies – are posed and solved. An analysis of analogs – solutions of the variational problem of optimal development, the disadvantage of which is the difficulty of obtaining information about the state of production and the external environment, was carried out. The new solution is based on the resource approach, when external resources are taken into account in the cost of production resources. A generalized model of optimal development is used, in which the planned period of the investment project is divided into intervals. At the beginning of each interval, the optimal development strategy is adjusted taking into account the clarification of information about the future state of the active environment: actions of competitors, consumers, world markets. To determine the optimal amount and optimal distribution of credits between subsystems, the maxima of the criterion – the parameterized function of the system's efficiency – are determined at each interval. A new model has been developed based on the model of optimal development, which takes into account the use of external resources, such as loans. The method of including an external resource in the development function and the production function is considered. Examples of modeling are given.
Decision Support System Recommendation Housing Using AHP And Saw Method Palangka Raya City

Palangka Raya City, as one of the provincial capitals in Indonesia, which has an area of around 2,400,000 km2, is a strategic city as a service and distribution hub for the industrial, trade, government and education sectors. Regional Policy of the city government with the existence of a development plan in the City of Palangka Raya as an implementation of the city space with all the disadvantages of its designation resulting in a distribution pattern of urban land types which in fact is not evenly distributed throughout the city. This research was conducted based on the results of observations made in several Marketing Agents in the Palangkaraya Region, which included 5 districts where the survey results obtained several marketing agents for KPR housing with the aim of facilitating the purchase of KPR housing. The purpose of this study is to design a decision support system that is used to support the decision to purchase housing loans in the Palangkaraya area. Based on the research that has been done, it is expected that the results of the purchase decision support system for the KPR recommendation with the best value can be a recommendation for the purchase. This system is designed with the AHP and SAW methods to help prospective residents to determine the house based on the desired criteria.
Comparative analysis of Web application development on Java and PHP

This article presents a comparative analysis of two programming languages used to develop web applications. Two most popular programming languages Java and PHP were compared. The evaluated criteria were: implementation and performance, the time of performing specific operations on 100, 1000 and 10000 records was checked. A code analysis was carried out to determine in which language it is easier to implement an application with identical functionalities. Before the implementation of the application, articles, books and websites on a similar topic were reviewed. In order to test the efficiency, the Postman developer tool was used. The comparison shows that PHP is a more efficient language for developing web applications than Java.
Comparative of React and Svelte programming frameworks for creating SPA web applications

The aim of the article is to perform a comparative analysis of two programming frameworks on the example of one of the most popular currently React v17 and Svelte, which is gaining considerable popularity. Two applications with the same functionalities in both analyzed frameworks were implemented to conduct the research. The analysis mainly concerns the rendering times of views, but the application structures and their size are also compared. As a result of the research, it was found that the Svelte application is much more efficient compared to the React application.
A Letter as a Fact of Literature: Letters by L.Ya. Ginzburg and V.S. Baevsky

he archives of the literary critic V.S. Baevsky are kept in the fonds of the&#x0D; Literature Museum of Smolensk State University. The materials of the scholar’s&#x0D; archive have not been presented to the public yet. Meanwhile, the fonds contain&#x0D; a collection of more than a thousand letters from addressees from all over the&#x0D; world, including such outstanding scientists of the twentieth century as B.Ya. Bu-&#x0D; khshtab, M.L. Gasparov, L.Ya. Ginzburg, Yu.M. Lotman, B.F. Egorov and others.&#x0D; Correspondence was of particular importance for the philologists of that gener-&#x0D; ation, as it made it possible to combine life, history and science. Comparison of scientific views, the need for an independent censor and&#x0D; obtaining an objective assessment of scientific works are included in the topics&#x0D; reflected in the letters from L.Ya. Ginzburg to V.S. Baevsky. Their correspondence&#x0D; and memoirs testify to the coincident temporal rhythms and allow us to trace the&#x0D; scientific thought of the scholars that lived almost the entire twentieth century.&#x0D; This paper reconstructs the role of a letter as a fact of scientific autobiography.&#x0D; It shows its transformation from the genre of «human documents» (letters and&#x0D; diaries) into the genre of «intermediate literature» (memoirs). Thus, the personal&#x0D; letters of L.Ya. Ginzburg acquire the status of a literary fact, since they represent&#x0D; a narrative unit in V.S. Bayevsky’s life
A comparative analysis of performance of Flutter and Xamarin development frameworks

This article presents a comparative performance analysis of two cross-platform development frameworks Flutter and Xamarin. Using these technologies identical test applications running on Windows and Android were created. Each of these applications included functionalities to run test scenarios. They concerned calculating the 30th word of the Fibonacci sequence, sorting with the MergeSort algorithm lists consisting of five thousand and ten thousand elements, performing basic database operations on the database, such as record, reading, searching, modifying and deleting data. The scenarios were repeated ten thousand times, and the average execution times of the operations were analyzed. The results did not conclusively show which framework is more efficient. However, in general, it can be concluded that for applications running on Android and Windows that perform a lot of calculations or save large amounts of data or search and at the same time modify data, the Flutter framework will be a better solution.
Fuzzy Logic for Determination of Community Assistance Using the Tsukamoto Method for Residents of Kasreman Village, Rembang

The obstacle to regional progress and the main cause of social problems is due to the large number of poor people, so there must be a poverty management program by the government, one of which is citizen assistance. The selection process by the local village apparatus is very much needed in the process of determining the recipients of citizen assistance, because the quota for the recipients of citizen assistance is less than that of registrants for citizen assistance. The distribution of aid does not fall to the right party resulting in injustice to other underprivileged families so that it creates several problems, where the method that will be used is Tsukamoto's Fuzzy Logic. In this study, the data used are land area, income of residents, number of dependents of the family. The evaluation method carried out in this study is using a confusion matrix, for one test the level of accuracy produced is 92.74%. Based on the experiment, it can be concluded that the Tsukamoto algorithm is quite accurate in determining citizen assistance to the residents of Kasreman Village, Rembang.