Reviewerly/exHarmony huggingface dataset:
Online Project Processing Information System at the Foundation Sumatran Orangutan Lestari

The Online Project Processing Information System at the Sumatra Lestari Orangutan Foundation (YOSL-OIC) is of course website-based, which is a project report information system that is carried out online that allows users to access data in real-time and can make the right decisions in a short time and it also makes it easier for the industry to manage and work on projects in a planned, controlled, analytical and sustainable manner. Industry must also be able to control how project activities are carried out. At YOSL-OIC, Data processing information systems are being evaluated in a semi-manual fashion, which is inefficient in terms of both time and accuracy. The study's overarching goal is to create a web-based data system that streamlines data collecting and processing and can be accessed from any location, among other benefits. The application that is formed includes work data that shows features such as collecting pictures and videos during activities, lists of donors, and can input projects through the website directly without going through software such as Ms.Word or other Office parts and several others. Employees may quickly and easily save enormous volumes of data, generate comprehensive reports, and guarantee the safety of sensitive project information with the help of this program.
Implementation of K-Means and Hierarchical Clustering in Determining Levels of Smart City 2022 Based on Motion Index

Smart City is a city with an innovative development concept. However, not all Smart Cities in countries have the same standard because they are quite heterogeneous. Thus, a cluster analysis was carried out to classify Smart City. The result shows that Smart City is divided into two levels, those are high and low. k-means and hierarchical clustering is used for the method of this research. The grouping is based on the motion index, which consists of economic, environmental, mobilization and transportation indicators, and also international profiles represented by various variables. This research expects that Smart City at a certain level can be compared with other levels, in order to there are improvements and mutual learning about Smart City at a high level. This can also encourage other cities in the process towards Smart City.
UI/UX Design for the Klaten Boarding House Application in the Widya Dharma University Klaten Area using Figma

Looking for boarding houses has become something that is often questioned by students, especially new students who come from outside the area around the University. The very limited availability of information can cause difficulties for the community when searching for boarding houses. The purpose of this study was to produce a UI/UX design prototype for the Klaten Boarding House application which will display relevant and accurate information about the location of the boarding house, information on boarding facilities, costs and can place an order for the boarding house. The area that will be used as a research location is a boarding house around Widya Dharma Klaten University. In designing the UI/UX design, the Klaten Kos application prototype was made as attractive as possible by using Figma editing software. Data collection techniques carried out by the author are interviews, observation, and literature study. With this system, it is hoped that it will make it easier to find boarding houses according to your wishes and help boarding owners to promote boarding houses online.
Design and Implementation of a Web-based Leather Goods Inventory System using the SCM approachased Astina

UD. Astina is one of the small and medium enterprises (SMEs) engaged in the production of leather goods such as shoes and sandals. The problems that occur at UD. Astina, that is, there is often a shortage and accumulation of supporting materials which are sometimes unstable. UD. Astina still carries out a transaction system manually and has not been computerized so that it hampers the marketing process for the production of leather goods and slows down the attractiveness of customers at UD. Astina. This research aims to design a system at UD. Astina who assists in optimizing the supply process for leather raw materials that must be available when carrying out production by applying the SCM method.The results of research with the construction of Electronic Supply Chain Management (E-SCM) in managing raw materials at UD. Astina can help make UD work easier. Astina in placing orders with suppliers so that they can anticipate shortages of leather raw material stock. So it can be concluded that Electronic Supply Chain Management (E-SCM) can be applied at UD. Astina in the management of leather raw material supplies.
Utilization Of UML (Unified Modeling Language) In The Design Of Academic Information Systems Based On The OOAD Method

Junior high school is one of the organizations whose business processes involve teaching and learning activities under the principal's responsibility. SMP ABC is a junior high school that continually strives to improve the quality of education, one of which is its academic services. However, the process of storing and processing data at SMP ABC still uses Microsoft word and Microsoft excel. Hence, the data management process is not good and allows a lot of errors in processing academic data. The academic information system is a system that can help solve the problem of errors in data processing and make the educational service process better and more effective. So SMP ABC also needs to implement an academic information system. The results of this study are to analyze and design educational information systems using the OOAD method using UML (Unified Modeling Language) tools with several diagrams, including use case diagrams, sequence diagrams, and ERD, to facilitate academic data management and improve efficiency and effectiveness so that management data on SPM ABC can be computerized appropriately.
User Experience Testing on the UII Informatics Department Website using the User Experience Questionnaire

The website is one of the media used by some educational institutions to convey information. One of them is the Department of Informatics at the Universitas Islam Indonesia (UII). One of the basic things that need to be considered when developing a academic website is the user experience (UX) a user has when engaging with a website. User experience measures the comfort level of a user when interacting with a product. User experience needs to be evaluated regularly so that the website can continue to meet the expectations of its users. However, there has been no research related to evaluating or testing user experience on the UII Informatics Department website. This study aims to assess the level of experience of UII Informatics Department website visitors. This study employed quantitative research methods, specifically the User Experience Questionnaire (UEQ) method. Using the mean calculation, the UII Informatics Department website received positive assessments on the dimensions of perspicuity, efficiency, attractiveness, dependability, and stimulation, but received negative evaluations on the novelty dimension. According to the benchmark analysis, the aspects of perspicuity and efficiency are above-average. While the dimensions of novelty, stimulation, dependability, and attractiveness are below-average. The results of this study can be used as a reference for developing a website for the Department of Informatics with a version that is more in line with user needs.
Application of the Trend Moment Method to Predict Shoes Sales

The N+2 Fashion store is engaged in selling trend shoes which is located in Air Batu City. Activities in the N+2 Fashion Store include ordering and selling trending shoes. Checking the supply of trending shoes is done by checking them one by one, of course this will make it very difficult for employees to estimate the number of trend shoes they want to order from each trending type of shoe. The large number of trend shoe brands is an obstacle in carrying out business processes which results in wrong prices for these trend shoes. In predicting trend shoes must ensure the sales process, minimize costs and time required. The problem faced by the N+2 Fashion Store is the difficulty in predicting the number of items that must be available for the next period in order to meet customer needs and not cause a buildup of goods in the long term. To overcome this problem, predictions are made using the trend moment method which is a forecasting method used to see trends (data that have increased) based on historical data from one variable using time series data. The results of the research obtained a prediction that in September 2022 there would be 9 pcs of Adidas shoes sold with a MAPE error rate of 16.33%.
Smart Traffic Light Using YOLO Based Camera with Deep Reinforcement Learning Algorithm

Congestion is a common problem that often occurs in big cities. Congestion causes a lot of losses, such as in terms of time, economy, to the psychology of road users. One of the causes of congestion is traffic lights that are not adaptive to the dynamics of traffic flow. This final project tries to solve this problem using a Reinforcement Learning approach combined with a SUMO (Simulation of Urban Mobility) traffic simulator. The data used is the real video data of the KD Cowek intersection, Surabaya. The video data is processed using the YOLO algorithm which will detect and count vehicles. The output of the video processing will be used in Reinforcement Learning. The result of Reinforcement Learning is that the total length of the traffic queue at 06.00 – 09.00 has an average of 106 vehicles.
House Price Prediction using Multiple Linear Regression and KNN

The transition of BPHTB management from central taxes to regional taxes is a continuation of the regional autonomy policy. The difference between the market value and the prevailing NJOP poses a challenge for the Sintang District Government in determining the Tax Object Acquisition Value (NPOP) as the basis for imposing BPHTB. Machine learning has been extensively explored for predictions and can be an alternative that can help predict NPOP, especially house prices. This study uses backward elimination and forward selection methods to select the features used in this study and multiple linear regression and K-Nearest Neighbor methods to make house price prediction models. The results of model performance measurement using RMSE, Multiple Linear Regression method with feature selection using backward elimination resulted in a better model with an RMSE value of 44.02 (million rupiahs) and an R2 value of 0.707.
TRENDS AND BENCHMARKS FOR THE DEVELOPMENT OF EDUCATION IN THE ERA OF DIGITALIZATION

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
SOFT SKILLS – AN IMPORTANT COMPONENT OF PROFESSIONALISM OF GRADUATES OF HIGHER EDUCATION INSTITUTIONS OF UKRAINE

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
METHODOLOGICAL FEATURES OF ORGANIZING E-LEARNING COURSES USING GOOGLE CLASSROOM

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
THE PROCESS OF INTELLECTUAL PROPERTY MANAGEMENT: APPROACHES TO UNDERSTANDING

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
MAIN MODELS OF NATIONAL INNOVATION SYSTEMS OF THE WORLD COUNTRIES

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
INNOVATIVE EDUCATIONAL TECHNOLOGIES: EUROPEAN EXPERIENCE AND ITS IMPLEMENTATION IN UKRAINE

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
TOPICAL ISSUES OF HUMAN RESOURCE MANAGEMENT IN HIGHER EDUCATION INSTITUTIONS

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
PEDAGOGICAL INNOVATIONS IN HIGHER EDUCATION: A PRACTICAL DISCOURSE

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
STRATEGIC DIRECTIONS OF THE COUNTRY’S ECONOMIC RECONSTRUCTION IN THE POSTWAR PERIOD

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
PROJECT-BASED BUSINESS-ORIENTED APPROACH TO REGIONAL DEVELOPMENT MANAGEMENT

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
Robust Tracking via Fully Exploring Background Prior Knowledge

Typical Siamese-based trackers focus on the target region and pay less attention to the background area. However, the background area can provide the tracker with prior knowledge about the target surroundings. Nonetheless, since the tracker can naturally utilize the target template for localization, importing additional background knowledge requires proper design so that the background area prior knowledge can be fully explored. Furthermore, the introduction of the entire background regions is redundant. Instead, the part background distractors in the regions are more meaningful for the discrimination of the tracker. In this work, we propose a background prior knowledge fully explored tracker for robust tracking. Firstly, we present a Transformer-based explicitly and fully background-utilizing scheme by boosting the tracker to independently exploit the background for localization. Specifically, a target-distractor independent decoder explicitly utilizes the background knowledge by making the target and the distractors independently perform fusion with the search feature. Secondly, we design a simple yet efficient discriminative distractors mining module to refine the background prior knowledge by replacing the whole background region with the mined background distractors. Extensive experiments demonstrate that the proposed method performs favorably against state-of-the-art trackers on nine benchmarks.
Adaptive Activation Network for Weakly Supervised Semantic Segmentation

Class activation maps generated by image classifiers are widely used as priors for image-level weakly supervised semantic segmentation. However, these activation maps mainly focus on the sparse discriminative regions, which has been a bottleneck for the segmentation task. Based on our observations, the activation maps actually capture almost the entire target regions, and some regions with lower activation values are easily to be neglected. Thus, to solve the issue, we propose an adaptive activation network with two branches to recalibrate the low-confidence regions in the activation maps. Specifically, an activation enhancement branch is designed to redistribute the activation values by leveraging attention mechanism. Since multi-scale images can provide complementary information, a scale adaptation branch is paralleled to supervise the activation enhancement branch. The mutual supervision and fusion of the two branches can promote the less-discriminative parts, and deactivate the background regions. Based on them, a simple yet effective denoising module is proposed to further improve the quality of pseudo masks, which makes use of the large scale predictions of the trained segmentation network. Extensive experiments on the PASCAL VOC 2012 and MS COCO 2014 benchmarks show that our method achieves state-of-the-art performance, demonstrating the effectiveness of our algorithm. Code will be made publicly available.
Multiview Spectral Clustering Based on Consensus Neighbor Strategy

Multiview spectral clustering, renowned for its spatial learning capability, has garnered significant attention in the data mining field. However, existing methods assume that the optimal consensus adjacency matrix is confined within the space spanned by each view's adjacency matrix. This constraint restricts the feasible domain of the algorithm and hinders the exploration of the optimal consensus adjacency matrix. To address this limitation, we propose a novel and convex strategy, termed the consensus neighbor strategy, for learning the optimal consensus adjacency matrix. This approach constructs the optimal consensus adjacency matrix by capturing the consensus local structure of each sample across all views, thereby expanding the search space and facilitating the discovery of the optimal consensus adjacency matrix. Furthermore, we introduce the concept of a correlation measuring matrix to prevent trivial solution. We develop an efficient iterative algorithm to solve the resulting optimization problem, benefitting from the convex nature of our model, which ensures convergence to a global optimum. Experimental results on 16 multiview datasets demonstrate that our proposed algorithm surpasses state-of-the-art methods in terms of its robust consensus representation learning capability. The code of this article is uploaded to https://github.com/PhdJiayiTang/Consensus-Neighbor-Strategy.git.
Selecting critical features for biomedical data classification

In this paper, the application of machine learning methods on large data sets with numerous features was investigated, with a focus on the identification of critical features in order to reduce the data and produce more accurate results. The research discusses feature extraction techniques for classifying two biomedical data sets with 62 and 71 features, respectively. The results were compared and presented using four classification techniques. The acquired results demonstrate that the selected important features typically produce more accurate results, or at least the same results while reducing the size of the data set and making data collecting easier.
Allocating Dynamic and Finite Resources to a Set of Known Tasks

We consider a generalization of the task allocation problem. A finite number of human resources are dynamically available to try to accomplish tasks. For each assigned task, the resource can fail or complete it correctly. Each task must be completed a number of times, and each resource is available for an independent number of tasks. Resources, tasks, and the probability of a correct response are modeled using Item Response Theory. The task parameters are known, while the ability of the resources must be learned through the interaction between resources and tasks. We formalize such a problem and propose an algorithm combining shadow test replanning to plan under uncertain knowledge, aiming to allocate resources optimally to tasks while maximizing the number of completed tasks. In our simulations, we consider three scenarios that depend on knowledge of the ability of the resources to solve the tasks. Results are presented using real data from the Mathematics and its Technologies test of the Brazilian Baccalaureate Examination (ENEM).
USE OF PROBLEM-BASED TEACHING METHODS FOR STUDENTS OF ECONOMIC SPECIALTIES AS A BASIS FOR THE DEVELOPMENT OF DUAL EDUCATION

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
The Application of the Crime of Picking Quarrels and Provoking Trouble in Cyberspace in China

In 2013, The Supreme People’s Procuratorate, the Supreme People’s Court issued “about the use of information network defamation criminal cases of applicable law” (hereinafter referred to as “the interpretation”), the judicial interpretation of article 5 on the Internet intimidation, abuse others or trouble, can also establish stir-up-trouble crime. This article in “Interpretation” expands the scope of the crime of picking quarrels and provoking troubles to cyberspace, forming a mode of the crime of picking quarrels and provoking troubles. Although the introduction of the Interpretation has played a positive role in regulating the behavior of picking quarrels and provoking troubles, the threshold of the crime of picking quarrels and provoking troubles is vaguer than the traditional identification of the crime of picking quarrels and provoking troubles, which to some extent leads to the confusion of the crime of picking quarrels and provoking troubles. Therefore, based on the theory and judicial basis of the crime of picking quarrels and provoking troubles, combined with the actual needs of the current cyberspace governance, it is necessary to study the related problems of the crime of picking quarrels and provoking troubles.
Federated knowledge amalgamation with unbiased semantic attributes under cloud–edge collaboration for heterogeneous fault diagnosis

Federated fault diagnosis attracts increasing attention in industrial cloud–edge collaboration scenarios due to the consideration of privacy and security issues. However, statistical heterogeneity caused by non-independent and identically distributed (non-IID) fault data is a ubiquitous problem. Previous methods designed for this problem mainly apply weighted averaging or knowledge distillation to integrate biased local knowledge from clients, ignoring the discrepancy between knowledge extracted from non-IID data solely. This is prone to result in slow and unstable model convergence and global model drift. In this article, a Federated Knowledge Amalgamation (FedKA) framework is proposed to alleviate the knowledge discrepancy caused by statistical heterogeneity. To mitigate the knowledge discrepancy, we first reveal and mine unbiased public knowledge for industrial fault diagnosis, i.e., fault attributes, which describe the fault characterization and mechanism without changing with data distribution. Then, a generative mapping between unbiased fault attributes and fault data is established for clients to incorporate unbiased knowledge into the diagnostic model. Moreover, to mitigate knowledge discrepancy and overcome the global model drift, a knowledge amalgamation algorithm is designed. Through the proposed layer-wise alignment, the multi-level fault semantic knowledge that stems from fault attributes is finely exploited and aggregated. Additionally, unlike the common fixed weighting strategy, we explore an adaptive weighting mechanism through the sample-wise diagnostic confidence to further alleviate the global model drift. Extensive experiments on a real thermal power plant process demonstrate the superiority of the proposed method for federated fault diagnosis.