Reviewerly/exHarmony huggingface dataset:
Creation of UI Design Prototype Mobile App Textile Museum to Increase The Number of Visitors, Tourism Marketing and Brand Awareness

Textiles are an important part of Indonesia, both as a component of clothing and as ritual and ceremonial items. The Textile Museum is a museum founded in 1976 which was pioneered by Mr. Ali Sadikin as the governor of DKI Jakarta at that time. However, over time, interest in visiting museums has decreased, especially with the C19 pandemic, many learning tourist attractions such as museums have experienced a decline in visitors. The role of the mobile app interface can make it easier for visitors or potential visitors and anyone to get various information easily and practically. This research will create a prototype UI design mobile apps interface for the Textile Museum using Adobe XD software. The research begins with mapping the problem, determining various solutions, determining the best solution, forming prototypes, and testing the results on target consumers, testing marketing concepts and brand awareness to capture the number of visitors to come to the Textile Museum.&#x0D;
Profitability from training and development programmes

The purpose of this document is to provide a conceptual overview of the employee training and improvement software and its benefits. This post examines the nature and drivers of employee training and software improvement, and then discusses the impact on quality for employees and the organization. Organizations are finding it difficult to live aggressively in today's global economy. The importance of developing worker improvement software is for companies looking to gain a competitive edge. Staffs are valued for help from the agency and achievement or failure of the relief agency in the overall staff performance. Therefore, companies fund large amounts of education and training programs for employees. In addition, in education software is a great support for companies to highlight the knowledge, understanding and potential of employees. There is a great dialogue between specialists and researchers about the impact of enhancement software on every employee and agency. The overview defined here is a careful review of the literature on essential worker enhancement software and its benefits for companies and employees.
An experiment to build an open source application for the Internet of Things as part of a software engineering course

Software engineering education is a field in which innovation is constantly taking place, especially in terms of the methods used to achieve the competencies and learning objectives to be taught. The Internet of Things is a relatively new area that offers accessible but complex SE challenges that the authors believe are an excellent example for inclusion in the educational process. Over the course of two academic years, we have explored the integration between SE and the Internet of Things with the additional goal of building an open-source application repository to foster academic research and enhance industrial tools.
Inventory, Preservation and Digitalization of Manuscripts as a Historical Source in Jambi

&lt;em&gt;&lt;em&gt;Inventory, preservation and digitization activities at the Jambi City Museum are activities motivated by information on the large number of manuscript collections at the Jambi Museum which have not been preserved and digitized. Therefore, the purpose of this service is to analyze and describe inventory, preservation and digitization activities at the Jambi City Museum. This community service uses a Community Based Participatory Research (CBPR) approach. The results of this dedication show that many of the manuscripts that are in the Jambi City Museum and which are still in the private collection of the community are historical sources for Jambi City. To record these manuscripts, an inventory was carried out. Meanwhile, to maintain the preservation of manuscripts at the Jambi City Museum, physical and non-physical preservation activities as well as digitization of manuscripts were carried out. This article argues that there are still some deficiencies which are considered for evaluation by the Jambi City Museum regarding the preservation of manuscripts as historical sources in Jambi. Both related to improving the quality of human resources, the availability of tools and equipment for preservation, as well as the involvement of various parties in the process of preserving manuscripts.&lt;/em&gt;&lt;/em&gt;
Research on Reading Promotion Strategy of University Library Based on Short Video

The development of short video has changed the reading habits and ways of college students.Faced with the change of college students' reading needs, many college libraries have tried to use short video to innovate and promote library resources and services, which is gradually becoming a new form of college library reading promotion.How to apply short video in the reading promotion of university library and make college students pay attention to reading, get used to reading and fall in love with reading is an urgent problem that needs to be solved.On the basis of analyzing the necessity of promoting short video reading in university libraries, this paper takes the works published by 11 university libraries on B station platform as samples, and studies the contents of their works by means of network investigation and content analysis.The short video operation of Chinese university libraries has been ignored, the content production mechanism has not been formed, and the quality of works is poor, it also puts forward optimization strategies such as attaching importance to account operation, professional content and online and offline activities, so as to promote the reading promotion of university libraries on the short video platform and promote the reading of the whole people.
Trusted Computing System Based on Blockchain

Cloud computing is an important part of the current digital society.With the popularization of cloud computing, the defects of its opaque calculation process and unverifiable calculation process and results cannot be ignored, and cloud administrators may also be concerned about the calculation results due to their own interests.to modify.Although the emergence of Ethereum smart contracts has solved the problem of insufficient decentralization of calculations and unverifiable calculation results and calculation processes, due to the repeated calculation of all nodes, it leads to low efficiency and intelligent credible calculations with limited computing power.In response to the above problems, proposes a trusted computing system based on blockchain.By adopting off-chain computing + on-chain verification, it not only retains the high-performance advantages of off-chain computing, but also realizes the decentralization of computing.Make the calculation process and results more reliable.
Research on the Application of Knowledge Graph in Big Data Analysis

With the wide and in-depth application of big data technology in various fields, descriptive data analysis plays a more and more important role in big data analysis.Aiming at the descriptive analysis of big data, focusing on the problem identification and data preparation in the data analysis process, starting with extracting data semantics and establishing the logic between data, this paper discusses the application of knowledge graph, studies the knowledge representation in it, and puts forward the method of combining RDF and semantic network to describe data.This idea enriches the means of descriptive analysis of big data, and lays a necessary foundation for further correlation analysis among big data.
Impact of Blockchain Investment on the Market Performance of Public Companies in China

This paper examines the impact of blockchain investments on the market performance of Chinese listed companies.By utilizing event study methodology and data from 208 listed companies that made blockchain investment announcements between 2008 and 2023, the findings indicate that such announcements have a significant positive effect on short-term market value, but this effect diminishes over time.Furthermore, companies with greater scale and more robust innovation capabilities are able to attain higher stock prices and returns.This positive market response is evident across various industries, with industry distinctions having minimal impact on the market performance of corporate blockchain investment.
Counting with Self-Weighted Multi-Scale Fusion Networks

Because of the large-scale variation, counting in scenes of different densities is an extremely difficult task. In this paper, based on the attention mechanism, we propose a new self-weighted multi-scale fusion network structure named SMFNet to solve the problem of multi-scale changes and can significantly improve the effect of crowd counting in monitoring scene. The proposed SMFNet uses VGG as the backbone network to extract multi-scale features, uses a SMFNet as the neck to fuse multiple-scale features, and uses the atrous spatial pyramid pooling (ASPP) network and ordinary convolution as the head to generate both the attention map and the density map. The attention map highlighting crowd regions in the image contributes to a high-quality density map, and the density map records the crowd distribution. The number of crowd in the image can be obtained by summing the pixel values of the density map. We conduct experiments on three crowd counting datasets and one vehicle counting dataset to show that our proposed SMFNet can improve the state-of-the-art counting methods.
Incremental News Mining Using Evolving Clustering with Functional Operators

Online media publish journalistic products, one of which is news online (online news). This is in line with the findings of the Ministry of Communication and Informatics (Kemkominfo), that in 2018 there were 43,000 online media in Indonesia. On generally in getting actual news, humans tend to read the news on online media one by one. The activity is not effective because of the news that produced by online media have the same information with each other news. In this study, we propose an innovative solution to this issue by developing a news mining system that employs clustering based on an evolving system. This system has the potential to improve the effectiveness of news retrieval by grouping similar news together and identifying key information trends, ultimately enhancing the ability of individuals to obtain actual news. Based on research observations, the performance of news clustering using an evolving clustering system with functional operators is quite good, as evidenced by an accuracy of 83%.
Modified K-Means Clustering with Semi Grouping Perspective : A Study

In this note, we will provide some results of a literature study related to one of the clustering methods, namely K-Means, but with some modifications, devoted to the case of computation time. Modifications were made at the time of determining the cluster center by previously applying principal component analysis (PCA), other researchers [4] proposed this method first, which differs in this note, namely in the preprocessing of the data before principal component analysis is carried out. Comparison of the accuracy of the cluster results is also given in this note.&#x0D;
Comparison of Machine Learning Algorithm Models in Bitcoin Price Sentiment Analysis

Bitcoin is one of the digital payments that is currently booming, fast delivery makes bitcoin in great demand by many people, currently there are many digital currency exchanges that can be used, one of the well-known ones in Indonesia, namely Indodax. Indodax is a cryptocurrency exchange, not only an exchange, Indodax also provides a chat room containing investors' opinions. Opinions contained in the Indodax chat room can be used to determine whether comments are positive, neutral or negative, so that it can be an investor's decision to sell or buy bitcoin using sentiment analysis. The sentiment analysis process begins with collecting data using an instant data scraper on the Indodax website, data preprcoessing, labeling using vader lexicon, TF-IDF as word weighting, data splitting, naïve Bayes algorithm and support vector machine, feature selection xgboost and gradient boosting, model evaluation with confusion matrix, then comparing the results of the two algorithms. Based on the tests that have been carried out, naïve bayes obtained the best accuracy value of 70.7%, naïve bayes combined with XGBoost obtained the best accuracy value of 86.6%, while the Support vector machine obtained the best accuracy 86.1%, support vector machine combined with gradient boosting obtained the best accuracy value of 88%. Based on these results the use of feature selection can increase the accuracy value of the algorithm.
STUDENTS’ RESPONSE TOWARDS THE IMPLEMENTATION OF FOREIGN INSTRUCTOR PROGRAMME IN HELPING WITH STUDENT’S SPEAKING SKILL

The purpose of this study is to describe students’ response on how effective it is in hiring professional native English speakers in helping new learners to master their speaking skill. Foreign instructors can not only help in improving the speaking skill of the beginners, but they can also help to modify the optimal learning environment that can familiarise the beginners in the learning activities they are doing. Foreign professionals who are proposed to educate beginners must master teaching skill and be good at using 21st century technology. In this research paper, the experimental method is used to study this experiment. The main purpose is to find out if hiring native English speakers is very effective in helping beginners to master their speaking skills. The results found show that hiring a professional native English speaker is very effective in helping beginners in mastering speaking skills because in addition to the knowledge gained, speaking with a native English speaker directly helps beginners in speaking English.&#x0D;
Sentiment Analysis Against Digital Payment “GoPay”, “OVO”, “DANA”, and “ShopeePay” Using Naïve Bayes Classifier Algorithm

Financial technology is a term used by companies that offer technological innovations in the financial sector. One of the products from Fintech is digital payments. The purpose of this study is to analyze the sentiment of digital wallet services using the Twitter social media. The digital wallets taken in this study are Gopay, Ovo, Dana, and ShopeePay. This research uses the Naïve Bayes Algorithm classification method. Comment data was taken from Twitter social media which were divided into two sentiment classes, namely positive sentiment and negative sentiment. The results of this study are the most widely accepted positive sentiment among the four digital wallets is ShopeePay, the most widely received negative sentiment among the four digital wallets is Dana. Customers who have skills in using digital payments can be grouped as Well Literate in financial literacy.
Students Perception on Using Eldiru as Asynchronous Learning Media

Pandemic condition due to Covid-19 disease pushes a new trend in teaching learning activity. It forces all levels of education implementing online learning platforms. Online learning platform gives freedom for students to learn autonomously. There are many online learning platforms available in this pandemic condition. One of the online learning platforms is eLDirU (e-Learning Jenderal Soedirman University). eLDirU is an online learning platform developed by Jenderal Soedirman University. It functions as a digital literacy learning innovation for native digital generation. In eLDirU, students and lecturers can interact in many activities provided in it. Students utilize eLDirU as synchronous and asynchronous learning media. This study observes studentsâ€™ perception on using eLDirU as asynchronous learning media. 66 students worked as the subject of this study. 66 feedbacks were collected from studentsâ€™ answers on questionnaires. The result of the questionnaire showed that eLDirU as asynchronous leaning media was proven to be helpful for the students in learning process. Students had a chance to explore themselves more in eLDirU. It also made them more active during learning process because lecturers facilitated the learning process by uploading related materials, videos, and other learning sources in it. Students also found no difficulties in accessing eLDirU, although they stayed in distant area. It might make them enjoy learning on eLDirU. The second result pointed out the use of eLDirU as asynchronous learning media. Students preferred using eLDirU when it came to asynchronous learning. In happened because they could explore their learning sources more with the help of eLDirU. But, for synchronous learning, the use of other learning content management system, such as Zoom meeting and Google Meet, would be studentsâ€™ top preference. It might ease studentsâ€™ understanding on the topic better.
Photonic Advantage of Optical Encoders

This research presents an optical/digital hybrid ANN which demonstrates a "photonic advantage" in intermediate classification accuracy over pure electronic ANNs with the same power and latency. The optical encoder uses incoherent light which is suitable for operation in ambient light with no additional optical power needed. Further research is needed to explore its efficiency in more complicated datasets, training algorithms, and nonlinear activation in the optical domain.
Deployment-Oriented Classical/Quantum coexistence in X-haul fiber link for B5G Networks

Coexistence of weak coherent pulses and intense light is experimentally demonstrated in a practical 5G X-haul scenario. Acceptable QBER measurements were obtained over 5 km fiber transmission by optimizing the parameters of single-photon sender/receiver stations.
Gap Analysis of Ford-Fulkerson Algorithm and Edmonds-Karp Algorithm as Machine Learning Approach for Augmentation Path in the Maximum Flow Problem

The maximum flow problem is popular with many researchers as it plays a significant role in many areas. This problem is about obtaining the maximum flow where there is one source and sink in the network flow. In solving maximum flow problems, many algorithms are applied such as the most used are the Ford-Fulkerson Algorithm and the Edmonds-Karp Algorithm. The goal of this study is to identify the gaps of these two algorithms and sufficiently provide an analysis and comparison of their time complexity, simplicity, and effectiveness to solve the augmentation path in the maximum flow problem. The Ford-Fulkerson and Edmonds-Karp algorithms performed well and sufficiently to solve the augmentation path in the maximum flow problem. However, both algorithms consist of different gaps like having slow time complexity and difficulty in implementation and execution.&#x0D; Keywords: Maximum Flow Problem, Augmentation Path, Ford Fulkerson Algorithm, Network Flow, Edmonds-Karp Algorithm
Sistem Pengenalan Telapak Tangan pada Interaksi Manusia Robot sebagai Perintah untuk Mobile Robot

Palm recognition system on human-robot interaction as commands for mobile robots is aresearch that aims to develop a system that allows robots to receive commands and instructions non-verbally from humans through palm recognition. Using image recognition and data processing technology, the system aims to create an intuitive interaction between humans and robots, allowing robots to recognize hand gestures and understand commands given. The research includes several stages of development. First, image processing and analysis is carried out to identify and understand the shape and movement of the human palm. Then, the data is linked with the appropriate commands to be communicated to the mobile robot. The training process is carried out to improve the accuracy of the palm recognition system and reduce the likelihood of errors in the interpretation of commands. The results of this study are expected to have a positive impact on the use of mobile robots, especially in complicated or dangerous environments where verbal communication may be limited or impossible. More natural and easy human-robot interaction is expected to improve efficiency and safety in the operation of mobile robots.&#x0D;
DynaQ: online learning from imbalanced multi-class streams through dynamic sampling

Abstract Online supervised learning from fast-evolving data streams, particularly in domains such as health, the environment, and manufacturing, is a crucial research area. However, these domains often experience class imbalance, which can skew class distributions. It is essential for online learning algorithms to analyze large datasets in real-time while accurately modeling rare or infrequent classes that may appear in bursts. While methods have been proposed to handle binary class imbalance, there is a lack of attention to multi-class imbalanced settings with varying degrees of imbalance in evolving streams. In this paper, we present the Dynamic Queues (DynaQ) algorithm for online learning in multi-class imbalanced settings to fill this knowledge gap. Our approach utilizes a batch-based resampling method that creates an instance queue for each class to balance the number of instances. We maintain a queue threshold and remove older samples during training. Additionally, we dynamically oversample minority classes based on one of four rate parameters: recall, F1-score, $$\kappa _m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>κ</mml:mi> <mml:mi>m</mml:mi> </mml:msub> </mml:math> , and Euclidean distance. Our learning algorithm consists of an ensemble that uses sliding windows and a soft voting schema while incorporating a drift detection mechanism. Our experimental results demonstrate the superiority of the DynaQ approach over state-of-the-art methods.
RESTORASI ARSIP STATIS TEKSTUAL DALAM MENJAGA KHAZANAH INFORMASI DI DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA SURABAYA

The purpose of this study is to determine the restoration activities of textual static archives in maintaining information treasures at the Surabaya City Library and Archives Office and the factors that become challenges in its implementation. This research uses a descriptive qualitative approach that will describe the factual situation contained in the form of narrative text. Data were collected through participatory observation, interviews, and documentation. The collected data is selected based on the focus of the research, analyzed, and then concluded based on the problem studied. The results of this study show that the restoration of textual static archives at the Surabaya City Library and Archives Office is very effective in maintaining or preserving the wealth of information contained there because its implementation is in accordance with Law No. 43 of 2009 concerning Archives and Regulation of the Head of ANRI No. 23.
Analisis Sentimen Ulasan Aplikasi Binar Pada Google Play Store Menggunakan Algoritma Naïve Bayes

Binar is an online learning platform that provides courses and certifications in the digital field. The Binar app has been downloaded 500,000 times and has a rating of 3.6 on the Google Play Store. However, user ratings sometimes do not match their reviews. In application development, not only the number of ratings but also user opinions need to be considered. Therefore, developers must be able to interpret every opinion given, and sentiment analysis was conducted using the Naïve Bayes Multinomial and Bernoulli algorithms along with Information Gain feature selection to interpret user opinions. This study used the Knowledge Discovery in Database (KDD) method. The data used consisted of 713 reviews of the Binar app, including 518 positive and 195 negative reviews. The best results were obtained in the 9:1 data split scenario with the Bernoulli Naïve Bayes model achieving an accuracy of 93.06%, precision of 87.04%, recall of 100%, f1-score of 93.07%, and AUC of 0.988.