Reviewerly/exHarmony huggingface dataset:
Promotional Techniques Used as Marketing Strategies for Library Resources and Services

Abstract This paper is based on the strategies for marketing the library resources and services. Marketing of library information resources and services is creating an enabling environment, arranging the prints and non – prints materials available in the library for easy accessibility and keeping the users abreast of the availability of the resources. The promotional techniques used in marketing of library resources are libraries’ produced materials like: (brochures, flyers, library e-mail library websites, reading lists, bibliographies, Newsletters, posters and others);special programmes and events like:( library orientation, workshops and seminars)and social media like Face book, YouTube, Blog and others. Marketing of library resources and services increases users’ patronage, justifies the funds expended on the library and enables it to compete with other information stakeholders. Keywords: Marketing, strategies, promotional techniques, library display, library orientation.
PuoBERTa: Training and evaluation of a curated language model for Setswana

Natural language processing (NLP) has made significant progress for well-resourced languages such as English but lagged behind for low-resource languages like Setswana. This paper addresses this gap by presenting PuoBERTa, a customised masked language model trained specifically for Setswana. We cover how we collected, curated, and prepared diverse monolingual texts to generate a high-quality corpus for PuoBERTa's training. Building upon previous efforts in creating monolingual resources for Setswana, we evaluated PuoBERTa across several NLP tasks, including part-of-speech (POS) tagging, named entity recognition (NER), and news categorisation. Additionally, we introduced a new Setswana news categorisation dataset and provided the initial benchmarks using PuoBERTa. Our work demonstrates the efficacy of PuoBERTa in fostering NLP capabilities for understudied languages like Setswana and paves the way for future research directions.
Penerapan Logika Fuzzy Metode Sugeno dalam Penentuan Harga Jual Tas Pria Branded

In today’s development, one of the things that is vast in demand is fashion, one of the fashion is a bag. From various groups, such as the lower class, middle class or upper class, men or women of all ages often use bags. The function of the bag itself is to place items and even support one’s appearance. CV Osric Sebastian is one of the few fashion bag distributors located in Batam, which started its bussiness in selling fashion bags since 2010. With the increasing need for the bags, there has been a lot of competition among bag distributors that competing with each other. Market competition among entrepreneurs will arise if the types of products produced and sold have similar types of models. The fashion bag industry itself must be able to sell products that compete in the market. One of the serious decisions that must be taken in making a sale is determining the value of the sale price of a product. The intend of this research was to determine the value of selling price of fashion bags at CV Osric Sebastian using the Sugeno fuzzy logic method. The final result of this research will be in the form of output selling price value of a fashion bag for the company based on the rules that have been made in the MATLAB program. From the results of fuzzy logic testing on the determination of the selling price of fashion bags on the system that was made can facilitate the company in making decisions to determine the value of the selling price of fashion bags at CV Osric Sebastian.
Short Text Similarity Detection Based on Graph Attention Networks and Knowledge Augmentation

Text similarity detection is a crucial task within the field of natural language processing Traditional word embedding methods consider about either semantics or structure of the text, and rarely consider knowledge-based information. Therefore, these methods have insufficient information for text representation and are ineffective in dealing with word polysemy and deep interactions among words. In this paper, firstly HowNet, as a knowledge base, is introduced in the graph attention networks to encode the sememes in HowNet. Secondly, we design the self-attention mechanism to strengthen the correlation among words obtained from the graph attention networks. Finally, the enhanced word vectors are fused with the word vectors generated by the pre-trained BERT model to obtain a more comprehensive text representation. The experiments conducted on three Chinese datasets demonstrate the superior performance of our model outperforms other conventional text similarity detection models.
Sentence Problem pada Penulisan Essay Mahasiswa Program Studi Sastra Inggris

Essay writing is a skill that must be mastered by students, especially English Literature Study Program students. Writing essays in English is a challenge in itself which of course has a lot of problems in the process. In writing essays in English, of course, you have to follow the correct writing rules. Mastery of several important aspects of writing including grammar, vocabulary, content, mechanism and organization is very important to be able to create good and easy-to-understand writing. Besides that. there are several things that need to be considered to create accuracy in writing such as sentence structure, cohesive devices in writing, and so on. In this study, the focus is on the problem of writing sentences. This study investigates sentence writing problems such as sentence fragments, run-on sentences, parallel structures, comma connections, and so on which are often encountered. This study involved semester student essays 6 English Literature study programs. The reason for selecting sixth semester students for the English Literature study program is because at this level students have already taken Writing and Grammar courses. This study aims to explain the phenomena of the sentence problems. This research is expected to contribute to the academic field. The contribution in question is a contribution in improving learning strategies related to writing. Therefore the author chose the research title "Sentence Problem in essay writing for students of the English Literature study program, Pamulang University".
Exploiting Topic Modelling for the Identification of Untapped Scientific Collaborations

Finding potential collaborators has become a challenge due to the growing number of scientists in organizations such as universities, research institutes, or companies. Collaboration Recommendation Systems (CRSs) have been developed to help researchers identify possible collaboration partners, but they often rely on citation graphs or paper abstracts which may not be readily available in organizational databases or online sources. However, scientific publication titles provide consistent bibliometric data that can provide insights into research areas. TOMOSCO is a topic modelling framework that uses transformer-based methods to extract research area information from small amounts of text, such as publication titles or brief project descriptions. TOMOSCO can classify, cluster, and match research topics across different disciplines, uncovering relationships among scientists and suggesting potential interdisciplinary collaborations. In experiments, TOMOSCO was able to identify existing collaborations with over 90% accuracy based solely on publication titles and propose new collaborations based on previously unseen publications and project descriptions.
Exploring Anchor-Free Approach for Reading Chinese Characters

Scene text spotting has achieved an impressive performance over recent years. Currently, most text localization methods are designed with the text line instance. We argue that building a character-level spotting network is more suited to recognize the Chinese of text and Chinese is also common in scene text images. In this paper, we explore an anchor-free spotting framework that treats a character as a single point. To better capture Chinese character features, we first use the Canny edge detectors and superimpose the obtained edge information onto the RGB image channel. After that, a feed-forward network is set up and the inference can be processed in a single network forward-pass, without complex post-processing steps. Experiments are performed on the Chinese text dataset and the quantitative comparisons demonstrate the effectiveness of the anchor-free approach.
In-situ nonvolatile and volatile modulation for optical neural networks

<i>Advanced Photonics</i>, co-published by SPIE and Chinese Laser Press, is a highly selective, Gold Open Access, international journal publishing innovative research in all areas of optics and photonics, including fundamental and applied research.
Digital Learning Hubs as a Component of the Information and Digital Learning Environment

The article aims to analyse digital education in terms of modern information and the digital learning environment. Based on the use of such theoretical pedagogical research methods as system analysis, concretisation comparativistic approach the study is investigated. The results demonstrate the functioning of educational hubs as a sociocultural phenomenon in modern educational policy, the modern experience of using digital hubs in education, particularly, the experience of CISCO and other influential players, the possibility of forming digital learning hubs based on libraries of higher education institutions. It is shown that important directions for further research are the coverage of the development of technologies and the impact of this process on the evolution of educational environments in the future. In conclusion, it was noted that the use of educational digital hubs contributes to the decentralisation of the educational system. The effectiveness of the use of regular test competitions are noted, which contributes to the formation of a competitive environment.
Digitalization of the Educational Process in the Field of Culture and Art: Challenges and Prospects

The aim of this article is to assess the challenges and opportunities presented by the digitalization of the educational process within the realm of culture and art. To achieve this objective, a range of analytical methods such as analysis, synthesis, prognostication, systematic examination, and comparison were employed. The findings underscore the favorable impact of digitalization on the educational landscape of culture and the arts. A key innovation lies in the potential widespread integration of cutting-edge solutions into the educational framework, as well as the utilization of virtual and augmented reality, facilitating the development of essential competencies required to mold a new generation of digital-savvy professionals. The conclusions consolidate strategies for surmounting the primary challenges encountered by digitalization in the field of cultural studies and the arts within the Ukrainian context. The study highlights several pivotal areas crucial for the advancement of digital education in culture and the arts. These areas encompass the establishment of a digitalized educational environment, the cultivation of digital and informational proficiencies, the exploration of innovative digital learning modalities and techniques, and the fostering of virtual engagement with artistic creations. To ensure the progression and effectiveness of art education in the digital era, it is imperative to strike a harmonious balance between traditional pedagogical approaches and the imperatives of contemporary digital society. The central emphasis should revolve around aligning the organization of art education with the evolving demands of the modern world.
Diverse instrument symphonic ensembles: Making sustainable music amid unsustainable situations

Music technology-focused ensembles such laptop orchestras have become mainstays at higher education institutions. Nevertheless, the perpetual innovation inherent in the mandate of such a group presents some unique challenges in the post-pandemic academic landscape. In the post-COVID period of supply chain breakdowns and budget-obsessed administrations, building an ensemble focused on technology may become prohibitively expensive. As traditional music departments struggle with lower enrolment, expanding large ensembles to include a more diverse corpus of instruments would solve multiple problems. Assuming the challenges inherent in starting such groups could be overcome, ensembles that expand their instrumental diversity could allow for innovative study, increase enrolment and even save the host institution much-needed funds. The author groups extant technology-focused musical ensembles into categories by their focus and then proposes a novel category featuring intentionally diverse instrumentation, which may solve many of the problems associated with funding such groups while increasing and diversifying enrolment.
Lingua e genere

This book is a study on gender representations in the language of the Italian TV news, adopting an interdisciplinary approach that integrates linguistic analysis with media analysis. The first part provides an overview of the theoretical and research framework on ‘language and gender’ and ‘media and gender’, with a brief presentation of the international theoretical models and an annotated review of the state of the art at a national level. Convergence and shared notions in the common perspective of ‘gender’ will be highlighted across the two fields. The second part features a corpus-driven analysis of the female and male representations in the Italian TV news, which explores both the gender-based variations in the speech of the sources of the news and the thematization of women and men in news content. The results show a complex picture: the Italian news language still actively spreads stereotypical representations, yet it also indicates signs of innovation compared to the past. In particular, the findings show that a more gender-aware news language conveys representations more gender-fair.
Assessing the combined effects of local climate and mounting configuration on the electrical and thermal performance of photovoltaic systems. Application to the greater Sydney area

Extremely high urban temperatures adversely affect photovoltaic (PV) system performance. Accurate PV cell temperature assessment relies on local weather conditions, exacerbated by urban overheating, often overlooked by inadequate temperature models and non-local data. This study investigates the electrical and thermal PV performance, considering mounting configurations and local conditions. Data from ten weather stations in Greater Sydney (NSW) during 2016–2017, including a hot summer, are used. The Sandia model is used to predict cell temperatures and power output for four mounting configurations, from open rack to building-integrated (BIPV). A PV thermal model is implemented to analyse daytime convection, crucial for understanding PV impact on local climate. Results show peak cell temperatures of 60 °C (open rack) to over 90 °C (BIPV), causing up to 50% power loss and 11% reduction in monthly performance ratio. Local climate variations impact PV energy output up to 6%, with mounting configuration effects up to 11%. Daytime convective flux averages 150–180 W/m2, peaking at 700 W/m2. Convective release varies up to 22% based on local climate, generally higher for open rack than close roof mounts, with potential reversals under low wind speed conditions. These findings can improve the knowledge of PV performances in urban areas facing extreme temperatures.
Word Find Games for Learning Vocabulary in Junior High School

This research discussed learning vocabulary in 8th grade junior high school through mobile games (Word Find). To get the results, researchers used the pre-experimental research design which was carried out to 8th junior high school at MTsN 5 Tangerang. The population used is 35 students in one class. The researchers used purposive sampling. Researchers used tests in the research. There are two tests, namely pre-test and post-test. Pre-test and post-test were conducted to compare whether the implementation of mobile games was effective or not in this research. The data were collected using pre-test and post-test. The result showed that the average pre-test result is 5.22 and the average post-test result is 8.1. According to the data, the value of is bigger than, = 0,046 &gt; = -2,032. So, Ho is rejected and Ha is accepted. It means this application is effective and the teachers can use a Word Find application as vocabulary learning media.
Question Classification of University Admission using Named-Entity Recognition (NER)

The university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. The manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. The research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. The purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. The type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from Binus Online Learning websites and data on questions frequently asked by users, especially prospective students. The results of the question classification are presented using descriptive statistics. The results show that the question classification with users’ most frequent question type is “What” by 63.3%, and the answer category is “tuition fees” by 22.4%. It can be concluded that the most frequent question type asked by users is “What”, and the most frequent topic of interest is “tuition fees”. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees.
Functional Requirement-Based Conversational Recommender System to Recommend Gadget Using Ontology

The increasing availability of gadgets in the market makes it difficult for customers to find gadgets that suit their needs. Gadget recommender systems have been developed to help users find products according to their preferences. However, these systems often ask questions about technical specifications, thus confusing novice users. For this reason, a gadget recommender system is needed in which the question interaction uses functional requirements so that all users are able to understand it. Because most users are more familiar with and find it easier to express their needs through gadget functions, for example, a user needs a gadget for live streaming activities, playing HD quality games, online meetings, and programming but does not need editing activities. Therefore, we develop a conversational recommender system (CRS) that recommends gadgets based on functional requirements. The system mimics the interaction of a knowledgeable salesperson, guiding users toward products that suit their needs. Our proposed system uses ontology as a knowledge base to map functional requirements to product specifications. Through performance evaluation and user satisfaction, our system achieved a recommendation accuracy score of 84.04% and a user satisfaction score of 0.76. These results show that users are satisfied with the system’s ability to recommend gadgets based on functionality requirements.
Modulation recognition based on deep learning network

The current radio recognition technology has used relatively mature deep learning networks,but the current network learning parameters and computational complexity are relatively high,based on this,a lightweight network with simple computational complexity MobileNet is designed,and its prediction accuracy is analyzed in combination with the frequently used VGG network and ResNet.In this paper,a dataset is first generated for network training and verification,which simulates real signals,considers its clock offset,frequency offset,signal-to-noise ratio and channel fading,and generates a total of 11 modulation signals.The simulation results show that VGG network and ResNet have a prediction accuracy of 5%∼10% higher than MobileNet in each signal-to-noise ratio due to their computational complexity and network depth,but the recognition accuracy of MobileNet has reached the extent that it can be used under the high signal-to-noise ratio,and can be used for radio signal detection and recognition under the premise of known signal type and noise characteristics.
Enhancing the Robustness of a Three-Layer Security Electronic Voting System Using Kerberos Authentication

The rapid advancements in technology have spurred interest in electronic voting systems as a means to modernize and enhance the democratic electoral process. However, ensuring the security and integrity of electronic voting systems remains a critical challenge. In this study, we present a highly secure and efficient model for an electronic voting system, featuring a robust three-layer security architecture that includes the device authentication, user authentication and network security. The proposed system incorporates Kerberos authentication and Advanced Encryption Standard (AES) to fortify device and user authentication. The segmentation of the system network into zones that includes the Virtual Private Network, the Public Network and the Demilitarized Zone also helps to mitigate potential cyber-attacks and fraudulent activities. Leveraging biometric security (fingerprint) and user tokens (voter's cards), the system ensures the accurate identification and authentication of voters, thereby enhancing the overall integrity of the voting process. Through a series of tests involving 500 enrolled users, the proposed electronic voting system demonstrated remarkable efficacy, achieving a 100 percent accuracy rate in ballot tallying. Having addressed key concerns related to security and transparency, the proposed voting system has the potential to instil trust and confidence in electoral processes.
AN ANALYSIS OF ENGLISH WRITING ERRORS IN EFL LEARNERS’ RECOUNT TEXT

This research discusses the analysis of students' errors in writing recount texts. This study aims to analyze the types of errors made by undergraduate students’ writing scores at the English Language Teaching Department at one of the universities in Cirebon. This study focuses on writing errors taken from three categories of English proficiency levels (low, average, high). This study used the descriptive qualitative method. There were six participants. The data were taken from their recount text writing results. The results of this study show that there are four types of errors based on Surface Strategy Taxonomy by students: Omission, Addition, Misinformation, and Misordering. The result of the total number of student errors is 37 which contains 14 Omission (37.83%), three Addition (8.11%), 19 Misinformation (51.36%), and one Misordering (2.70%). The most errors were made by Misinformation, with 19 errors (51.36%). It can be concluded that the ability of undergraduate students at one university in Cirebon is still low in writing recount text.