Reviewerly/exHarmony huggingface dataset:
Meningkatkan Penulisan Karya Ilmiah Mahasiswa dengan Menggunakan Mendeley Sebagai Referensi Manager

The purpose of perpetuating the Society To increase knowledge for participants, namely students, the importance of the benefits of the mendeley application as reference management of library sources in research. To improve the ability of the participants, namely students, to be able to operate the mendeley application properly and correctly. As a reference database, reference files such as books or articles from journals in PDF form can be stored and given appropriate information to help facilitate searching. Saved PDF files can also be opened, read, and annotated with sticky notes or highlights. Writings created with Microsoft Word, Open Office or LaTex can be linked to Mendeley software so that citations and bibliographies can be compiled automatically.Tujuan dari pengabadian Masyarakat Untuk meningkatkan pengetahuan bagi para peserta yakni mahasiswa pentingnya manfaat aplikasi mendeley sebagai manajemen referensi sumber-sumber pustaka dalam penelitian. Untuk meningkatkan kemampuan para peserta yakni mahasiswa agar dapat mengoperasikan aplikasi mendeley dengan baik dan benar.Sebagai database referensi, file referensi seperti buku atau artikel dari jurnal dalam bentuk PDF bisa disimpan dan diberi keterangan yang tepat untuk membantu mempermudah pencarian. File-file PDF yang disimpan juga bisa dibuka, dibaca, dan diberi catatancatatan dengan sticky notes atau highlight. Tulisan yang dibuat dengan Microsoft Word, Open Office atau LaTex bisa dihubungkan dengan software Mendeley sehingga sitasi dan daftar referensi (bibliography) bisa disusun secara otomatis
Research on Interrupted Sampling and Forwarding Jamming Recognition Method Based on Rectangle Integral Bispectrum Feature

Interruptted sampling and forwarding jamming based on digital radio frequency memory (DRFM) technology is an important means of ECM at present. In this paper, a feature extraction method based on rectangular integral bispectrum is proposed to characterize the feature difference between target echo and jamming signal, and neural network and other methods are used to classify and recognize target signal and jamming signal. The simulation results show that the integrated bispectrum feature can distinguish the target signal and the jamming signal well, and improve the recognition effect.
INVESTIGATING EFL LEARNERS’ READING DIFFICULTIES AND THE POSSIBLE SOLUTIONS

This study investigates the reading difficulties faced by EFL learners and proposes possible solutions for English Education students in an Indonesian university. Reading comprehension is a vital component of the English education curriculum, as it enables students to comprehend and analyze texts effectively. To conduct this study, a descriptive strategy was employed in combination with a qualitative approach. The questionnaire was designed to identify common challenges encountered by students, including difficulties in text translation, enhancing motivation, and the teaching methods employed by instructors. Data was collected through interview sessions, questionnaire responses, and documentation. The findings revealed that students predominantly struggled with sentence comprehension. By addressing these issues and fostering a desire to read, related challenges such as limited vocabulary, grammar proficiency, and sentence interpretation can be effectively tackled. Moreover, some students faced difficulties arising from inadequate teaching methods employed by their instructors. Consequently, this study concludes that students' reading difficulties primarily revolve around understanding the text itself, which in turn leads to other complications such as grammar mastery, vocabulary limitations, and text translation problems. These factors emerge from both students themselves and the instructional approaches adopted by their teachers.
The Urgency of Linguistic Skills to Read Kitab Tafsir in Boarding Schools in Indonesia: Analysis of Students’ Learning Issue

The study is aimed at investigating learners’ issues on reading kitab tafsir in boarding schools in Indonesia. This study employs the case study approach of the qualitative method. Data is collected through in-depth interviews with ten informants (teachers and learners) selected by using purposive sampling technique. The data is thematically analyzed by using interactive analysis technique of Miles &amp; Huberman model of NVivo 12. The findings of the study reveal that there are nineteen learners’ linguistic issues to understand the kitab tafsir. All issues are grouped into four categories, namely: two issues are related to ashwat (phonology), six issues are connected to sharaf (morphology), six issues are about nahwu (syntax), and other five issues are related to dilalah (semantic). All issues revealed in the study are crucial to be solved to understand the kitab tafsir in boarding schools all over Indonesia. Thus, teachers should be creative to find interesting and innovative learning techniques so that reading the kitab tafsir is no longer an issue for learners. The nineteen issues are deeply and comprehensively analyzed and discussed in the article.
Sistem Pakar Diagnosa Hama Penyakit Tanaman Tomat Menggunakan Metode Forward Chaining

The tomato (Solanum lyco persicum) is a plant from the solanceae family, native to Central and South America, from Mexico and Peru. Tomatoes have a short life cycle and height from I to 3 meters. Currently, farmers often ignore pests and diseases of tomatoes due to lack of knowledge. They also assessed that it finally happened at the time of planting, experts on pests and diseases of tomatoes were eliminated, there was a lack of socialization from agricultural officials about pests and diseases in tomatoes, but they did not know how to control them and to eliminate diseases quickly or decrease productivity. This study uses the Forward Chaining method, which is a description that starts from known facts and then is matched with IF and IF-THEN rules. Pests and diseases with a high prevalence in tomatoes are pests. Some pests are: fruit caterpillars, earthworms, green aphids, white flies, fruit flies (bractrotera) and further diseases namely: Fusarium wilt, leaf spot disease, bacterial spots, fruit rot. This expert system application provides information on tomato diseases and disease diagnosis, including control solutions that can be used to reduce the risk of damage to tomato plants. This study aims to build an expert system application that can diagnose tomato plant pests and diseases using the forward chaining method. This application is expected to help the community in diagnosing the types of tomato plant pests. The implementation of the application of an expert system for pests and diseases of tomato plants is expected to provide easy access for users and the wider community, through the use of web-based media.&#x0D;
Pengajaran Bahasa Inggris dan Intercultural Communication Competence: Analisis Pidato Helena Merschdorf

Intercultural Communication Competence (ICC) is becoming more important nowadays because of the modern social issues in society. Previous studies tried to insert ICC into English Language Teaching (ELT) in order to help solving these issues. However, little attention has been paid to the dimension and components of ICC. Library research method was conducted with the script of the speech from Helena Merschdorf regarding intercultural communication as the primary source and several other books and articles as secondary resources. There are two primary aims of this study: 1. To analyse the components and dimensions of ICC based on Helena Merschdorf’s speech. 2. To know the components and dimensions of ICC based on Helena Merschdorf’s speech that can be inserted in ELT. The result indicate that the speech include all components and dimesions of ICC and each component and dimension of ICC can be inserted into ELT.
Segment-Based Risk Mapping for Lithium Brine Prospectivity

Summary Lithium is a critical energy transition element. Mineral demand forecasts suggest that the global lithium-ion battery value chain will expand, with lithium mining having the potential to become a $34 billion industry by 2030 a five-fold increase from 2022 ( McKinsey &amp; Company, 2023 ). Essential to meeting the rising mineral demand is the execution of high-efficiency de-risked exploration methods capable of fast-tracking new search spaces. Thus far, lithium exploration has largely been conducted in a piecemeal fashion. Focus has been limited to evaluating specific targets, allowing no clear predictions to be made about neighbouring sites, nor a holistic view of the lithium potential of a particular region. CGG has utilised its years of experience in oil and gas exploration, understanding of large datasets and in-depth lithium mineral system analysis to consider how a play/systems-based approach may be applied to mineral exploration. We have been working on bringing the concepts of a systems-based approach, and the communication technique of a common risk segment (CRS) map, to a regional case study of lithium brine exploration across western Argentina, with an aim to highlight areas of interest for further analysis.
EVALUATION OF THE EFFICIENCY OF THE EDUCATIONAL ORGANIZATIONS SITES AS A TOOL FOR SERVICE QUALITY MANAGEMENT

The article proposes a methodology for analyzing the effectiveness of websites of educational organizations based on building a system for evaluating a website, primarily as a set of information-logical elements, technical and visualgraphic solutions. An open presentation of information about the activities of educational organizations is currently an integral attribute of the provision of educational services. Information technologies for providing information are different, but, based on the regulatory requirements for the activities of educational organizations and the established practice of interaction with consumers of services, the main channel for obtaining the necessary information about the activities of institutions are official websites. This product allows you to build constructive communication between an educational organization and the target audience, therefore, it must comply not only with the requirements of regulatory authorities, but also with the needs of consumers of this product, the ideas of students and their parents about the information resource of the educational organization.
LGBT Cyberbullying Detection in Thai Language Utilizing Transformers-Based Algorithms

Cyberbullying is a growing issue worldwide, particularly among minority groups such as the LGBT community. While research has shown that LGBT individuals are at a higher risk for experiencing cyberbullying, there is a lack of studies focusing on detecting cyberbullying directed towards this group in the context of the Thai language. This research aims to identify incidents of cyberbullying targeting the LGBT community in the Thai language on the social media platform, Twitter by using specific Thai derogatory keywords for data collection. In terms of model development for the detection of LGBTcyberbullying, deep learning-based algorithms, including CNN, LSTM, and transformer models have been applied to the Thai corpus to classify the cyberbullying messages. The results show that CNN performs better than other algorithms with 99.98% accuracy and 99.99% F1 score. The study aims to develop effective tools and strategies for detecting cyberbullying and ultimately contribute towards creating a better society that values diversity and inclusion of LGBT.
Trend of Continuous Auditing Research: A Bibliometric Analysis

The purpose of this study was to find out how the development of citations, publication trends, a collaboration of authors, trends in title terms, and trends in keyword terms in Continuous Auditing articles (2013-2022). Data collection using publish or perish (PoP) software in bibliographic search a
Research on Pathways to Enhance Learning Abilities of Vocational College Students

For students in vocational colleges, enhancing their learning abilities is crucial for their personal growth and future career development. This article aims to explore the significance of cultivating learning abilities in vocational college students in the new era, analyze the current status and issues related to students' learning abilities in vocational colleges, and propose corresponding pathways for improvement. Through in-depth research on the learning situations of vocational college students, identifying areas where learning may be lacking, this study aims to help students in vocational colleges better enhance their learning abilities. It also seeks to explore effective pathways to boost students' learning abilities and provide a reference basis for education and teaching in vocational colleges.
Enhancing Trust in Supply Chain Management with a Blockchain Approach

Blockchain technology has the potential to significantly enhance trust in supply chain management by providing a secure and transparent system for recording and tracking transactions. A blockchain is essentially a distributed ledger that is maintained by a network of nodes, and each node holds a copy of the same ledger. Transactions validated and recorded by the nodes in the network, and once a transaction is recorded, it cannot be altered or deleted. One of the key benefits of using blockchain technology in supply chain management is the ability to provide end-to-end traceability of products. By using a blockchain-based system, every transaction that occurs within the supply chain can be recorded and tracked, allowing for greater transparency and accountability. This can help to reduce the risk of fraud, counterfeiting, and other illegal activities within the supply chain. By using a decentralized system for recording and tracking transactions, there is less need for intermediaries and intermediaries, which can reduce costs and increase the speed of transactions. Overall, blockchain technology has the potential to significantly enhance trust in supply chain management by providing a secure and transparent system for recording and tracking transactions. However, there are still some challenges that need to be addressed, such as interoperability between different blockchain systems, and the need for standardization of data formats and protocols.
An Actor-Network Theory Instrument for Design Practitioners

In this paper, we explore a novel idea of theory instruments: tangible artifacts that encourage participatory design practitioners in the industry to reflect on interactions with technological devices in the light of a particular theory.Our Actor-Network Rings instrument is based on Actor-Network Theory as this is one way to challenge taken-for-granted assumptions about what effects digitalization have on product users and their lives.The instrument consists of wooden rings, wooden balls, clothespins, and magnets.By adding wooden clothespins as actors and plastic ones as actants on wooden network rings, upholding a wooden ball becomes visible and even tangible as a program of action.Shifting or removing clothespins visualizes concepts of imaginary substitution and delegation.We analyze two cases of how designers and design researchers in companies use the instrument to investigate dilemmas of people and technology.With transcripts from video documentation and hand-drawn visualizations, we show that the instrument elicits broader insights into the dilemmas and even opens ideas for future scenarios.We argue that there is a potential for developing such theory instruments that, in a playful manner, offer fundamental theory perspectives to inform understandings of digitalization.
Toward Robust Discriminative Projections Learning Against Adversarial Patch Attacks

As one of the most popular supervised dimensionality reduction methods, linear discriminant analysis (LDA) has been widely studied in machine learning community and applied to many scientific applications. Traditional LDA minimizes the ratio of squared <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_2$</tex-math> </inline-formula> norms, which is vulnerable to the adversarial examples. In recent studies, many <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_1$</tex-math> </inline-formula> -norm-based robust dimensionality reduction methods are proposed to improve the robustness of model. However, due to the difficulty of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_1$</tex-math> </inline-formula> -norm ratio optimization and weakness on defending a large number of adversarial examples, so far, scarce works have been proposed to utilize sparsity-inducing norms for LDA objective. In this article, we propose a novel robust discriminative projections learning (rDPL) method based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_{1,2}$</tex-math> </inline-formula> -norm trace-ratio minimization optimization algorithm. Minimizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_{1,2}$</tex-math> </inline-formula> -norm ratio problem directly is a much more challenging problem than the traditional methods, and there is no existing optimization algorithm to solve such nonsmooth terms ratio problem. We derive a new efficient algorithm to solve this challenging problem and provide a theoretical analysis on the convergence of our algorithm. The proposed algorithm is easy to implement and converges fast in practice. Extensive experiments on both synthetic data and several real benchmark datasets show the effectiveness of the proposed method on defending the adversarial patch attack by comparison with many state-of-the-art robust dimensionality reduction methods.
Traffic Anomaly Detection: Exploiting Temporal Positioning of Flow-Density Samples

It is of paramount importance to detect traffic data anomalies in a real-time manner as it helps efficient traffic control and management. Several unsupervised anomaly detection algorithms are proposed previously in the literature; however, lack of proper ground truth labels for traffic data has been always a substantial barrier to deploy and evaluate them. In this paper, we introduce a concept named Temporal Positioning of Flow-Density Samples (TP-FDS) that can be used by domain experts for fast and reliable traffic data labeling. We mathematically show that deviations in two-dimensional TP-FDS completely reflect point and subsequence anomalies previously defined in the literature of time series data. Furthermore, benefiting from this concept, we propose a novel anomaly detection framework with the help of Fast Angle Based Outlier Detection (Fast-ABOD) to be used for traffic data. Extensive data labeling experiments are conducted with the opinions of 20 different experts. Implementation of several machine learning algorithms, like KNN, OC-SVM, iForest, and LOF, is also adapted with two different setups of hyper-parameters to be used in the proposed framework. Results indicate that our framework integrated with Fast-ABOD is able to detect anomalies in traffic data better than other machine learning and state-of-the-art deep learning algorithms with more than 72% and 96% of F1 score and AUC.
Hybrid Non-Intrusive QoE Assessment of VoIP Calls Based on an Ensemble Learning Model

While the Mean Opinion Score (MOS) is the most well-known way to quantify Quality of Experience (QoE), it only provides average insight. In this paper, we will demonstrate that instead of only relying on the MOS value, predicting users' perceived quality through probabilistic metrics provides service providers with more accurate insight to improve their network decisions, relying on more accurate measures of how many of them are satisfied or not with the provided service. Furthermore, a hybrid non-intrusive ensemble learner based on the selection of multiple base learners is designed to estimate the QoE of the VoIP signal. The performance of our proposed model is compared to that of individual learners, demonstrating that the proposed scheme outperforms earlier schemes. In addition, in contrast with earlier schemes that relied on their own laboratory-generated dataset, another key advantage of our approach is that it extracts a wide variety of different system parameters, such as noise type and echo delay, and signal parameters, such as fundamental frequency, only from the degraded signal. Finally, we show how the more accurate predicted QoE values can be used by service providers to properly modify network parameters to get closer to the required QoE levels.
Role of scientific evidence in the judiciary system: A Systematic Review

Background: Scientific evidence plays a crucial role in modern legal systems, enabling fact-finders to arrive atlogical conclusions in a variety of cases. However, the credibility of scientific evidence can be a major challenge forjudges and juries. Evidence laws provide limitations on the admissibility and presentation of evidence in criminalproceedings, protecting a defendant’s right to a fair trial and enabling the efficient conduct of criminal trials.Forensic science has become an essential component of criminal justice, aiding in the identification of the guiltyand the prevention of wrongful convictions. As such, the use of scientific evidence in criminal proceedings mustcontinue to be prioritized and protected.Aim-The goal is to examine how forensic science plays a part in the judicial system, particularly how it may beused to establish facts, establish guilt, and provide justice.Methods-A review of the literature on a few of the older studies from various journals and databases wasconducted. They searched a computerized database. Studies that were deemed relevant to the current objectivewere picked at random.Result- Forensic evidence plays a crucial role in the criminal justice system in India. It provides an objective andscientific basis for establishing the guilt or innocence of a suspect, identifying perpetrators of crime, and establishingthe facts of a case. The use of forensic evidence in criminal trials promotes transparency and accountability in thecriminal justice system and helps to ensure that justice is served.
Automatic Taxonomy Generation and Incremental Evolution on Apache Spark Parallelization Framework

The term “Big Data” refers to a large volume of information usually in terabytes and petabytes. It includes both structured and unstructured data. Unstructured data is conventionally text-heavy, but may also contain data such as facts, dates and numbers. To use this unstructured information effectively, it needs to be processed and organized. Taxonomy is considered as a powerful way of organizing the information. For automatic taxonomy generation, various techniques have been proposed in the past. However, the substantial nature of big data presently crosses the processing abilities of traditional techniques. Thus, to meet this challenge an extensible and scalable technique is required to potentially accelerate the process of taxonomy generation and its evolution upon arrival of new data, hence catering large amount of unstructured big data. This paper proposes a technique for both the taxonomy generation and evolution on Apache Spark infrastructure. The proposed technique is evaluated on a text dataset of a computing domain. The evaluation results show that the technique presented in this paper outperformed the existing techniques in terms of time and quality metrics. The time and quality-based evaluation showed that the use of MapReduce environment has resolved the scalability issues of current taxonomy generation and evolution process.&#x0D;