Reviewerly/exHarmony huggingface dataset:
Peculiarities of the introduction of infocommunication technologies in the practice of higher education and their influence on increasing the competitiveness of higher education institutions

The main features of introduction of infocommunication technologies in practice of higher school and modern tendencies of use of infocommunication technologies in high institutions are considered in the article. Possibilities for improvement of educational process in high institutions which arise at introduction of infocommunication technologies are defined. The advantages of application of infocommunication technologies in higher institutions are formulated. The problems of introduction of infocommunication technologies in the practice of higher institutions are analyzed and possible obstacles are identified. The term «competitiveness of higher education» is studied. The influence of infocommunication technologies on increasing the competitiveness of higher education institutions is determined. Prospects for the development of infocommunication technologies in universities are outlined. Recommendations for the integration of information technology in higher institutions practice are given.
Perancangan Sistem Informasi Pada UMKM Ikan Hias Nusatic

In the trading business world, namely the business of selling a product, many obstacles are found in processing data such as incoming and outgoing goods, because there are still companies that use manual systems that have not been computerized, the data processing system in the business world today is one of the most dominant problems in the business sector engaged in community services. This research aims to simplify the decision-making process so that it can utilize more effective and efficient cost, effort, and time in Nusatic UMKM. By using the prototype method to realize the desired software model, this application can simplify administrative activities which include sales data, purchasing data, and delivery in stages starting from the registration process to payment transactions.
Enhancing Cyber Security in Organisations by Establishing Attributes Towards Achieving Cyber Resilience

The rapid changes in technology on a global scale, combined with the widespread adoption of business operations in cyberspace, have intensified the need for robust protection against escalating risks posed by cyber threats. This research paper aims to identify fundamental cyber resilience management attributes that enable organizations to manage cybersecurity, sustain, and adapt amidst evolving cyber risks and threats. By integrating resilience theory and security theory, this study establishes the attributes for resilience within cyber domains, making a novel contribution to cyber resilience management in organizations. The study introduces a model featuring seven main variables: Rationale, Reliable, Readiness, Resistance, Robust, Rebound, Reflective, and sub-variables across the Physical, Logical, and Social cyber domains, providing a converged framework for achieving cyber resilience. The findings of the study highlight the significance of fundamental attributes for enhancing cyber resilience management in organizations, such as clarity in purpose, vision, and values for security management, an empowered culture, availability of resources, avoidance of single points of failure, development, and coordination of resources to respond to threats and risks, promotion of continual improvement, and the sharing of information and knowledge. In conclusion, this research paper presents a model for managing cybersecurity in organizations by identifying key attributes for achieving cyber resilience.
PENGEMBANGAN BAHAN AJAR BERBASIS WEB PADA MATERI SISTEM PENCERNAAN MANUSIA KELAS V SEKOLAH

A B S T R A K&#x0D; Penelitian ini bertujuan untuk menghasilkan produk bahan ajar berbasis web pada materi sistem pencernaan manusia kelas V sekolah dasar. Penelitian ini menggunakan metode penelitian dan pengembangan dengan mengadopsi model ADDIE yang terdiri dari lima langkah yaitu analysis, design, development, implementation, evaluation. Sumber data penelitian ini yaitu data kuantitatif diperoleh dari hasil penilaian produk oleh 3 orang validator serta dari hasil penilaian angket kepraktisan oleh 1 orang guru dan 20 siswa kelas V Sekolah Dasar Negeri 30 Pontianak Selatan. Data kualitatif diperoleh dari komentar dan saran dari setiap validator. Tingkat validitas bahan ajar diperoleh dari penilaian ahli desain, ahli materi dan ahli bahasa sedangkan tingkat kepraktisan bahan ajar diperoleh dari respon siswa. Hasil validasi produk bahan ajar berbasis web oleh ahli desain adalah “sangat valid” dengan rata-rata 4,2 sedangkan ahli materi memberikan penilaian “sangat valid” dengan rata-rata 4,3, dan ahli materi memberikan respon dengan kategori “sangat valid” dengan rata-rata 4,6. Hasil kepraktisan bahan ajar berbasis web pada uji coba skala kecil adalah “sangat praktis”: dengan rata-rata 3,45, dan uji coba tahap besar adalah “sangat praktis” dengan nilai rata-rata 3,39. Hasil penilaian kepraktisan oleh guru diperoleh kategori “sangat praktis: dengan rata-rata 3,62. Dapat disimpulkan bahwa bahan ajar berbasis web ini sangat layak dan sangat praktis digunakan oleh guru dan siswa saat proses pembelajaran daring.&#x0D; &#x0D; A B S T R A C T&#x0D; This study aims to produce web-based teaching materials for the human digestive system material for fifth grade elementary school. This study applied research and development method by adopting the ADDIE model encompasses analysis, design, development, implementation, evaluation. The source of quantitative data was obtained from product assessment result by 3 validators and the result of practicality assessment was conducted by 1 teacher and 20 students of class V Elementary School 30 South Pontianak. Qualitative data was obtained from validators’ comments and suggestions. The level of teaching material validity was measured by design expert, material expert and linguist. The level of teaching material practicality was assessed by students. The results of product validation of web-based teaching materials by design experts were "very valid" with an average of 4.2 while materials expert gave a "very valid" assessment with an average of 4.3, and teaching material expert responded in the "very valid" category with an average of 4.6. The practicality result of web-based teaching material in small-scale trial was "very practical": with an average of 3.45, and large-scale trial was "very practical" with an average value of 3.39. The practicality assessment performed by the teachers was in the "very practical" category: with an average of 3.62. It can be concluded that this web-based teaching material was very feasible and practical to be used by teachers and students during the online learning process.
PENYUNTINGAN NASKAH TERJEMAHAN SAMARKAND KARYA ANONIM

The objectives of this research are as follows: (1) to explain the process of editing translated manuscripts; (2) to describe the difficulties/challenges in editing translated manuscripts; and (3) to provide solutions in addressing the existing difficulties/challenges. This research employs a qualitative approach using descriptive analysis method. This means that the researcher edits the manuscripts, describes the process, identifies the arising issues, and provides alternative solutions. The analyzed stages of mechanical editing include translation and editing of words, phrases, and sentences. This classification is done because there are still many errors found in translating words, phrases, and sentences. The difficulties faced by translators, which also pose challenges for the researcher as an editor, are as follows: (1) ambiguity of the meaning of words, phrases, and sentences; (2) difficulty in finding equivalent words, phrases, and sentences; (3) inadequate familiarity with cultural words in the source text; (4) influence of the form and type of words, phrases, and sentences in the source language; and (5) influence of the source language. These difficulties can be overcome by: (a) referring to the source text and comparing it with the translated manuscript to be edited; (b) employing editing techniques specific to translated manuscripts; (c) mastering the theme and having knowledge of the translated manuscript; (d) consulting directly with the translator; and (e) if possible, consulting with the original author of the manuscript as well.
Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network

Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.
Thermal Infrared Single Image Dehazing and Blind Image Quality Assessment

Image dehazing is a method to reduce the effects of haze, dust, or fog in images in order to provide a clear view of the observed scene. A large variety of traditional and machine learning-based approaches exists in the literature. However, these approaches mostly consider color images in the visual-optical spectrum. Apparently, the thermal infrared spectrum is much less affected by haze due to its longer wavelength. But atmospheric perturbations during long-range observation can cause image quality degradation in the thermal infrared (TIR) spectrum as well. In this paper, we propose a method to generate synthetic haze for TIR images. Then, we analyze the already existing blind image quality assessment measure Fog Aware Density Evaluator (FADE) for its applicability to the TIR spectrum. We further provide a comprehensive overview of the current state-of-the-art in image dehazing and empirically show that many approaches originally designed for visual-optical images perform surprisingly well when applied to the TIR spectrum. This is shown in experiments conducted on the recently published M3FD dataset.
Socio-Psychological Aspects of Digitalization of Modern Education

Digitalization processes affect all spheres of human activity, including the system of modern education. As a result, there are many open questions and socio-psychological problems associated with digitalization. Studies of the ‘influence of numbers’ on the health of people, in particular students of different age groups, are relevant. In this context, it is advisable to study the impact of digitalization on schoolchildren and students from the point of view of medical and psychological approaches. The purpose of this study is to analyze the features of the influence of modern digital technologies in the field of education on the psychological and somatic state of students in connection with the level of general social health of the population.
Are Emotions Conveyed Across Machine Translations? Establishing an Analytical Process for the Effectiveness of Multilingual Sentiment Analysis with Italian Text

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages. hor[RU,RA]{\large Richard Anderson} \address[RU]{\large Rutgers University, USA} \address[RA]{\large rick.anderson@rutgers.edu} \author[RU]{\large Carmela Scala} \author[RU]{\large Jim Samuel} \author[UC]{\large Vivek Kumar} \address[UC]{\large University of Cagliari, Italy} \author[RU]{\large Parth Jain}
A Comparative Study of Large Language Models in Explaining Intrinsically Disordered Proteins

(1) Background: Artificial Intelligence (AI) models have shown potential in various educational contexts. However, their utility in explaining complex biological phenomena, such as Intrinsically Disordered Proteins (IDPs), requires further exploration. This study empirically evaluated the performance of various Large Language Models (LLMs) in the educational domain of IDPs. (2) Methods: Four LLMs, GPT-3.5, GPT-4, GPT-4 with Browsing, and Google Bard (PaLM 2), were assessed using a set of IDP-related questions. An expert evaluated their responses across five categories: accuracy, relevance, depth of understanding, clarity, and overall quality. Descriptive statistics, ANOVA, and Tukey&amp;#039;s honesty significant difference tests were utilized for analysis. (3) Results: The GPT-4 model consistently outperformed the others across all evaluation categories. Although GPT-4 and GPT-3.5 were not statistically significantly different in performance (p&amp;amp;gt;0.05), GPT-4 was preferred as the best response in 13 out of 15 instances. The AI models with browsing capabilities, GPT-4 with Browsing and Google Bard (PaLM 2) displayed lower performance metrics across the board with statistically significant differences (p&amp;amp;lt;0.0001). (4) Conclusion: Our findings underscore the potential of AI models, particularly LLMs such as GPT-4, in enhancing scientific education, especially in complex domains such as IDPs. Continued innovation and collaboration among AI developers, educators, and researchers are essential to fully harness the potential of AI for enriching scientific education.
WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI’s terms <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://infernolia.github.io/WEDGE.
Study on Effectiveness of Cooperative Language Learning on Improving Students’ English Speaking Skills; the Case of Guto Adulan Secondary School Grade Ten in Focus

This study looked into how effective cooperative learning is in helping students improve their English speaking skills.Guto Adulan high school in West Shoa Zone, Bako Tibe Woreda was chosen as the study site and two EFL teachers and 54 students in grade ten were the participants.Questionnaires, observation and interviews were used as data collection methods and the analysis of the data was done through frequency, percentage and mean score as well as qualitative methods.Results showed that most EFL teachers had been using traditional approaches or teacher-centered language learning methods and the main cause of the low level of cooperative learning usage was the number of students in the classroom, seating arrangements, textbook volumes, misunderstanding of cooperative learning and the students' poor English language background.Suggestions were provided based on the findings to help the effectiveness of cooperative language learning and improve student speaking skills.These include motivating, encouraging and giving attention to group discussions, observing and evaluating group work, conducting feedback in groups, giving teachers more training in this area and students continuing to maintain what they have achieved and having more practice using cooperative learning.Further research on the topic is recommended as well as school management reducing the number of students in the classroom, requesting additional EFL teachers from the concerned body and supervising teachers in the classroom.
Evaluating the persuasive influence of political microtargeting with large language models

Recent advancements in large language models (LLMs) have raised the prospect of scalable, automated, and fine-grained political microtargeting on a scale previously unseen; however, the persuasive influence of microtargeting with LLMs remains unclear. Here, we build a custom web application capable of integrating self-reported demographic and political data into GPT-4 prompts in real-time, facilitating the live creation of unique messages tailored to persuade individual users on four political issues. We then deploy this application in a pre-registered randomized control experiment (n = 8,587) to investigate the extent to which access to individual-level data increases the persuasive influence of GPT-4. Our approach yields two key findings. First, messages generated by GPT-4 were broadly persuasive, in some cases increasing levels of support for an issue stance by nearly 50%. Second, in aggregate, the persuasive impact of microtargeted messages was not statistically different from that of non-microtargeted messages (5.68% vs 7.32%, respec- tively, P = 0.082). These trends hold even when manipulating the type and number of attributes used to tailor the message. Taken together, these findings suggest — contrary to widespread speculation — that the influence of current LLMs may reside not in their ability to tailor messages to individuals, but rather in the persuasiveness of their generic, non-targeted messages. This work secondarily contributes by offering a robust and replicable approach – through a custom web-based pipeline – to integrating LLMs into experimental designs, and a novel dataset, GPTarget2023, containing metadata for thousands of tailored AI-generated messages.
Anomaly detection of industrial motors under few-shot feature conditions based on causality

Abstract It is observed that previous research studies focusing on few-shot feature data for fault diagnosis or anomaly detection have a limitation, that is, feature extraction methods to solve few-shot feature data problems will also have scenarios where they may not always be applicable. In this paper, a motor anomaly detection model with generalization performance is proposed to meet the anomaly detection needs in the above scenarios. The model consists of a reinforcement unit and a diagnosis unit. Firstly, the reinforcement unit extracts the adjacent features with different timestamps through ensemble learning. Secondly, the temporal convolutional network (TCN) model is nested to increase the receptive field of the reinforcement unit. Additionally, a residual network is introduced to improve the generalization performance. Finally, features obtained from the reinforcement unit are used for final anomaly detection through neural networks in the diagnosis unit. Experimental results indicate that the proposed model achieve an anomaly detection accuracy of 97.96% in factory motor dataset, while the model has the superior generalization ability.
Analisis Pemanfaatan E-book Sebagai Bahan Penunjang Pembelajaran Oleh Pengguna Perpustakaan SMAN 1 Kedungwaru Tulungagung

Current technological advances lead us to rapid development. One of the developments that occurred was in the library. The information needs of library users are facilitated by the existence of e-books which can be used as learning support. . E-books are library materials in digital form which are considered as library materials that can be accessed whenever and wherever we need them. This research was conducted to find out how the use of e-book library materials as a support for learning at SMAN 1 Kedungwaru. The research method used in this study is qualitative and presented in a descriptive form using observation, interviews and documentation. Data analysis was carried out by collecting data, reducing data, presenting data, and drawing conclusions/verification. The results of the study show that students at SMAN 1 Kedungwaru have used e-books as a learning support. They use e-books as reading material to add insight. This is considered a practical matter to obtain learning materials, especially during a pandemic. The existence of this e-book also has a big influence on student academic achievement. E-books are also considered to make it easier for students to fulfill academic information. The results of this study also show that there are still students who have not been able to make full use of e-books. What's more, the collections provided are still limited, so students choose to look for library materials in physical form. However, librarians try their best to add library materials and carry out promotions so that students can make the most of e-books. E-book library materials can be put to good use and are also effective in utilizing the facilities provided. The e-book is used as a reference in supporting the learning process. The suggestion of this research is the need for encouragement or motivation so that users can make the most of e-books.
A Deep Web Data Extraction Framework Enhancement Method

The solutions for the data extraction problem are based on an analysis of the HTML DOM trees and the response page tags. These techniques rely highly on HTML specifications, even though they can produce good results. To effectively disclose in-depth online data, this research provides a methodology with two stages to address the problem. To find the user’s text query, the suggested system first performs “normal crawling.” A method is suggested based on the crawler’s received moved forward weighting work (ITF-IDF) to choose important websites. “data region extraction” is carried out in the second stage to gather data records. The suggested data extractor extracts visual blocks using the blocks’ visual characteristics. According to the suggested technique, the visual blocks should be grouped into similar formats based on format trees and appearance similarity. The visual blocks that will be extracted as information records from the cluster with the highest weight are those that are selected. The test reveals that the system’s suggested outline is superior to earlier information extraction efforts.