Reviewerly/exHarmony huggingface dataset:
The effect of digital technology on students' reading Behavior

The study discusses the effect that technology can have on a student's reading behavior. Where students now a day spend a lot of time with cell phones. Rapid advances in telecommunications technology, media and information technologies and the widespread development of global information infrastructure transform learning and reading patterns into global paradigms. It was this paradigm that became dominant and effectively became involved in the revolution of information networks, which would later determine the future of national welfare. Technology, however, remains the initial hypothesis before research is carried out. In this method is use qualitative method. Study using qualitative method provided a comprehensive and clear picture of the social situations studied, comparative events from one social to another or from one time to another; or be able to find patterns of connections between certain aspects and others, and be able to find hypotheses and theories. In a study that described the work done by 12 respondents. Based on Moleong, the aim of the writer is to examine how the student's behavior, habits, and perceptions of how technology affects reading habits for them. The purpose of this study is to know whether technology has a major impact on student reading behavior. The results of this study indicate that technology had a positive impact on the interest in reading students, but most students prefer reading nonfiction rather than reading lessons. And technology can has a negative impact on the student as student interest is distracted and they are sedated with the modern technological sophistication of gadgets.
The Merits and Limitations of YouTube as a Method of Informal Learning

YouTube offers multiple channels for informal learning. Participants may learn from video content in the form of lectures, tutorials, guides, and even vlogs. YouTube also enables forms of incidental learning in which participants discover new information, content, or skills, while engaged in other activities. Informal learning on YouTube impacts participants’ learning strategies and learners’ progress. Selecting a new beginner of the Ukulele may help me to figure out some benefits and limitations of YouTube as a method of informal learning.
Android Application of English Learning as Learning Media for Junior High School Students

One form of technological advances in computers is a mobile device. At this time English is a very important requirement in all fields of education and work even in daily life, because English itself has become an international language where most countries use this language. However, most junior high school students often have difficulty in learning English. This research aims to create an English learning application based on the existing curriculum using Adobe Animate CC 2017 which is offline. The stages used to make the application in this study are problem analysis, problem, problem solving strategies and system design. The global system design uses UML modeling which consists of Use case Diagrams, Story Boards, Activity Diagrams, Sequence Diagrams and Flow Charts. This application makes it easier for junior high school students to learn English so that it helps make it easier for teachers to convey material and also this application considers all aspects of development, is meaningful, interesting, and functional. features after testing on several smartphone devices.
Analysis of Web-Based Academic Information System at Harapan Kartasura Vocational School

Technological advancements are continuously developing rapidly, initiated or based on the world of education that provides knowledge, guidance and practice on how to create updated technology. The cause of the creation of technology stems from the world of education, so the world of education should implement a technology-oriented information system. HarapanKartasura Vocational School in processing academic data still uses a simple computerized system and still uses paper. Even though it is supported by a computer, it only uses simple applications such as Microsoft Excel and Word applications, thus allowing a lot of errors in processing academic data. This can hinder academic services to students and teachers as well as result in difficulties in finding data and consuming time in preparing reports. The purpose of this study is to design an academic information system at HarapanKartasura Vocational School so that it can facilitate the processing of academic data such as student data, teacher data, class data and subject data and can overcome existing problems.The result of this research is an academic information system that can help accelerate school performance and make it easier for schools to get the information they need precisely, quickly and accurately.
Face Recognition with Backpropagation Artificial Neural Networks Using the Sigmoid Activation Function

In the current development of information technology facilities that are so rapid and complex, the reliability of a system that processes data properly will produce good information. The use of computers today is not only to help human work, but has also replaced human work in various fields. The purpose of this research is to design an image model in an application program. This existing digital image can then be used for various purposes. One way is to take photos on a driver's license (SIM), when taking photos directly using a digital camera and facial photo files are also directly stored in the police database. So this facial photo recognition program can actually also be applied by the police to recognize the faces of criminals. From the 10 experimental samples, it can be concluded that the best LearnRates with the lowest ErrorLevel for BMP format files are for learnrate 0.2 with an error rate of 18,54229 and a match rate of 81,45771 with a recognition rate of more than 60% in photo 2, while with an error rate of 51.00543 and a match level 48,99457
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models

Nowadays, pretrained language models (PLMs) have dominated the majority of NLP tasks. While, little research has been conducted on systematically evaluating the language abilities of PLMs. In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM). In our study, we design four evaluation dimensions, i.e. memory, comprehension, reasoning, and composition, to measure ten widely-used PLMs within five categories. Our empirical results demonstrate that: (1) PLMs with varying training objectives and strategies are good at different ability tests; (2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size and distribution; (3) PLMs have excellent transferability between similar tasks. Moreover, the prediction results of PLMs in our experiments are released as an open resource for more deep and detailed analysis on the language abilities of PLMs. This paper can guide the future work to select, apply, and design PLMs for specific tasks. We have made all the details of experiments publicly available at https://github.com/RUCAIBox/ElitePLM.
Formation of digital competence of future teachers-psychologists

The relevance of the article is due to the changes taking place today in the world community. Today we are no longer talking about a post-industrial, but about a data-centric world in which data itself, its collection, storage and further application come out on top. Therefore, the presence of digital competence is a necessary element of human existence in the modern information space. This article analyzes the works of authors developing the problem of competence of educational workers in a digital society. The author considers the concept of digital consumption in the process of application in educational activities as one of the components of the digital competence of the future teacher-psychologist. The article highlights the results of diagnostics of the level of formation of digital consumption as a component of digital competence of the future teacher-psychologist at the ascertaining and control stages of the experiment conducted on the basis of the I.Ya. Yakovlev Chuvash State Pedagogical University. The study was conducted between October 2021 and May 2022. In the course of the study, the level of formation of digital consumption of students of the Faculty of Psychology and Pedagogy was checked. The methods of the Russian Center for Internet Technologies, the All-Russian Center for the Study of Public Opinion, and the National Agency for Financial Research were used in the process of diagnosing volunteer students. The ascertaining stage of the experiment on the formation of digital competence revealed that few students use digital technologies at a sufficient level in their activities. During the formative experiment to improve digital competence, students were taught the skills of using modern information technologies and using digital tools in their professional activities. At the control stage of the experiment on the formation of digital competence of the future teacher-psychologist, we found that the indicators of achieving digital consumption in the field of professional activity of a teacher-psychologist significantly increased after lectures and practical classes in the field of digital consumption. The article may be useful for further research in the field of digitalization of vocational education
Off-chain trading for micro grid systems

As micro grids and blockchain gained the interest and attention of both academia and the industry, the interaction between the two technologies seems inevitable. However, there are challenges to overcome in order to actually realize the integration between micro grids and blockchains. In this article, we review the solutions proposed to enhance micro grids with blockchains. We discuss the scalability challenges and the opportunities derived from the off-chaining computing techniques. In this context, we draft a design to implement a micro grid-based peer-to-peer local energy market, powered by an off-chain computing protocol called DIVERSITY. DIVERSITY aims to shift the computational burden from a main blockchain to an intermediate layer of nodes, aggregating data and executing smart contracts off-chain. We simulate different data logging approaches, and it is found that DIVERSITY allows an actual saving on fees and power consumption derived from using a public blockchain platform, such as Ethereum, in order to assure a truly decentralized renewable energy distribution at a lower cost.
EVALUATION OF THE APPLICATION OF DOUBLE-EXPOSURE SPECKLE PHOTOGRAPHY WITH DIFFERENT APERTURES IN EXPERIMENTAL MECHANICS

The article presents an experimental evaluation of the use of double-exposure speckle photography with circular and annular apertures in experimental mechanics using the example of determining the modulus of elasticity in tension. The description of the process of registration of speckle photography and its subsequent processing is given. Based on the results of calculations of the finite element model of the sample, the areawhere the boundary conditions do not affect its tension is determined. A description is given of the procedure for processing speckle photographs to determine the modulus of elasticity and the error in its measurement with different apertures.
A STUDY OF BLOCKCHAIN APPLICATION IN THE BANKING INDUSTRY

Blockchain technology is a core, underlying technology with promising application prospects in the banking industry. As such, blockchains could revolutionize the underlying technology of the payment clearing and credit information systems in banks,thus upgrading and transforming them.Blockchain applications also promote the formation of “multicenter,weakly intermediated”scenarios,which will enhance the efficiency of the banking industry.
On the Audio-Visual Emotion Recognition using Convolutional Neural Networks and Extreme Learning Machine

The advances in artificial intelligence and machine learning concerning emotion recognition have been enormous and in previously inconceivable ways. Inspired by the promising evolution in human-computer interaction, this paper is based on developing a multimodal emotion recognition system. This research encompasses two modalities as input, namely speech and video. In the proposed model, the input video samples are subjected to image pre-processing and image frames are obtained. The signal is pre-processed and transformed into the frequency domain for the audio input. The aim is to obtain Mel-spectrogram, which is processed further as images. Convolutional neural networks are used for training and feature extraction for both audio and video with different configurations. The fusion of outputs from two CNNs is done using two extreme learning machines. For classification, the proposed system incorporates a support vector machine. The model is evaluated using three databases, namely eNTERFACE, RML, and SAVEE. For the eNTERFACE dataset, the accuracy obtained without and with augmentation was 87.2% and 94.91%, respectively. The RML dataset yielded an accuracy of 98.5%, and for the SAVEE dataset, the accuracy reached 97.77%. Results achieved from this research are an illustration of the fruitful exploration and effectiveness of the proposed system.
Hinders of Cloud Computing Usage in Higher Education in Iraq: A Model Development

Cloud computing (CC) is a trendy technology that is being used in business and daily life. However, limited studies is found on higher education usage. The barriers and obstacles that confront the usage is not clear and in particular in developing countries. The purpose of this study is to examine the barriers and obstacle that confront the usage CC services in Barash University in Iraq. Using the technology organization environment framework and the internal external factor (IE-TOE), the study proposed the conceptual framework. The data was collected from academic, non-academic staff and students using convivence sampling technique. The data was analyzed using Smart PLS. The findings showed that organizational obstacle followed by technological, internal and external factors, and environmental factors are the most severe obstacles that confront the university in using CC services. Decision makers can benefit from the developed model to ease the implementation of CC.
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding

Transformer-based models are widely used in natural language understanding (NLU) tasks, and multimodal transformers have been effective in visual-language tasks. This study explores distilling visual information from pretrained multimodal transformers to pretrained language encoders. Our framework is inspired by cross-modal encoders' success in visual-language tasks while we alter the learning objective to cater to the language-heavy characteristics of NLU. After training with a small number of extra adapting steps and finetuned, the proposed XDBERT (cross-modal distilled BERT) outperforms pretrained-BERT in general language understanding evaluation (GLUE), situations with adversarial generations (SWAG) benchmarks, and readability benchmarks. We analyze the performance of XDBERT on GLUE to show that the improvement is likely visually grounded.
Diagnosa Penyakit Bells Palsy Menerapkan Metode Tsukamoto

Bell’s Palsy is paralysis of the facial muscles that causes one side of the face which causes one side of the face to sag. In Indonesia, there are many cases of bell's palsy but it is definitely difficult to determine. In this case, there was a case of bell's palsy in Indonesia of 19.55%, from all the most frequent cases of neuropathy that were found in the age of 20-50 years, and the incidence increased with increasing age after 60 years. About 60-85% of sufferers can recover completely within 3 weeks and only 15% require 3 months. But there are 30% of those who have a lifetime disability due to the patient not getting better for 4 months. Therefore an expert system was designed to diagnose bells palsy. The method used in the expert system is the Tsukamoto Method. The Tsukamoto method is a development of the Mamdani and Sugeno methods which use control and are also used by humans instead of machines. Fuzzy Tsukamoto is one method that is very flexible and has a tolerance for existing data. This expert system can provide solutions and ways to prevent bell's palsy with the diagnosis process carried out efficiently and save time in helping the general public in the prevention that occurs. This web-based expert system application was built with the PHP programming language and MySQL data storage
Penerapan Pewarnaan Graf Pada Penjadwalan Mata Kuliah Program Studi Matematika UIN Imam Bonjol Padang

A graph is a discrete structure consisting of vertices and edges connecting these vertices. One of the discussions in graph theory is graph coloring. In this research, graph coloring will be applied in making the schedule for the Mathematics study program at UIN Imam Bonjol Padang by assuming the subject as a vertice and the lecturer as an edge. One of the benefits of applying graph coloring in scheduling makes scheduling arrangements easier. The graph coloring algorithm use the Welsh Powell Algorithm. Based on the application of graph coloring with the Welsh Powell Algorithm obtained the lecturer's teaching schedule according to the SKS, time and classroom given.
Prediksi Kesiapan Kerja Mahasiswa menggunakan Algoritme K-Means dan C4.5

Student work readiness is quite influential on the existence of a university. The waiting time required by students to get a job can affect the mentality of students and the value of higher education from the assumptions of society. This will greatly affect the interest of parents or prospective students to continue their education at universities that have a bad image. Therefore, predictions of student work readiness before graduation are needed for consideration by higher education institutions to overcome the problem of waiting time for student work after graduation. The source of this research data is obtained from the Surakarta University database by utilizing alumni data from tracer studies as train data and 6-semester active student data as test data. The initial step taken is preprocessing to eliminate noise that can interfere with or affect the final result. The research method that will be used is to implement the K-Means and C4.5 algorithms for grouping and prediction processes. The data train used is 150 data and the testing data is 59 data. The results obtained by the K-Means algorithm can cluster 143 data correctly by comparing with the original data. The best cluster value obtained is K = 3.
Aplikasi Network Analysis Metode CPM (Critical Path Method) Model AON Untuk Menentukan Penjadwalan Proyek Pembangunan Gedung Perpustakaan UIN Malang

The problem The with development projects is to determine the project schedule so that it can be completed in an optimal (minimum) time. Scheduling with Network, among others, uses the CPM (Critical Path Method). The network diagram representation of the method is in the form of a directed graph (digraph) which is presented in two models, namely AOA (Activity On Arc) and AON (Activity On Node. Activities in the AON model are placed on nodes. This study aims to determine the application of network analysis using the CPM method. The AON model on the scheduling of the library development project at UIN Malang The methods used are forward pass, backward pass, and total slack so that it can be known the fastest or late time an activity can be started or ended and the amount the allowable delay time for an activity and its critical activities.Previous research only used the PERT (Program Evaluation and Review Technique) method and the Gantt Chart method, so in this study the author will use another method, namely the CPM Model AON method. pen time the construction project schedule is 293 days starting from 01-07-2022 to 27-05-2023. From the results of the analysis using the CPM method, it was obtained that from 298 project preparation activities, 17 activities were critical activities so that their implementation could not be delayed because it would result in a delay in project completion time.
Research support activities of library, university of Moratuwa : bringing to a next level via research commons

Research Commons is dedicated library space for researchers. Research Commons in libraries are important to develop research culture in the university environment [1]. In order to review and enhance the current status of research support activities of the Library, University of Moratuwa (UoM) a, a survey was carried out among all academic staff members and postgraduate students at the UoM. The survey aimed to investigate the usage and importance of current research support services and find out the importance of some proposed research support services. As the research instrument, a questionnaire was developed after conducting a pilot study among eminent researchers and focus group interviews with library academics. Current library services as well as new library services were ranked using average weighted scores [2]. Average weighted score was calculated to determine overall order of preference as shown below. Largest weight was given for most preferred choice.
Embedding Hallucination for Few-Shot Language Fine-tuning

Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences. In such settings, fine-tuning a pre-trained language model can cause severe over-fitting. In this paper, we propose an Embedding Hallucination (EmbedHalluc) method, which generates auxiliary embedding-label pairs to expand the fine-tuning dataset. The hallucinator is trained by playing an adversarial game with the discriminator, such that the hallucinated embedding is indiscriminative to the real ones in the fine-tuning dataset. By training with the extended dataset, the language learner effectively learns from the diverse hallucinated embeddings to overcome the over-fitting issue. Experiments demonstrate that our proposed method is effective in a wide range of language tasks, outperforming current fine-tuning methods. Further, we show that EmbedHalluc outperforms other methods that address this over-fitting problem, such as common data augmentation, semi-supervised pseudo-labeling, and regularization. The code will be made available at: https://github.com/yiren-jian/EmbedHalluc.
Imagination-Augmented Natural Language Understanding

Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations. Such abilities enable us to construct new abstract concepts or concrete objects, and are essential in involving practical knowledge to solve problems in low-resource scenarios. However, most existing methods for Natural Language Understanding (NLU) are mainly focused on textual signals. They do not simulate human visual imagination ability, which hinders models from inferring and learning efficiently from limited data samples. Therefore, we introduce an Imagination-Augmented Cross-modal Encoder (iACE) to solve natural language understanding tasks from a novel learning perspective -- imagination-augmented cross-modal understanding. iACE enables visual imagination with external knowledge transferred from the powerful generative and pre-trained vision-and-language models. Extensive experiments on GLUE and SWAG show that iACE achieves consistent improvement over visually-supervised pre-trained models. More importantly, results in extreme and normal few-shot settings validate the effectiveness of iACE in low-resource natural language understanding circumstances.