Reviewerly/exHarmony huggingface dataset:
DSC Index: Measuring the Digital Supply Chain Practice among the Higher Education Institutions Community in Least Developed Countries

The 4th Industrial Revolution, more commonly referred to as Industry 4.0, has brought about a wave of multifaceted changes across the industrial spectrum around the world, and it has triggered the digitalisation of supply chains and their management regardless of the type of organisation. With increasing interconnectivity through various sectors, digital supply chain (DSC) practices and intentions have also become integral to higher education institutions. As streamlined, automated administrative processes and virtual classes conducted through online platforms become the norm, digitalisation has been catalysed in the education sector. However, several sociocultural, economic, and psychographic factors influence the adaptation of new technologies, especially in developing countries such as Bangladesh. This study uses the composite index approach to determine the Index derived from the correlation between the factors and their impact on the DSC practices and intentions. The study indicates that Trust (T) is the primary influencer, along with Performance Expectancy (PE), closely followed by Facilitating Value (FV), Facilitating Conditions (FC), and Digital Literacy (DL).
A Novel Jamming Signal Recognition Method Based on Data Augmentation Using 1D-GAN under Small Sample Condition

Various jamming signals in the complex electromagnetic environment pose a serious threat to radar detection. Effective recognition of jamming type is of great significance for anti-jamming. In recent years, jamming recognition algorithms based on deep learning have been proposed. With a sufficient number of samples, these algorithms can obtain high recognition accuracy. However, in the actual battlefield environment, it is difficult to accurately obtain large amounts of measured samples with clear labels, and the accuracy of jamming recognition cannot be guaranteed. This paper proposes a jamming recognition method based on a one-dimensional adversarial generative network (1D-GAN) for small sample conditions. Extra samples are generated by using this 1D-GAN and these generated samples are combined with the original data to form an augmented data set. Then a one-dimensional convolutional neural network (1D-CNN) is used for jamming recognition. The fidelity of the generated samples is verified in three dimensions data domain, feature domain, and jamming recognition accuracy. The experiment results show that the generated data has high similarity with the real data, and data augmentation by GAN can effectively improve the jamming recognition performance in the case of a small number of samples.
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers

Adversarial attacks have highlighted the vulnerability of classifiers based on machine learning for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) tasks. An adversarial attack perturbs SAR images of on-ground targets such that the classifiers are misled into making incorrect predictions. However, many existing attacking techniques rely on arbitrary manipulation of SAR images while overlooking the feasibility of executing the attacks on real-world SAR imagery. Instead, adversarial attacks should be able to be implemented by physical actions, for example, placing additional false objects as scatterers around the on-ground target to perturb the SAR image and fool the SAR ATR.In this paper, we propose the On-Target Scatterer Attack (OTSA), a scatterer-based physical adversarial attack. To ensure the feasibility of its physical execution, we enforce a constraint on the positioning of the scatterers. Specifically, we restrict the scatterers to be placed only on the target instead of in the shadow regions or the background. To achieve this, we introduce a positioning score based on Gaussian kernels and formulate an optimization problem for our OTSA attack. Using a gradient ascent method to solve the optimization problem, the OTSA can generate a vector of parameters describing the positions, shapes, sizes and amplitudes of the scatterers to guide the physical execution of the attack that will mislead SAR image classifiers. The experimental results show that our attack obtains significantly higher success rates under the positioning constraint compared with the existing method.
A Compound Jamming Signals Recognition Method Based on One-Dimensional Multi-Label Convolutional Neural Network

With the continuous development of interference technology, and interference equipment constantly updated, the future of the electromagnetic interference environment presents a wide attack spectrum, interference style, a combination of many forms, the use of variable strategies, spatial and temporal changes, and other characteristics, the interference environment faced by the radar from a single jamming signal environment changes to a compound jamming signals environment. Therefore, it is important to accurately identify the jamming components and combination styles of compound jamming signals. In this paper, a compound jamming signals recognition method based on a one-dimensional multi-label convolutional neural network (ID-MLCNN) is proposed. The method uses a two-way convolutional neural network to extract the features of the real and imaginary parts of the compound jamming signals data and uses a multi-class threshold optimization method to optimize the multi-label recognition results. The method is applied to the compound jamming signals recognition experiments, and the experimental results show that the method can effectively identify the jamming components and combination styles of the compound jamming signals.
EMOTIONAL BURNOUT DURING THE WAR

The article gives a theoretical analysis of the problem of emotional burnout. Emotional burnout syndrome - a state of physical and mental exhaustion - is a response to emotional overstrain. In addition, the syndrome of emotional burnout can be a mechanism
Comprehensive Analysis of Feature Extraction Techniques and Runtime Performance Evaluation for Phishing Detection

The digital landscape is continually evolving, bringing with it numerous cybersecurity challenges, notably the rise of phishing websites targeting unsuspecting users. These deceptive websites jeopardize digital identities, emphasizing the critical need for precise detection mechanisms. This research provides a deep analysis of feature extraction nuances and critically evaluates the runtime performance of detection models. Through intensive refinement of Random Forest classification models, an integrative approach is adopted, which encompasses feature selection, outlier mitigation, and hyperparameter optimization using advanced data mining techniques. Leveraging a pre-established dataset with 87 distinct features from 11,430 URLs, this research narrows down the features to a pivotal set of 56. The outcome is a robust model that achieves an accuracy of 97.069% and a precision rate of 97.326%. A noteworthy aspect of this study is the incorporation of ensemble models, which amplify prediction accuracy by harnessing the capabilities of multiple algorithms. By employing the ensemble approach, the research ensures the model’s heightened accuracy and adaptability, making it resilient against ever-changing phishing strategies. The findings underscore the symbiotic relationship between comprehensive feature extraction techniques and the paramount importance of runtime efficiency, laying the groundwork for a fortified digital landscape.
Smart Greenbox Design for Indoor Horticulture

Indonesia is an agrarian country where most of the population is engaged in agriculture. One of the biggest commodities in Indonesian agriculture is the cayenne pepper. Cayenne pepper is a commodity with the highest level of production and demand every year. However, cayenne pepper is a plant that is prone to crop failure. The biggest causes are climate change and lack of knowledge about the parameters that mostly affect the growth of cayenne pepper plants. Therefore, this study aims to help horticulture enthusiasts plant and grow cayenne pepper plants using Smart Greenbox. The Smart Greenbox measures several parameters that affect the growth of a plant, including temperature, humidity, light intensity inside the box, and moisture with the pH level of the soil. All the data measurements will be sent to the IoT cloud so other Smart Greenbox can learn from each other. To help the learning process, a website and growth prediction model using machine learning will be a delivered solution to obtain the most dominant parameters to give optimal growth. The historical data measurement is also stored and displayed on the website to provide feedback for the user to monitor the plant's growth. The overall system test result reports that the device can run optimally 24/7 for 45 days while sending data to Google Firebase every 5 minutes. The website for dashboard functionality can display all information regarding the Cayenne Pepper plant inside the Smart Greenbox.
Decoding User Feedback: A Convolutional Neural Network Approach to Sentiment Analysis in Event Management

In a committee event, the organizer usually evaluating their event once the event is done. One way to evaluate an event is to ask the participant to write a feedback about the event. The amount of feedback that comes in will impact on the time it takes to analyse it. The more feedback comes in, then it will take more time for the committee to analyse it. With Machine Learning and Deep Learning, feedbacks that comes in will be analyse with short amount of time and can be determined the sentiment value. In this research, a sentiment analysis model will be designed using Convolutional Neural Network (CNN) classification method. The output of this sentiment analysis model will be determining whether the sentiment value of a feedback is positive or negative. This sentiment analysis model later will be tested with Performance Evaluation. There are 4 parameters that will be tested, Accuracy, Recall, Precision, and F1 Score. The result of this research hopefully will be helpful to determine the overall feedback that comes in on an event with short amount of time. From the result of the test, the model with the highest accuracy of 94,4% is model with data partition of 70:30, epoch value of 100, batch size value of 32, and learning rate value of 0,00003.
BASIC EDUCATIONAL CONCEPTS AS THE BASIS OF PROFESSIONAL TRAINING OF IT INDUSTRY SPECIALISTS

The article analyzes the basic educational concepts that will form the basis of modeling the professional training of IT specialists. The analysis of the leading educational concepts – personally oriented learning, educational development environment, practice-oriented learning clearly demonstrates their fundamental importance for the further modeling of the educational process of formation of value-deontological competences of future specialists in the IT industry. At the same time, we note that all these concepts have a universal (and general) character for pedagogical theory and practice, in particular for the field of professional training. Therefore, we believe that the purpose of our presentation is to single out a number of educational concepts that are of special importance for the professional training of IT specialists.&#x0D; The Ukrainian and foreign practice of relevant research was analyzed, the definitions of educational concepts were interpreted and summarized. Our analysis made it possible to single out the following educational concepts - universal and special, which will serve as a theoretical and methodological basis for modeling the educational process of forming the value-deontological competences of future specialists in the IT industry.&#x0D; Thus, the article points to the prospect of the selection of certain methods, techniques, technologies and forms of organization of educational activities aimed at the formation of axiological and deontological competences, which we consider factual as confirmation of the formation of future IT, precisely guided by the approaches and guidelines of the specified educational concepts - specialists of axiological culture and deontological type of thinking.
Psychological Aspects and Opinions about Some Typical Robots and Robots in General

It is a well-known challenge for robot designers, developers and manufacturers that certain personalities and characteristics of people can result in resistance towards or outright refusal of human-robot interactions. With this in mind, we conducted qualitative research to collect information about what people think about some typical robots (Spot, Atlas, Lovot). Fifty-eight participants were interviewed during focus group sessions. Based on responses, we could conclude that the Spot robot was considered ugly and primarily useless, the Atlas was regarded neutrally and in Lovot’s case participants blamed developers of such robots and societies that encouraged this type of robots. Our main contribution is qualitative data about opinions of these robots, which can be crucial feedback information for developers.
Performance of a Rainwater Harvesting Tank Under Under Varying Non-Potable Demand: Case Study in Kubang Semang, Penang

Rapid development and climate change has endangered the availability of water resources worldwide. This study look into the potential of the applying a rainwater harvesting system (RWHS) for non- potable water usage in a doublestorey residential terrace house using Tangki NAHRIM 2.0 (TN2.0). TN2.0 is a web application that adopt the yield-after-spillage (YAS) convention to identify the optimum rainwater tank size. It is found that for a house of size 86.86 m&lt;sup&gt;2&lt;/sup&gt;, the optimum tank size is 2 m&lt;sup&gt;3&lt;/sup&gt; with water-saving and storage efficiency values of 51% and 63.2% respectively. Additionally, the performance of the optimum tank size of 2 m&lt;sup&gt;3&lt;/sup&gt; was further examined under different water demand. It was found that a 2 m&lt;sup&gt;3&lt;/sup&gt; tank was still able to save water up to 42.1% for a family of 10 persons. Meanwhile, the storage efficiency shows a little increment from 63.2% to 67.8%.
Practicality and Effectiveness of Student Learning Using Smart Apps Creator Media to Improve Critical Thinking Abilities and Student Learning Outcomes

Efforts made by the government to break the chain of spread of the Covid-19 virus include changing the face-to-face learning system to online distance learning, causing learning to be disrupted. The limited knowledge and skills of teachers in operating IT-based learning facilities, for example laptops and electronic learning media, have an impact on teachers having difficulty delivering learning effectively. Likewise, students who during the learning process do not have adequate supporting facilities, and many students cannot even operate IT-based learning media, causing the learning process to be ineffective. So that low student motivation and enthusiasm for learning causes student learning outcomes to decrease. The solution that researchers use in the learning process is to use the learning media "Smart Apps Creator" to facilitate effective learning because the use of Smart Apps Creator media is very practical in its operation, whether used in the online or offline learning process, it can even be used wherever students are, whether outside the schedule learning, so that it can make learning effective online. This can have an impact on improving student learning outcomes. This research aims to create practical learning using Media Smart Apps Creator. This type of research is quasi-experimental research with a learning practicality test using the average percentage of teacher and student responses, and a learning effectiveness test using a paired sample t-test. The average percentage of practical response test results from teachers and students was 93%, meaning that the Smart Apps Creator media is very practical to use as an online learning medium. Continuing research on effectiveness testing with an average pretest score of 53.9 and posttest of 84.4, the results of the paired sample t-test with a significance value of 0.000 means that the Smart Apps Creator media has high effectiveness in improving student learning outcomes. From the results obtained, it can be concluded that the use of Smart Apps Creator learning media is very practical and effective when learning online
The Impact of Data Augmentation Techniques on the Recognition of Script Images in Deep Learning Models

Deep learning technology is widely used for recognizing character images, including various regional characters and diverse ancient scripts. Deep learning models require large data sets to recognize images accurately. However, creating a dataset has limitations in terms of quantity, including the Komering script dataset used in this study. Data augmentation techniques can be applied to expand the dataset by modifying existing images to increase data diversity. This study aims to investigate the impact of augmentation techniques on the performance of deep learning models in the case of Komering script recognition. The dataset consists of 500 images for five classes of Komering script characters. Three augmentation techniques, namely random rotation, height shift, and width shift, were applied to the five characters, which were then used to test the model trained to recognize characters in the Komering dataset. This research contributes to providing insights into the effect of augmentation techniques on robust confidence prediction of deep learning models for recognizing newly augmented data. The results demonstrate that the deep learning model can recognize modified data using augmentation techniques with an average accuracy of 80.05%.
Digital Assessment: A Survey of Romanian Higher Education Teachers’ Practices and Needs

Within the European Commission’s Digital Education Action Plan (2021–2027) and the DigCompEdu framework, our research focuses on the competence area of teachers’ assessment practices and needs. We designed a 24-item online questionnaire for Romanian higher education teachers who are using digital technologies for assessing students’ learning, learning outcomes and practical skills. The present paper analyzes how the 60 respondents from Romanian universities evaluate their own digital competence and how they are using digital assessment, but also what training needs they have in these regards. This study, carried out in May–June 2022, therefore attempts to identify the main concerns, challenges and obstacles higher education teachers encounter when designing and using digital assessment. Our findings indicate the importance of empowering teachers through continuous learning, embracing flexible hybrid models and reimagining assessment strategies for digital literacy. The ANOVA analysis reveals variations among three groups categorized by self-reported digital competencies in their utilization of digital tools. Responsible knowledge-sharing, AI literacy and adaptive curriculum design emerged as critical imperatives. Our study advocates for a transformative shift towards AI-based pedagogy, emphasizing personalized learning that aligns with teachers’ competencies and specific assessment needs while adhering to fundamental teaching principles.
Literasi Numerasi dalam Pembelajaran Operasi Hitung Pecahan pada Peserta Didik Kelas 5 SDN 2 Ngambarsari Wonogiri Tahun Pelajaran 2021/2022

The aims of this study were (1) to describe the implementation of numeracy literacy in learning fractional arithmetic operations for fifth grade students during the pandemic; (2) Explain the obstacles to implementing numeracy literacy in grade 5 students; (3) Describe the results of learning fractional arithmetic operations during implementing numeracy literacy in grade 5 students. This type of research uses a qualitative descriptive approach. The research was conducted at SD Negeri 2 Ngambarsari, Wonogiri Regency. The research subjects were all fifth grade students. The implementation of numeracy literacy was carried out through 3 (three) stages, namely the habituation stage, the development stage which is oriented towards understanding the basic concepts of mathematics and the last stage is the application stage of mathematical concepts. Obstacles to implementing numeracy literacy in grade 5 students, namely: students' low interest in reading, the mindset of teachers who only encourage but do not provide role models for reading habitually, limited facilities and infrastructure for numeracy literacy media. The teacher's obstacles are: the teacher's lack of understanding of the concept of numeracy literacy, so that during learning the teacher is not able to apply learning material in everyday problems, and constraints related to time, namely reduced learning time as a result of the policy to control the spread of Covid-19. Class V numeracy literacy skills in learning fractional arithmetic operations obtained 3 (three) categories, namely: first low level, second low level, and medium level. The ability of numeracy literacy in learning fractional arithmetic operations is strongly influenced by how often students encounter arithmetic operations questions
Analisis Pendekatan Kontekstual dalam Pembelajaran Menulis Cerita Pendek Siswa Kelas VII SMP Negeri Satu Atap Riangpuho

This study aims to provide an overview and explanation of (1) the contextual approach used by the teacher in learning to write short stories, (2) the factors that influence the teacher's success in analyzing contextual approaches, especially learning short stories and (3) the results achieved from applying this approach this contextual. The subjects of this study were Indonesian teachers and Grade VII students of One Roof Public Middle School Riangpuho and the object of the study was short story learning using a contextual approach. Data collection methods used are observation, interviews and documentation. Data obtained through these three methods were analyzed using descriptive qualitative. The results of the study showed that there was an increase in writing short stories using a contextual approach as follows: (1) contextual learning really helps students in the learning process, especially in writing short stories, especially writing experiences; (2) the teacher acts as an informator, motivator, facilitator, evaluator, inspirer, and mediator. Students have a high enthusiasm for learning because it is in accordance with their life experiences, and the school environment supports learning. Meanwhile, the use of language that is easy for students to understand, the deepening method used by the teacher, namely associating the material with students' daily experiences and the intense assistance that the teacher provides when students begin to write stories, are supporting factors in the application of a contextual approach; (3) the results obtained by students from 29 people have a value above the average of 83.66. This shows that learning using a contextual approach can increase the value of short stories to students.