Reviewerly/exHarmony huggingface dataset:
Development of A Metaverse-Based Teaching Material on Rainforest Ecosystem for Nigerian High School Biology

This study focused on the development of a metaverse-based rainforest ecosystem for high school students and tested its effectiveness in terms of students’ academic achievement and learning satisfaction. In this study, the metaverse platform ZEP is used to simulate a rainforest ecosystem. ZEP jointly developed by Supercat and Naver Z was formally launched in Korea on March 16, 2022. ZEP is a platform where users can integrate through avatars and integrates the metaverse’s virtual space map, video conferences, materials, movies and presentations displayed in object form. Ease of access, cost-effectiveness, interactivity, and user-friendliness are some of the strengths of the ZEP metaverse platform. The ZEP metaverse materials for high school biology were developed to serve as a hybrid world between the real and the virtual world. Data collection for the metaverse group was carried out using the pretest-posttest method, comparing it with two control groups—one using textbooks and the other using videos. The results indicate that the experimental group with metaverse technology is effective in improving students’ learning achievement and satisfaction. Therefore, it is proposed that the metaverse in education is valuable for teachers and learners because it could lead to student engagement and motivation in the classroom.
The Use of Learning Media-Based Augmented Reality (AR) to Improving Integrated Science and Social Studies Literacy

The use of learning media-based Augmented Reality (AR) to improving integrated science and social studies literacy . This study aimed to explore the use of learning media-based Augmented Reality to enhance integrated science and social studies literacy in primary school. AR-based learning media was integrated into the IPAS subject. The participants in this study were 48 fifth-grade students from elementary schools, divided into experimental (24 students) and control groups (24 students). Each class employed a cooperative learning approach, with students in the experimental groups engaged in using AR-based learning media to support their learning process. The research method used was non-equivalent pretest-posttest design. The instruments used to test literacy skills (both pretest and posttest) consisted of 5 items. The results indicate a significant difference in the enhancement of students’ literacy skills in the experimental groups compared to the control group. This suggests that the use of the AR-based learning approach enhanced students’ literacy skills in the context of the IPAS subject. Keywords: ar learning media, ipas learning, science literacy, social studies literacy. Abstrak: P enggunaan media pembelajaran berbasis Augmented Reality (AR) untuk meningkatkan literasi IPA dan IPS terpadu. Penelitian ini bertujuan untuk mengeksplorasi penggunaan media pembelajaran berbasis Augmented Reality untuk meningkatkan literasi IPA dan IPS terpadu (IPAS) di sekolah dasar. Media pembelajaran berbasis AR diintegrasikan ke dalam mata pelajaran IPAS. Partisipan dalam penelitian ini adalah siswa kelas lima sekolah dasar yang berjumlah 48 orang, dibagi menjadi kelompok eksperimen (24 siswa) dan kelompok kontrol (24 siswa). Setiap kelas menggunakan pendekatan pembelajaran kooperatif, dimana siswa pada kelompok eksperimen diikutsertakan dalam penggunaan media pembelajaran berbasis AR untuk mendukung proses pembelajarannya. Metode penelitian yang digunakan adalah non-equivalent pretest-posttest design. Instrumen yang digunakan untuk tes kemampuan literasi (baik pretest maupun posttest) terdiri dari 5 item. Hasil penelitian menunjukkan adanya perbedaan yang signifikan peningkatan kemampuan literasi siswa pada kelompok eksperimen dibandingkan dengan kelompok kontrol. Hal ini menunjukkan bahwa penggunaan pendekatan pembelajaran berbasis AR meningkatkan kemampuan literasi siswa dalam konteks mata pelajaran IPAS. Kata kunci: Media pembelajaran AR, pembelajaran IPAS, literasi sains, literasi social. DOI: http://dx.doi.org/10.23960/jpp.v13.i3.202328
Leveraging Data Geometry to Mitigate CSM in Steganalysis

In operational scenarios, steganographers use sets of covers from various sensors and processing pipelines that differ significantly from those used by researchers to train steganalysis models. This leads to an inevitable performance gap when dealing with out-of-distribution covers, commonly referred to as Cover Source Mismatch (CSM). In this study, we consider the scenario where test images are processed using the same pipeline. However, knowledge regarding both the labels and the balance between cover and stego is missing. Our objective is to identify a training dataset that allows for maximum generalization to our target. By exploring a grid of processing pipelines fostering CSM, we discovered a geometrical metric based on the chordal distance between subspaces spanned by DCTr features, that exhibits high correlation with operational regret while being not affected by the cover-stego balance. Our contribution lies in the development of a strategy that enables the selection or derivation of customized training datasets, enhancing the overall generalization performance for a given target. Experi-mental validation highlights that our geometry-based optimization strategy outperforms traditional atomistic methods given reasonable assumptions. Additional resources are available at github.com/RonyAbecidan/LeveragingGeometrytoMitigateCSM.
The Influence of Library Utilization and Social Environment on the Level of Economic Literacy in Class XI IPS Students at MA Nurul Huda Sidoarjo

This study aims to determine the effect of library utilization on the level of economic literacy of students of class XI IPS at MA Nurul Huda Sidoarjo. The influence of the social environment on the level of economic literacy of students of class XI IPS at MA Nurul Huda Sidoarjo. The subjects of this study were 72 students of social science class at MA Nurul Huda Sidoarjo. Data collection in this study used a questionnaire by making the entire study population into a sample or saturated sample. The method used is quantitative descriptive. Data analysis uses multiple linear regression. Research results showed that the variable utilization of the library and social environment simultaneously has no significant effect on the level of economic literacy of students in class XI IPX MA Nurul Huda Sidoarjo. The magnitude of the effect of the use of variables X1 and X2 on the variable Y uses the coefficient of determination by looking at the SPSS model summary output display. Based on the results of the simultaneous significance test (f test) in the significance table, the value is 0.425 above the significance requirement of 0.05. Based on the basis of simultaneous test decision making in regression analysis, it can be concluded that the variable utilization of library (X1) and social environment (X2) does not significantly influence the level of economic literacy of students in class XI IPS MA Nurul Huda Sidoarjo.
THE FORMATION OF FUTURE PSYCHOLOGIST’S DIAGNOSTIC COMPETENCE

In the given article the formation problem of future psychologists’ diagnostic competence. Nowadays, in order to implement the increasingly complex tasks which face the new schools and teachers-psychologists at present period of time, it is necessary a complete comprehensive change of their upcoming professional work, i.e. the founding of diagnostic competence, professional and personal development of students. The degree of solving the issues of formation diagnostic competence which are given in the psychological and pedagogical sources is considered, as well as the content and structure the future psychologist teachers’ diagnostic competence is disclosed. The conducted theoretical and methodological review in psychological and pedagogical literature describes that the problem of the future teacher-psychologist’s diagnostic competence is a relevant topic., The following methods are used to reach the aim, they are: theoretical analysis of sources on psychology and pedagogics, modeling method, survey, experimental data’s qualitative and quantitative analysis. The conducted theoretical and methodological review in the studied by us psychological and pedagogical sources show that the formation problem of future teachers-psychologists’ diagnostic competence is believed as the topical i.e., relevant.
Unsupervised Multi-Scale Image Enhancement using Generative Deep Learning Approach

To produce super-resolution images, it is essential to eliminate the noise elements and give a clear noisefree output. To achieve this purpose multiscale image representation is found to be effective in many ways for its accuracy of correct feature extraction capacity. This denoising approach is integrated as a chosen enhancement tool in the form of an ensemble GAN model, and accordingly, the generator-discriminator training concept is transformed to adopt the approach as per the desired demands. In this research, a multiscale image approach is implemented using an ensemble GAN model with hybrid discriminator architecture. No one form of noise is "ideal" to eliminate while denoising with the proposed model. Instead, based on the properties of the data and the noise inherent in it, the proposed ensemble GAN can handle various sorts of noise.The technique optimises training through simultaneous generator and discriminator model updates, improving output quality, by using the least loss value for discriminator selection. Inception Score (IS) and Fréchet Inception Distance (FID) evaluations show that it outperforms pixel-based denoising, with an amazing accuracy of 99.91%.
Enhancing Holistic Education through AI-Integrated Learning Environments: A Comprehensive Framework

In our country, the youth play a pivotal role in bringing pride and accolades to our nation. However, it is essential to question the prevailing notion that everyone must aspire to become a doctor or an engineer. Instead, why not encourage them to pursue their passion for singing, dancing, or any other non-conventional path? by integrating embedded artificial intelligence and models into our educational system, we can revolutionize the way we nurture and support young talents. AI-driven platforms identify and nurture individual strengths, tailoring resources for creative pursuits. They analyze learning styles, interests, and aptitudes, providing a customized educational experience. Extracurriculars are crucial for talent development. AI organizes and ensures access to aligned opportunities. Smart scheduling balances academics and co-curriculars. Career counseling and mentorship, powered by AI, offer tailored guidance, boosting confidence and self-esteem. Feedback tools promote resilience and confidence. Interdisciplinary learning connects academic and creative interests, preparing students for the modern world. This well-rounded approach empowers youth to excel and contribute meaningfully. AI unlocks every student's potential, shaping a generation of accomplished individuals.
Deep Learning Based Fall Detection System

A number of organizations, both medical and non-medical, have adopted assistive technology, such as medical wireless wristbands and fall detection devices. Most research on fall detection using deep learning techniques are based on image data sets, which are not readily available. So, researchers have focused on making a deep learning-based classifier that takes numerical input of sensors data and perform effectively, so that medical costs can be reduced. Deep learning-based fall detection is an assistive device to detect fall particularly for elderly people which can significantly reduce the mortality rate from falls by providing a response as part of a medical system. This paper developed a deep learning methodology to increase classifier accuracy using numerical data for fall detection. With the help of Deep Learning-Artificial Neural Network (DL-ANN), accelerometer, gyroscope, and infrared dataset are feed into DL-ANN consisting of convolution layer, pooling layer, and fully connected layer containing hidden layers to improve the classification accuracy. The DL-ANN was implemented on Jupyter Notebook with the help of a Python library for this Artificial Neural Network approach. This research also employs framework such as NumPy, Pandas, Scikit-Learn, Matplotlib, and Tensor Flow libraries. Moreover, the accuracy of the DL-ANN classifier was tested on eight optimizers with the help of different Keras layers in sequential model. The adaptive moment estimation (ADAM) algorithm performed the best in terms of loss while training the model. Deep Learning-Artificial Neural Network method helped to improve the classification accuracy of the wearable fall detection devices by up to 97%. This was done by training the data with a lot of computing power. Also, if falls could be reliably detected using a deep learning-based classifier like ANN, which got a 97% accuracy, 96% precision, 97% recall, and 96% F1-score rate in this paper, it would pave the way for the development of a fall detection solution that would work for lifelong.
Gesture Recognition Machine Vision Video Calling Application Using YOLOv8

Gesture recognition pertains to a computer system’s capacity to identify and understand hand gestures, categorizing them into captions or text. This technology holds vast potential for applications in education, communication, and accessibility, especially for individuals with hearing disabilities. This research introduces a gesture recognition video calling application, enabling communication among individuals with and without speech and hearing abilities. To enhance accuracy, the application employs Darknet’s YOLOv8 for object detection. YOLOv8 is a robust and efficient system that excels in various computer vision tasks, such as object detection, instance segmentation, and image classification. The paper proposes a trained model for image classification, encompassing ten different hand gesture classes, which processes each video frame during the call, achieving a remarkable 98% accuracy with a low latency of 10ms. The application displays the classification results on top of the interface during the video call.
A Novel Weights-less Watermark Embedding Method for Neural Network Models

Deep learning-based Artificial Intelligence (AI) technology has been extensively used recently. AI model theft is a regular occurrence. As a result, many academics focus their efforts on safeguarding the Intellectual Property (IP) of trained Neural Network (NN) models. The majority of the most recent white-box setting watermark embedding methods rely on modifying model weights. Weights updated for the NN model during training must take into account the initial task as well as the embedding of watermarks. As a result, the accuracy of the initial task will be affected, necessitating more training time. This research proposes a novel weights-less watermark embedding method for deep neural networks to address this issue. Without actually embedding the watermark within the NN model weights, it uses a principle of code matching between the watermark and the weights. The proposed method requires less time than existing white-box setting watermark embedding methods, and the accuracy of the original task is not much diminished. Additionally, since the NN model weights are left alone, their statistical distribution will remain unchanged, giving the model increased resistance to watermark detection. The experiments in this paper demonstrate the effectiveness, efficiency, and robustness of our method.
Deep Learning-based Image Quality Assessment Metric for Quantifying Perceptual Distortions in Transmitted Images

An Image Quality Assessment (IQA) metric measures the quality degradation of an image and is used to optimize parameters of an image processing algorithm. The IQA score can be also an important indicator of the target downstream application performance. With the popularity of deep learning (DL)-based applications in resource constrained domains, most of the DL computations are outsourced to avail remotely located resources. The image data transmission for this purpose is susceptible to distortions due to the imperfect communication environment. The existing IQA metrics used to evaluate the quality of these images mainly rely on human judgment and do not account for the perceptual distortions that are responsible for the degradation in DL model performance. To address this issue, we propose a convolutional autoencoderbased IQA metric that compares images in low dimensional feature space and can be used to monitor image degradation occurred during data transmission. The simulation analysis shows that the proposed method introduces at best 0.13% error while on average 8% error compared to the application model accuracy. Importantly, the proposed IQA score coincides with the DL model performance on a downstream task and can be used to optimize the parameters of a communication system.
Spatiotemporal Mammography-based Deep Learning Model for Improved Breast Cancer Risk Prediction

Breast cancer is one of the most prevalent cancers among women. It is the second leading cause of death in cancer-related deaths. Early detection and personalized risk assessment can reduce the mortality rate and improve survival rates. Classical risk prediction models which rely on traditional risk factors produce inconsistent results among the different populations. Thus, they are not routinely used in screening programs. Deep learning was proven to improve the results of breast cancer risk prediction. CNNs can detect risk cues from screening mammograms. However, the deep learning models utilize the spatial information of each screening mammogram independently. This study aims to further improve the risk prediction models by exploiting the spatiotemporal information in multiple screening time points. We implemented a Siamese neural network for spatiotemporal risk prediction and compared the results against CNN trained using two different time points (T1 and T2) independently. We tested our results on 191 cases, 61 of which were diagnosed with cancer. The Siamese model showed a superior AUC of 0.81 against 0.75 and 0.77 at T1 and T2 respectively. The Siamese network also exhibited higher accuracy and F1-score with values of 0.78 and 0.61 while CNNs have the same accuracy of 0.76 with an F1-score of 0.54 at T1, and 0.59 at T2. The results suggest that spatiotemporal risk prediction can be a more reliable risk assessment tool.
Assessment of Flash Flood Vulnerability Index in a tropical watershed region: a case study in Ciliwung Hulu watershed, Indonesia

Flash floods, an unpredicted swift climatological disaster, frequently occur in Indonesia. However, there are limited vulnerability assessments, especially in urban and vital regions such as Bogor District. The study aims to assess the vulnerability index of Ciliwung Hulu Watersheds as one of the most susceptible areas in the district. Flash flood vulnerability index (FFVI) is selected to be calculated as the indicator. Data were obtained from the official government offices and processed using the FFVI formula referring to the work of Nasiri et al., (2019) and Perka BNPB No. 2/ 2012 and then mapped using ArcGIS 10.3. The results and the maps show that the study area is categorized as highly to very highly vulnerable to flash flood disasters. The attained results help facilitate the governance interplay processes in building a more disaster-ready management plan and to construct a more resilient society.
Digital currencies and the Nigerian economy: Evidence from selected coins

This study emphasizes the implication of dynamic connection between digital currency and Nigerian economic growth rate by focusing attention on Bitcoin, Ethereum and Litecoin with respect to their returns and volatility from 2010Q4 to 2022Q3. As a way to have a robust estimation, we model our analysis using ARDL model and granger causality test. This model is rather useful to have both short and long run estimations. Importantly the study’s outcome conforms with the fundamentals. By findings from the study, the trend analysis suggests that the country’s exchange rate moves in line with digital currency activities while at the same time signifies some implication on the growth rate of the Nigerian economy. While lower returns for Bitcoin and Litecoin increase growth rate, the return for Ethereum rather move in the same direction as the growth rate. This indeed suggest that most Nigerians into digital currency activities often engage in portfolio diversification among available coins. The study further found that low volatility in the market will raise (significantly especially for Ethereum) growth rate of the economy while causal implication run from returns and volatilities of these coins to growth and exchange rates. Indeed, the findings have important policy implication for the Nigerian economy which suggests paying good attention to digital currency activities in the country and formulating necessary policies to improve it.
Barriers in Implementing Electronic Management During the COVID-19 Pandemic Crisis in Jordanian Universities: A Comparative Study Between Public and Private Institutions

This study aimed to explore the barriers to the implementation of electronic management during the COVID-19 pandemic crisis in Jordanian universities. It also sought to determine whether there were statistically significant differences in the perceptions of administrative staff in Jordanian universities regarding these barriers, based on the type of institution (public or private). A questionnaire was developed for field survey purposes and to gather the opinions of respondents. A simple random sample consisting of 700 administrative staff members was selected. The study's findings revealed that the extent of barriers to the implementation of electronic management during the COVID-19 pandemic in Jordanian universities was significant. However, no statistically significant differences were observed in the barriers to electronic management implementation between public and private universities Keywords: Barriers, Electronic Management, COVID-19 Pandemic, Jordanian Universities DOI: 10.7176/IKM/13-7-03 Publication date: December 31 st 2023
Perancangan Desain Packaging Untuk Aksesoris Pernikahan Wedding Planner Dodok Lawang Banyumanik Semarang

Product packaging has a very important role in attracting consumers, making consumers select the goods and serving as a tool for brand promotion. Using images, colors, and text, graphic designers can apply design principles to produce visually appealing packaging, highlight the benefits of a product, and give it a unique personality. Dodok Lawang runs a business in the form of services, namely making dowries, gifts, ring boxes and gift boxes. In this research, we developed packaging making/with the aim of renewing old packaging so that it appears more attractive. The form of packaging for the wedding planner is in the form of boxes and paper bags, as well as leaflets with a simple but still elegant design concept. validation from media experts and material expert have a result that the product is valid to be used as a protecting/wrapping of product properly. From the user's side, this design is effective because the information and visuals acquired from the packaging design are better understood by the user.&#x0D; &#x0D;