Reviewerly/exHarmony huggingface dataset:
User Interface Design of Jaipong Dance Applications for Elementary School using the User-Centered Design (UCD) Method

The Jaipong dance is one of the traditional Indonesian dances, the nation's cultural heritage originating from West Java.Introducing dance to elementary school-level children is one way to maintain and preserve regional culture.However, the limited learning time causes the material to be delivered less in-depth, so students feel bored, lose motivation, and get some critical information from the material.This study aims to build a mobile-based learning application for Jaipong dance learning activities at the elementary school level using the User-Centered Design (UCD) method.The application evaluation involved fifteen public elementary school students in Ciamis, West Java.Participants were asked to answer a quantitative survey using the Quality Use Integrated Measurement (QUIM) instrument to discover their experience using the application during Jaipong dance lessons.The test results get an average score of 91%, included in the "excellent" category.These results were strengthened by the user's understanding of the Jaipong dance.As many as 86.7% of users who use the Jaipong dance learning application understand the material well.In contrast, only 33.3% of YouTube users understand the material well.This paper shows that the UCD method contributes to the design process according to the needs and characteristics of the user to design the user interface of the Jaipong dance learning application.
Giant Finger: Visuo-proprioceptive congruent virtual legs for flying actions in virtual reality

An immersive experience in virtual reality occurs when visual sense accompanies congruent somatosensation. However, as users should not fall down and physical abilities vary among individuals, existing methods may exhibit limited or predefined motion flexibility. To address this issue, we present Giant Finger, a flying action method that mimics human gait using two big virtual fingers. We confirmed its ownership via proprioceptive drift and questionnaires, and found that it performed better than existing methods in tasks such as flying and kicking.
A Virtual Farm Tour for Public Education about Dairy Industry

In this paper, we present an immersive virtual farm simulation developed to provide realistic on-farm experiences to the public. Users could visit the virtual farm, walk through various sites where dairy cows are raised, and learn how the dairy products are produced through the virtual experience. Public users' responses about the virtual experience were collected in various measures, e.g., user experience and learning efficacy, via showcases at local libraries. We present preliminary results regarding the potential of the simulation as an effective agricultural education, and discuss our future plans.
Simulants: Synthetic Clinical Trial Data via Subject-Level Privacy-Preserving Synthesis.

Clinical trials capture high-quality data for millions of patients each year, yet these data are largely unavailable for research beyond the scope of any individual trial due to a combination of regulatory, intellectual property, and patient privacy barriers. Synthetic clinical trial data that captures the analytical properties of the source data, could provide significant value for research and drug development by making insights widely available while protecting the privacy of the participants. We present a method "Simulants" for generating research-grade synthetic clinical trial data from a real data source. We compared the fidelity and privacy preservation performance of Simulants to the state-of-the-art deep learning synthesizers and found that Simulants had superior performance when applied to clinical trial data as assessed both by established metrics and when considering critical clinical features. We also demonstrate how Simulants' privacy settings may be configured to conform to specific privacy policies governing data sharing.
Contemporary Approaches and Challenges in Education: An Implication in the Use of Mobile Technologies in the New normal

Globally, COVID-19 pandemic which spread worldwide, affecting many aspects of life, does not excepted the educational industry. Hence, teachers are required to prepare for education necessities, decisions, choices, and adaptations to meet student expectations, teacher education requirements, and as well the conditions under which universities and schools operates. The substantive situation thus raises the importance of technology integration in educational system for effective teaching &amp; learning process. Notably before now educational provisions at all levels have been bedevilled with numerous challenges which demands new approaches especially at this post-covid period. It has been envisaged that, the use of mobile technology in this new-normal will go a long way to reduces inherent challenges and improve on the instructional process for students and this is so because the 21st Century students are digital native. The mode anchored on the generic umbrella of Information and Communication Technology (ICT), which is helpful in making the process of teaching &amp; learning more meaningful. It was on this premises that, the paper examined some typologies of mobile technologies devices and their instructional uses, such as: Smart phones, E-book reader, Personal Digital Assistance (PDA) among others. Similarly, challenges associated with their uses were identified and they include: Inadequacy of facilities and resources; Swift switch to online learning; Gap in teachers’ skills &amp; competency in handling collaborative learning; Poor infrastructure; Poor internet connectivity etc. The Paper equally identified measures to improve on the uses of mobile technologies for instructional process. Finally, conclusion and recommendations were highlighted and such recommendations made are: Need for adequate funding of education, Provision of adequate infrastructural facilities, provides induction training to improve on skill &amp; competency gap in teachers and ensure good connectivity &amp; provision of the needed internet sources within the school system
Competition for gradient-free tuning of large language models: approaches, results, current challenges and future directions

This perspective presents a brief overview of the background of Gradient-free tuning for large language models competition, the championship scheme, as well as the challenges and future directions.
SEBARAN KEKERINGAN HIDROLOGI BERDASARKAN DEBIT ALIRAN DI KABUPATEN BIMA

A phenomenon that often occurs in Indonesia which is also an annual disaster that hit several areas is drought. Drought is a condition in which an area, land, or community experiences a shortage of water so that it cannot meet their needs. Drought can be caused because an area does not experience rain or is dry for a long period of time or the rainfall is below normal, so that the water content in the soil is reduced or even non-existent. The areas to be analyzed are the Hidirasa watershed, the Rontu watershed, and the Jangka watershed in Bima Regency, West Nusa Tenggara. The analysis drought aims to determine the deficit, drought duration, drought criteria based on deficits and surpluses that occur by analogy to Oldeman's criteria, hydrological drought index analysis, and hydrological drought distribution maps. In this study, data were used for the Tawali watershed in the Hidirasa watershed, the Sari watershed for the Jangka and the Kumbe water for the Rontu watershed to determine the Q50 and Q80 thresholds using the method Flow Duration Curve (FDC) to obtain hydrological drought characteristics. In the analysis of hydrological drought in several watersheds in Bima Regency, the results show that the maximum deficit in the Hidirasa, Term and Rontu watersheds was 1.09 m³/s, 0.14 m³/s, 0.49 m³/s which occurred in 2001, 1995, and 2001. From the deficit, it was found that the average drought period occurred 8-9 months from May to December and even some until January which indicated that the drought criteria were in zone 2 according to Oldeman. The largest hydrological drought index is the Hidirasa watershed, the Jangka Watershed and the Rontu watershed, respectively, -0.45, -1.00, -1.00, with a very strong drought category.
Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

The amount of 3D data stored and transmitted in the Internet of Medical Things (IoMT) is increasing, making protecting these medical data increasingly prominent. However, there are relatively few researches on 3D data watermarking. Moreover, due to the particularity of medical data, strict data quality should be considered while protecting data security. To solve the problem, in the field of medical volume data, we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform (PCT and 3D-DCT). Each slice of the volume data was transformed by PCT to obtain feature row vector, and then the reshaped three-dimensional feature matrix was transformed by 3D-DCT. Based on the contour information of the volume data and the detail information of the inner slice, the visual feature vector was obtained by applying the perceptual hash. In addition, the watermark was encrypted by a multi-sensitive initial value Sine and Piecewise linear chaotic Mapping (SPM) system, and embedded as a zero watermark. The key was stored in a third party. Under the same experimental conditions, when the volume data is rotated by 80 degrees, cut 25% along the Z axis, and the JPEG compression quality is 1%, the Normalized Correlation Coefficient (NC) of the extracted watermark is 0.80, 0.89, and 1.00 respectively, which are significantly higher than the comparison algorithm.
Sketch Less Face Image Retrieval: A New Challenge

In some specific scenarios, face sketch was used to identify a person. However, drawing a complete face sketch often needs skills and takes time, which hinder its widespread applicability in the practice. In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig. 1). Firstly, we developed a method to generate the data of sketch with drawing process, and opened such dataset; Secondly, we proposed a two-stage method as the baseline for SLFIR that (1) a triplet network, was first adopt to learn the joint embedding space shared between the complete sketch and its target face photo; (2) regarding the sketch drawing episode as a sequence, we designed a LSTM module to optimize the representation of the incomplete face sketch. Experiments indicate that the new framework can finish the retrieval using a partial or poor drawing sketch. (https://github.com/ddw2AIGROUP2CQUPT/SLFIR)
Review On Prevention of Data Leakage in Cloud Server by Utilizing Watermarking and Double Encryption Techniques

In today's world, the exchange of multimedia data has become an essential part of people's everyday routines, as they utilize various systems, services, and applications. Real-world cloud storage systems frequently experience data leaks, making secure data transmission and copyright protection of multimedia information difficult. To tackle the problem of safeguarding copyright, the implementation of digital watermarking has been proposed. This technique involves adding hidden information, such as confidential data, to digital media to authenticate its ownership. Encryption techniques are also employed to secure the data and prevent unauthorized access. The proposed approach suggests using both watermarking and proxy re-encryption techniques to efficiently exchange multimedia content. To secure confidential information, the process involves encrypting a private key with a specific encryption method that requires a key. Next, this encrypted key is combined with the user's private key and inserted into an image using the Least Significant Bit (LSB) technique. After the confidential information has been incorporated into the image, it can be encoded using the ECC Encryption technique. However, copyright protection remains a challenging and complex issue in the face of increasing internet and digital technology usage. The resulting image can be encoded using the ECC Encryption method. However, copyright protection remains a challenging and complex issue in the face of increasing internet and digital technology usage.
Identifying Factors Affecting the Relationship between Department and Graduation Level of Informatics Engineering Students using Apriori Algorithm: A Case Study at Pamulang University

To cultivate the next generation of leaders, it is essential for teenagers to receive a high level of education. Typically, this education is acquired through attending lectures that produce a high GPA, which is considered a valuable achievement for students. The level of graduation achieved within the appropriate timeframe can also impact campus accreditation, especially for engineering students, particularly those pursuing informatics engineering. To improve graduation rates, it is necessary to use data mining to identify patterns and trends among graduating students. The a priori algorithm was used in this study to analyze school majors, the length of study, and student graduation rates. Through this algorithm, it was possible to identify one or more rules that can be used as benchmarks for predicting graduation rates. Based on the results and discussions of 30 students, the most effective rule for predicting graduation is a combination of the student's previous school major, a study period of 4 years or less, a GPA of 2.51-3.00, and passing all courses on time. Using the a priori algorithm, the rule was found to have a confidence value of 16 and a support value of 71.4%. This indicates that the rule is a reliable predictor of student graduation rates.
A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological Image

Pathological primary tumor (pT) stage focuses on the infiltration degree of the primary tumor to surrounding tissues, which relates to the prognosis and treatment choices. The pT staging relies on the field-of-views from multiple magnifications in the gigapixel images, which makes pixel-level annotation difficult. Therefore, this task is usually formulated as a weakly supervised whole slide image (WSI) classification task with the slide-level label. Existing weakly-supervised classification methods mainly follow the multiple instance learning paradigm, which takes the patches from single magnification as the instances and extracts their morphological features independently. However, they cannot progressively represent the contextual information from multiple magnifications, which is critical for pT staging. Therefore, we propose a structure-aware hierarchical graph-based multi-instance learning framework (SGMF) inspired by the diagnostic process of pathologists. Specifically, a novel graph-based instance organization method is proposed, namely structure-aware hierarchical graph (SAHG), to represent the WSI. Based on that, we design a novel hierarchical attention-based graph representation (HAGR) network to capture the critical patterns for pT staging by learning cross-scale spatial features. Finally, the top nodes of SAHG are aggregated by a global attention layer for bag-level representation. Extensive studies on three large-scale multi-center pT staging datasets with two different cancer types demonstrate the effectiveness of SGMF, which outperforms state-of-the-art up to 5.6% in the F1 score.
Polyglot Modal Models through Lingua Franca

Complex software systems often feature distinct modes of operation, each designed to handle a particular scenario that may require the system to respond in a certain way. Breaking down system behavior into mutually exclusive modes and discrete transitions between modes is a commonly used strategy to reduce implementation complexity and promote code readability.
Measuring and estimating Key Quality Indicators in Cloud Gaming services

The gaming industry has proposed the concept of Cloud Gaming (CG), a paradigm that enhances the gaming experience on reduced hardware devices. However, this paradigm puts a lot of pressure on the communication links that connect the user to the cloud. As a result, the service experience becomes highly dependent on network connectivity. In this context, the present work proposes a framework for measuring and estimating the most important E2E (end-to-end) metrics of the CG service, namely Key Quality Indicators (KQIs). Therefore, different machine learning (ML) techniques are evaluated to predict KQIs related to the CG user experience. For this purpose, the most important KQIs of the service, such as input lag, freezes or perceived video frame rate, are collected in a real network deployment. The results show that ML techniques can be used to estimate these indicators solely from network-related metrics. This is seen as a valuable asset for the delivery of CG services over cellular networks, even without access to the user’s device, as it is expected for telecom operators.
Covid-19 Patient Count Prediction Using LSTM

In Wuhan, China,the COVID-19 pandemic first emerged, in December 2019, and it has reached to over nearly 200 countries in a matter of weeks.Every country that was affected by the disease began taking the necessary precautions to avert it from spreading, give sick individuals the best medical care possible, and take preventative measures to restrict the spread.The fact that those with modest symptoms and others who don't experience any negative effects might distribute COVID-19 is the cause of its extensive distribution.Individuals' pre-existing medical conditions, like a cardiovascular problem, high blood pressure, diabetes, weakened immune system, or age-related frailty, are also linked to critical cases and fatality rates.The time series prediction approach was used by the forecasting model.A dataset repository, such as the UCI repository, serves as the input for the Pakistan Covid-19 dataset.The pre-processing procedure must then be carried out.The data separation must then be put into practise.We must divide the data into test and train groups in this phase.The feature selection approach must then be put into practise.The LSTM algorithm must then be used to forecast the covid-19 patient population in Pakistan.Last but not least, the research findings indicate that the MAPE (Mean absolute Percentage Error).
An Adaptive Plug-and-Play Network for Few-Shot Learning

Few-shot learning (FSL) requires a model to classify new samples after learning from only a few samples. While remarkable results are achieved in existing methods, the performance of embedding and metrics determines the upper limit of classification accuracy in FSL. The bottleneck is that deep networks and complex metrics tend to induce overfitting in FSL, making it difficult to further improve the performance. Towards this, we propose plug-and-play model-adaptive resizer (MAR) and adaptive similarity metric (ASM) without any other losses. MAR retains high-resolution details to alleviate the overfitting problem caused by data scarcity, and ASM decouples the relationship between different metrics and then fuses them into an advanced one. Extensive experiments show that the proposed method could boost existing methods on two standard dataset and a fine-grained datasets, and achieve state-of-the-art results on mini-ImageNet and tiered-ImageNet.
2DSBG: A 2d Semi Bi-Gaussian Filter Adapted for Adjacent and Multi-Scale Line Feature Detection

Existing filtering techniques fail to precisely detect adjacent line features in multi-scale applications. In this paper, a new filter composed of a bi-Gaussian and a semi-Gaussian kernel is proposed, capable of highlighting complex linear structures such as ridges and valleys of different widths, with noise robustness. Experiments have been performed on a set of both synthetic and real images containing adjacent line features. The obtained results show the performance of the new technique in comparison to the main existing filtering methods.
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for State Estimation

Estimation of the state of a discrete-time state-space model from noisy measurements is a crucial aspect of signal processing. The extended Kalman filter (EKF) is widely used as a low-complexity solution based on a state evolution and measurement model of the state-space model. However, obtaining precise information about these models can be difficult in practice, and model mismatch greatly reduces state estimation accuracy. In this paper, we introduce Split-KalmanNet, a robust EKF algorithm that utilizes the power of deep learning for state estimation. Split-KalmanNet is inspired by the recent KalmanNet and calculates the Kalman gain using the Jacobian matrix of a measurement function and two recurrent neural networks (RNNs) with a split structure. The RNNs independently learn the covariance matrices of the prior state estimate and the innovation from data. The proposed split structure in calculating the Kalman gain allows for compensation of both state and measurement model mismatch effects. Numerical simulations show that Split-KalmanNet outperforms traditional EKF and the state-of-the-art KalmanNet algorithm in various scenarios of model mismatch.
The palm tree optimization: Algorithm and applications

A novel metaheuristic algorithm has been presented based on the physical significance of palm tree leaves and petioles, which can themselves water and fertilize with their unique architecture. Palm tree leaves collect almost all the raindrops that fall on the tree, which drags the nutrient-rich dropping of crawlers and birds that inhabit it and funnel them back to the palm tree’s roots. The proposed Palm Tree Optimization (PTO) algorithm is based on two main stages of rainwater before it reaches the trunk. Stage one is that the rainwater drops search for petioles in the local search space of a particular leaf, and stage two involves that the rainwater drops after reaching the petioles search for trunk to funnel back to the root along with nutrients. The performance of PTO in searching for global optima is tested on 33 Standard Benchmark Functions (SBF), 29 constrained optimization problems from IEEE-CEC2017 and real-world optimization problems from IEEE-CEC2011 competition especially for testing the evolutionary algorithms. Mathematical benchmark functions are classified into six groups as unimodal, multimodal, plate &amp; valley-shaped, steep ridges, hybrid functions and composition functions which are used to check the exploration and exploitation capabilities of the algorithm. The experimental results prove the effectiveness of the proposed algorithm with better search ability over different classes of benchmark functions and real-world applications.