Reviewerly/exHarmony huggingface dataset:
Pengembangan E-Modul Discon Berbasis Android (E-Modul Disroid) Materi Bunyi bagi Siswa Sekolah Dasar

The online learning process is not effective because the media used in the science learning process is limited to books and Whatapps Group applications. This study aims to develop learning media (module) based on android on sound material for students IV SD. This research is a research and Development with the ADDIE model, which consists of five stages, namely, Analyze, Design, Development, Implement, and Evaluation. The population in this study are fourth-grade students of SDN 8 Mambo. The research instrument used a media validation questionnaire sheet, material validation, and user questionnaire (teachers and students). Data analysis using descriptive analysis techniques. Based on the results of the data analysis obtained, the validity value of the material from the aspect of self-instruction of 87.6% category is very valid, the self-contained aspect of 91.7% category is very valid, stand-alone aspect of 91.7% category is very valid, adaptive aspect of 83.3% category is valid, and user-friendly aspect of 100% category is very valid. The validity of the media on indicators of system quality and information quality with a value of 100% is a very valid category. The user practicality sheet obtained a value of 77.9% with the practical type. So it can be concluded that the Android-based e-module (E-module DISROID) developed in the criteria is very valid and very practical so that this module can be used in science learning, especially for practicum activities in elementary school Class IV.
Development and Effectiveness of a University Student Coaching Program Using Metaverse Space

This study aims to develop and implement a coaching program in the metaverse space to address issues faced by university students during their life stage and verify their effectiveness. The overall program consists of four sessions: "Opening," "Metaverse-based Coaching Clinic (2 sessions)," and "Coaching Clinic Reflection and Feedback." A total of 20 participants were selected through S University's extracurricular program system. To assess the effectiveness of the program, surveys, as well as focus group interviews (FGIs) were conducted. Using SPSS 26.0, significant differences were observed in the participants' career decision self-efficacy and resilience. The FGIs were conducted with a total of four participants divided into two groups, with each group lasting for one hour. The analysis revealed key phases, including the introductory phase comprising participants' motivation and expectations, the main phases consisting of program operation, coaching perception and outcomes, metaverse environment, and metaverse coaching perception and outcomes, as well as the concluding phase in which recommendations are suggested for subsequent programs. The study confirmed the significant impact of coaching in the metaverse space and highlighted the maximization of coaching effectiveness through mutual anonymity facilitated by avatars. This research holds significance as an exploratory study that practically verifies the potential expansion of the metaverse environment in the coaching field.
Audio Encryption Framework Using the Laplace Transformation

Digital information, especially multimedia and its applications, has grown exponentially in recent years. It is important to strengthen sophisticated encryption algorithms due to the security needs of these innovative systems. The security of real-time audio applications is ensured in the present study through a framework for encryption. The design framework protects the confidentiality and integrity of voice communications by encrypting audio applications. A modern method of securing communication and protecting data is cryptography. Using cryptography is one of the most important techniques for protecting data and ensuring the security of messaging. The main purpose of this paper is to present a novel encryption scheme that can be used in real-time audio applications. We encrypt the sound using a combination of an infinite series of hyperbolic functions and the Laplace transform, and then decrypt it using the inverse Laplace transform. The modular arithmetic rules are used to generate the key for the coefficients acquired from the transformation. There is no loss of data or noise in the decryption sound. We also put several sound examples to the test
Design Decoupling Control and Wavelet Type-2 Fuzzy Brain Imitated Neural Network (WT2FBINN) for rotational actuator (TORA) System

In this article, a new wavelet type-2 fuzzy brain imitated neural network (WT2FBINN) is presented for nonlinear system decoupling control. A sliding mode control combined with a decoupling control is illustrated. The control system comprises of a proposed WT2FBINN and a robust controller. The WT2FBINN is used as the main controller to approach an ideal controller to achieve desired control performance, and the robust controller is used to eliminate the approximation error to achieve system stability. The WT2FBINN contains a wavelet type-2 fuzzy system and a brain imitated neural network. The parameters of the proposed WT2FBINN can be updated by the gradient descent method. Then, the WIT2FBINN is applied to control underactuated systems, such as a rotational actuator (TORA) system. Finally, simulation results have validated the effectiveness of the proposed control approach. A comparison to a recently developed method is useful for better illustrating the benefits of the proposed method.
MSLS: Meta-graph Search with Learnable Supernet for Heterogeneous Graph Neural Networks

In recent years, heterogeneous graph neural networks (HGNNs) have achieved excellent performance. The efficient HGNNs consist of meta-graphs and aggregation operations. Since manually designing meta-graph is an expert-dependent and time-consuming process, the performance of HGNNs is limited. To address this challenge, the differentiable meta-graph search has been proposed to obtain promising meta-graph automatically. However, the previous differentiable meta-graph search constructs the supernet without learnable aggregation operations, which limits the semantics extracting ability of HGNNs with automatically designed meta-graph for downstream tasks. To solve this problem, we propose the Meta-graph Search with Learnable Supernet for Heterogeneous Graph Neural Networks (MSLS). Specifically, to obtain better performance HGNNs, MSLS constructs a supernet with learnable aggregation operations based on the meta-graphs. MSLS adopts decoupling training to train the learnable supernet and obtains the optimal meta-graph with learnable aggregation operations using a constrained evolution strategy. Extensive experiments show that our method (MSLS) achieves the best performance in different tasks.
Privacy-Preserving OLAP via Modeling and Analysis of Query Workloads: Innovative Theories and Theorems

This paper proposes innovative theories and theorems in the context of a state-of-the-art paper that computes privacy-preserving OLAP cubes via modeling and analyzing query workloads. The work contributes to actual literature by devising a solid theoretical framework that can be used for future optimization opportunities.
Early ICU Mortality Prediction with Deep Federated Learning: A Real-World Scenario

The generation of large amounts of healthcare data has motivated the use of Machine Learning (ML) to train robust models for clinical tasks. However, limitations of local datasets and restrictions on sharing patient data impede the use of traditional ML workflows. Consequently, Federated Learning (FL) has emerged as a potential solution for training ML models among multiple healthcare centers. In this study, we focus on the binary classification task of early ICU mortality prediction using Multivariate Time Series data and a deep neural network architecture. We evaluate the performance of two FL algorithms (FedAvg and FedProx) on this task, utilizing a real world multi-center benchmark database. Our results show that FL models outperform local ML models in a realistic scenario with non-identically distributed data, thus indicating that FL is a promising solution for analogous problems within the healthcare domain. Nevertheless, in this experimental scenario, they do not approximate the ideal performance of a centralized ML model.
LearnedSort as a learning-augmented SampleSort: Analysis and Parallelization

This work analyzes and parallelizes LearnedSort, the novel algorithm that sorts using machine learning models based on the cumulative distribution function. LearnedSort is analyzed under the lens of algorithms with predictions, and it is argued that LearnedSort is a learning-augmented SampleSort. A parallel LearnedSort algorithm is developed combining LearnedSort with the state-of-the-art SampleSort implementation, IPS4o. Benchmarks on synthetic and real-world datasets demonstrate improved parallel performance for parallel LearnedSort compared to IPS4o and other sorting algorithms.
Privacy-Preserving Redaction of Diagnosis Data through Source Code Analysis

Protecting sensitive information in diagnostic data such as logs, is a critical concern in the industrial software diagnosis and debugging process. While there are many tools developed to automatically redact the logs for identifying and removing sensitive information, they have severe limitations which can cause either over redaction and loss of critical diagnostic information (false positives), or disclosure of sensitive information (false negatives), or both. To address the problem, in this paper, we argue for a source code analysis approach for log redaction. To identify a log message containing sensitive information, our method locates the corresponding log statement in the source code with logger code augmentation, and checks if the log statement outputs data from sensitive sources by using the data flow graph built from the source code. Appropriate redaction rules are further applied depending on the sensitiveness of the data sources to preserve the privacy information in the logs. We conducted experimental evaluation and comparison with other popular baselines. The results demonstrate that our approach can significantly improve the detection precision of the sensitive information and reduce both false positives and negatives.
Data Driven Dimensionality Reduction to Improve Modeling Performance✱

In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer the features for tasks such as machine learning, instead, we explore dimensionality reduction (DR) as a data-driven approach for engineering these low-dimensional representations. Through a careful examination of available feature selection and feature extraction techniques, we propose a new class named feature clustering. These new methods could utilize different forms of clustering to help evaluate the relative importance of features and take on properties different from the well-known DR algorithms. To evaluate these algorithms, we develop a parallel computing framework that optimizes their hyperparameters on a sample of application datasets. This framework harnesses the parallel computing power to examine a large number of parameter combinations and enables hyperparameter tuning and model tuning purely based on observed performance. This optimization framework provides mechanism for users to control computational cost and is able to examine many parameter choices in seconds. On a set of building energy data where the key features are known based on domain knowledge, the optimized DR algorithms indeed identify the expected main drivers of building electricity usage: outdoor temperature and solar radiance. This shows the automated optimization procedure is able to find known features. In terms of modeling accuracy, a distance correlation-based feature clustering method outperforms other DR algorithms including the well-known KPCA, LLE, and UMAP on two different tests.
Decoupled Graph Neural Architecture Search with Variable Propagation Operation and Appropriate Depth

To alleviate the over-smoothing problem caused by deep graph neural networks, decoupled graph neural networks (DGNNs) are proposed. DGNNs decouple the graph neural network into two atomic operations, the propagation (P) operation and the transformation (T) operation. Since manually designing the architecture of DGNNs is a time-consuming and expert-dependent process, the DF-GNAS method is designed, which can automatically construct the architecture of DGNNs with fixed propagation operation and deep layers. The propagation operation is a key process for DGNNs to aggregate graph structure information. However, DF-GNAS automatically designs DGNN architecture using fixed propagation operation for different graph structures will cause performance loss. Meanwhile, DF-GNAS designs deep DGNNs for graphs with simple distributions, which may lead to overfitting problems. To solve the above challenges, we propose the Decoupled Graph Neural Architecture Search with Variable Propagation Operation and Appropriate Depth (DGNAS-PD) method. In DGNAS-PD, we design a DGNN operation space with variable efficient propagation operations in order to better aggregate information on different graph structures. We build an effective genetic search strategy to adaptively design appropriate DGNN depths instead of deep DGNNs for the graph with simple distributions in DGNAS-PD. The experiments on five real-world graphs show that DGNAS-PD outperforms state-of-art baseline methods.
Detection of moving objects in multi-complex environments using selective attention networks (SANet)

Object detection studies aim to solve safety problems at industrial sites; however, improving object detection performance and ensuring real-time capabilities simultaneously remains challenging in multi-complex industrial environments. This paper proposes an unconditionally protected detector to address this problem. The detector's backbone network uses a residual block with a bottleneck structure to reduce computation and enhance real-time performance. Additionally, a selective attention network enhances object detection by extracting important features based on the morphological characteristics of the feature map in the neck structure. The method includes a whole-body estimation to locate individuals obstructed by obstacles, thereby preventing collisions with vehicles and avoiding hazardous situations in restricted areas. The proposed method enables real-time object detection and risk assessment for nearby objects, enhancing safety for industrial vehicle drivers and workers. Moreover, it can lead to further improvements in object detection performance in research fields such as autonomous driving and AI CCTV utilizing cameras.
Analysis of Optimum 3-Dimensional Array and Fast Data Movement for Efficient Memory Computation in Convolutional Neural Network Models

CNN-based inference engine’s performance and efficiency always depend on the computational and dataflow-control complexity. Instead of considering a 2-dimensional (2D) feature array for processing, a 3D array of features/weights would improve the dataflow movement & memory computation. The optimum 8 × 8 × 32 3D-feature array size was chosen based on the factor of on-chip memory requirement, data reuse, and PE utilization. Using the optimum 8 × 8 × 32 feature array, seven different combinations of data-flow scheduling strategies were analyzed by varying row, column, and depth-wise parameters on the workload model using a MATLAB environment. From the analysis, strategy-V (depth-wise parallel & row/column-wise sequence) is found to be the best with a 4 × 8 processor array. Compared to the state-of-the-art processor strategy, strategy-V achieves the data transfer rate (off-chip to on-chip) and on-chip memory requirement of 3.3 times (higher) and 16 times (lesser) with a small overhead of processor cost.
A Simple Hybrid Local Search Algorithm for Solving Optimization Problems

Optimization in engineering is an important domain of operations research that gains a lot of attention nowadays. Optimization may be constrained or unconstrained; single objective or multi-objective. With the extent of computing power available today, optimization algorithms are capable of handling several constraints and more variables. This paper proposes a new, simple hybrid local search metaheuristic algorithm for solving single objective, un-constrained problems in the optimization domain. The new algorithm is a hybrid one that generates the initial population randomly and, iteratively move towards optimal/near-optimal solutions. It uses one tuning parameter and a single random number. The performance is analyzed using ninety-three benchmark functions including the 100-digit challenge test suite of IEEE Congress on Evolutionary Computation (CEC2019). The number of dimensions varies from one to 100. The results obtained are compared with a few efficient algorithms including Sine Cosine Algorithm and Arithmetic Optimization Algorithm. The analyses show that the newly proposed hybrid local search algorithm is effective and competitive.
Forecast of Movie Sentiment Based on Multi Label Text Classification on Rotten Tomatoes Using Multiple Machine and Deep Learning Technique

Intent of the paper is to investigate a comparison of different multi-classification models using machine learning and deep learning methodologies to categorize the sentiment of sentences from the rotten tomato’s movie review dataset. In order to achieve best performance three machine learning and three deep learning algorithms were estimated: logistic regression, linear support vector classifier, and Multinomial Naive Bayes (MNB), Recurrent Neural Network (RNN)/LSTM, Convolutional Neural Network (CNN) and CNN + Gated Recurrent unit network (GRU). In this work, it is observed that linear support vector classifier shows significant improvement among all other machine learning algorithms. Moreover, when deep leaning algorithms were included in the model, one of the deep leaning algorithms, CNN provides highest accuracy of 80% among all the algorithms. After optimizing the fitted model, new text data are considered for testing of correct predictive model and prediction of new text data. Optimized predicted model, i.e., CNN seems to give correct predictions for new test data. Hence, CNN model supposed to be used as one of the most suitable models for sentiment analysis of Rotten Tomatoes Movie Reviews since there is improvement of multi-class classification accuracy.
Generation Z’s Satisfaction with Artificial Intelligence Voice Enabled Digital Assistants

Artificial Intelligence voice enabled digital assistants, such as Alexa, Siri and Google assistants are increasingly used by Generation Z’s Day to day life. This study attempts to study Gen Z satisfaction level of these new age voice enabled digital assistants and its determinants. 235 valid responses were collected using online survey. The results show usefulness is the most important determinants for using digital assistants, which is followed by user’s competency. The research shows that privacy perception is negatively related to satisfaction.
An Ensemble Approach to Hostility Detection in Hindi Tweets

In today’s world, social media platforms are prevalent in all walks of life. However, the content on these platforms is often created with hostile intent. With the increase of such hostile content in social media, there has arisen a need to effectively detect such posts so as take appropriate measures against them. Hence, extensive studies have been performed in this field for the English language, however, similar work is relatively sparse in the case of Indian languages like Hindi. The work done here introduces a transfer learning-based approach with an ensemble model on top for identifying and flagging posts of hostile intent on social media platforms written in the Hindi Devanagari script. For this purpose, we have leveraged the pre-trained Fasttext model trained on Hindi text. Our model achieves F1-scores of 76% on the Defamation class, 80% on the Fake class, 69% on the Hate class and 79% on the Offensive class on the provided test set.
False Positive Reduction in Mammographic Mass Detection

A mortal disease affecting women globally is breast cancer. The survival rate rises in the matter of breast cancer when the presence of mass is identified ahead of time through a mammogram. The mass is obscured in dense breast tissues and hence the sensitivity is limited in the case of mammography. A computer aided diagnosis (CAD) system helps in overcoming the sensitivity issue in mammography but in turn the system is prone to many false positives. The proposed work incorporates the development of automated density-specific models for false positive reduction. A feature-classifier combination that performs the false positive reduction efficiently in each expert model has been identified. Normal and abnormal mammograms of all the four density types from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database have been employed in this work, resulting in the classification accuracy of 96%, 80%, 76% and 88% and false positive rates (FPR) of 0, 0.26, 0.25 and 0.12 for each density-specific mass detection models respectively.
Legal Challenges of Cryptocurrency

Cryptocurrencies are one type of digital currencies that have been widely used in recent times. The use of these currencies in commercial transactions raises many legal challenges. This study aims to address several challenges including determining the nature of the virtual currency, whether it is a subject of ownership and taxation, and the legal regulation of this currency. Moreover, using cryptocurrencies in money laundering and terrorism financing leads to difficulty in discovering these crimes. This article concludes that the legality and legal nature of cryptocurrencies are still controversial. Some legislation approves using cryptocurrencies as a means of payment without considering them as money. Some criminal legislation includes these currencies within the concept of money obtained from the primary crime. It also requires virtual asset service providers to combat money laundering and terrorist financing.
The Impact of Digital Transformation on the Financial Performance of Jordan Media Institute (Alrai)

The study aimed to demonstrate the impact of digital transformation on the financial performance of the Jordan Media Institute (Al-Rai) in terms of Financial leverage ratios and liquidity ratios. The study used a descriptive analytical methodology. The importance of this study lies in the fact that it is one of the few studies that investigated the impact of digital transformation, measured by the rate of growth in the number of digital subscribers, on the financial performance of Al-Rai newspaper, which is one of the most prominent Jordanian newspapers. It is expected to contribute to opening new horizons for researchers and academics in various sectors. The study concluded that there is a statistically significant positive effect of the digital transformation on the liquidity ratios and that there is no statistically significant effect of the digital transformation on the leverage ratios of the Jordan Media Institute. The study recommended the need to take into account the results of this study and the orientation of decision-makers at the Jordan Media Institute to improve financial performance through leverage indicators, increase the efficiency of credit management, and reduce dependence on debt in financing its assets. The study also called on researchers to take into account the results of this study and conduct similar studies in different sectors, and compare the results to provide new insights and horizons about digital transformation and its impact on financial performance in various sectors.