Reviewerly/exHarmony huggingface dataset:
Manuscript Rainfall Prediction using Multilayer Perceptron (MLP)

In this study, we undertook an analysis focused on forecasting hourly rainfall. This analysis framed the problem as binary classification, with rainfall events classified into two categories: "rainy" (the positive class) and "non- rainy" (the negative class). We leveraged independent climatic variables from the current hour to predict rainfall conditions for the following hour. Our data source was the CST weather station, providing records of 8 hourly weather parameters. To make predictions, we harnessed a commonly used machine learning model, the Multilayer Perceptron (MLP), resulting in an accuracy rate of 79%. Key Words: rainfall, MLP, time series
Cryptocurrency and central bank digital currency: an insight from the regulatory perspective

The scope of direct transactions globally with almost zero transaction cost and keeping transactions anonymous without facing any centralized control has caused a huge demand for cryptocurrency worldwide whereas its excess volatility, no underlying backings, no centralized control, and anonymity behind transactions have contributed significant risk for the entire financial system. Unlike cryptocurrency, Central Bank Digital Currency, being a virtual currency backed by the central bank or the monetary authority of a country serves all the functions of money, such as a store of value, a unit of account, and a medium of exchange, facilitates the attainment of macroeconomic goals and financial stability. To ensure the safeguarding of the interest of the people, business enterprises, and the financial system of a country from a broader perspective, adequate regulatory measures regarding cryptocurrency are a must. There should be proper harmonization and coordination among the countries regarding the regulatory approach as the markets of cryptocurrency are integrated globally.
MELESTARIKAN METODE BACA AL-QURAN MENGGUNAKAN AKSEN BUGIS DI KECAMATAN LAMURU KABUPATEN BONE

Learning to read the Qur'an is central to education that needs attention. Various religious problems are caused by the inability of people to read and understand the Qur'an properly. The method of recitation using the bugis accent, is a classical Qur'anic teaching method. Therefore, preservation is needed so that the Bugis accent as a method of reading the Quran can be maintained properly. The research method used is to use descriptive qualitative, using in-depth interview techniques. The results found were stakeholder efforts to realize the method of reading the Quran using the Bugis language and there were problems in preserving the method of reading the Quran using the Bugis language.
A Discrete Sine Cosine Algorithm with Enhanced Comprehensive Learning for Feature Selection

Feature selection (FS) plays a crucial role in enhancing the accuracy and response time of machine learning techniques. Due to the NP-hardness of the FS problem, an Enhanced Comprehensive Learning Discrete Sine Cosine Algorithm (ECLDSCA) incorporates several key components to effectively remove redundant features and identify the optimal feature subset. In the ECLDSCA, a tournament selection is first used to replace all individual update modes and maintain population diversity. Three enhanced comprehensive learning operators are then designed, including the global best learning operator, which learns the best optimization information in the search area; the population information learning operator, which learns in the local area; and the feature correlation learning operator, which refines the search space using feature information. In addition, the algorithm incorporates an enhanced greedy local search and random removal perturbation based on feature correlation. These mechanisms enhance the algorithm’s exploration capability, allowing for a more thorough search of the solution space. The tests were carried out on 10 benchmark datasets and compared with recent similar algorithms, the ECLDSCA demonstrated superior performance in terms of accuracy and the number of selected features.
PURCHASE MANAGEMENT INFORMATION SYSTEM DESIGN AT PT. NOP INDONESIA BASED ON DESKTOP APPLICATION

&lt;p&gt;The Purchasing Department is an important&lt;br /&gt;part of a company when it comes to the procurement of goods&lt;br /&gt;because, without a Purchasing Department, the company's&lt;br /&gt;operational continuity can be disrupted. PT. NOP Indonesia as&lt;br /&gt;a manufacturing company that produces various kinds of&lt;br /&gt;flexible fine packaging made of resin (plastic seeds) requires a&lt;br /&gt;system that can control the availability of raw materials,&lt;br /&gt;production equipment, and office supplies to support business&lt;br /&gt;and company operations. The purpose of this system is to&lt;br /&gt;facilitate the process of purchasing goods/items, to help manage&lt;br /&gt;and control purchase data, and to assist Purchasing Managers in&lt;br /&gt;controlling and approving purchase documents or data.&lt;/p&gt;
A Novel Reversible Data Hiding Method with Contrast Enhancement for Color Medical Images

Medical images contain sensitive information about patients' health conditions and medical history. Therefore, it is important to protect these images to ensure patient privacy and prevent unauthorized access, tampering, or theft of these sensitive medical records. Additionally, protecting medical images can help prevent medical identity theft, which is a growing concern in the healthcare industry. To address these concerns, data hiding with image contrast enhancement can be used to ensure that the hidden data is not easily detectable by unauthorized parties while still providing a visually appealing and interpretable medical image. The proposed method achieves higher performance in terms of embedding capacity as well as overall image quality compared to some previous methods.
Building Multimodal Teaching Materials as an Alternative to Increase Learning Interest of Grade IV Elementary School Students

Education as an effort to humanize humans is a unity that cannot be separated from students. Effective learning can be created by utilizing teaching materials as an important element in learning. Distance learning during the COVID-19 pandemic uses online learning patterns. Face-to-face lea
Solitonic Neural Network: A novel approach of Photonic Artificial Intelligence based on photorefractive solitonic waveguides

Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software systems and electronic neuromorphic models. Unlike computers, the brain uses a unified geometry whereby memory and computation occur in the same physical location. The neuromorphic approach tries to reproduce the functional blocks of biological neural networks. In the photonics field, one possible and efficient way is to use integrated circuits based on soliton waveguides, ie channels self-written by light. Thanks to the nonlinearity of some crystals, propagating light can write waveguides and then can modulate them according to the information it carries. Thus, the created structures are not static but they can self-modify by varying the input information pattern. These hardware systems show a neuroplasticity which is very close to the one which characterize the brain functioning. The solitonic neuromorphic paradigm this work introduces is based on X-junction solitonic neurons as the fundamental elements for complex neural networks. These solitonic units are able to learn information both in supervised and unsupervised ways by unbalancing the X-junction. The storage of information coincides with the evolution of structure that changes plastically. Thus, complex solitonic networks can store information as propagation trajectories and use them for reasoning.
Nintendo 3DS Forensic Examination Tools

This chapter describes a suite of digital forensic examination tools for the Nintendo 3DS series of game consoles. The Nintendo 3DS is a handheld game console with capabilities that include video recording, photography, web browsing and network communications. Originally released in 2011, the consoles remain popular, with almost 76 million units sold. Since the consoles can enable illegal activities, they are potential containers of evidence in criminal investigations. Previous research has highlighted the artifacts found on Nintendo 3DS and other similar devices. However, this chapter expands the body of research focused on automating artifact extraction and validation. The extraction and validation efforts would be of interest to forensic practitioners as well as researchers focusing on small-scale embedded devices.
Lightweight Underwater Garbage Detection Algorithm Based on YOLOX-S

With the rapid development of the economy and society, the output of garbage increases with it, and more and more garbage flows into water. In order to facilitate multi-platform deployment and extend the detection of underwater garbage, this paper improves on the YOLOX-S algorithm as a lightweight underwater garbage detection algorithm, which has a higher detection speed and detection accuracy while having a smaller number of parameters. Firstly, Shufflenetv2 is experimentally selected to enhance the backbone feature extraction network and reduce the parameters of the model. Secondly, the ECA attention mechanism is experimentally selected to enhance the effective information of the feature map, effectively distinguish irrelevant information in underwater scenes, and improve the capability of detecting different shapes of underwater garbage. Finally, experimental results on the underwater trash image dataset collected in this paper show that the present model has a better balance between speed and accuracy and model size than the mainstream lightweight models, and is more conducive to multi-platform deployment for underwater trash detection tasks. This paper provides a new approach for lightweight underwater garbage detection algorithms.
A Cloud Native Zero Trust Full Process Video Image Authentication Encryption Method to Protect Video Data Security

With the popularity of video data applications, malicious tampering against video information and data theft are on the rise. In the face of increasingly frequent video data security threats, research and development of effective video data protection technology has become an important development direction for network and information security strategies. Therefore, this paper investigates a cloud-native zero-trust video encryption method for protecting video data security. The uncertainty of chaotic system results can be better combined with video encryption. This paper constructs a key system with double PWLCM chaotic mapping on the cloud-native platform, based on the zero-trust principle, to encrypt video, protect video data security and achieve zero-trust security of the system. This paper first introduces the basic concepts of chaotic encryption algorithm design ideas and multi-level encryption schemes, followed by the design of video encryption schemes and finally the analysis of encryption efficiency.
The Effectiveness of Using Practicum-Based Algorithm and Programming Module on Cognitive Learning Outcomes of Mathematics Education Students

&lt;p&gt;This study aimed to 1) analyze the effect of using practicum-based Algorithm and Programming modules on cognitive learning outcomes of Mathematics Education students, and 2) analyze the effectiveness of practicum-based Algorithm and Programming modules in improving the cognitive learning outcomes of Mathematics Education students. The learning outcomes in question were in the form of a pretest and posttest. The learning outcomes in question are in the form of a pretest and posttest. The subjects of this study were the second semester students in class 02 of Mathematics Education Study Program Universitas Tidar in the even semester of the 2019/2020 academic year who were selected with the &lt;em&gt;cluster random sampling technique&lt;/em&gt;. The research design used was &lt;em&gt;pre-experimental&lt;/em&gt; with the form of &lt;em&gt;one group pretest-posttest&lt;/em&gt; &lt;em&gt;design&lt;/em&gt;. This study used instruments in the form of observation sheets, interview guidelines, and tests. The data analysis techniques used were the prerequisite test (normality), the Wilcoxon test, and N-Gain. The results showed that there was a significant effect of the use of practicum-based Algorithm and Programming modules on the cognitive learning outcomes of Mathematics Education students and the use of practicum-based Algorithm and Programming modules was effective in improving the cognitive learning outcomes of Mathematics Education students. The results of the calculation of the N-Gain value showed that there was an increase in the results of the pretest and posttest of 0.7742 which is included in the high category and in the percentage of 77.42% is included in the category of effective interpretation.&lt;/p&gt;
A Model Compression Method Using Significant Data and Knowledge Distillation

With the emergence of complex neural network models, the running cost of the models shows an increasing trend, while the larger the model, the exponential increase in training cost. Therefore, this paper proposes a method to compress the model of Convolutional Neural Network (CNN) by combining data selection and knowledge distillation, so that the performance of the original model can be maintained with low computational cost. (1) For data selection, we propose an algorithm to rank the importance of training data based on influence scores, and select some training data with greater improvement in model performance as the new training data set based on the ranking results. (2) A knowledge distillation method with low data volume is proposed to input some important training data into the teacher model to generate knowledge to guide the training of the student model, so as to reduce the model running cost while maintaining the model performance of the teacher model, and such a method can also availably avoid the overfitting phenomenon when the data volume is too small. By compressing both the data and the model, the training and running costs of the model can be effectively reduced.
UID-Auto-Gen: Extracting Device Fingerprinting from Network Traffic

The number of Internet device vulnerabilities has been quickly rising in recent years, rendering an explosion of network attacks. Device fingerprinting serves as the primary means for vulnerability awareness and attacker tracking. The current device fingerprinting approach can only achieve model-level identification within the Internet scope or individual-level identification for specific protocols (e.g., SSL) or scenarios (e.g., LAN). However, it is still difficult for these methods to achieve individual-level identification on a global Internet scale. In this paper, we propose a fingerprint extraction approach that is accurate to the individual level of the device by using a combination of clustering, multiple sequence alignment, and based on the geographic location stability of the device. In a continuous 3-month observation for several cities around the world, at least 1.54% of devices can be accurately extracted with unique IDs, with an accuracy rate of 99.30%, which is capable of being used in production environments.
An Adaptive Sampling Strategy for Federal Graph Neural Networks in Internet of Things

The Internet of Thing systems have contributed a considerable scale of graphs via numerous devices, like the network topology. These graph data are used in wireless communication and mobile computing for a variety of tasks such as traffic prediction and vehicle communication. Therefore, the analysis of these graph data has shown great significance towards the management of IoTs. Compared to other distributed computing scenarios, IoTs require a wider range and more frequent transmission of data. Considering the constrained communication resources and concerns about data privacy, IoT devices tend to keep the graph data locally. Therefore, it is meaningful to adopt the idea of Federal Graph Neural Network for IoTs, where the fancy Graph Neural Network can be implemented in a distributed manner over graphs in IoTs. This paper proposes a sampling-based framework towards the purpose. It assumes the graphs are vertically partitioned over devices, which means all devices hold an identical set of vertices and edges, and each owns a subset of features. It fits the fact that different devices own heterogeneous sensing capabilities. Then a sampling-based method is proposed for multi-round training of Federal Graph Neural Network, and only partial devices join in the training in each round. The sampling strategy applies the local accuracy of validation of each device to adjust the sampling probabilities accordingly, so as to accelerate the convergence of the global model. Finally, extensive analysis and numerical evaluation verify the advancement of the framework.
Improving Learning Outcomes of Science About the Solar System Using the Role-Playing Method

Science learning activities of 6th grade students of SDN Karangreja are still many who obtain learning outcomes below the KKM. Therefore, an effort is needed to improve the learning outcomes of science, one of which is through the use of the role-playing method. This study aims to determine the impr
Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems.
The quality evaluation of psychometric scale reply base on eye tracking

During psychometric tests, test subjects sometimes deliberately or unconsciously avoid choosing options that clearly reflect the disorder, which makes it difficult to ensure the quality of the collected psychometric scales. When test subjects use a mobile device to answer a psychological scale test, the front camera of the eye-tracking device is used to achieve eye-tracking during the answer with the help of a convenient and easily accessible auxiliary tag, and the content of the answer is combined with the eye-tracking information to analyze the quality of the response selected by the test subject when answering the question. An analysis model was designed to give an overall composite quality score after the quality of each subtopic was estimated.
RugbyNet: an efficient CNN architecture for offline unconstrained handwritten Chinese character recognition

Recently, neural networks have been used for handwritten Chinese character recognition (HCCR). While we have seen very high recognition accuracy for constrained Chinese handwriting, it remains a challenging task to recognize unconstrained Chinese handwriting. This paper proposes a new network architecture based on convolutional neural networks (CNNs), called RugbyNet. A special characteristic of the network is the rugby-like structure which is capable of reducing the number of multiply-accumulate operations and the required storage. And the convolutional blocks in the rugby-like structure are guided and designed by the speed, a direct indicator of computational complexity, to keep the model efficient while reducing the cost of memory access. Besides, We further proposed three data augmentation methods, extrusion, expansion, and distortion, to generate synthetic samples from the fixed sample set. These synthetic samples can then be used for training. Experimental results show that the recognition accuracy of the proposed RugbyNet on the ICDAR-2013 offline HCCR competition dataset reaches 97.77%, and enables a relative 4.70% error reduction compared to the current single-network method MCANet trained only on handwritten dataset.
TABLE 2 from Predicting Mammogram Screening Follow Through with Electronic Health Record and Geographically Linked Data

&lt;p&gt;Results of the study cohort selection&lt;/p&gt;
Special issue on Advanced statistical modeling and causal inference with complex data for better decision making

The special issue on Advanced Statistical Modeling and Causal Inference with Complex data for Better Decision Making has been inspired by the developments of innovative models and methods to answer challenging substantive questions and support policy choices and decisions. It includes a selection of twelve papers with cutting-edge methodological developments motivated by unique applications with challenging data structures from various fields of science.
Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility

Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies [Formula: see text]-differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.