Reviewerly/exHarmony huggingface dataset:
Improvement of Vision-Based Hand Gesture Recognition System with Distance Range

Hand gestures are widely explored for the human action interface, reducing the complexity of interaction between humans and computers. Hand gesture-based interaction for computer applications enables touchless operation without the need for other devices such as a keyboard and mouse. It also guarantees sterility and safer interaction for multiple users. The issue of HGR has a low range of detection. This study aims to improve the distance range for HGR from the user to the computer camera using the zoom algorithm that is processed by OpenCV and the Mediapipe library. To verify the proposed system, a Graphical User Interface (GUI) for four applications is developed in HGR mode. The GUI includes the development of cursor mouse event commands and a virtual keyboard display. The proposed method was then tested for some time, and the result indicates that the maximum range of detection increased with the accuracy of the HGR. The proposed method meets the expected outcome and can be used for applications such as slide presentations and watching video applications.
Rancangan Pengembangan Modul Penggajian pada Sistem Informasi Akuntansi Terintegrasi dengan Kerangka Kerja COSO

PT XYZ is a food and beverage trading company, in its business process it has implemented SIA for purchasing, sales, accounts payable, accounts receivable, bank cash, inventory and general ledger modules. The company has 200 employees, while the payroll system is still done manually, which can increase the risk of errors and fraud. The accounting cycle related to payroll is the HR/Payroll cycle. This study aims to determine the extent to which the current payroll system procedures are effective in terms of internal controls, identify weaknesses in internal control of payroll procedures and provide alternative solutions to correct existing weaknesses. The data analysis technique uses interactive analysis as standard for the Committee of Sponsoring Organizations (COSO) framework, then creates a new development plan for the payroll module in an integrated SIA based on COSO standards. This research uses an experimental method, its development uses the Object Oriented Analysis Design (OOAD) method, which is oriented towards model development using the Unified Model Language (UML) facility. The results of the study indicate that there are weaknesses in the form of errors in reporting, reporting takes a long time and payroll controls are still weak. Solving the problem to overcome these weaknesses requires improving the payroll system and developing a new computer-based payroll system that is integrated with the existing SIA, so that the developed system can be used efficiently and effectively.
Coming Soon...Diving into ARPA History

I’ve taken to calling the pieces I write for FreakTakes “administrative histories.” The reason I opted for that name instead of “progress studies histories” or “metascience histories” is that it was the name that drew the fewest confused stares and/or eye rolls from the scientists and engineers whom I hope to attract.
Meningkatkan Hasil Belajar Tematik Kelas III Menggunakan Model Pembelajaran PBL dengan Berbantuan media PPT

This study aims to improve student learning outcomes by applying the Problem Based Learning (PBL) learning model using Power Point (PPT) media. The subjects of this study were 26 students in grade III of the 2022 school year. Learning outcome data are obtained from evaluation tests, learning outcome data are analyzed by quantitative descriptive methods, and obstacles encountered in research are analyzed by qualitative descriptive methods. The results of the research that have been carried out obtained data that in the pre-cycle completeness of student learning outcomes 42.30% with an average score of 68.61. Furthermore, in cycle I the percentage of completeness of learning outcomes was 69.24% with an average score of 77.11%, in cycle II the percentage of completeness of student learning outcomes was 88.47%. It can be concluded that the application of the PBL learning model can improve the thematic learning outcomes of grade III students with the help of PPT media can increase students' active participation in the learning process.
Autonomous Navigation of Tracked Robot in Uneven Terrains

Since microdosing of chemical, biological, radiological, and nuclear (CBRN) contaminants is enough to cause great damage to humans, operating robots are widely used to handle CBRN-related tasks. However, how to improve the automation capabilities of these robots in uneven environments, such as autonomous navigation, is still a huge challenge. Current navigation methods usually set the scene as flat pavement, without considering the situation that the land slope exceeds a certain threshold. In order to explore ways of autonomous navigation in uneven environments, a 3D path planning and navigation method for the tracked robot is proposed in this paper, respecting applicable traversability constraints in uneven terrains. Firstly, a 3D graph-based map is built according to the occupancy map of the uneven environment. A set of spatial points, ensuring collision-avoidance of the robot, is randomly sampled in free space, and a vertex map is generated based on these vertices for robot traversing. Then, regarding the robot’s climbing and obstacle crossing ability as constraints, a path planning algorithm is used to search for the best path based on the Dijkstra algorithm. Finally, a fusion SLAM method based on LiDAR, IMU and RGB-D camera is used to achieve real-time localization, and the pure pursuit algorithm is used for navigation. The simulation results show that the proposed method can provide a safe and effective 3D path for the tracked robot and enable the robot’s autonomous navigation in uneven environments.
RANCANG BANGUN APLIKASI PERHITUNGAN GAJI KARYAWAN BERDASARKAN PRESENSI KARYAWAN PADA PT EPSINDO JAYA PRATAMA PRABUMULIH BERBASIS WEB

PT. Epsindo Jaya Pratama Prabumulih is a company engaged in the manufacturing and development of Electric Submersible Pump Systems (ESPS) and Horizontal pumping systems (HPS). This equipment is widely used in the oil and gas mining industry both in Indonesia and abroad. The company is also very aware of the importance of quality human resource (HR) services to encourage company development, so it must improve its employee information system to process employee salary calculations based on employee attendance.However, the problem with this in the HR department is that it is still difficult to make employee salary calculation reports, they still use manual calculations for employee salaries, namely inputting them in Microsoft Exel. The purpose of the research is how to design and build an application for calculating employee salaries based on employee presence at PT Epsindo Jaya Pratama Prabumulih Web-Based. The research method uses a qualitative descriptive method with data collection techniques in the form of observation, interviews and literature study. The type of data consists of qualitative data and data sources consist of primary and secondary data. The device development method uses the waterfall method. System design tools used are use case diagrams, class diagrams, activity diagrams. This application was built using a website with the PHP programming language and MySQL database, and to print reports using a pdf component.
Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Abstract Readout errors caused by measurement noise are significant source of errors in quantum circuits, which severely affects the output results and is an urgent problem to be solved in noisy-intermediate scale quantum (NISQ) computing. In this paper, we use the bit-flip averaging (BFA) method to mitigate frequent readout errors in quantum generative adversarial networks (QGAN) for image generation, which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance, successfully solving problems with high cost of measurement for traditional error mitigation methods. Our experiments were simulated in Qiskit, using the handwritten digit image recognition dataset, under the BFA-based method, the Kullback-Leibler(KL) divergence of the generated images converges to 0.04, 0.05, and 0.1 for readout error probabilities of p=0.01, p=0.05, and p=0.1, respectively. Additionally, by evaluating the fidelity of the quantum states representing the images, we observe average fidelity values of 0.97, 0.96, and 0.95 for the three readout error probabilities, respectively. These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.
The Development of Chatbot Provided Registration Information Services for Students in Distance Learning

In recent years, chatbots have become crucial, particularly for assisting students with real-time registration information. This research focused on 1) synthesizing registry works related to information provided for students, 2) designing chatbots and conversation structures in the form of interactive conversations between students and robots for answering questions and providing information tailored to their needs, and 3) examining and evaluating the use of chatbots in providing information services to students, while analyzing the accuracy and suitability of the developed chatbot. This study, based on research and development, utilized a sample consisting of 16 staff directly involved in the provision of registration information to students and 255 undergraduate students from Sukhothai Thammathirat Open University, with respondents being selected through a simple random sampling technique. The synthesis of the research results revealed the following findings: 1) A qualitative study revealed that the registration information related to students, called STOU Journey, consisted of 10 issues, and was required for the whole learning period. 2) The result of the design and development of the chatbot revealed that the developer chatbot could be used on both the website and the LINE application. It was also found that the chatbot could answer most questions correctly and completely. The chatbot responded quickly and was easy to use. The chatbot used language that was easy to understand and natural, while 3) satisfactory evaluation results from 255 undergraduate students showed that overall, students who had used the completed version of the chatbot were satisfied with the use of the chatbot at a high level (Mean = 4.19, SD = 0.98) while they also felt that the chatbot was easy to use (Mean = 4.33, SD = 0.95) and the using the chatbot felt like a natural conversation (Mean = 4.22, SD = 0.99).
Contextual Client Selection for Efficient Federated Learning over Edge Devices

Federated learning (FL) has emerged as a prominent distributed learning paradigm, enabling collaborative training of neural network models across local devices with raw data stay local. However, FL systems often encounter significant challenges due to data heterogeneity. Specifically, the non-IID dataset in FL systems substantially slows down the convergence speed during training and adversely impacts the accuracy of the final model. In our paper, we introduce a novel client selection framework that judiciously leverages correlations across local datasets to accelerate training. Our framework first employs a lightweight locality-sensitive hashing algorithm to extract client features while respecting data privacy and incurring minimal overhead. We then design a novel Neural Contextual Combinatorial Bandit (NCCB) algorithm to establish relationships between client features and rewards, enabling intelligent selection of client combinations. We theoretically prove that our proposed NCCB has a bounded regret. Extensive experiments on real-world datasets further demonstrate that our framework surpasses state-of-the-art solutions, resulting in a 50% reduction in training time and a 17% increase in final model accuracy, closing to the performance in the ideal IID case.
Deep Bayesian Quantization for Supervised Neuroimage Search

Neuroimage retrieval plays a crucial role in providing physicians with access to previous similar cases, which is essential for case-based reasoning and evidence-based medicine. Due to low computation and storage costs, hashing-based search techniques have been widely adopted for establishing image retrieval systems. However, these methods often suffer from nonnegligible quantization loss, which can degrade the overall search performance. To address this issue, this paper presents a compact coding solution namely Deep Bayesian Quantization (DBQ), which focuses on deep compact quantization that can estimate continuous neuroimage representations and achieve superior performance over existing hashing solutions. Specifically, DBQ seamlessly combines the deep representation learning and the representation compact quantization within a novel Bayesian learning framework, where a proxy embedding-based likelihood function is developed to alleviate the sampling issue for traditional similarity supervision. Additionally, a Gaussian prior is employed to reduce the quantization losses. By utilizing pre-computed lookup tables, the proposed DBQ can enable efficient and effective similarity search. Extensive experiments conducted on 2, 008 structural MRI scans from three benchmark neuroimage datasets demonstrate that our method outperforms previous state-of-the-arts.
Efficient and Secure Access Control for IoT-based Environmental Monitoring

Environmental monitoring devices based on IoT collect a large amount of data about the environment and our surroundings. These data are collected and processed before being uploaded to third-party servers and accessed and viewed by ordinary or specialized users. However, they may hold sensitive information that should not be exposed to unauthorized users. Therefore, accessing this sensitive information must be strictly controlled and limited in order to prevent unauthorized access. This research intends to create an access control mechanism based on distributed ledger technologies. The idea is to use a hybrid of IOTA technology and Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) technology. The permissions to access these data are written in a token, and this token will be sent to the Tangle after being signcrypted with CP-ABSC. Consequently, the data will be safeguarded, their confidentiality and integrity will be maintained, and unauthorized individuals will be unable to access the information. The proposed system was evaluated in terms of performance and the results showed that the system is straightforward, rapid, and convenient to use. Furthermore, a security assessment was conducted by running several scenarios to evaluate its feasibility and protection.
Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation

Multi-site brain magnetic resonance imaging (MRI) has been widely used in clinical and research domains, but usually is sensitive to non-biological variations caused by site effects (e.g., field strengths and scanning protocols). Several retrospective data harmonization methods have shown promising results in removing these non-biological variations at feature or whole-image level. Most existing image-level harmonization methods are implemented through generative adversarial networks, which are generally computationally expensive and generalize poorly on independent data. To this end, this paper proposes a disentangled latent energy-based style translation (DLEST) framework for image-level structural MRI harmonization. Specifically, DLEST disentangles site-invariant image generation and site-specific style translation via a latent autoencoder and an energy-based model. The autoencoder learns to encode images into low-dimensional latent space, and generates faithful images from latent codes. The energy-based model is placed in between the encoding and generation steps, facilitating style translation from a source domain to a target domain implicitly. This allows highly generalizable image generation and efficient style translation through the latent space. We train our model on 4,092 T1-weighted MRIs in 3 tasks: histogram comparison, acquisition site classification, and brain tissue segmentation. Qualitative and quantitative results demonstrate the superiority of our approach, which generally outperforms several state-of-the-art methods.
An improved intelligence approach to handling data leakagerisks in the corporate information security process

Confidential information is of great interest to competing firms. This causes aggression and attacks. Many peopleunderestimate the importance of the threat, and as a result, it can lead to collapse and bankruptcy for the company.Even a single case of malpractice can result in millions in damages and the loss of customer trust. Threats aresubject to data on organization structure, status, and operations. Sources of such threats are its competitors,corrupt officials, and criminals. They are introduced with certain value-protected information and modified inorder to cause financial damage. Even 20% of such a decision can result in information leakage. Sometimesthe loss of company secrets can be due to the inexperience of employees or a lack of security systems. In thispaper, an improved intelligence approach to handling data leakage risks in the corporate information securityprocess is proposed. Accounting automatically calculates weighted relative class systems through a complete,complex security of the most important processes and technical and organizational measures. Their combinationis an antivirus system, a firewall, and protection from electromagnetic radiation. Systems protect information onelectronic media sent through communication channels, access exemptions for various documents, create backupcopies, and recover confidential information after damage.
Image Recovery and Object Detection Integrated Algorithms for Robots in Harsh Battlefield Environments

Battlefield environments are harsher than normal environments, and the images captured by imaging equipment are more prone to degradation. Degraded images seriously affect the analysis of military intelligence and the deployment of intelligent weapons. To address this issue, we propose an image recovery algorithm, which recovers degraded battlefield images based on a physical imaging model and uses a light-weight network to estimate the parameters of physical model. In addition, we propose a strategy to joint training of the image recovery module and the object detection module. Specifically, we integrate the recovery module to the front of YOLO detector to jointly optimize the two modules with detection loss. The image recovery module is light-weight without significant adverse impact on the real-time running of object detection, which can be easily deployed to intelligent unmanned devices such as battlefield robots. The experimental results show that the proposed algorithm achieves better recovery performance in battlefield environments, and the joint training strategy effectively improve the accuracy of object detection.
Implementation and Prediction of Accurate Data Forecasting Detection with Different Approaches

The ability to recognize anomalies and make accurate forecasts is essential to the practice of many disciplines, including finance, economics, meteorology, and anomaly detection. The installation and prediction of reliable forecasting detection using various methodologies are the primary objectives of this abstract's discussion. The accuracy of forecasting and detection models has been improved through the development of a variety of strategies, methodologies, and approaches. Among these are the more common statistical methods, algorithmic approaches to machine learning, and more recent deep learning methods. Each method has advantages and disadvantages, and the degree to which it is successful is highly dependent on the nature of the data as well as the specific application area. In order to put accurate forecasting detection into action, the data must first be preprocessed, then appropriate features must be identified, and finally, models must be trained utilizing the method that was selected. After then, the models are judged according to how well they can forecast the outcomes of future occurrences or identify deviations from the norm. The purpose of this abstract is to investigate and evaluate the effectiveness of several methodologies regarding accurate forecasting detection. This article addresses the benefits and drawbacks of each technique, as well as the difficulties associated with putting them into practice and their capacity to make accurate forecasts. In addition, the abstract investigates how the accuracy of predicted detection is affected by a variety of aspects including data quality, model complexity, feature selection, and training approaches. This demonstrates the significance of utilizing dependable metrics of evaluation and processes of validation in order to produce accurate and trustworthy predictions. The facts and results that are discussed in this abstract help to a greater knowledge of the many methods that can be utilized for accurate forecasting detection. They provide insights into the strengths and limitations of each strategy and help in picking the technique that is best appropriate for specific forecasting tasks by assisting in making these evaluations. In general, the identification of correct forecasting plays an important part in the decision-making processes, as well as risk management and anomaly detection. This abstract is meant to serve as a foundation for additional study and is intended to inspire the development of predictive detection models that are both more accurate and efficient across a variety of domains.
Perancangan dan Implementasi Sistem Pencatatan Akuntansi dengan Microsoft Access pada Sinar Bulan Bintang

This study aims to design an accounting record system using Microsoft Access to overcome the constraints and deficiencies faced by Sinar Bulan Bintang. Sinar Bulan Bintang is one of the MSMEs in Batam that operates as a minimarket. This business has been running for 18 years and during that time the owner recorded receipts and expenses manually and even making estimates based on memory. As a result, partners experience difficulties recording and bookkeeping, which results in inaccurate decisions. Therefore, the right solution to deal with this problem is to design a computerized accounting recording system through Microsoft Access. The method of collecting data is by interviewing, observing, and documenting the owners and members of Sinar Bulan Bintang. The results of this research are in the form of a system designed with Microsoft Access and available various features needed by partners. One of these features also involves generating financial reports in the form of income statements, balance sheets, capital reports, trial balance reports, and sales reports. This system allows owners and partners to make more accurate and informed decisions during their daily operations.