Reviewerly/exHarmony huggingface dataset:
Application of The Speed-Up Robust Features Method To Identify Signature Image Patterns On Single Board Computer

Through the development of a signature pattern recognition program on SBC Beagle-bone Black, this research seeks to determine how to differentiate between real and false signatures. Three techniques of gathering data were employed in this study: interviews, observations, and a review of the literature. The quick application development method is the approach that is applied. The rapid, efficient, and brief development cycle (RAD) is emphasized. This study uses a use-case diagram to illustrate the application's logic and data flow. In this study, OpenCV is used as a digital image processing library along with the C++ programming language and QT creator as an integrated development environment (IDE). This application was subjected to both accuracy and functional testing. The following conclusions are drawn from the findings of the investigation and testing that was done: Using the fast library approach for approximate nearest neighbors (FLANN) and the speeded-up robust features (SURF) feature extraction method, the signature pattern recognition program on the Beagle-bone black SBC can differentiate between real and fraudulent signatures. Through the processes of generating image scale space, feature localization, and feature description, the SURF approach extracts feature from signature images. This signature pattern recognition application is one of the digital image processing apps that can be run on the Beagle-bone Black single board computer. This indicates that the specifications of the SBC Beagle-bone Black for digital image processing are good.
3D Geometric Shape and Colors Interactive Learning Media using Raspberry Pi, OpenCV, and TensorFlow Lite

This study aims to create learning media for 3D geometric shapes and colors for early childhood. Early childhood can enter 3D objects into the device. Then, the device will mention and explain the shape and color of the object in question. As a contribution, this research provides a brief introduction and learning related to geometric shapes for early childhood. The method used is experimental. The hardware components of this system are Raspberry Pi 3, RPi Camera, PIR Sensor, 3.5inch LCD, and Speaker. Python, OpenCV, and TensorFlow Lite are used from the software side. OpenCV is used to detect colors. TensorFlow Lite is used to detect the shape of geometric objects. In this study, the model used is a custom model specifically for TensorFlow Lite, which was trained through Google Colab. This media has a learning mode and a question mode. In learning mode, early childhood only needs to insert objects into the learning media and will get information in the form of images and sounds related to the object. In question mode, the learning media will provide instructions and questions to enter objects with a specific color. After that, the learning media will determine whether the answer is right or wrong and give the score. The results of tests that cover both modes reach a success percentage of 100%, where this learning media can recognize and explain every shape and color of objects in both modes.
Phishing and Spoofing Websites: Detection and Countermeasures

Website phishing and spoofing occur when unsuspecting users are tricked into interacting with a fraudulent website designed to impersonate a legitimate one. This is done with the intention of stealing login credentials or other personal information. The goal of this project is to develop a multi-layered URL-based malicious website detection system to counter such attacks. The proposed system employs several defence mechanisms, including whitelist filtering, API requests to domain blacklist providers, and string comparison algorithms, to accurately identify and classify websites as either legitimate or malicious. In brief, the first layer provides an initial check by matching the domain of the intended website with a predefined whitelist, while the second layer queries APIVoid (a domain blacklist provider) to conduct additional checks for domain age and reputation. Finally, to prevent typographical errors that could unintentionally redirect users to a malicious website, the last layer compares the domain of the intended website with entries in the whitelist to identify any significant similarities using the Levenshtein distance algorithm. To evaluate the system's performance, a comprehensive testing phase was conducted on a dataset containing 30 randomly selected websites, encompassing various scenarios of malicious and legitimate websites. The results show a high true positive rate of 0.94 and an overall accuracy of 0.93, indicating the system's ability to accurately classify legitimate and malicious websites. The proposed system shows promising results in accurately classifying websites and enhancing user awareness to prevent phishing and spoofing attacks.
Uncover the Body: Occluded Person Re-identification via Masked Image Modeling

Person re-identification (ReID) has attracted tremendous attention and achieved significant progress on holistic data where the whole body of a pedestrian is completely presented. However, in a more realistic scenario where pedestrians are partially occluded, the discriminative ability of the existing ReID methods is severely limited since the visual information of the pedestrians becomes noisy and unreliable. To alleviate this issue, current solutions mostly pay more attention to visible body parts for extracting fine-grained features. Nevertheless, different occluded parts on different images of the same pedestrian always result in inaccuracy matching. In this paper, we propose an Uncover the Body Network (UBN) which exhibits the ability to remove the occlusion and attempt to restore the full body of a pedestrian. The proposed UBN can alleviate the noise brought by occlusions and extract more robust feature representations. To achieve this, we propose a MIM (Masked Image Modeling) based method for its powerful representation of partial images to the whole. Instead of randomly masking the images, we propose a Mask Prediction Module (MPM) to readily locate the occluded patches, and an occlusion-guided masking strategy is adopted to facilitate the learning. Extensive experimental results on both the occluded and holistic ReID benchmarks have demonstrated the superiority of UBN against the state-of-the-art approaches.
Intellectual property rights in the metaverse

The contribution is examining the exciting technological phenomenon of the metaverse through the lens of Intellectual Property Rights (IPR) and their potential application in this virtual ecosystem. More precisely, the focus of the paper is on three types of Intellectual Property Rights – Copyright, Patents and more extensively Trademarks. Firstly, the author offers some definitions and pinpoints the main features of the emerging virtual realities, as well as clarifies their interplay with blockchain technologies. Also, she underlines the flexibility of IPRs and the intangible nature of their protected subject matter, which makes it easier to extend the implementation of their legal norms from the real world to the realms of the metaverse. Further, the paper explores for each of the three IPRs the potential to contribute to the development of the metaverse and its economy, e.g. through the creation of virtual works of art and trading with “art NFTs”; registering Patens for hardware systems and devices that enable access to the metaverse (e.g. AR or VR glasses), and creating, trading and protecting through Trademarks virtual goods, which represent intangible twins of their branded real-life products.
Aplikasi Handling Problem Unit IT Infrastruktur Divisi Teknologi Dan Sistem Informasi Pada Bpd Sumsel Babel Berbasis Website

The Regional Development Bank (BPD) of South Sumatra and Bangka Belitung (Sumsel Babel) is a financial institution with a complex and important IT infrastructure system in its operations. The purpose of this research is to develop an application that can help in handling these problems. The method used in this research is the waterfall method which consists of requirements analysis, design, implementation, testing, and maintenance. The proposed solution design is a website-based problem handling application. This application will provide access to the IT Infrastructure Team to submit problem reports and monitor repair status in real-time. Additionally, this application will also be equipped with user management features, access rights management, and notification systems that will inform users of repair status. It is hoped that with this application, the efficiency and effectiveness of IT infrastructure problem handling can be better, thus speeding up response time in problem solving and increasing customer satisfaction. In conclusion, the use of website-based problem handling applications at the Regional Development Bank (BPD) of South Sumatra and Bangka Belitung (Sumsel Babel) can help improve the performance of the IT infrastructure unit of the Technology and Information Systems Division, thereby expected to better meet user needs.
WSAD-Net: Weakly Supervised Anomaly Detection in Untrimmed Surveillance Videos

Weakly supervised anomaly detection (WSAD) is a newfangled and challenging task, the goal of which is to detect anomalous activities in untrimmed surveillance videos with no requirement of temporal localization annotations. A few methods have been proposed to detect anomalies under the weakly supervised setting. In order to combat the issue even further, we present a weakly supervised anomaly detector network (WSAD-Net), which is composed of a pre-trained feature extractor and an anomaly-specific subnetwork. To learn the anomaly-specific parameters of WSAD-Net, we design a Classification Loss based on the multiple instance learning (MIL) and two novel losses, namely, Compactness Loss and Magnetism Loss, which play an important role in evaluating the correlation of features. During the test phase, we introduce the Anomaly-specific Temporal Class Activation Sequence (Ano-TCAS) to generate the anomaly score. We evaluate WSAD-Net on two benchmarks, i.e., the UCF-Crime and Live-Videos datasets, and experiments on these two benchmarks show that WSAD-Net outperforms or competes with current state-of-the-art methods.
A Motion Analysis System for Pointing and Calling Considering Safety Checks for Soldering Work

In Japan, pointing and calling are carried out to improve the safety, reduce human errors and prevent accidents. Via pointing and calling concept the safety is improved by pointing to the work object or tool and voicing the situation in order to predict the accidents during the work. In factories, soldering is part of the handicraft industry and one of the career options for people with disabilities. During soldering skill, it is necessary to memorize and repeat the same work, which takes a long time. Also, there are human errors caused by accidents and lack of safety checks or experience. In addition, ensuring worker safety requires continuous monitoring of worker motion, which is a significant burden for instructors. In this paper, we propose a motion analysis system for pointing and calling. The proposed system uses a depth camera to capture images of workers during pointing and calling. Also, the proposed system considers beforehand safety checks for soldering operations to prevent accidents and injuries. The experimental results of the pointing orientation show that the proposed system is effective for safety checks and can support beginners and people with disabilities to continue soldering work safely.
Custom Deep Learning Model for the Diagnosis of Cervical Carcinoma

Cancer is the second most common cause of death in the majority of the world due to late diagnosis. Most cancer cases are typically discovered at an advanced stage, which lowers the likelihood of recovery because proper therapy cannot be given at that time. In particular, for incurable cancers, which may result in a reduced life expectancy due to the rapid progression of the disease, the sooner cancer is identified, the more effective the therapy may be. Early detection also lessens the financial effects of cancer because treatment in the early stages is much cheaper than treatment in later stages.The method suggested is an end-to-end deep learning method in which the input photos are sent directly to the deep model, which makes the decision. The proposed Ensemble of deep learning modelIV3-DCNN to detect cancer in pap-test images. The model's precision, FScore, Specificity, Sensitivity, and accuracy of 99.4%, 99.23, 95.48, 97.9, and 99.2%. Last but not least, the suggested strategy would be very beneficial and successful, especially in low-income nations where referral mechanisms for patients with suspected cancer are frequently lacking, resulting in delayed and fragmented care.
Learning a Deep Fourier Attention Generative Adversarial Network for Light Field Image Super-Resolution

Human eyes can see the three-dimensional (3D) world because they receive the light emitted by objects, and the light field (LF) is a complete representation of the set of light in the 3D world. Light field image super resolution (LFISR) aims at reconstructing high-resolution LF images from their low-resolution counterparts which captured by the LF camera. In recent years, although convolutional neural networks (CNNs) can bring good performance to LFISR tasks, they cannot recover more real finer texture details. Therefore, it is a difficult challenge to generate realistic images of LF that can satisfy human perception. To address these problems, we propose a novel LFISR model by learning a deep Fourier attention generative adversarial network (GAN). Specifically, the generator network and loss function of the traditional single image super-resolution reconstruction (SISR) GAN model are improved for LFISR reconstruction. Furthermore, we design an attention module based on deep Fourier channel, which leverages the frequency content difference across distinct features to learn precise hierarchical representations of high-frequency information of diverse structures. Extensive experiments are performed on the mainstream LF datasets, leading to the state-of-the-art results of our method.
The Use of Chatbot as an Artificial Intelligence Tool to Improve Intelligent Digital Marketing

Digital eras convert the way of work and make it easy for all industries including marketing. The emergence of artificial intelligence technology and machine learning technology leads marketing beyond digital marketing to intelligent digital marketing. In intelligent digital marketing, the company will be able to classify the customers based on their preferences, which ends up having different segmentations for customers. A chatbot is a tool that was discussed in this research to provide customer service to the customers. Chatbots depending on artificial intelligence, machine learning, and natural language processors can provide 24/7 services to customers. Other than chatbot benefits to help customers, it will help the company to understand customer needs and to target the required segment of customers for the specific service or product. Chatbot also has some limitations and user resistance which the researcher believes will shrink over time. Digital marketing is a huge industry that impacts both the customer and the company. Focusing on digital marketing with the use of artificial intelligence will create a new way of competitiveness and will create a data market. This paper focused on highlighting both digital marketing and chatbot as artificially intelligent tools to support the customer in digital marketing. The paper covers many facts and theories about this topic.
Early Detection of Breast Cancer using Versatile Techniques - A Study

Among all cancer types, breast cancer is the most prevalent. For females, it ranks as the second most common cause of cancer-related mortality. Every 1 person per 28 people in lifetime have a chance of developing breast cancer, according to statistics. Each year, it is estimated that over two million women encounter it. The high-risk group in India has an average age of 43–46 years, but in the west, women between the ages of 53 and 57 are more likely to get breast cancer. While there is no known cure for breast cancer, early detection and diagnosis significantly improves chances of survival. Treatment for breast cancer patients may be possible if the disease is identified and diagnosed early. Diagnosing benign from malignant tumours and determining whether a breast cancer case is early or progressed presents a number of challenges for cancer researchers. This study compares many methods for detecting breast cancer and provides a detailed analysis of each, highlighting the methods that are most accurate and economical. This study's primary goal is to comprehend the fundamental principles behind the various technologies used in breast cancer diagnosis. It is simple to save lives by spreading awareness of the latest and most varied detection and screening techniques.
Factors Affecting the Use of ChatGPT in Mass Communication

The research discusses the sudden and significant development in the applications of artificial intelligence (GPT chat) in the field of mass communication, prompting us to study this application with the considerations raised by this application. As it is within a few years, media professionals may turn to these applications to accomplish their work, and they may also have negative effects on reducing the human media workforce and relying on it to accomplish various works. The results indicated that this application has been used to power chatbots, allowing them to provide more personalized responses and improve the user experience.
IMPLICATIONS OF BEHAVIORISTIC LEARNING THEORY IN PRIMARY SCHOOL LEVEL STUDENT LEARNING

According to behavioristic theory, learning is a change in behavior and overall student personality. At the elementary school level both SD / MI, one of the apparent learning successes is the change in student behavior. Elementary / MI students have several characteristics such as: (1) full of inquiry or curiosity, (2) spontaneously responding (paying attention) to various aspects of the world around them, (3) active in doing anything even though small things, and so on. Students at the elementary school level will be more motivated to learn by giving reinforcement (reinforcement) by the teacher in the form of rewards, rather than providing motivation in the form of motivational sentences. Behavioristic theory is seen as one learning theory that has positive implications in the learning process. This is because behavioristic theory emphasizes the existence of stimulus and response, thus making the role of the teacher here very vital. The teacher acts as the center of learning activities. Before the teacher carries out learning, the teacher makes a learning plan by taking into account indicators of learning success, identifying learning objectives, conducting learning analyzes and identifying student characteristics. Then in implementing learning, the teacher provides stimulus to students in the form of motivation, reinforcement, assignments and so on. After that the teacher gives feedback at the end of learning. In addition to having positive implications, this behavioristic theory also has weaknesses in its application in the learning process, because this theory emphasizes more on what can be seen, namely behavior and not paying attention to what is happening in the mind because it cannot be seen. Thus the behavioristic learning process is considered more as a mechanical and automatic process without discussing what happens in students while learning.
HQFS: High-Quality Feature Selection for Accurate Change Detection

High-quality features play a crucial role in achieving accurate change detection (CD). However, current CD methods often rely on simple combinations of features or the computations of feature differences between bi-temporal images, which cannot fully explore the representative capacity of features. In this paper, we design a high-quality feature selection module, called HQFS, to generate high-quality change-related features for accurate CD. Firstly, we analyze the pros and cons of the features utilized in recent deep learning-based CD methods. Based on this analysis, we construct a feature pool containing various change-related features. The proposed HQFS module is then employed to learn the importance of these features and assign higher weights to the high-quality features to amplify their contributions in computing the change maps. To further enhance the performance, we propose two additional components: the fully attention enhanced feature extraction network and the attention pyramid fusion & change mask prediction module. The former focuses on extracting features from images, while the latter leverages attention mechanisms and pyramid fusion techniques to generate accurate change predictions. Comprehensive experiments on three benchmark datasets demonstrate the superiority of our method over seven state-of-the-art change detectors.
Efektivitas Media Pembelajaran Quizizz terhadap Keaktifan Belajar PAI Kelas XII SMAN 1 Lareh Sago Halaban

The use of learning media is a learning strategy that must be mastered by teachers, especially interactive learning media. But in reality there are still many teachers who have not mastered interactive learning media so that it has an impact on student learning activities such as learning activeness. The research method is quantitative experiment with questionnaire distribution techniques on a sample of 32 respondents, data analysis techniques using the help of the SPPS 26 program. The results of the research on the T test show that 0.001 &lt;0.05 which means Ho is rejected and Ha is accepted. Then the effectiveness test shows that the value of R &lt; 1 (0.72) &lt; 1. Based on the results of these tests, it can be concluded that Quizizz learning media is effective in increasing the activeness of PAI learning class XII at SMAN 1 Lareh Sago Halaban.
Evaluasi Penerapan Sistem Informasi Manajemen Rumah Sakit di Bagian Rekam Medis Rumah Sakit Bhayangkara POLDA DIY

Background: RS Bhayangkara POLDA DIY is one of any hospital who used SIMRS since 2011, but in fact they never do any evaluating which very important to the goods for the hospital itself.Objective: To evaluate the implementation of SIMRS in the medical records section of the Bhayangkara POLDA DIY Hospital.Methods: The data was collected by observation, interview, and documentation. Research population are all of 6 medical record employees, and the sample is from all of those 6 employees.Results: The performance and information aspects are considered not good, the economic, control, efficiency, and service aspects are considered good. The user's satisfaction, it has been considered good but there are still obstacles faced by the user, namely the system performance is not optimal, system error and network are slow.Conclusion: The hospital should follow up this complain from user and coordinate with IT officer for optimizing the system as soon as possible
A Video Scene Segmentation Approach for Learner Monitoring

Recently, for audio-visual communication are utilized many video conference systems and video on demand systems. Also, new live streaming and e-Learning systems are implemented and used for education and learning. However, during operation of these systems, the participants or learner do not watch the video because they may have also other tasks. In this paper, we discuss the video scene segmentation for monitoring a learner, where the video is divided the video scenes using a frame correlation matrix according to the learner’s behavior.