Reviewerly/exHarmony huggingface dataset:
Improving Students’ Speaking Skills Through Social Interaction In The Classroom

This research purpose to find out improvement of the students’ speaking skills used social interaction at the eighth grade students of SMAN 5 Selayar. The design of this research used a classroom action research which consisted of two cycles. Each cycles consisted of three meetings. The procedure of this research in every cycle consisted of four phases They had plans to action, observation, and reflection Subject matter for the research of class were the pupils XI which consisted of 20 students. The instrument of this research used interview. The result of students speaking skills in terms pronunciation and vocabulary in D-test, cycle I, and cycle II had improved in different scores. The research demonstrated there were was a significant improvement of the students speaking skills used by social interaction models. This statement could prove by the students’ mean score of diagnostic test was 44, but it had increased to 62.6 in cycle I. And in Cycle II was 77.5. it means that the students’ speaking skills from cycle I to cycle II was classified successful. Social Interaction could improve the students’ speaking skills.
Based Attack Detection on Machine Learning Algorithm

Abstract-Due to increased digitization and the advent of new technologies such as the Internet of Things, the use of machine learning (ML) algorithms is rapidly expanding (IoT). ML algorithms are being used in healthcare, IoT, engineering, finance, and other fields in today's digital world. However, in order to predict/solve a specific problem, all of these algorithms must be taught. There's a good chance that the training datasets have been tampered with, resulting in skewed findings. As a result, we have proposed a blockchain-based approach to protect datasets generated by IoT devices for E-Health applications in this article. To address the aforementioned issue, the proposed blockchain-based solution makes use of a private cloud. For assessment, we created a solution that dataset owners can use to secure their data.
Design and Development of Digital Intelligent Fracturing Platform

There are many problems in data monitoring of fracturing construction site, such as many data types, large amount of data, single admission mode, inconsistent format, non sharing of operation equipment data, fragmentation and easy loss of data. In order to scientifically plan and uniformly manage the fracturing construction resources, the digital intelligent fracturing platform adopts intelligent technologies such as edge computing, AI video analysis and Internet of things to digitally process, accurately collect and centrally store all types of data on the fracturing construction site. It has the characteristics of real-time monitoring, intelligent diagnosis, automatic disposal and intelligent optimization. The platform has five functional modules: operation data monitoring, material data monitoring, equipment status monitoring, material level status monitoring and intelligent security monitoring. It realizes the digital operation of the whole process of fracturing construction.
Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap

Stochastic Gradient Descent (SGD) is one of the commonly used optimizers in deep learning. Therefore, in this work, we modify stochastic gradient descent (SGD) by adding a fixed variable. We will then look at the differences between standard stochastic gradient descent (SGD) and stochastic gradient descent (SGD) with additional variables. The phases performed in this study were: (1) optimization analysis, (2) fix design, (3) fix implementation, (4) fix test, (5) reporting. The results of this study aim to show the additional impact of fixed variables on the performance of stochastic gradient descent (SGD).
Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy

Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p&lt;0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.
ANALISIS NILAI MORAL DAN NILAI SOSIAL DALAM NOVEL SEJARAH MAJAPAHIT BALA SANGGRAMA KARYA LANGIT KRESNA HARIADI SEBAGAI BAHAN PEMBELAJARAN BAHASA INDONESIA DI SMA

This study aims to determine the moral and social values contained in the historical novel Majapahit Bala Sanggrama by Langit Kresna Hariadi. As well as to determine the effectiveness as a literary learning material in teaching Indonesian. The research was carried out using qualitative research methods. Data collection techniques used reading techniques and note-taking techniques. The data analysis technique used descriptive analytic technique. The results of data analysis on the historical novel majapahit bala sanggrama by Langit Kresna Hariadi are the moral values contained in the historical novel, namely determination, longing, restlessness, oscillation between several choices, fear, revenge, disappointment, caring for others, respect for others, not forcing will, conspire, have good prejudice, pray, surrender to God, trust in God. The social values contained in the historical novel are devotion, mutual assistance, kinship, loyalty, care, belonging, discipline, empathy, tolerance and cooperation. The results of the analysis of moral values and social values in the historical novel majapahit bala sanggrama by Langit Kresna Hariadi can be used as an alternative material for learning Indonesian in high school.
Boosting Semantic Segmentation by Conditioning the Backbone with Semantic Boundaries

In this paper, we propose the Semantic-Boundary-Conditioned Backbone (SBCB) framework, an effective approach to enhancing semantic segmentation performance, particularly around mask boundaries, while maintaining compatibility with various segmentation architectures. Our objective is to improve existing models by leveraging semantic boundary information as an auxiliary task. The SBCB framework incorporates a complementary semantic boundary detection (SBD) task with a multi-task learning approach. It enhances the segmentation backbone without introducing additional parameters during inference or relying on independent post-processing modules. The SBD head utilizes multi-scale features from the backbone, learning low-level features in early stages and understanding high-level semantics in later stages. This complements common semantic segmentation architectures, where features from later stages are used for classification. Extensive evaluations using popular segmentation heads and backbones demonstrate the effectiveness of the SBCB. It leads to an average improvement of 1.2% in IoU and a 2.6% gain in the boundary F-score on the Cityscapes dataset. The SBCB framework also improves over- and under-segmentation characteristics. Furthermore, the SBCB adapts well to customized backbones and emerging vision transformer models, consistently achieving superior performance. In summary, the SBCB framework significantly boosts segmentation performance, especially around boundaries, without introducing complexity to the models. Leveraging the SBD task as an auxiliary objective, our approach demonstrates consistent improvements on various benchmarks, confirming its potential for advancing the field of semantic segmentation.
Workshop Penulisan Proposal dan Laporan Karya Ilmiah untuk Dosen dan Mahasiswa

This PKM activity started from the interest of lecturers at the Mathematics Education Study Program at Tanjungpura University to write proposals and reports for lecturers and students in West Kalimantan following the regulations of the Ministry. The form of this PKM is a workshop with the title "Workshop on Proposal Writing and Scientific Work Reports for Lecturers and Students at STKIP Singkawang." This workshop was attended by 25 people consisting of lecturers and final semester students at STKIP Singkawang. The data collection technique is observation, interview, and documentation. The PKM activity plans are 1) Preparation: determining the location and obtaining an implementation permit in this workshop activity; 2) Implementation: conducting workshops, analyzing test results, and assisting in the preparation and preparation of scientific article publications, conducting workshops in class; 3) Final: The making of this scientific proposal and report produces a scientific paper proposal and report. The result of this PKM is the implementation of the Workshop on Writing Proposals and Scientific Work Reports for Lecturers and Students following the steps that have been determined and the proposals and scientific reports of STKIP Singkawang lecturers.
Tank Battle And Trivia Quiz Games Development Using Construct 2 To Improve Critical Thinking Ability

Game is something that is fun and entertaining 'of all time' and will always be in this world and can be enjoyed by both children, and adults. There are various types of games that are often played, including shooting and quiz games. Shooting Game has the main theme of shooting where players will beat each other to become the winner. While Quiz Game or often called Trivia Quiz is a game where players try to answer the questions given to get various kinds of prizes and become a winner. Construct 2 is a program for making games without writing programming code, because most of the game's logic can be arranged via the menu.This study aims to create a game design that is a combination of Shooting Game and Trivia Quiz where players can play both types of games simultaneously. While the program used is construct 2. And the sample used is 16 years and over. The methodology used is object-oriented design methodology. The modeling tools used are use case diagrams, activity diagrams and class diagrams. The results of making this game show that combining more than one type of game can be done and make a game more interesting.
Detection of Breast Cancer Based on Feature Extraction Using WPSO in Conjunction with CNN

According to cancer reports from the past few years in India, thirty percent of instances are breast cancer, and furthermore, it is possible that this percentage would increase in the near future. In addition, one woman is given a diagnosis every two minutes, and another woman passes away every nine minutes as a result of her condition. People who are diagnosed with cancer at an earlier stage have a better chance of survival. Micro calcifications are one of the most important symptoms to look out for when trying to diagnose breast cancer in its earlier stages. Several scientific investigations have been carried out in an effort to combat this illness, for which techniques related to machine learning can be utilized to a significant extent. Particle swarm optimization, often known as PSO, is acknowledged as one of several effective and promising methods for identifying breast cancer. This method helps medical professionals administer treatment that is both timely and appropriate. The weighted particle swarm optimization (WPSO) approach is utilized in this work for the purpose of extracting textural information from the segmented mammography picture for the purpose of classifying micro calcifications as normal, benign, or malignant, hence increasing the accuracy. A portion of the cancerous growth is removed from the breast region using optimizing techniques. In this article, Convolutional Neural Networks (CNNs) are presented for the purpose of identifying breast cancer in order to cut down on the amount of manual overhead. The CNN framework is built in order to extract features as effectively as possible. This algorithm was developed to identify areas in mammograms (MG) that are suspicious for cancer and to classify those areas as normal or abnormal as quickly as possible. This model makes use of MG pictures that were gathered from a variety of hospitals in the surrounding area.
An Edge-Guided Weighted Image Interpolation Algorithm

For the problem of poor performance of linear image interpolation and high complexity of nonlinear image interpolation, an edge-guided weighted image interpolation algorithm is proposed. Firstly, the edge feature of the low-resolution image is detected by the Sobel operator, and the edge strength and direction of the high-resolution image are calculated by the bilinear interpolation algorithm. Then, the warped distance of the reference pixels and weighted Lanczos kernel are calculated by the edge features of the high-resolution image. Finally, the reference pixels are weighted to obtain the resulting high-resolution image. The simulation results show that the proposed interpolation algorithm is more accurate than traditional interpolation algorithms, and the computing time of the upscaling by a factor of 2 for the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{256}\times \mathbf{256}$</tex> image with the proposed algorithm is less than 50ms, which is attractive for real-time communication and image applications.
Survey of Evaluation Methods and Metrics for Face Clustering

Face clustering is one of the basic problems in the field of image processing. In recent years, face clustering technology was widely used. Mostly researchers focus on algorithm optimization, while it should be noted that evaluation methods are the standard of algorithms. How to evaluate the quality of face clustering rapidly and efficiently has academic research significance and practical application value. In this paper, evaluation methods of face clustering are divided into three kinds: accuracy, relevance and performance measures. The main work in this paper includes the following: (1) presenting a short review of face clustering and traditional classification of evaluation methods; (2) after exploring several main databases, summarizing publications and presenting a statistic result; (3)analyzing and comparing evaluating methods of face clustering, and discussing the suitable situations of measures. Finally, the expected research work on face clustering effect assessment is summarized and prospected. In the future, it is still needed to be in-depth study on systematic methods in this field.
Research on the Integrated Earthquake Network Monitoring and Management Platform in Beijing

Earthquake network is the most important infrastructure in earthquake prevention and disaster reduction work, which can provide monitoring data support for earthquake prediction, earthquake emergency response, and related scientific research. Therefore, how to scientifically and effectively manage earthquake networks is an important research issue for earthquake prevention and disaster reduction. This paper takes the Beijing region as an example to implement an integrated earthquake network monitoring and management platform. The core functions of the system include: site visualization management, equipment status management, business data management, operation and maintenance duty management, user permission management, and so on. Based on this platform, the full process visualization management of earthquake network has been achieved, greatly improving the quality of operation and maintenance and the level of data management, which has positive significance in practical work.
Topology Control of an Unmanned Aerial Vehicle in 3D Network

The topological control approach for Unmanned Aerial Vehicles (UAVs) in 3D settings is proposed in this research. The technique builds a minimal weight tree using the Spanning Tree Protocol (STP), which acts as a backbone network for effective communication between the UAVs. To allow precise localization and routing of the UAVs, two space partitioning techniques–the Grid-based Space Partition Method and the Quadtree-based Space Partition Method–are introduced. The usefulness of the suggested strategy in creating network connectivity and communication effectiveness in a 3D environment is demonstrated by experimental findings.
E - Voting Using Blockchain

In general, building an online voting system that complies with the laws set forth by legislators has always been a difficult task. An innovative development in the field of information technology is distributed ledger technology. Blockchain technology provide a limitless number of sharing economy-friendly applications. This research attempts to assess how well distributed online voting systems may be implemented using blockchain technology. The project explains the specifications for developing online voting systems and outlines the technical constraints of using blockchain to implement such systems. A few of the well-familiar frameworks that uses blockchain as a service are first evaluated for the project. Then put forth a cutting-edge blockchain-based online voting system that fixes all the issues found. In a broader sense, this project assesses the possibilities of distributed ledger technologies by telling a case study, specifically the conduct of an election and the execution of a blockchain built application that improves privacy and reduces the cost of holding national elections.
A Trustworthy Federated Learning Framework for Individual Device Identification

IoT scenarios face cybersecurity concerns due to unauthorized devices that can impersonate legitimate ones by using identical software and hardware configurations. This can lead to sensitive information leaks, data poisoning, or privilege escalation. Behavioral fingerprinting and ML/DL techniques have been used in the literature to identify devices based on performance differences caused by manufacturing imperfections. In addition, using Federated Learning to maintain data privacy is also a challenge for IoT scenarios. Federated Learning allows multiple devices to collaboratively train a machine learning model without sharing their data, but it requires addressing issues such as communication latency, heterogeneity of devices, and data security concerns. In this sense, Trustworthy Federated Learning has emerged as a potential solution, which combines privacy-preserving techniques and metrics to ensure data privacy, model integrity, and secure communication between devices. Therefore, this work proposes a trustworthy federated learning framework for individual device identification. It first analyzes the existing metrics for trustworthiness evaluation in FL and organizes them into six pillars (privacy, robustness, fairness, explainability, accountability, and federation) for computing the trustworthiness of FL models. The framework presents a modular setup where one component is in charge of the federated model generation and another one is in charge of trustworthiness evaluation. The framework is validated in a real scenario composed of 45 identical Raspberry Pi devices whose hardware components are monitored to generate individual behavior fingerprints. The solution achieves a 0.9724 average F1-Score in the identification on a centralized setup, while the average F1-Score in the federated setup is 0.8320. Besides, a 0.6 final trustworthiness score is achieved by the model on state-of-the-art metrics, indicating that further privacy and robustness techniques are required to improve this score.
Security and Scams

In the world of cryptocurrency, security is a top priority. With digital assets, there is a risk of hacking, theft, and scams, which can result in the loss of significant amounts of money. In traditional finance, if you are a victim of fraud or theft, you can typically file a dispute with your bank or credit card company to recover your funds. However, in the world of cryptocurrency, there is no central authority that oversees transactions or provides refunds. Cryptocurrencies are decentralized, which means that there is no central authority, such as a bank or government, that controls them. Instead, transactions are validated and recorded on a decentralized public ledger.
Understanding the Blockchain

Let me start by apologizing that I will have to go into a bit of technical detail here and I realize not everyone is as passionate about technology as I am. However, blockchain technology is such an important part of this book that I feel it’s important for you to at least understand the basics of the technological aspects of it. It is the foundation that crypto is based upon. So I will keep it as light as possible, but you need to get through this to understand the full dynamics of crypto and the impact it has on the world. Enjoy.
Final Thoughts: The Future of Crypto

To conclude this book, the average person like you and me is likely to notice the impact of crypto and blockchain on their daily life in some of the following ways:
Anomaly Detection in Surveillance Videos Using Spatio-Temporal Context Information

Several computer vision algorithms have been proposed to detect anomalous activities (robberies, murders, vandalism, among others) in videos. According to the learning approach, they can be classified into probabilistic distribution modeling, sparse coding, and deep learning based methods. The main drawbacks of these approaches are (i) extraction of low-level features that do not capture complex behaviors of instances on the scene, (ii) generation of features from irrelevant regions, (iii) overlooking of relationships among objects, and (iv) omission of long-term dependencies. To solve these issues, we propose a deep learning architecture that leverages the relationships among objects. It achieves this by using an attention mechanism and learning long-term dependencies using a multilayer recurrent neural network (multilayer LSTM). An AUC score of 0.749 on the UCF-Crime dataset confirms that the proposed algorithm competes effectively against several state-of-the-art approaches for anomaly detection in surveillance videos. It also explains the relationship between regions in the video frames and the anomaly detections.