Reviewerly/exHarmony huggingface dataset:
Design of an Arduino Uno-Based Garden Security System for Corn from Pests Using RTC as a Timer

This study aims as a security system for corn plants from Arduino-based pests using a DC to AC inverter with a 220V output as the output, and designing the Arduino Uno microcontroller system to work automatically by using the RTC as a timer, aims to make it easier for corn farmers to operate this equipment. Swine pests are a serious threat to farmers because they can cause crop failure, many farmers are already bothered by the presence of pig pests. The results of this study are a 12 Volt 6 AH battery as a voltage source to provide voltage to the Arduino Uno microcontroller, RTC relay, and inverter circuit as output. The RTC sensor is used as a timer, which disconnects and connects the battery current to the inverter using a 5v dc relay. This tool works at night for 12 hours, from 18.00 to 06.00 in the morning, and from 06.01 to 18.01 the RTC module will actively give a signal to the relay to connect and disconnect the battery voltage current to the inverter. With this tool, it makes it easier for farmers to monitor the safety of the garden corn plants from pests, and no longer need to go to the garden at night for manual installation.
The Determinants of Cryptocurrency Returns

This study examines the determinants of cryptocurrency returns, namely Trading Volume, Price Volatility, and Market Capitalization. This study uses Bitcoin, Ethereum, Tether, USD Coin, and BNB, which are the Top 5 largest Cryptocurrency Market Capitalizations in 2022. The testing method used in this study is a panel data regression analysis. The results of this study prove that Trading Volume and Price Volatility have a significant and positive effect on Cryptocurrency Returns. In contrast, Market Capitalization does not substantially impact Cryptocurrency Returns. If investors want to earn high returns, then it is advisable to choose cryptocurrencies with high trading volume and high price volatility. But keep in mind, with high price volatility can also mean a greater risk of the coin.&#x0D; Keywords: Cryptocurrency; Trading Volume; Price Volatility; Market Capitalization; Return &#x0D; &#x0D; Abstrak&#x0D; Penelitian ini bertujuan untuk menguji berbagai factor penentu imbal hasil cryptocurrency yaitu Volume Perdagangan, Volatilitas Harga dan Market Capitalization. Studi ini menggunakan Bitcoin, Ethereum, Tether, USD Coin, dan BNB yang merupakan Top 5 Market Capitalization Cryptocurrency terbesar tahun 2022. Metode pengujian yang dilakukan dalam penelitian ini yaitu uji analisis regresi data panel. Hasil dari penelitian ini membuktikan bahwa Volume Perdagangan dan Volatilitas Harga berpengaruh signifikan dan positif terhadap Return Cryptocurrency, sedangkan Market Capitalization tidak memiliki pengaruh yang signifikan terhadap Return Cryptocurrency. Jika investor ingin mendapatkan return yang tinngi, maka disarankan untuk memilih cryptocurrency yang memiliki Volume Perdagangan dan Volatilitas Harga yang tinggi. Namun yang perlu diingat adalah, dengan tingginya Volatilitas Harga berarti pula tingginya risiko koin crypto tersebut.&#x0D; Kata Kunci: Mata uang kripto; Volume Perdagangan; Volatilitas Harga; Kapitalisasi pasar; Pengembalian
WUJUD BUDAYA INDONESIA SEBAGAI PEMANTIK MOTIVASI BELAJAR BAHASA INDONESIA SEBAGAI BAHASA ASING

Various Indonesian cultures can be used as teaching materials in BIPA learning. The purpose of this research is to examine Indonesian culture which is used as teaching material in BIPA learning from YouTube videos. This study used descriptive qualitative method. The objects of this research are three YouTube videos entitled Intoduction to Wayang Culture in BIPA Learning, Culture in BIPA Teaching, and Cross-Cultural Understanding in BIPA Learning. Data Analysis wa carried ut through data reduction, data description, and drawing conclusions. Based on data analysis, it is known that the Indonesian culture used as teaching materials in BIPA learning varies. The results pf this study can be used by BIPA teachers to utilize all aspects related to culture such as wayang, museums, cross-cultural understanding, and others through YouTube videos for BIPA teaching materials.
Ketidaksantunan Berbahasa Mahasiswa dalam Proses Perkuliahan (Studi pada Program Studi Tadris Bahasa Indonesia UIN Fatmawati Sukarno Bengkulu)

This study aims to describe the form of impoliteness in student language in the lecture process. The study was conducted on students of the Indonesian Language Tadris Departement at Fatmawati Sukarno Bengkulu State Islamic University (UIN). The data analysis method used is descriptive-qualitative. This study found violations or impoliteness in: 1) maxims of wisdom in 3 utterances; 2) modesty maxim of 4 utterances; 3) sympathy maxim of 2 utterances; 4) the maxim of appreciation is 4 utterances; 5) the maxim of agreement is 1 utterance; 6) the maxim of agreement is not found. This research concludes that the impoliteness in the language of Indonesian Tadris Departement students at UIN Fatmawati Sukarno is found in the maxims of impoliteness, tact, humility, sympathy, appreciation, and agreement
Peningkatan Kemampuan Siswa dalam Menulis Teks Deskripsi melalui Model Pembelajaran Quantum Writing

This study aims to find out how the quantum writing method can help improve the ability to write descriptive text in junior high school students. This study took the case of class VII students of MTs Nurul Ula Burai. The population in this study were all 47 class VII students of MTs Nurul Ula Burai. The sample in this study was obtained using cluster random sampling technique. So that class VII 1 with a total of 22 students was chosen as the experimental class and VII 2 with a total of 25 students as the control class. This study uses the experimental method. The data analysis technique used is the test data analysis technique and documentation. The results showed that the average value of the test results (experimental class) that used the quantum writing method in the ability to write descriptive text with an average test result score of 82.88, was higher than the control class that did not use the quantum writing method with an average score of average test result 62.62. For the results of hypothesis testing using the T test, it is known that the t-count is 8.20 compared to the t-table of 2.01, so the results of the t-count are greater than the t-table (8.20 &gt; 2.01), so it can be concluded that the model learning quantum writing has a major influence on improving the ability to write descriptive texts for junior high school students.
The Pre-Service Early Childhood Education Teacher in Developing Child's Story Book

&lt;em&gt;This research aims to describe prospective PAUD teacher students' ability to develop their creativity when making storybooks for early childhood. This qualitative descriptive study involved 12 prospective PAUD teacher students taking the Creativity and Talents course in the PAUD PG Study Program at a university in Surabaya. The data was obtained from the results of collecting assignments to make story books, while data analysis was done by assessing the percentage through observing the results of making story books based on indicators of creativity in novel story ideas, making storylines, and arranging words in story narrative sentences as well as illustrating pictures and coloring them. The results of research on the creativity of PAUD teacher candidates show that creativity in developing storybooks is good. This can be seen from the results of the percentage of new ideas (84.09%), storylines (76.87%), language (61.12%), and illustrated pictures (98.17%). Based on these results, it is hoped that future PAUD teacher students will be able to make storybooks for children on the theories and needs that exist in their respective schools and it turns out that they can overcome limitations in illustrating pictures with the help of technology and big data available on the internet.&lt;/em&gt;
A Comprehensive Review of the Evolution and Future Directions of Digital Forensic Investigation Model

Digital forensics investigations play an essential role in modern law enforcement, national security, and civil litigation. To conduct a successful investigation, it is crucial to choose an appropriate digital forensics investigation model that fits the investigation's context, scope, purpose, and methods. This paper presents an overview of the main factors that should be considered when selecting a digital forensics investigation model, including the type of incident, legal and ethical requirements, technical and operational capabilities, and complexity of the scenario. Additionally, this paper describes 11 digital forensics investigation models, ranging from simple to complex, each with its own strengths and weaknesses. By understanding the advantages and limitations of each model, digital forensics professionals can choose the most suitable model for their specific investigation. Keywords—Digital forensics investigations, Forensic analysis, Investigation models, Forensics Model
Path planning for intelligent vehicles based on improved D* Lite

Typical path planning algorithms are good for static obstacles avoidance, but not for dynamic obstacles, so path planning of intelligent vehicles in uncharted regions is a fundamental and critical problem. This study suggests an improved D* Lite algorithm to address the issues of large corner, node redundancy and close to obstacles in the path planned by D* Lite algorithm. Firstly, in order to increase the safety of the path, the D* Lite algorithm sets the safety distance between the intelligent vehicle and obstacles. Then, the kinematic constraints of intelligent vehicles are introduced to increase the path search direction and avoid path corners exceeding the steering maneuverability of intelligent vehicles. Next, the path is optimized, and the optimization process of removing redundant points is employed to tackle the problem of curved search path and redundant nodes, and the path is smoothed by using third-order Bezier curve to generate a path with continuous curvature. Finally, the enhanced D* Lite algorithm is fused with the improved dynamic window approach to achieve real-time obstacle avoidance based on the global optimal path for moving obstacles. Simulation studies in static and dynamic contexts are used to demonstrate the usefulness of the revised D* Lite algorithm. The results show that compared with other path planning methods, the path generated by the proposed method has more safety and smoothness features, and improves the path quality. Therefore, the proposed algorithm has certain effectiveness and superiority in path planning problems in static and dynamic environments.
Current trends of library automation Academic library in Bangladesh

This research focuses on current trends of library automation in Rajshahi University Central Library. This research try to find out RUCL various work sections and services present status. The work is confined on one university library in Bangladesh. It try to investigate on different aspects and function. Finally the work find out some problems and suggestions on rajshahi university central library automation system.&#x0D; This study provides a bird’s eye on automation system rajshahi university central library view but don’t give a total idea on library automation system in Bangladesh
Analysis of the kinetic path of some physical measurements and kinetic abilities in terms of the direct attack in the foil

Through the researchers' acquaintance with the previous studies, the problem was identified as that the preparation of training curricula in all its units must be based on accurate scientific foundations. Positively affect the type of attack and its implication in the presence of correlational relations, whether direct or indirect, i.e., precedence in training and in preparing units Therefore, the researcher decided to build a causal model to know the relationships to show the best model of the direct straight attack. The study aimed to build a causal model for the most important physical measurements and kinetic capabilities of the direct straight attack in the research sample. The two researchers used the descriptive approach in the manner of causal relationships, so the two researchers conducted the experiment on a sample of the second stage students, whose number is (85) students out of (109) students, and they were chosen according to the equation. Stephen Sampson After conducting the exploratory experiment and performing the measurements and tests of the research, they were classified and treated statistically, so they conclude the validity of the causal model in stating the precedence of the variables.
Sustainable Technological Development Planning of Artificial Intelligence-Based Learning Platforms (AILPs)

To date, the selection of AI tools has lagged behind work on other software-specific aspects of modern learning platform development. We designed a novel technology relevance assessment methodology based on an expert Delphi survey and multicriteria analysis. Then, we applied the results to plan further development of the prototype platform and to build its exploitation strategy. The lessons learned while planning and developing this platform can be applied to a large class of similar systems.
The Non-Zero-Sum Game of Steganography in Heterogeneous Environments

The highly heterogeneous nature of images found in real-world environments, such as online sharing platforms, has been one of the long-standing obstacles to the transition of steganalysis techniques outside the laboratory. Recent advances in identifying the properties of images relevant to steganalysis as well as the effectiveness of deep neural networks on highly heterogeneous datasets have laid some groundwork for resolving this problem. Despite this progress, we argue that the way the game played between the steganographer and the steganalyst is currently modeled lacks some important features expected in a real-world environment: 1) the steganographer can adapt her cover source choice to the environment and/or to the steganalyst’s classifier, 2) the distribution of cover sources in the environment impacts the optimal threshold for a given classifier, and 3) the steganalyst and steganographer have different goals, hence different utilities. We propose to take these facts into account using a two-player non-zero-sum game constrained by an environment composed of multiple cover sources. We then show how to convert this non-zero-sum game into an equivalent zero-sum game, allowing us to propose two methods to find Nash equilibria for this game: a standard method using the double oracle algorithm and a minimum regret method based on approximating a set of atomistic classifiers. Applying these methods to contemporary steganography and steganalysis in a realistic environment, we show that classifiers which do not adapt to the environment severely underperform when the steganographer is allowed to select into which cover source to embed.
Permasalahan Sekolah Modern dan Tantangan yang Dihadapinya

This article discusses the challenges faced by modern schools in providing quality education and the challenges they must overcome to address these issues. Common problems in modern schools include overloaded curriculum, technological dominance, excessive workload, and limited facilities and infrastructure. One major issue is an overloaded curriculum, where students face pressure to absorb a vast amount of information within limited timeframes, leaving little room for the development of their interests and talents. Technological dominance also has a significant impact, as excessive reliance on technology can reduce social interaction and hinder students' critical thinking abilities. Excessive workload is another commonly faced problem for students in modern schools, resulting in stress and an imbalance between academic activities and personal life. Additionally, limitations in facilities and infrastructure, such as inadequate facilities and insufficient infrastructure, can restrict students' learning experiences. The challenges faced by modern schools include the development of relevant and flexible curricula, as well as the wise integration of technology to optimize learning. Managing a balanced workload for students and improving school facilities and infrastructure are also challenges that need to be addressed. In addressing these challenges, collaboration among schools, teachers, parents, and relevant stakeholders is key. Continuous teacher training, the implementation of innovative teaching models, and adequate funding are necessary to achieve sustainable improvements in education. Through a deep understanding of the problems faced by modern schools and the challenges they encounter, it is hoped that this article will provide valuable insights for stakeholders in addressing educational issues and promoting better progress for students and society as a whole.
A generative target detection algorithm based on diffusion model

Inspired by the Diffusion Model, this paper proposes a Self-attention-generating object detection algorithm (SAG-D). The algorithm uses self-attention to extract features and fuse them into the backbone network, so as to obtain more effective features to guide the decoder to find the appropriate RoI, and uses the principle of diffusion model to continuously correct the simulation box with noise to generate the predicted ground truth box. In the end, The experiments show that the proposed algorithm achieves very good results on MS COCO dataset.
HierChain: A Hierarchical Blockchain-Based Data Management System for Smart Healthcare

Healthcare is a crucial element of human lives that produces a substantial amount of medical data every year. A major difficulty faced by e-Health systems is the secure storage and sharing of this data without compromising its integrity and privacy. Being a trustless, traceable, and immutable technology, blockchain has the potential to address these issues. In this paper, we propose HierChain, a hierarchical blockchain-based data storage and sharing system for healthcare. This framework provides a trustless environment that is decentralized and tamper-resistant for efficient management of data. We first introduce an optimization problem aimed at identifying the optimal data storage solution for maximum efficiency. Next, we classify the health data based on its specific features, sensitivity, and storage requirements. This classified data is then stored on three different blockchains (Ethereum, Hyperledger Sawtooth, and MultiChain) according to their optimal attributes. We utilize fog nodes for providing computational services to the resource-constrained Internet of Things (IoT) nodes, enabling us to perform data preprocessing and eliminate redundant information from the collected data. Moreover, we use differential privacy on the fog layer to ensure that sensitive medical data remains protected throughout the analysis process. Finally, we present a comprehensive attack evaluation and performance analysis by implementing our framework on a real-world medical dataset. The simulation results indicate that HierChain outperforms existing algorithms in terms of scalability and security of health data without degrading the performance of the IoT network.
Towards Continual Egocentric Activity Recognition: A Multi-Modal Egocentric Activity Dataset for Continual Learning

With the rapid development of wearable cameras, it is now feasible to considerably increase the collection of egocentric video for first-person visual perception. However, the development is hindered by a shortage of multi-modal egocentric activity datasets. Furthermore, the catastrophic forgetting problem of multimodal continual activity learning, as a branch of continual learning, has not been thoroughly explored, which makes accumulating a larger collection of multi-modal activity data more urgent. To address this shortage, we propose a multi-modal egocentric activity dataset for continual activity learning named UESTC-MMEA-CL in this paper. The dataset is collected using our self-developed glasses with a first-person camera and wearable sensors, and it contains synchronized data of video, accelerometers, and gyroscopes for 32 types of daily activities performed by 10 participants who wore our glasses. Statistical analysis of the sensor data is given to show the auxiliary effects of activity recognition. We report the results of egocentric activity recognition of three modalities (RGB, acceleration, and gyroscope) separately and jointly on a base network architecture. We thoroughly evaluated four baseline methods with different multimodal combinations to explore the catastrophic forgetting in continual learning on UESTC-MMEA-CL. We hope that the UESTC-MMEA-CL dataset can act as a facilitator for future studies on continual learning for first-person activity recognition in wearable applications. You can download preliminary data from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ivipclab.github.io/publication\_uestc-mmea-cl/mmea-cl</uri> . The data is currently used to solve the problems of multimodal continual learning of activities.
5G Aviation Networks Using Novel AI Approach for DDoS Detection

The advent of Fifth Generation (5G) technology has ushered in a new era of advancements in the aviation sector. However, the introduction of smart infrastructure has significantly altered the threat landscape at airports, leading to an increased vulnerability due to the proliferation of endpoints. Consequently, there is an urgent requirement for an automated detection system capable of promptly identifying and thwarting network intrusions. This research paper proposes a deep learning methodology that merges a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) to effectively detect various types of cyber threats using tabular-based image data. To transform time series features into 2D texture images, Gramian Angular Fields (GAFs) are utilized. These images are then stacked to form an N-channel image, which is fed into the CNN-GRU architecture for sequence analysis and identification of potential threats. The provide solution GAF-CNN-GRU achieved an accuracy of 98.6% on the Cranfield Embedded Systems Attack Dataset. We further achieved Precision, Recall and F1-scores of 97.84%, 91% and 94.3%. To evaluate model robustness we further tested this approach, using a benchmark random selection of input features, on the Canadian Institute for Cyber-Security (CIC) 2019 Distributed Denial-of-service attack (DDoS) Dataset achieving an Accuracy of 89.08%. Following feature optimisation our approach was able to achieve an accuracy of 98.36% with Precision, Recall and F1 scores of 93.09%, 95.45% and 94.56% respectively.