Reviewerly/exHarmony huggingface dataset:
E-Modul Puisi Berbasis Aplikasi Desia untuk Siswa Kelas X SMA

&lt;div align="center"&gt;&lt;table width="645" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="439"&gt;&lt;p class="Abstrak"&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This research development has the goal of producing poetry &lt;em&gt;e-modul &lt;/em&gt;and DESIA&lt;em&gt;, &lt;/em&gt;describing the appropriateness, and the effectiveness of e-&lt;em&gt;modul&lt;/em&gt;. This development follows the phases of ASSURE such as analyze learner characteristics, state performance objective, select methods, media, and materials, utilize materials, requires learners’ participants, evaluate and revise. The experiment carried out in control as well as experiment group to create poetry e-&lt;em&gt;modul&lt;/em&gt;&lt;em&gt; &lt;/em&gt;with DESIA basis, the acquired score of targeted teaching and learning activities proven to be successfully passed and effectively to be used in the activities of teaching for tenth grade Senior high school students. This e-&lt;em&gt;modul&lt;/em&gt; has been positively accepted by the students and the Indonesian Language teachers.&lt;/p&gt;&lt;p class="Abstrak"&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Penelitian pengembangan ini bertujuan untuk menghasilkan &lt;em&gt;e-modul &lt;/em&gt;puisi berbasis aplikasi DESIA&lt;em&gt;, &lt;/em&gt;mendeskripsikan kelayakan &lt;em&gt;e-modul&lt;/em&gt;, dan mendeskripsikan tingkat efektivitasnya. Model pengembangan yang digunakan adalah ASSURE. Uji coba dengan desain kuasi eksperimen di kelas kontrol dan kelas eksperimen menghasilkan &lt;em&gt;e-modul &lt;/em&gt;puisi berbasis DESIA, skor kelayakan bahan ajar dengan kategori sangat layak, dan bahan ajar terbukti efektif untuk diaplikasikan pada pembelajaran puisi bagi siswa kelas X. &lt;em&gt;E-modul &lt;/em&gt;ini juga direspon positif oleh siswa dan guru bahasa Indonesia.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
Meta Analisis Pengaruh Model Blended Learning terhadap Hasil Belajar IPS

&lt;div align="center"&gt;&lt;table class="KisiTabel1" width="645" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="439"&gt;&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The purpose of this study was to reanalyze the existence of a blended learning model to improve social studies learning outcomes. The method used in this research is meta-analysis. This research activity includes problem formulation, tracing, and analyzing relevant research results. Data collection was carried out by searching electronic journals through Google Scholar with the keywords "learning outcomes", "blended learning", "SD social studies learning". Based on the search results, 65 articles were obtained, but based on these articles, only 10 relevant articles were obtained. Data analysis was carried out using a qualitative descriptive method. The results of data analysis showed that the blended learning model was able to improve student learning outcomes starting at the lowest result of 1.65% to the highest result of 64.32% with an average result of 23.32%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Tujuan penelitian ini adalah untuk menganalisis kembali adanya model &lt;em&gt;blended learning&lt;/em&gt; terhadap peningkatan hasil belajar IPS. Metode yang digunakan dalam penelitian ini adalah meta analisis. Kegiatan penelitian ini, meliputi perumusan masalah, menelusuri, dan menganalisis hasil penelitian yang relevan. Pengumpulan data dilakukan dengan menelusuri jurnal elektronik melalui Google Scholar dengan kata kunci “hasil belajar”, “&lt;em&gt;blended learning&lt;/em&gt;”, “pembelajaran IPS SD”. Berdasarkan hasil penelusuran tersebut diperoleh 65 artikel, namun berdasarkan artikel tersebut hanya diperoleh 10 artikel yang relevan. Analisis data dilakukan dengan metode deskriptif kualitatif. Hasil analisis data menunjukkan bahwa model &lt;em&gt;blended learning&lt;/em&gt; mampu meningkatkan hasil pembelajaran siswa dimulai pada hasil terendah 1,65% sampai pada hasil yang tertinggi 64,32% dengan hasil rata-rata 23,32%.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
Mapping of Evacuation Paths and Gathering Points in Efforts to Reduce Disaster Risk in Taman Baru Village Sekotong West Lombok to Create a Disaster-Responsive Village

Taman Baru Village, Sekotong, West Lombok Regency is one of the areas under a hill valley and surrounded by hills with a height of about 0-1000 meters above sea level. The average level of rainfall in this area is 1381 mm with an average number of rainy days of 120 days so that this area is often affected by disasters, such as floods and landslides. Therefore, the Taman Baru Village needs to be wary of, considering that disasters can happen at any time. Thus, it is necessary to have disaster mitigation, namely the mapping of evacuation routes and gathering points and their visualization. These goals of this program are the existence of evacuation routes and gathering points, which are devoted to tidal floods, earthquakes and landslides. The results of this work program are also expected to be the best means that can facilitate the community in evacuating themselves when a disaster occurs. These output of this activity were a disaster mitigation leaflet in Taman Baru Village, a map of evacuation routes and gathering points in Taman Baru Village and their visualization (installation of evacuation route signs and gathering points that can provide instructions for people who want to evacuate themselves in the event of a disaster). The outcome of this activity is the increased awareness and understanding of the people of Taman Baru Village on disaster mitigation, especially earthquakes, tsunamis, landslides and floods
Efficient Object Detection Acceleration Methods for Autonomous-driving Embedded Platforms

Object detection in autonomous vehicles is typically operated in an embedded system to reduce power consumption. The use of an object detection algorithm with high accuracy and real-time detection speed in the embedded systems is essential for ensuring safe driving. This study proposes a parallel processing method for GPU and CPU operations to enhance the detection speed of the model. In addition, this study proposes data augmentation and image resize techniques that consider the camera input size of autonomous driving, which increases the accuracy significantly while improving the detection speed. The application of these proposed schemes to a baseline algorithm, tiny Gaussian YOLOv3, improves the mean average precision by 1.14 percent points (pp) for the Berkeley Deep Drive (BDD) dataset and 1.34 pp for the KITTI dataset compared to the baseline. Furthermore, in the NVIDIA Jetson AGX Xavier, which is an embedded platform for autonomous driving, the proposed algorithm improves the detection speed by 22.54 % for the BDD, and 24.67 % for the KITTI compared to the baseline, thereby enabling high-speed real-time detection on both datasets.
Digital Image Encryption and Decryption based on RSA Algorithm

Digital images are crucial in many areas, including online communication, multimedia systems, medical imaging, and military communications. Color images are being stored and transmitted over the internet and wireless networks in large amounts and thus it is necessary to protect them from any unauthorized user access. Cryptography is the art of codifying messages, so that the messages become unreadable, this way it plays a vital role in the field of security of data. There are several Cryptographic Algorithms to encrypt and decrypt Images. This paper aims to Encrypt and Decrypt Images based on RSA Algorithm providing Authentication and using Image Hash functions for additional Security and Integrity of images. This project also makes sure the Image retains its quality and is not corrupted even after decryption.
The study on the opinion survey on the professionalism security plan of public officials in charge of youth development : Focusing on in-depth interviews with youth-related public officials in Gyeonggi-do

The purpose of this study is to explore opinions on securing the professionalism of public officials in charge of youth development through in-depth interviews with youth-related public officials in Gyeonggi-do. The research method is qualitative research, and the main data collection method is in-depth interview. In-depth interviews were conducted for 8 participants. As for the criteria for in-depth interview, five job performance skills(tools) were extracted and implemented through a theoretical background for a plan to secure professionalism. The main analysis results of the plan to secure professionalism are as follows. First, public officials in charge of youth development shall be increased and assigned to the department in charge of youth development. Second, a systematic policy delivery system, such as using the name 「youth」 should be established centering on the department in charge of youth development. Third, the scope of job of public officials in charge of youth development should be legislated. Fourth, the public official in charge of youth development should carefully consider the appointment plan from a term-based public official to a local professional career officer. Fifth, long-term education should be continuously received to improve the professionalism of public officials in charge of youth development. Finally, a professional organization should be established to defend the rights and interests of public officials in charge of youth development. Based on these results, it is intended to be used as basic research data.
Game on Random Environment,  Mean-field Langevin System and Neural Networks

In this paper we study a type of games regularized by the relative entropy, where the players' strategies are coupled through a random environment variable. Besides the existence and the uniqueness of equilibria of such games, we prove that the marginal laws of the corresponding mean-field Langevin systems can converge towards the games' equilibria in different settings. As applications, the dynamic games can be treated as games on a random environment when one treats the time horizon as the environment. In practice, our results can be applied to analysing the stochastic gradient descent algorithm for deep neural networks in the context of supervised learning as well as for the generative adversarial networks.
Development of a Knowledge-Based System for Diagnosing of Diesel Engines

Knowledge-Based Systems apply Artificial Intelligence techniques to solve difficult problems in complex systems that well-trained experts can only manage. These systems can support the decision-making of inexperienced people with the necessary tools to do work that requires high expertise. These systems depend on three main resources: human expertise, experiments, and previous observations. Furthermore, Knowledge-Based Systems reduce the complexity of operation and implementation, making them flexible and easy to understand. The combination of knowledge-based diagnostic methods with recording and monitoring of operating variables; furthermore, adding them to the knowledge base improves the efficiency and reliability of detecting the machine’s behaviour and the effectiveness of the whole system. The aim of the study is to develop a Knowledge-Based System including five stages that could be improved separately to optimize the operation of the machines. In addition, this system allows the evaluation, updating, modification, and integration of the rules in the knowledge base, which results in efficiency improvement of the machines’ operation. Firstly, this paper briefly introduces the different methods to analyze knowledge obtained from human experts in the most effective way. We aimed to maintain the quality of the information and define the effect that experts and the types of machines being understudied on selecting the most suitable method to deal with this information to form the final knowledge base. After it, we reviewed the theories that deal with uncertain and qualitative information and the most appropriate theory for the Knowledge-Based System. Finally, different directions in software tools for Expert Systems development were reviewed. The main added value of the study is the development of the new Knowledge-Based System, which can be handled more flexibly by inexperienced users and increase the reliability and efficiency of the marine diesel engines.
Instant power

Quantum batteries that recharge in a flash could accelerate the electric car revolution, says Jon Cartwright
Quantum Cryptography and its Application

Present cryptography algorithms are based on mathematical problem such as factoring two large prime numbers and N vs NP problem. But computing power and evolution in mathematics increases day by day so it’s easy to reverse one way function to quickly such as that of factoring large integers. So the solution is to build a cryptosystem which secure our communication even the computing power is to high so we introduce quantum physics in cryptography which lead to evaluation of quantum cryptography. Quantum cryptography one of the emerging topics in field of computer industry. This paper is an attempt to review fundamentals of quantum cryptography and also analyses few application areas of quantum cryptography and it’s limitation.
Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants

In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games. We explore how to transfer trained parameters of these architectures based on shared semantics of channels in the state and action representations of the Ludii general game system. We use Ludii's large library of games and game variants for extensive transfer learning evaluations, in zero-shot transfer experiments as well as experiments with additional fine-tuning time.
Sentiment Analysis of Twitter Data using Naive Bayes Method

Asian Journal of Organic & Medicinal Chemistry includes communications, full papers (articles), short papers, green chemistry, reviews, and perspectives. Specifically, these include synthesis, synthetic methods and regents, mechanism of reactions, biologically active compounds with their possible mechanism of action, natural products, in vitro and in vivo tests of compounds, computational chemistry, physical-analytical-inorganic chemistry, nanotechnology, drug carriers, proteomics and genomics. Rapid synthesis of new compounds following eco-friendly methods and their functions as medicinally active agents is certainly the central part of chemical and medicinal research in modern times. On the basis of this fact, significant emphasis is placed to rapid publication and disseminates original discoveries in this new journal.
Continual Learning in Neural Networks

Artificial neural networks have exceeded human-level performance in accomplishing several individual tasks (e.g. voice recognition, object recognition, and video games). However, such success remains modest compared to human intelligence that can learn and perform an unlimited number of tasks. Humans' ability of learning and accumulating knowledge over their lifetime is an essential aspect of their intelligence. Continual machine learning aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn online from a non-stationary and never-ending stream of data. A key component of such a never-ending learning process is to overcome the catastrophic forgetting of previously seen data, a problem that neural networks are well known to suffer from. The work described in this thesis has been dedicated to the investigation of continual learning and solutions to mitigate the forgetting phenomena in neural networks. To approach the continual learning problem, we first assume a task incremental setting where tasks are received one at a time and data from previous tasks are not stored. Since the task incremental setting can't be assumed in all continual learning scenarios, we also study the more general online continual setting. We consider an infinite stream of data drawn from a non-stationary distribution with a supervisory or self-supervisory training signal. The proposed methods in this thesis have tackled important aspects of continual learning. They were evaluated on different benchmarks and over various learning sequences. Advances in the state of the art of continual learning have been shown and challenges for bringing continual learning into application were critically identified.
Scalable Building Height Estimation From Street Scene Images

Building height estimation plays an essential role in many applications such as 3D city rendering, urban planning, and navigation. Recently, a new building height estimation method was proposed using street scene images and 2D maps, which is more scalable than traditional methods that use high-resolution optical images, RADAR, or LiDAR data, which are proprietary or expensive to obtain. The method needs to detect building rooflines to compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images where buildings occlude each other or are blocked by other objects such as trees since rooflines can be difficult to locate. To address these limitations, we propose a robust building height estimation method that computes building height simultaneously from street scene images with an orientation along the street and images facing the building with an upward-looking view. We first detect roofline candidates from both types of images. Then, we use a deep neural network called RoofNet to classify and filter these candidates and select the best candidate via an entropy-based ranking algorithm. When the true roofline is identified, we compute building height via the pinhole camera model. Experimental results show that the proposed RoofNet model yields a higher accuracy on building corner and roofline candidate filtering compared with state-of-the-art open-set classifiers. Our overall building height estimation method outperforms the baseline by up to 11.9% in accuracy and achieves 92.8% in height estimation error within 4 meters on the collected data set.
A Concise Explanation of the Universe

The dynamic universe field plays an ongoing picture of coverages of space by versions of numbering systems.
Book review

The expectation maximization (EM) algorithm is the most enduring way to estimate the parameters of Gaussian mixture models. However, use the EM algorithm needs to know in advance the true number of mixing components. Therefore, unless this key information is available, it is usually not straightforward to perform this algorithm. On the other hand, its performance highly depends on the initial parameters. To alleviate these problems, a new model selection criterion, i.e., the desirability level criterion, is proposed to choose the number of components. In particular, we proposed a variable step until find either coincides with the actual number or slightly exceeds it, which maximize the value of the desirability level criterion that provides an efficient index to quantify the distance between the Gaussian mixture model fits the observation data. Furthermore, unwanted components can be suppressed by setting the threshold of the desirability level criterion. Numerical examples are provided to illustrate the effectiveness of our desirability level criterion.
RGB Based Secure Share Creation in Steganography with ECC and DNN

Image steganography is a technique for concealing sensitive data inside an image. Since traditional image steganography encrypts sensitive data in the host image, the payload capability is often disregarded, and steganographic image quality for the Human Visual System (HVS) must be improved. As a result, we proposed a new deep learning-based multiple image steganography approach in this work. The secret image is embedded into the cover image using a Deep Neural Network (DNN), and the embedded cover image is then encrypted using Elliptic Curve Cryptography (ECC) is used to improve the image’s anti-detection properties. The Deep Neural Network is a combination of Pre-processing, Hiding, and Revealing networks that enable steganography and extraction of full-size images to enhance steganographic performance. The results of the experiments show that the network can successfully embed up to three secret images into the cover image without causing a major difference in the Structural Similarity Index (SSIM). Furthermore, the image created using this steganography method has higher SSIM and Peak Signal to Noise Ratio (PSNR) values, with values of 0.99 and 35 dB, respectively.
Medicine Supply Chain Using Ethereum Blockchain

For the past several years, pharma companies are dealing with many problems in tracing their products in the supply chain procedure; due to this problem, it gets effortless for counterfeiters to feature their fake medicines into the drug retail. Every year enterprises lack around $200 billion because of those counterfeit drugs. According to the WHO survey report, in many under-developed countries, every 1 in 10 drugs used by consumers is fake. Thus, there is a requirement of such systems that can trace and track drug delivery at each part so that this forged medicine problem can be resolved. Blockchain Technology has an unlimited potential to overcome such problems in the provision chain method very efficiently. In our proposed system, we have implemented a Drug supply chain management system built on Ethereum Blockchain and can continuously observe and keep track of the drug delivery process.
Motivational factors among social work undergraduates in online education during the post-COVID-19 era

This essay summarises social work students' experiences with online learning during a pandemic and investigates motivational factors post-pandemic. Post-COVID, 56 percent prefer hybrid learning. Students reported that they were the primary source of motivation for online classes, followed by course instructors and the environment.
Template-based balloon-marker and guidewire detection for coronary stents in cardiac fluoroscopy

The placement and visualization of coronary stents during fluoroscopy depends mainly on the detection of balloon markers and their connecting guidewires. In this paper, a novel template-based approach is proposed to detect balloon markers and guidewires in cardiac fluoroscopic images. In particular, guidewires are detected based on balloon markers only, without prior knowledge of the background or guidewire elements. Also, while earlier techniques used circular models of balloon markers, we propose a more realistic elliptical model. Training and the testing datasets for balloon marker and guidewire detection were collected from different Cathlab systems and annotated by an application specialist with 10 years of experience in this field. The balloon-marker detector achieved a precision of 98.5%. Within 3-pixel tolerance, the guidewire detector achieved a matching percentage of 99.5% with the true guidewire using a customized evaluation method. Moreover, the guidewire detector achieved a mean Hausdorff distance of 3.3 pixels (0.6 mm) and a longest-common-substring (LCS) distance with a mean matching percentage of 87% within 1-pixel tolerance. Clinical Relevance- The proposed novel technique of detecting the guidewire offers a constant computational time and insensitivity to the body structures or the guidewire-like elements (such as the surgical wires). This leads to improved stent visualization and reasonable processing times.