Reviewerly/exHarmony huggingface dataset:
PENGARUH MOTIVASI BELAJAR TERHADAP HASIL BELAJAR PADA SISWA KELAS XI SMA NEGERI 73 JAKARTA

Education plays an important role in the development of science and technology by creating skilled human resources. In learning activities, motivation and of course the learning outcomes themselves cannot be separated. Experts say that good learning outcomes are influenced by high motivation. The purpose of this research is to find out whether there is influence between students' learning motivation and student learning outcomes. This research was conducted at SMA Negeri 73 Jakarta Class XI in the even semester of the academic year. This study uses a survey method to find the relationship between variables with a simple linear regression technique. Sampling used was purposive sampling, amounting to 150 students. Instruments in the form of a questionnaire / questionnaire learning motivation and daily test scores of all students of class XI. Based on the results of the hypothesis test, it is known that there is an influence between the learning motivation variable on the learning outcomes of class XI students in the compulsory mathematics subject at SMA Negeri 73 Jakarta. The results showed that the simple regression model can be used to predict learning motivation variables on learning outcomes, or there is an effect of learning motivation variables (X) on learning outcomes (Y), with a significance level of 0.000 &lt; 0.05.
SECURITISATION OF CYBER SPACE - FROM PRISM OF DETERRENCE STRATEGY

&lt;p&gt;Deterrence in cyber space has most often succumbed to the technical and legal limitations of digital and physical world respectively. Cyber deterrence aims to deter an (unwanted) cyber interaction by not only rendering it technically unfeasible but also increasing the ex-post facto costs for the aggressor. Despite major challenges confronting realization of "perfect in domain cyber deterrence", this paper aims to develop a novel subject-object relationship in a given cyber environment and makes a case of achieving credible "cyber deterrence" through comprehensive application of denial and punishment strategies. Doing so, while capitalizing on snap history of cyber interactions, it also projects the potential "cyber escalation ladder" and forecasts the future of cyber deterrence, which can guide the policy makers to meet desired ends. &lt;/p&gt;
KEEFEKTIFAN MODEL GORDON DAN MOODY PADA PEMBELAJARAN MEMBACA PUISI SISWA SMP COBIG ISLAMIC SCHOOL MAKASSAR

This research aimed to; (1) Revealing the ability to read poetry by applying Gordon model; (2) Revealing the ability to read poetry by applying the Moody model; and (3) Revealing whether learning to read poetry through the applying of the Gordon model is more effective than the Moody model. This research used a quasi-experimental design. The instrument was an assessment rubric. The results were; (l) The ability of students to read poetry by applying the Gordon model is in the fairly skilled category with an average score of 62.50; (2) The ability of students to read poetry by applying the Moody model is in the fairly skilled category with an average score of 64.25; and (3) The data analysis showed that, values are obtained sig. (2 tailed) of 0.431 &gt; 0.05 then H0 is accepted and HA is rejected. It can be concluded that the application of the Gordon model is not more effective than the Moody model. Thus, both models are equally effective in learning to read poetry at class VIII junior high school students of COBIG ISLAMIC SCHOOL Makassar.
APAKAH HASIL BELAJAR EKONOMI DIPENGARUHI OLEH MOTIVASI BELAJAR DAN KEAKTIFAN BELAJAR?

Education aims to educate students to become human beings who are knowledgeable, creative, independent, capable, noble and responsible, these goals can be achieved with learning motivation and active learning in learning so that the learning outcomes obtained are good, especially in economic subjects. The purpose of this study was to determine 1) the partial effect of learning motivation variables on economic learning outcomes, 2) the effect of learning activities on economic learning outcomes, 3) the simultaneous influence of learning motivation variables and learning activities on economic learning outcomes in class X IPS SMAN 1 Cerme. The sampling method used is purposive sampling resulting in a sample of 86 students. This study uses primary data obtained from questionnaires. The results of this study indicate that there is a partially significant positive effect between learning motivation on economic learning outcomes with a sig value of 0.021 &lt;0.05 and t count 2.353 &gt; t table 1.98896, and there is a significant positive effect of learning activity variables on economic learning outcomes with sig value 0.019 &lt; 0.05 and t count 2.399 &gt; t table 1.98896. Also simultaneously there is a significant positive effect between learning motivation and learning activities on economic learning outcomes with a sig value of 0.000 &lt;0.05 and a calculated F value of 27.079 &gt; F table 3.11 and with an Adjusted R-squared value of 0.380 the contribution of the independent variables is 38%. .
Amadeus Migration Process a Simulation-Driven Process to Enhance the Migration to a Multi-Cloud Environment

With the development of the cloud offers, we observe a prominent trend of applications being migrated from private infrastructure to the cloud. Depending on the application’s complexity, the migration can be complex and needs to consider several dimensions, such as dependency issues, service continuity, and the service level agreement (SLA). Amadeus, the travel industry leader, had partnered with Microsoft to migrate its IT ecosystem to the Azure cloud. This work addresses the specificity of cloud-to-cloud migration and the multi-cloud constraints. In this paper, we summarise the Amadeus Migration process. The process aims to drive the migration from an initial private cloud environment to a target environment that can be a public or hybrid cloud. Further, the process focuses on a prediction phase that guides the migration process. This paper expects to provide an efficient decision-making process that guides managers and architects to optimise and secure their migration process while considering micro-servicesoriented applications targeting an efficient deployment over multi-cloud or hybrid cloud. The prediction relies on the network simulation to predict applications’ behaviour in the cloud and evaluate different scenarios and deployment topologies beforehand. The objective is to predict migrated applications’ behaviour and identify any issue related to the performance, the application’s dependency on other components, or the deployment in the cloud. The migration process proposed in this paper relies on SimGrid, a toolkit developed by INRIA[52] for distributed application modelling. This framework offers a generic process to model IT infrastructure and can assist cloud-to-cloud migration. Specific attention is given to predictive and reactive optimisations. The first results show predictive optimisation's impact on securing KPI and reactive optimisation to optimise the solution cost. Thus, we reach an average cost reduction of 40% in comparaison with the same deployment strategy while keeping the same SLA.
IoT in Practice: Investigating the Benefits and Challenges of IoT Adoption for the Sustainability of the Hospitality Sector

Enhancing the sustainability of the hospitality sector with technology is essential to achieving growth whilstalso reducing the hotel’s impact on the environment. Indeed, the Internet of Things (IoT) concept has recently gained popularity as a new research topic in various industrial disciplines, including the hospitality industry. IoT is being seen and used to transform the hospitality industry for the newly desired sustainable growth. However, it is not all ‘smooth sailing’ as multiple challenges must be addressed by organisations in the hospitality industry when installing IoT. These challenges include cost, security, infrastructure and IoT protocols. Considering the diversity of IoT applications, the paper will examine IoT’s use in hotels whilst also highlighting the challenges hotels face when using IoT. In particular, it will cover the effect of cyber security, including IoT’s protocol layers, potential monitoring and sensor technologies.
Verifiable Data Sharing Scheme for Dynamic Multi-Owner Setting

One of scenarios in data-sharing applications is that files are managed by multiple owners, and the list of file owners may change dynamically. However, most existing solutions to this problem rely on trusted third parties and have complicated signature permission processes, resulting in additional overhead. Therefore, we propose a verifiable data-sharing scheme (VDS-DM) that can support dynamic multi-owner scenarios. We introduce a management entity that combines linear secret-sharing technology, multi-owner signature generation, and an aggregation technique to allow multi-owner file sharing. Without the help of trusted third parties, VDS-DM can update file signatures for dynamically changing file owners, which helps save communication overhead. Moreover, users independently verify the integrity of files without resorting to a third party. We analyze the security of VDS-DM through a security game. Finally, we conduct enough simulation experiments and the outcomes of experimental demonstrate the feasibility of VDS-DM.
Semantic Framework for Query Synthesised 3D Scene Rendering

View synthesis allows the generation of new views of a scene given one or more images. Current methods rely on multiple input images, which are practically not feasible for such applications. While using a single image to create a 3D scene is difficult since it necessitates a solid understanding of 3D settings. To facilitate this, a complete scene understanding of a single-view image is performed using spatial feature extraction and depth map prediction. This work proposes a novel end-to-end model trained on real images without any ground-truth 3D information. The learned 3D features are exploited to render the 3D view. Further, on querying, the target view is generated using the Query network. The refinement network decodes the projected features to in-paint missing regions and generates a realistic output image. The model was trained on two datasets, namely RealEstate10K and KITTI, containing an indoor and outdoor scene.
Efficient ECC-Based Authentication Scheme for Fog-Based IoT Environment

The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. In this paper, we have chosen the safe curve types of elliptic-curve cryptography (ECC) such as M221, SECP256r1, curve 25519, Brainpool P256t1, and M-551. These types of curves are the most secure curves of other curves of ECC as their security is based on the complexity of the ECDLP of the curve. And these types of curves exceed the complexity of the ECDLP. A valid signature can be generated without reestablishing the whole private key. ECDSA ensures data security and successfully reduces intermediate attacks. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.
On Run-Length-Constrained Binary Sequences

A class of binary sequences, constrained with respect to the length of zero runs, is considered. For such sequences, termed (d, k)-sequences, new combinatorial and computational results are established. Explicit expressions for enumerating (d, k)-sequences of finite length are obtained. Efficient computational procedures for calculating the capacity of a (d, k)-code are given. A simple method for constructing a near-optimal (d, k)-code is proposed. Illustrative numerical examples demonstrate further the theoretical results.
How to use Classical Operation in Digital Bits to Simulate Quantum Bits for the RSA Cryptosystem

In quantum algorithm, the Shor's algorithm can find prime decomposition of very big numbers easily and break RSA encryption much faster and more efficiently than in the classical case. How to complicant the public key to slow down the speed and efficiency of Shor's algorithm to secure our RSA encryption scheme is main issue in this study. By using classical random simulation to operation as quantum bits to complicate the RSA cryptosystem, even Shor's algorithm can not to find prime decomposition of very big numbers n with qubits random simulation easily.
Classifying Galaxy Images Using Improved Residual Networks

The field of astronomy has made tremendous progress in recent years thanks to advancements in technology and the development of sophisticated algorithms. One area of interest for astronomers is the classification of galaxy morphology, which involves categorizing galaxies based on their visual appearance. However, with the sheer number of galaxy images available, it would be a daunting task to manually classify them all. To address this challenge, a novel Residual Neural Network (ResNet) model, called ResNet_Var, that can classify galaxy images is proposed in this study. Subsets of the Galaxy Zoo 2 dataset are used in this research, one contains over 28,000 images for the five-class classification task, and the other contains over 25,000 images for the seven-class classification task. The overall classification accuracy of the ResNet_Var model was 95.35% for the five-class classification task and 93.54% for the sevenclass classification task.
Uncertainty Estimation in Neural Networks Through Multi-Task Learning

The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Previous studies have found out that ensemble of NNs typically produce good predictions and uncertainty estimates. Inspired by these, this paper presents a new framework that can quantitatively estimate the uncertainties by leveraging the advances in multi-task learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.
Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks

Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learning, is an optimized solution to localize and detect the presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage object detectors whose resultant model not only detects the presence of weapons but also classifies with respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.
Data Security Through Crypto-Stegano Systems

Unauthorized access and hacking are major issues for internet users, and numerous articles have been published on various approaches to solving this issue. This study proposes a novel method for encoding a hidden message within the text by combining text steganography and substitution cryptography. While steganography and cryptography can be used to protect data, neither is sufficient to provide better security as they can be broken by steganalysis and cryptoanalysis. Therefore, the terms "cryptography-stegano" should be combined for improved security. In this method, data is first encrypted using a substitution cryptography technique to produce the cypher text, which is then encrypted using text steganography to produce a more secure cypher text sent to the recipient. Contrary to popular belief, using image steganography and cryptographic methods is unnecessary for effective data encryption. The study shows that combining text steganography with cryptographic techniques is an excellent data security method. Additionally, other combinations, such as audio and video steganography, should also be considered for better security
Süper Çözünürlük Yönteminin Uydu İmgelerinin Sınıflandırma Performansına Etkisi

The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution.
VISUALIZATION AND REALITY IN PACKAGED BEVARAGE ADVERTISING

Visual and audio advertising media as information broadcast to audiences must consider advertising ethics because bombastic and manipulative visualizations and narrative sentences deceive the public by providing untruthful information. According to advertising ethics, ontology, and epistemology studies, revising the media’s presentation of product advertisements is imperative. Accordingly, this study utilizes a descriptive-quantitative method based on previous studies and direct observation of the "Adem Sari Chingku Sparkling" advertisement in 2017. Conclusions are drawn using the constructivism method. The results suggest that the presentation of "Adem Sari Chingku" advertisements in the modern era tends to be manipulative. The advertisements are presented with bombastic visuals and biased narratives to manipulate suggestions and lead the audience to develop certain assumptions about the advertisement. The suggestions made by the advertising media do not refer to the composition of the product's raw materials. The audience is led to believe that the product can relieve and cure heartburn.
Keterampilan Membaca Pemahaman Siswa Kelas IV Sekolah Dasar Dalam Mengikuti Program Literasi

This study aims to analyze the reading comprehension skills of fourth grade students at SD Muhammadiyah 24 Surabaya while participating in the literacy program. This study also uses descriptive qualitative research with data collection techniques and triangulation techniques and sources. The data collection technique was triangulation technique, namely observation, interviews and documentation, while the source triangulation used was students and homeroom teachers for grade IV and school principals. The data analysis is a qualitative data analysis technique through the reduction stage, data presentation and the final stage is drawing conclusions or verification. The results of the study showed that there was an increase and change in students' reading comprehension skills after participating in the literacy program held by the school as seen from students being able to capture reading content, summarizing reading content, answering questions according to reading content and students being able to retell reading content. The supporting and supporting factors for the implementation of the literacy program are the human resources, namely students, teachers and schools.
The development of educational game-based learning media in natural science subject for elementary school students

This research is based on the lack of innovative learning media that is easily accessible and suitable for the characteristics of students. This research aims to produce a learning media in the form of science learning educational games aimed at fifth-grade elementary school students. This educational game was developed using Construct2. This type of research is research and development (RnD) with the 4D development model (Define, Design, Develop and Disseminate). This educational game validation was obtained from media experts consisting of lecturers, practitioners, and teachers whose fields correspond to the media being developed. The questionnaire distributed to media experts obtained an average value of 0.97 which was categorized as valid. This educational game was also tested on users consisting of 21 fifth grade elementary school students, and obtained an average score of 94.15 in the very good category, this indicates that the science learning educational game for elementary school children is also very suitable for grade students. V elementary school which is evident from the assessment of grade V elementary school students who stated that this media was very good.