Reviewerly/exHarmony huggingface dataset:
Strategi Menciptakan Budaya Literasi di MTsN 10 Tanah Datar

The purpose of this research is to reveal the strategy of the madrasa principal in seeking an educated academic environment at MTsN 10 Tanah Datar. This type of research is qualitative research with a case study approach. The research location is at MTsN 10 Tanah Datar. There were 6 research informants, namely school principals, curriculum representatives, public relations representatives, teachers, librarian heads, and students at MTsN 10 Tanah Datar. Data collection techniques used are through interviews, observation and documentation. The technique of data analysis is by means of triangulation, namely triangulation of sources and triangulation of techniques. The results of the study show that the Madrasah Principal's strategy in creating a Literacy Culture at MTsN 10 Tanah Datar is to strive for a literate academic environment with a). Planning literacy activities, namely creating literacy programs and forming a solid work team. B). Implementation of literacy activities, namely encouraging activities that smell of literacy, involving students to diligently read every day, enlivening the literacy corners of the madrasa, providing motivation to write to all madrasa residents to synergize, cooperate, and set an example. C). Evaluation of literacy activities, namely monitoring and follow-up.
DESIGN UI/UX E-LEARNING ENGLISH MOBILE USING USER CENTERED DESIGN (UCD) METHOD

E-learning English Mobile application design is a Mobile-based application design needed by SMAN 2 Purwakarta for future learning purposes. With this application design SMAN 2 Purwakarta has a picture of learning applications that will facilitate students and teachers in learning and teaching activities.&#x0D; In this application design research, there are several methods that are widely used by previous researchers. One of the methods used in this research is the User Centered Design (UCD) method which is a new method in system development. UCD is a language that is widely applied in describing designs. The concept of UCD is the user as the center of the system development process, and the goals, the system environment are all based on the user experience.&#x0D; The results of this study produced a learning application prototype, namely E-learning English Mobile. After testing using the System Usability Scale (SUS). The average value obtained is 78. It can be concluded that the design of the E-Learning English Mobile application is acceptable because it meets the Acceptable category
A Chaotic Discriminant Algorithm for Arrival Traffic Flow Time Series Based on Improved Alternative Data Method

&lt;p&gt;Chaos discrimination is a prerequisite for the application of chaos theory modeling. Since the average orbital period of an air traffic flow system is long, it is difficult to obtain time series with a small time scale and many data points, so the Small-Data Method is often adopted to quantitatively calculate the chaotic characteristic quantity. However, when using the power spectrum method, it is found that the Small-Data Method is prone to false judgments when the data volume is small. To reduce spurious judgments, we apply a chaos discrimination algorithm based on an Improved Alternative Data Method combined with the Small-Data Method for air traffic flow and analyze it by example. The algorithm was experimentally demonstrated to correct the false judgment results of the Small-Data Method. In particular, when the data volume is only 150, the discrimination accuracy of the improved algorithm is as high as 80%, which is 26% higher than the discrimination accuracy of the Small-Data Method. Moreover, the improved algorithm has better discriminative performance than the Small-Data Method under the same data volume condition, which is suitable for the chaotic discriminative analysis of the arrival traffic flow time series.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Quality Evaluation for Online Distance Education Based on Grey Relational Degree

&lt;p&gt;Aiming to address the problems of low accuracy and long time consuming of existing education quality evaluation models, a new online distance education quality evaluation model based on grey correlation algorithm is proposed. Firstly, calculate the membership of education quality data, and complete the data collection through cluster mining. Secondly, construct the evaluation index system according to the set evaluation principles. Finally, the grey correlation degree algorithm is used to mine the correlation degree relationship of education quality index data and complete the construction of the evaluation model. The comparison test results show that the evaluation accuracy of the built model is 0.98, the response time is only 0.7s, the CPU proportion is only 27%, and the satisfaction rate is more than 0.97. Therefore, the model has higher application value.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Analisis Indeks Pencemar Kualitas Air Tukad Nyuling di Kabupaten Karangasem

Tukad Nyuling is a watershed with a watershed area of 7,436 ha (BPDAS Undaanyar) and a length of 13 km, flowing year-round generally flowing to the south. Period I monitoring of BOD and detergent parameters in the upstream, middle and downstream of Tukad Nyuling has exceeded the class II water quality standard. Parametar total coliform in the downstream Tukad Nyuling condition has exceeded the standard of grade II water quality, with a concentration of 7,000/100 ml. COD parameters in the middle of Tukad Nyuling conditions that have exceeded the standard of class II water quality, where COD concentration reaches 31.10 mg/L. Monitoring period II BOD parameters in the upstream, middle and downstream Tukad Nyuling has exceeded the standard water quality class II, BOD concentration ranges from 5.28 mg/L to 5.79 mg/L. Concentrated detergent in the middle and downstream Tukad Nyuling has exceeded the standard of class II water quality, the concentration of detergent in the middle reaches 16.21 mg/L while in the downstream reaches 18.46 mg / L. Based on the status of water quality with the method of index pollutant upstream has a mild pollutant status while the middle and downstream have a moderate pollutant status.
Intelligent Task Scheduling in Multi UAVs During the Flood Relief Operation

An increasing interest in the design of Unmanned Systems (US) in firefighting, surveillance, delivering some living supplies, mapping for disaster recovery, and search and rescue (SAR) missions. However, only a limited number of studies have been reported on US scheduling in a 3D environment. Dynamic task scheduling plays a very critical role in coping with emergent tasks. In this paper, we investigate a multi-autonomous system collaborating unmanned aerial vehicles (UAV) and unmanned surface vehicles (USV) systems to execute various tasks in SAR operations in flood-isolated areas. A search technique has to be carried out to scan the damaged region, the number of UAVs to be used for a particular SAR operation, speeding up the searching process, and find any probable survivors, sending USV to rescue the survivors. The autopilot is present in the UAVs are optimized with a controller, which monitors the reference path while maximizing flight duration, while optimization of flight paths may include other features, such as the determination of arrival time at each point, utilization of UAVs, battery capacity considerations, and battery recharging considerations. Task scheduling is a significant difficulty for researchers and practitioners involved in multi-UAV operations. Here, we propose a conceptual model of a scheduling system for UAV and USV deployments and specify dependencies between them.
Virtualized intelligent genetic load balancer for federated hybrid cloud environment using deep belief network classifier

Abstract Load balancing is major issue in federated cloud environment. Various services can be offered by different cloud service providers. As per current working environment cloud computing is used in major applications such as education, online shopping, multimedia services, etc. Dynamic load balancing is required to handle the resources. Federated cloud has various services offering system with computing resources, resource pooling, internet access services and storage. Intelligent Genetic algorithm is proposed to provide efficient load balancing service in hybrid cloud environment. Virtualized Intelligent Genetic Load Balancer algorithm consists of load balancer and resource provisioning system to allocate the resources. Enhanced Load Balancer is used to preserve the load and minimize the span time based on resource provisioning method. In this work we analyse automated virtual machine services by using runtime resource provision. Here we use enhanced load balancer to measure the performance using virtual machine placements, resource utilization and automated quality requirements. We design a deep belief network based on requirements and measure the accuracy using TensorFlow. The simulation results test the accuracy and compare the results. Virtualized Intelligent Genetic Load Balancer system is achieving the accuracy of 95% based on overall capacity requirements. We compare Virtualized Intelligent Genetic Load Balancer system performance with existing simulations results and compared the results.
On Hybrid Curves

In this paper, we first define the vector product in a special analog Minkowski Geometry (R^3,) which is identified with the space of spatial hybrids. Next, we derive the Frenet-Serret frame formulae for a three dimensional non-parabolic curve by using the spatial hybrids and the vector product. However, we present the Frenet-Serret frame formulae of a non-lightlike hybrid curve in R^4 and an illustrative example for all theorems of the paper with MATLAB 2016a codes.
Comparison of Augmented Reality Technologies Applied to Assembly Processes in Aerospace Industry

The assembly of an aircraft is executed by performing a series of assembly operations, which are carried out following specific instructions called work instructions. In addition, the quality control of these operations is performed following verification instructions. Traditionally, these work and verification instructions were based on 2D documentation (on paper or digital screen), prepared by manufacturing engineering, and made available to operators by manufacturing engineering systems (MES) when required. Over the last decade, Airbus has made significant progress in ensuring the digital continuity of processes, exploiting information from the industrial digital mock-up (iDMU) downstream, favoring the preparation and use of assembly and quality instructions through new techniques, such as lightweight 3D viewers, or augmented reality. This paper describes experiences of implementation of augmented reality (based on laser or holographic technology) in the workshop, both for assembly and quality processes. Several use cases are analyzed, comparing both technologies, explaining their pros and cons, and justifying the use of each of them in each process.
Cryptocurrency price prediction

In this research paper, we&apos;ll talk about cryptocurrency with the help and development of deep learning, and AI-assisted trading has gained immense popularity. To regulate the splendid engrossment from the part of cryptology we take the assistance of retailing (Deep learning &amp; AI-support). A specific period of data has been stored on daily bases to receive the outcomes in a company of the help of ultra-modern algos. With the references to various papers, I found out the pros and cons of cryptocurrency price prediction. Some simple algorithms &amp; architectures helped to grow the cryptocurrency market. Crypto trading became popular in 2017 and now more than 1500 cryptocurrencies are proactively trading. Crypto currencies can be smoothly created and used for online settlement. Bitcoin is also known as cryptocurrency and its values keep varying every second. Hence for predicting the rate of bitcoin cost I will use the infrastructure of LSTM. This infrastructure will help us in proving that LSTM will provide more accuracy. RNN is a category of ANN and connectivity for this type of network is made through nodes from the direct nodes along with a time-related progressions. LSTM is a RNN infrastructure which is a part of DL which handles the entire data as well as single data points.
Image Reconstruction for Rain Removal in Both Wavelet and Spatial Frequency Sub-bands Using W-CycleGANs

Although many techniques are devised in rain removal research recently, using either the Convolutional Neural Network (CNN) [1] or the Generative Adversarial Network (GAN) [2], they always require both rain and no-rain training images exist in pairs to train their networks. Recently, the Cycle-Consistent Adversarial Networks (CycleGAN) [3] has demonstrated successful results in removing real rain distortion, without the no-rain training images [4]. Building on this success, we propose a new technique called the Wavelet-CycleGANs (W-CycleGANs) that has the same advantage. In addition, the wavelet properties can be used for the CycleGAN to remove rain from images at their frequency sub-bands, in the Hue, Saturation and Value (HSV) color space [5]. We train and compare the W-CycleGANs’ to the CycleGAN’s performance fairly, using the same set of rain images as the CycleGAN [4]. Their quantitative results are compared using the Natural Image Quality Evaluator (NIQE) [6] as real rain images in pairs simply do not exist in the real world. In addition, their qualitative results are compared using visual check at zoomed-in regions. Both results have demonstrated the W-CycleGANs’ superiority in removing real rain distortions.
A Comparative Study of Signal Recognition Based on Ensemble Learning and Deep Learning

In this paper, based on the traditional artificial feature extraction method, the ensemble learning method and the convolutional neural network method are used to identify the types of 9 kinds of analog and digital modulation signals, and the performance is evaluated and compared. It is found that the convolutional neural network has higher SNR tolerance than the ensemble learning algorithm, and has better classification performance under complex SNR conditions. The ensemble learning algorithm has better classification effect on Chirp and OFDM signals than the convolutional neural network. This research result also provides a research basis for the combination of traditional machine learning methods and deep learning methods.
LSEPNet:Joint Prediction of Disparity and Semantics Based on Binocular Vision

Visual perception networks are often used to provide the necessary environmental information such as semantics and depth for mobile robots or autonomous driving. However, these networks can usually only complete one task. In practical applications, people often hope that an visual perception network can complete multiple tasks at the same time. In this work, We propose a lightweight visual perception network called LSEPNet with two branches of semantic segmentation and disparity estimation based on binocular vision system, which is composed of lightweight feature extraction module, feature fusion module based on attention mechanism, semantic decoder and disparity refinement module. Through feature fusion and joint loss function, LSEPNet can jointly train disparity estimation and semantic segmentation tasks end-to-end in real time. We evaluate the proposed network LSEPNet on KITTI2015 and Cityscapes datasets, and compare it with classic disparity estimation network and semantic segmentation network respectively, proving that LSEPNet can complete semantic segmentation task and disparity estimation task together in real-time with acceptable speed and accuracy.
Evaluation on the application mode of blockchain technology in water transportation engineering project management

Abstract Due to the continuous development and application of blockchain technology, more and more industries are exploring its application in project management. In the management of water transportation engineering projects, blockchain technology also has certain application potential. This article will start with the characteristics of blockchain technology, and the current situation and needs of water transportation project management, analyze the application mode of blockchain technology in water transportation project management, and explore its advantages, limitations, and practical applications. The experimental results of this article showed that when blockchain technology was applied to the management of water transportation engineering projects, its information sharing score was 93; its data storage and management score was 96.1; its smart contract score was 97.4; its fund management score was 97.1; its Internet of Things technology score was 96.5, which was far higher than the score of applying traditional technologies. It was shown that using blockchain technology for water transportation engineering project management can improve the efficiency and quality of project management, reduce costs, and extend to a wider range of water transportation lines and project fields in the future.
potential of renewable energy sources in Java Island: a systematic literature review

This study aims to reveal the various renewable energy sources that have the potential to be developed on the island of Java, Indonesia. Because it is cheap and easy to reach, non-renewable energy is widely used as a source of energy for people's lives. The negative side of non-renewable energy sources forces local governments to look for alternative energy sources that are more environmentally friendly. Environmentally friendly alternative energy is rarely studied, specifically on the island of Java, through literature studies. Therefore, this study used a research method: a literature study on 28 Scopus, WoS, and Sinta-indexed articles. This literature study yields the information that 10 types of renewable energy sources have the potential to be developed in Java. These renewable energy sources can be further developed using technology according to their potential in each province. Thus, this research has succeeded in linking various renewable energy sources that have the potential to be developed on the island of Java.
Variations on a tree

The family tree is like an inherited “object” that has been passed down through many generations, with many and varied definitions which distort the tree both as a combinatorial object and in its visual representations. Moreover, whether used by amateur genealogists or academic researchers, it is always contextualized by both validated exogenous knowledge and by implicit knowledge. In this paper, we explore introducing certain contextual information that is associated with a locally defined dissimilarity between individuals of the same generation. We propose a new heuristic based on a radial representation of a node-link model which seeks to preserve local proximities in the layout. This heuristic is applied in an original form, which is that of Pierre Rosenstiehl’s “scientific family tree”.
Sentential Logic (SL)

Called also Propositional Logic/Calculus, or Sentential Calculus, this logic is concerned with sentences, i.e., statements for which the value can be assigned of truth or falsity.
Data Security in Hybrid Cloud Computing Using AES Encryption for Health Sector Organization

The healthcare sector has a very broad volume of patient information that needs to be recorded, and the cloud provides the necessary infrastructure at a low cost with better quality. The patient health records are implemented as digital output to Electronic Health Records (EHRs). The EHR can contain sensitive patient data such as data, scanned images, and X-rays. Security of the data in the cloud is an important issue due to different kinds of security threats to the cloud, such as distributed denial-of-service (DDoS) and man-in-the-middle (MITM) attacks. This study emphasizes the importance of data security in the hybrid cloud for the health sector using encryption with the Advanced Encryption Standard (AES). We present Data, Speed Efficiency, and Electronics Health Record (DSE), which defines each of the major components required to implement data security in hybrid cloud computing. EHR security is provided by using an encryption technique such as AES, which is implemented and uploaded to the hybrid cloud system. We approach the DSE taxonomy according to its speed and encryption. This study's main contribution is that it establishes a strong link between data security and healthcare organizations, as well as the open issues and challenges for patient data security in the health field. As a recommended solution, EHRs are encrypted with a secret key before uploading to the cloud server. Thus, the data will be safe as it cannot be changed without the help of the data owner and healthcare organizations.
Renewable Just-In-Time Control-Flow Integrity

Renew (Rewriting Newly Executable pages after Writes) unites and extends recent advances in binary code analysis and transformation to solve a longstanding compatibility problem for binary code security hardening algorithms—support for arbitrary dynamically self-modifying code. Self-modification is now a mainstay of many consumer software products, including Just-In-Time (JIT) compiled languages, on-demand component loading, self-extracting installers, and self-hooking APIs; but it poses significant challenges for code hardening algorithms that rely on computationally heavy static analyses, source code information, or compiler-specific code generation patterns. As a result, many of the strongest protection mechanisms for code hardening have remained incompatible or significantly weakened for the large class of software that incorporates self-modification (either directly or within its underlying runtime systems).
Witnessing Erosion of Membership Inference Defenses: Understanding Effects of Data Drift in Membership Privacy

Data drift is the phenomenon when the input data distribution in testing time is different from the training time. This strengthens the generalization gap in a model, which is known to severely deteriorate the model’s performance. Meanwhile, previous studies state that membership inference attacks (MIA) take advantage of the generalization gap of a machine learning model. By transitive logic, we can deduce that data drift would affect these privacy attacks. In this work, we consider data drift when applied to the privacy threat of MIA. As the first work to explore the detrimental extent of data drift on membership privacy, we conduct a literature review on current MIA defense works under selected dimensions associated with data drift. Our study reveals that not only has data drift never been tested in MIA defense, but there is also no infrastructure to juxtapose data drift with MIA defense. We overcome this by proposing a design for simulating authentic and synthetic data drift and evaluate the benchmark MIA defense methods on various settings. The evaluation shows that data drift strongly enhances the attack success rate of MIA, regardless of defense. In this, we propose MIAdapt, a proof of concept of a MIA defense that allows update in data drift. From this evaluation, we provide security insight into possible solutions in negating the effects of data drift. We hope our work brings attention to the threat of data drift and instigates the development of MIA defense that are adaptable to data drift.