Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF ASSURE BASED DIGITAL LEARNING MODULES IN ENGLISH SUBJECT AT SMP ISLAM AL AZHAR 13 SURABAYA

This study aims to produce the appropriate digital learning modules.in English subject for grade 9 at SMP Islam Al Azhar.13. Surabaya, find out the appropriateness of digital learning modules in English subject. based on the criteria for teaching materials which were determined by the Board of National Standard Education, find out the results of responses of grade 9 students after using the digital learning modules.in English Subject. This research is a kind of development research. (Research and Development) with reference to the modified ASSURE.development model including (1) Analyze Learners (2) State Objectives (3) Select Methods, Media and Materials (4) Develop Products (5) Validate Product (6) Conduct Product Trial (7) Evaluate and Revise Product. The research subjects included media expert lecturer, material expert lecturer, subject matter expert teacher, and 20 students of grade 9 at SMP Islam Al Azhar. 13 Surabaya. The object of this research is digital learning modules. The instruments used to assess the appropriateness of digital learning modules included appropriateness' assessment sheet by media expert, appropriateness' assessment sheets by material experts, and student response questionnaires on the use of digital learning modules. The results of the study revealed that the appropriateness of the digital learning modules.in English subject in terms of the media included very appropriate categories. In term of materials included very appropriate categories. The learning process included a decent category.
Digital Forensic Analysis of Discord Mobile Application on Android Based Smartphones

Nowadays, the spread of social media in all areas of society and becoming a part of life has led to creative and innovative changes in the fields of communication. Instant messaging applications are widely used in communication between users around the world, and the Discord application is one of them. With the Discord application, more than 300 million registered users benefit from many services such as gaming, messaging, and video chat. The use of Discord by cybercriminals has also become one of the most common applications in forensic investigations. In this study, by examining the structure of the Discord application used in Android devices, a methodology has been presented on how to extract data and how to examine these data in terms of forensic analysis. The proposed analysis methodology shows how communication analytics, contact information, message information, deleted messages, group messages, how messages are extracted and how to examine these data structures, permissions, user information, and communication protocols can be analyzed. In the results of the study, a comprehensive analysis of the Discord application in terms of judicial reviews is presented.
Touchless Advertising Mobile Application - TaMa

Hand gesture recognition has been a promising area of research in the past decades, especially with breakthroughs in the field of computer vision, but the COVID-19 pandemic has brought much attention to this field. The emphasis on a higher sanitary standard has pushed for more touchless interactions to help mitigate viral contagion. This technology could prove to be highly beneficial for interactive devices located in public spaces, such as self-serving information kiosks and self-service check-outs. This paper proposes a touchless advertising mobile application as a proof of concept, to display and interact with advertisements, as a proof of concept. The application uses an implementation of the TensorFlow library to detect, extract, and classify hand gestures. This system is tested and verified to show its robustness. The results obtained show favourable performance and accuracy. The application is designed to offer a smoother learning curve, making it easy to use.
Efficient Computation of Mandelbrot Set Generation with Compute Unified Device Architecture (CUDA)

Compute Unified Device Architecture (CUDA) is one of the standards for interfaces in parallel programming implemented into NVIDIA's GPUs. GPU (Graphics Processing Unit) is a processor of many smaller, more specialized cores. With the implementation of CUDA, it is possible to find solutions to handle complex computational problems faster and more efficiently rather than the results of the computations performed by the CPU. Because creating images/videos requires many processes per pixel, experiments are applied to one example of computing problems related to them, which is fractal generation. The Mandelbrot set is one of the fractal generations that need a complex computational process to generate it. In this research, the execution of a Mandelbrot Set generation using the CPU and GPU (CUDA) will be done. Parallelization that occurs on CUDA’s Mandelbrot set generation, results in relatively faster execution time than sequential work on the CPU in certain cases.
Rice disease classification based on leaf damage using deep learning

Rice is the staple food of the Indonesian people. Food security is an absolute thing to do today to reduce rice imports. Various efforts were made to improve seed quality, resistance to pests, nutrition, and so on. It helps to Analysis identify diseases that may arise in rice plants. Efforts were made using the image of leaves affected by the disease and then analyzed using a deep learning algorithm. The architecture used is google net and customnet. The selection is based on a good level of accuracy and the computational time required to obtain the model. the convex hull algorithm helps to find the focus of disease objects on rice leaves. Data augmentation increases the variation in the amount of data and reduces unbalanced datasets. The results obtained are by using this algorithm, accuracy is obtained 80.94%, and the average computation time is three minutes and twenty seconds. Error calculation of classification are MSE 0.4227, RMSE 0.6501, and MAE 0.2555.
Development of an Inertial Sensor-Based Exergame for Combined Cognitive and Physical Training

Mild cognitive impairment (MCI) is a condition where older people have experienced cognitive decline, which can then transition into dementia. Hence, it is important to prevent further health decline. Therefore, we have developed an exergame that aims to prevent cognitive and physical decline in older people with MCI. The exergame uses inertial measurement units, worn on the user’s wrists and feet, to record their movements. The user steps in place to move through the game environment and interacts with different obstacles through movement. We performed an experiment to evaluate the technical game performance, exercise intensity, and game usability and enjoyment. We found that our movement detection algorithms were able to detect 90% of all movements after one attempt, on average between 1.7-3.5 seconds. While our young participants’ heart rates did not reach moderate exercise intensity while playing the game, we expect that the activity is suitable for the target population. Furthermore, young participants’ user feedback from questionnaires regarding usability and enjoyment was positive.
ISOMETRIC: An Intelligent System of Outpatient Monitoring Evaluation Towards Rehabilitation In Cloud

Gait analysis is a methodical study of how people move that can be applied to a range of settings, including sports, rehabilitation and clinical diagnosis. This paper describes a system for tracking the kinetic rehabilitation of hemiplegic patients with a combined use of gait analysis system and wearable devices. The concept is focused on training and extracting individualized knowledge from an intelligent information system during the patient’s stay in the rehabilitation facility. This knowledge can be utilized so that the patient may self-manage their rehabilitation regimen and monitor the improvement of their condition in their own place. Medical experts will then be able to monitor patients’ progress through a cloud-based platform.
Optical Limiting Thin Multilayer Films Based On Phase Transition In VO<sub>2</sub>

We propose a self-activating optical limiter device based on a multilayer film structure featuring a dielectric substrate, a metallic and a Vanadium Dioxide (V0 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) film. By exploiting the phase transition in VO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , the device optical transmittivity is reduced by ~ 70% with a recovery time of 5 ns.
System Architecture and Core Technology, Method and Data-Driven Technology of Digital Twin Riverbasin

Digital twin riverbasin is a riverbasin digital twins corresponding to the physical riverbasin unit object, With the help of riverbasin digital twins model, simulation, emulation, verification, prediction and deduction of the current physical riverbasin business behavior state and rule. Aiming at the problems of data, algorithm and twin model in the construction process of digital twin riverbasin, The proposed system Architecture and core technology of Digital twin riverbasin, the research on data acquisition, mapping image, twin model construction and data-driven technology of digital twin riverbasin is systematically carried out, and the method and data-driven mechanism based digital thread of digital twin riverbasin are put forward. The research results can provide theoretical, methodological guidance and technical support for the construction of digital twin riverbasin..
Design and Practice of DevOps Platform via Cloud Native Technology

With the rapid development of information technology, software version iteration and delivery are becoming more and more frequent, which brings great challenges to software operation and maintenance. Devops (development and operations) proposed to solve this problem has been paid attention to and used by more and more enterprises via promoting the collaborative work mode of development, operation and maintenance and QA. In order to achieve the goal of Devops operation and maintenance automation, a series of operation and maintenance tools have been developed, such as gitlab code management, Jenkins pipeline technology, docker container technology, kubernetes container orchestration technology, harbor image warehouse, etc. These tools work together to form the Devops platform, which provides the ability of continuous integration, continuous delivery and continuous deployment [1]. Based on cloud native technology, an integrated operation and maintenance platform was designed and applied in enterprise operation, which improves the efficiency of research and development.
Construction of Knowledge Graph on Debris Flow Prevention Domain

Debris flow disaster breaks out frequently and causes serious harm. Therefore, it is of great significance to construct of debris flow prevention knowledge graph to work on disaster prevention and mitigation. Aiming at the problem that the cognitive knowledge correlation in the field of debris flow disasters prevention is not strong, this paper not only proposes a method to construct a knowledge graph of debris flow disasters prevention from the data layer, technology layer, and application layer but also divides debris flow theoretical knowledge, disaster prevention strategies, debris flow disaster events and debris flow method models into four entity types and analysis correlation on the relationship between the four entity types. BiLSTM-CRF method and template matching method are used for knowledge extraction of the above four entities. The experiment result shows that 1233 debris flow entities and 2797 entity relationships are extracted. The accuracy rate of the extracted debris flow entities is about 80%. Finally, the neo4j graph database is used to store the extracted entities and relationships, and a knowledge graph of debris flow prevention for disaster prevention is constructed, which realizes the query and retrieval of debris flow prevention and control knowledge.
Research Review of Algorithm Model in Graphic Database System

Nowadays, with the establishment of social net-works, graph data has played a critical role in everyday life. A graph database should be capable of dealing with the corresponding graph data. Every node in the graph is a data point, and the edges between nodes denote the relationship between data. The graph database is expected to reach the relationship between nodes rapidly and accurately, thus benefiting areas in need of vast computational resources, such as business and social networks. Querying and indexing the graph database is the most crucial part to reduce the computational resources, which naturally becomes the focus of this paper. In this review, we concluded the existing approaches and techniques of querying and indexing and summarized the pros and cons for each of them. Possible future research directions were also provided based on the analysis of existing ones.
Development and Application of Experimental Platform for Time Series Analysis Course

With the development of information technology, the content and means of the current statistics practice course can no longer meet the teaching needs. In order to strengthen the experimental practice courses according to the requirement of “advanced, innovative and challenging,” taking the time series analysis practice course as an example, an experimental platform based on R-shiny application was designed and developed. The platform has an interactive interface with good user experience and provides all the steps required to complete the basic experiments as well as end-to-end advanced data analysis services to meet the needs of teaching and research including case studies and empirical analysis.
SLTFILL: Spatial and Light Transformer for Multi-Reference Image Inpainting

Image inpainting aims to restore the missing regions of corrupted images plausibly. Recent techniques of image inpainting handle it as a pure generation problem and utilize large-scale training data to learn semantic and texture information to fill the missing area. However, generative networks frequently distort local structures or introduce visible artifacts while filling large missing regions. To address this issue, we propose SLTFill, an image inpainting method that uses a set of reference images which share scene contents with the corrupted image to guide the synthesis of missing regions. A key challenge addressed by our approach is aligning the transformations of the reference images with the context of the corrupted image and making sure they are consistent in spatial structure and light space. Qualitative and quantitative experiments demonstrate the state-of-the-art performance of our approach on the image inpainting task.
Traffic Anomaly Detection Via Conditional Normalizing Flow

Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.
Integrated Global Path Planning for Autonomous Mobile Robots in Complicated Environments

Generating the optimal path curvature in complex environments and reducing the time cost of path planning are the key problems for global path planning. However, most of the existing methods based on graph-searching and optimization use inappropriate curvature calculation methods in the process of curvature optimization, which cannot ensure accurate path planning and optimal motion control, and redundant graph searches lead to a lot of time consumption. In this paper, we propose an integrated global path planning method, which can optimize the global path curvature to make the final trajectory more suitable for the structure of the environment. Besides, we also use the graph-searching method based on the high-definition map to fast search. We present the experimental re-sults of this method on different autonomous mobile platforms. The results showed that compared with the previous methods, the global path planning method proposed in this paper has better performance in complex path planning tasks, and can generate smoother global paths in less time.
Rapid Prototyping of the SmartAR Augmented Reality Control Platform for multiple use cases

The technology of augmented reality (AR) is still in its infancy, but given the significant influence of mobile devices like smartphones and tablets, it can be said to be the next big thing for the next generation. The goal of this study is to create, utilizing fast application development approaches, a low-cost platform that can be applied to a variety of Smart City and Smart Building use cases. The many AR use cases discussed in this paper are all implemented using the same methodology. The Node-Red platform, which is perfectly suited to the Internet of Things and online applications, was used to swiftly construct a number of augmented reality use cases for the platform.
A multiclass classification model to estimate Agile user stories

Effort estimation in Agile projects is extremely important in order to prevent schedule overrun, late product delivery and over-budgeting among others. These estimations are usually performed manually by making use of estimation techniques such as planning poker and expert judgment. However, there are several constraints associated with these techniques since estimation requires the presence of the whole project team and there might be divergence of opinions. The main objective of this study was to estimate Agile user stories using multiclass classification techniques. Three classifiers (Naïve Bayes, Decision Tree and Support Vector Machine) have been applied to user stories to estimate effort in terms of Story Points. Three machine learning models were generated and evaluated to determine the best algorithm by comparing metrics such as accuracy, error rate, precision, recall and f-measure. Using a single train:test sample in the ratio of 100:25, Naïve Bayes, Decision Tree and Support Vector Machine gave an accuracy of 64%, 80% and 100% respectively. Experiments performed showed that the accuracy of Support Vector Machine was better than the other two algorithms in estimating user stories.
Intersubjective Paradigm and Oracle Conceptualization as an Open-Closed Platform for Programming Technologicalization

The paper examines the role of the intersubjective paradigm and oracle conceptualization as an open-closed platform for programming technology. The closed part is a technological environment of programming, a kind of logical abstraction of all the variety of technical programming systems that is consistent. The basis of such a platform is intersubjective logic. The open part is the carrier of possible subject technological systems of programming focused on the subject. It is shown that the technological environment of programming is explicitly reduced to coexistent assimilations, with subsequent reduction to oracle schemes, and as a consequence to composites and compositions. Based on the consideration the technological system of programming based on primitive program algebra is analyzed.
Signals Recovery by Means of Three-Harmonic Equations Solutions

An intensive development of signal theory has been taking place for the last decades. Therefore, the range of such problems has expanded considerably. Problems closely related to the mathematical methods have become especially important. We can include here problems related to image processing, filtering audio and other signals, etc. It should be noted that these problems are often related to the methods of systems analysis and approximation theory. In particular, in many digital signal processing problems, interpolation is one of the classic methods of their solution. When we talk about the usage of interpolation polynomials in signal theory, we are used to using standard methods of spline-type interpolation, Lagrange interpolation polynomials or Hermite polynomials. However, signals are known to be approximated the best by periodic functions. Trigonometric analogues of Lagrange interpolation polynomials are the standard examples of interpolation that could be used in practice. But such methods do not always give good results or are not easy to use. To solve this problem, we should keep in mind that signals are harmonic functions. Therefore, the discrete Fourier transformation has been used in many scientific works. However, this approach does not always give the desired effect, so the question of constructing alternative interpolation polynomials arises. In this paper, we propose a new approach to signal recovery, which allows getting better signal recovery than that given by the most common methods of signal recovery. The usage of interpolation polynomials proposed in this paper provides an improvement in signal recovery compared to their classical counterparts.
Application Architecture For Obtaining Data From Scientometric Databases

Scientometric data from bibliographic and reference databases with a limited user interface are used to analyze the effectiveness of the scientific staff, educational and educational institutions in the field of science. Automation of the process of obtaining the author's scientometric indicators from bibliographic and reference databases through a limited user interface could accelerate the creation of a picture of the scientometric activity of the institution's employees. The main goal of quickly obtaining the author's scientometric indicators from bibliographic and abstract databases through a limited user interface is a significant saving of human resources during the manual analysis of these data. Also in this case, the possibility of errors during manual data collection is minimized. Data collection can be done in a variety of ways and means. Among those considered in the article: data parsing, receiving data through the application program interface and receiving data through third-party software solutions supplied by companies. The paper considers approaches to obtaining and processing these data.
The concept of a deepfake detection system of biometric image modifications based on neural networks

The concept of a system for detecting deepfake modifications in biometric images is proposed. The concept is deployed based on the functioning of a convolutional neural network and the algorithm of the biometric image classifier according to the structure “sensitivity-Youden's index-optimal threshold-specificity”.