Reviewerly/exHarmony huggingface dataset:
Evolution of Professional Competencies of a Web Developer

The relevance of the article lies in the fact that the activity under consideration — web development as a whole is a popular programming industry. Many companies transfer their business processes to the Internet, and provide users with access to their data, solutions and services. In this regard, there is a shortage of IT specialists of this qualification — web developers. And if we take into account the rather rapid change of technologies, which concerns both programming paradigms and just the release of new versions of software, such as server, programming languages, programming system, frameworks, then methods of professional development of web developers become especially relevant. The purpose of the study is to determine the most effective way to develop a web developer in the company. This includes determining the needs of the company and the skill level of the developer and reducing the difference between these indicators. The task is to determine the method of optimal assessment of the level of qualification, offer points of measurement of this level, and analyze the effectiveness of advanced training. The article raises the problems of effective professional development of employees of web development companies. The way of development of a ready-made specialist is considered, depending on the skill level of the developer, the level of project requirements and updating of the main software. Methods used: empirical research, classification methods. The methods of evaluation and training of employees were analyzed using the example of companies leading web development. The result of the research is a model of web developer competencies, recommendations for assessing the levels of knowledge and skills of employees, recommendations on the level of implementation of new technologies.
HMPT: a human–machine cooperative program translation method

Abstract Program translation aims to translate one kind of programming language to another, e.g., from Python to Java. Due to the inefficiency of translation rules construction with pure human effort (software engineer) and the low quality of machine translation results with pure machine effort, it is suggested to implement program translation in a human–machine cooperative way. However, existing human–machine program translation methods fail to utilize the human’s ability effectively, which require human to post-edit the results (i.e., statically modified directly on the model generated code). To solve this problem, we propose HMPT (Human-Machine Program Translation), a novel method that achieves program translation based on human–machine cooperation. It can (1) reduce the human effort by introducing a prefix-based interactive protocol that feeds the human’s edit into the model as the prefix and regenerates better output code, and (2) reduce the interactive response time resulted by excessive program length in the regeneration process from two aspects: avoiding duplicate prefix generation with cache attention information, as well as reducing invalid suffix generation by splicing the suffix of the results. The experiments are conducted on two real datasets. Results show compared to the baselines, our method reduces the human effort up to 73.5% at the token level and reduces the response time up to 76.1%.
Practical hardware for evolvable robots

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.
Application and Integration of Stochastic Mathematical Decision Tree Modeling Algorithm in Higher Vocational Mathematics Training Intelligent Platform

The classical decision tree algorithm in the data mining algorithm is analyzed and studied, the generation principle and steps of the decision tree algorithm are described, and the advantages and disadvantages of the commonly used decision tree algorithms ID3, C4.5 and C5.0 algorithms are analyzed.The strategy of deep integration of information technology in higher vocational mathematics teaching is explored.Deep integration of information technology and mathematical modeling in higher vocational mathematics teaching, optimizing teaching content and teaching mode, constructing perfect curriculum resources, improving teaching efficiency, and stimulating learning interest.
AN INTERDISCIPLINARY METHODOLOGICAL MODEL

The teaching of science content in junior high school offers great opportunities for the use of IT in the classroom. In this article, the authors present a model of an innovative educational methodology based on the use of cross-curricular connections. The main idea of the presented author’s model is to improve the effectiveness of teaching natural sciences by using the possibilities of information technologies. The model includes 8 stages, namely: idea of creating intersubjective connections; designing learning units; development of learning content; creation of educational materials; teaching in a new way; organization of study time; evaluating and measuring achievements; analysis and results of applied innovation. The authors of the article introduce us to the methodology of each of the stages and share examples of its implementation in the junior high school stage of basic education.
An Optimized Image Encryption Scheme, Leveraging the Arnold Transform and Chaotic Map

The traditional approach to image encryption faces significant challenges and issues, including inadequate safety, image degradation, low latency, low-key sensitivity, diffusion, and strong correlation.To address these shortcomings, a new image encryption algorithm is proposed in this work.The algorithm uses a combination of Discrete Cosine Transform (DCT) and Arnold transform along with chaotic scrambling to enhance the image security.The two-level encryption approach performs transformations in the position of each pixel without changing their values or statistical features.The proposed algorithm is evaluated and shown to offer several advantages, including robust performance, statistical efficacy, high key sensitivity, picture security, and real-time performance.Overall, the proposed algorithm represents a significant improvement over the traditional approach to image encryption.
Effectiveness of Focal Loss for Traffic Sign Detection Using Deep Neural Networks

Recent developments in autonomous driving have created a great demand for precise and computationally effective traffic sign-detecting systems.By assisting drivers and assuring their safety, such technology can lessen traffic accidents and fatalities.However, to make such a system deployable, a few crucial accuracy and processing performance problems must be resolved.Real-time performance is sometimes regarded as a must for such an application.RetinaNet, a focal loss-based single-stage object detector, is employed to strike a compromise between accuracy and processing speed concerning the most cutting-edge object detectors.The detector is suitable for traffic sign identification since it was developed to overcome the class imbalance problem that the single-stage detector had.(TSD).The efficiency of the detector was evaluated by combining feature extractors like ResNet-50 and ResNet-101 on two openly available TSD benchmark datasets.Various metrics like memory allocation, mean average precision (mAP), running time, amount of floating-point operations, and model parameters are taken into consideration.Evaluation of the detector on several datasets is required to examine the variance in the performance, and the RetinaNet model is the fastest and best model in terms of memory usage, making it the ideal option for the deployment of mobile and affordable embedded devices.
PERFIL EPIDEMIOLÓGICO DA TUBERCULOSE NO MUNICÍPIO DE PATOS-PB NO PERÍODO DE 2013 A 2017

Tuberculosis is an infectious disease of bacterial origin caused by the etiologic agent Mycobacterium tuberculosis.According to the World Health Organization (WHO), two billion people, referring to a third of the world's population, are infected with Mycobacterium tuberculosis.Yhe objectif is describer the epidemiological profile of tuberculosis cases in the municipality of Patos PB, reported in the SINAN (National Institute of Public Health Information System) from 2013 to 2017.This is an epidemiological, descriptive and quantitative study.The population was composed by the residents of the Municipality of choice, registered in SINAN in the estimated period.A total of 106 cases of pulmonary tuberculosis were reported.The highest numbers were male, 20-49, low schooling, and urban residents.Most of the cases did not present HIV associated with tuberculosis, in relation to the treatment most were cured.Conclusion: The study makes it possible to know the epidemiological characteristics of the studied population and the deficiency of completing the research file, in the municipality of Patos-PB.
An Immersive Learning Factory for AI &amp; Data Literacy: An Exploratory Study in the Wild

Artificial Intelligence (AI) has already made a strong impact on business and private life. Nonetheless, understanding how AI works and which role data plays in this context still remains unclear for many people. We argue that students with non-technical backgrounds should build up AI & data literacy to understand the key concepts of AI & data and leverage its potential in their field of study and research. For this purpose, we present the concept of an immersive learning factory, where students can explore AI & data concepts with interactive and immersive technologies. In this paper, we demonstrate our overarching idea, as well as the results of our exploratory evaluation with industrial engineering & management students from a data science lecture. Our main contribution includes the conceptual framework of the immersive learning factory, as well as design guidelines for creating immersive learning experiences concluded from the evaluation.
FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT CALON MAHASISWA BARU MENDAFTAR PADA FTII UHAMKA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN)

In accepting new students at Prof. University. Dr. Hamka, many prospective students or parents of students are looking for registration information, this is a great opportunity for Uhamka to gain the sympathy of prospective students to register at Uhamka, especially the Faculty of Industrial and Informatics Technology. The problem in this study is that there is no data processing related to the factors that influence the interest of prospective new students to choose the Faculty of Industrial and Informatics Technology (FTII) Uhamka. The purpose of this study was to determine the factors that influence the interest of prospective new students in choosing majors at the Faculty of Industrial and Information Technology (FTII) Uhamka. The attributes used in this study were 10 attributes, namely full name, major, tuition fee, FII location with domicile, presence of friends/family, accreditation, facilities, PMB services, PMB information, and information of interest. The method that researchers use in this study is the K-Nearest Neighbor Algorithm (K-NN). From the results of testing the researchers used the K-5 fold technique and the confusion matrix obtained an average accuracy of 72.5%, which means it is good.
Scalable Simulations of 3D Turbulence Fine Structure in Nanoflare Using a Novel Plasma Statistical Algorithm

Based on algorithms of solving directly MHD partial differential equation algorithm (e.g., ATHENA [1], NIRVANA [2], ZEUS [3], FLASH [4], , HPIC [9][10][11]), conventional simulation methods cannot attain the extreme range of scale for 3D turbulence fine-structure (Geometry and Physics) in flare-CME phenomena, especially for nanoflare heating problems.Here we present a parallel lattice Boltzmann algorithm based on plasma statistical physics, which allows us to reach even the relativistic regime necessary for modeling 3D turbulence fine-structure evolution.This innovative approach can simultaneously describe the continuous features of plasma at the macro-spatial-temporal scale, particle features of plasma at the micro-spatial-temporal scale, and particle features enforced by magnetic fields.However, the novel algorithm brings several challenges for fine-structure large-scale simulation, such as the gargantuan memory and storage requirements due to high dimensions and output data, and long simulation time, because each run takes a week.We propose optimization technologies for data access, communication, I/O, etc.These optimizations make it possible to achieve scalable and robust simulation (using up to 100,000 cores on the Tianhe-2 supercomputer) and the most extensive system ever run.For the first time, we can analyze the 3D turbulence fine structure by interacting with plasmas and magnetic fields in nanoflare.
Pelatihan Pembuatan Game Edukasi WordWall Bagi Guru SMA Dharma Amiluhur Yogyakarta

This community service aims to provide training on creating a WordWall educational game for teachers at SMA Dharma Amiluhur, Sedayu, Special Region of Yogyakarta. The training was held in May at the school and was attended by 25 teachers of all subjects. This activity begins with an introduction regarding the types of educational games, the advantages and disadvantages of each educational game, and the latest developments related to the benefits of using educational games for learning. In addition, this service is also focused on providing knowledge and understanding to teachers at Dharma Amiluhur High School regarding the benefits of the WordWall educational game, the features on the website and how to make it. The results of the dedication show that the success indicators have been achieved, namely ≥75% of teachers understand the benefits and features of WordWall, attendance at each meeting is 80%, and all teachers can make educational games WordWall with various features available even in learning that takes place after training, teachers at SMA Dharam Amiluhur have used the educational game WordWall as a learning evaluation website that students must work on. Therefore, this community service has achieved the set indicators of success and the goal of making teachers at the school able to understand and use the WordWall educational game has been achieved.
Design of a Productivity Monitoring System for an Asset Maintenance Group Using Design Thinking Methodology

In the Shared Services Center (SSC) of an organization in the Hazardous Chemical Cargo Transportation business, a need for a new group has emerged in order to serve a specific customer need. This new development opened the opportunity for improvement in the monitoring, measuring, and analyzing of its productivity. Establishment of these are needed to show the business that this group serves an important function and is a contributor for the organization to achieve its business objectives. The researcher was able to analyze the current system and put together the system pain points from interviews and through the design thinking methodology. The researcher then came up with a proposed productivity monitoring system which has been compared to the existing system and is expected to eliminate the risk areas prone to human error and address the need to have a means to generate the team's productivity summary.
O desenvolvimento da BVS Rede de Informação e Conhecimento e sua contribuição para a disseminação da informação técnico-científica na Secretaria de Estado da Saúde de São Paulo

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FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this article, we present a large-scale detailed 3D face dataset, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaceScape</i> , and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset releases 16,940 textured 3D faces, captured from 847 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniform. These fine 3D facial models can be represented as a 3D morphable model for coarse shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different from most previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.
Private Image Generation with Dual-Purpose Auxiliary Classifier

Privacy-preserving image generation has been important for segments such as medical domains that have sensitive and limited data. The benefits of guaranteed privacy come at the costs of generated images' quality and utility due to the privacy budget constraints. The utility is currently measured by the gen2real accuracy (g2r%), i.e., the accuracy on real data of a downstream classifier trained using generated data. However, apart from this standard utility, we identify the “reversed utility” as another crucial aspect, which computes the accuracy on generated data of a classifier trained using real data, dubbed as real2gen accuracy (r2g%). Jointly considering these two views of utility, the standard and the reversed, could help the generation model better improve transferability between fake and real data. Therefore, we propose a novel private image generation method that incorporates a dual-purpose auxiliary classifier, which alternates between learning from real data and fake data, into the training of differentially private GANs. Additionally, our deliberate training strategies such as sequential training contributes to accelerating the generator's convergence and further boosting the performance upon exhausting the privacy budget. Our results achieve new state-of-the-arts over all metrics on three benchmarks: MNIST, Fashion-MNIST, and CelebA.
A Gesture Recognition and Drone Control System based on Residual Neural Network

To achieve visual communication between Drone and operator, a static gesture recognition and Drone control system based on residual neural network is designed. The system segments static gestures through YCrCb colour space and elliptical skin colour detection model, extracts gesture features using the residual neural network ResNet34 and uses deep learning techniques to realise the combination of gesture recognition and UAV control. The system uses the gesture images taken from the UAV viewpoint as the dataset and will send the corresponding control signals to the UAV side according to the calibrated control commands to achieve stable control of the UAV, and the classification accuracy is used as an evaluation index to assess the performance of the model of this system in gesture recognition. Finally, the control performance of the system was verified in a real flight in a Tello vehicle platform, and the experimental results showed that the average accuracy rate could reach 90.43%.
Machine Learning with Bitcoin Heist Ransomware

In recent years, there has been a significant rise in the popularity of cryptocurrency amongst investors worldwide. One cryptocurrency that has been the forerunner in this new digital age is Satoshi Nakamoto’s Bitcoin. As much as it has augmented in value in the past several years, many issues have emerged as new points of concern surrounding cryptocurrency ransomware orchestrated by scammers. As a result of the growing scandals, one notorious case that has made the most headlines is the Bitcoin Heist. We have found a sizable dataset that traces back to the Bitcoin Heist incident. With the help of data science and machine learning fundamentals, we will explain different methodologies to determine whether transactions are malicious or not based on a given Bitcoin address. In this paper, we will explain cryptocurrency and ransomware and further insights into the machine learning concepts behind this issue through various models such as Adaptive Boosting (AdaBoost), Gradient Boosting, K-Nearest Neighbor (KNN), and Random Forest.