Reviewerly/exHarmony huggingface dataset:
Restoration and Design of Ice Sculpture Structure following Multivision Sensor and Three-Dimensional Reconstruction Technology

The purpose of the study is to improve the structure restoration and design of ice sculptures. This study discusses the concept and artistic expression of ice sculpting based on the relevant theoretical knowledge of visual sensing technology and three-dimensional reconstruction technology. Based on this, the ice sculpture structure restoration and design model based on multivision sensors and three-dimensional reconstruction technology is constructed, and the model is compared with the manual test. The results show that the ice sculpture structure restoration and design model based on a multivision sensor and three-dimensional reconstruction technology is feasible and has high efficiency and stability compared with artificial ice sculpture restoration. In the experiment of structure reconstruction and restoration of an ice sculpture, the manual group 1 spends 237 minutes, the manual group 2 spends 252 minutes, and the time spent by the model group is very stable, which is about 139 minutes. Due to the difference in manual group personnel, their carving time is also different. As the carving continues, the possibility of errors made by the two manual groups increases. Therefore, ice sculpture structure restoration and design based on multivision sensors and three-dimensional reconstruction technology will provide a reference for the study in the field of ice sculpture structure restoration and design.
A systematic literature review on knowledge tracing in learning programming

This Research Full Paper presents a systematic review on knowledge tracing of learning programming based on student performance data in exercises. Programming has become an essential skill to solve realworld problems in modern engineering disciplines. However, when students start to learn how to program, they face a lot of challenges in acquiring various programming knowledge and skills. While it is beneficial to customise learning material to fit individual learning progress, the widely different learning pace of students in an introductory programming course has made it impractical for teachers to track the knowledge acquisition of individual. Hence, many recent works take a data-driven approach to model students' learning progress based on the performance data in programming-related exercises, which include the submitted program codes and answers to closed-ended programming exercises. By analyzing these performance data, a system can evaluate the students' knowledge level of various concepts and skills in programming. This paper performs a systematic review and reports key information about recent works on programming knowledge tracing based on student performance data. An overview of the different choices of knowledge representation, domain knowledge model, performance measure and knowledge tracing algorithms is provided. The nature and granularity of knowledge components and the relationships between them are compared across the reviewed works. The different choice of programming knowledge representation leads to varied methods to assess knowledge levels from empirical performance data in programming-related exercises. Two broad categories of works are identified. The first is to overlay a student model on the domain knowledge model, and the student knowledge levels are updated in distinct time steps. The second trains temporal knowledge tracing models to predict students' future performance based on their performance in previous exercises. In addition, this review discusses the distinct challenges in knowledge tracing in programming education. It also points out limitations in current works and opportunities to improve knowledge tracing in learning programming.
Real-time motion detection with candidate masks and region growing for moving cameras

A fast and reliable motion detection method is proposed for moving cameras. Existing methods generally build background models and then apply background subtraction for moving pixels detection. In background modeling, statistical methods and/or features, such as edges, local binary patterns are used. Parameters of the proposed methods need to be updated adaptively for better performance. In recent studies, adaptive learning rates are used with main and candidate background models. Simple and adaptive thresholding methods are used for foreground detection in different studies. We build a statistical background model with hue–saturation–value color space and perform background subtraction with the contribution of optical flow vectors. Dense optical flow is applied between warped the background model and the current frame to estimate optical flow vectors. If a pixel has a larger magnitude, we set a weight value for each pixel in the background subtraction process. Then, we extract two candidate foreground masks and apply some postprocessing techniques to determine the final mask. Our proposed method works fast in real time, and the experimental results on two different datasets show that our approach has the highest performance compared with the methods in the literature.
BUILDING A COMPREHENSIVE CLOUD SECURITY AUDIT PROGRAM

Building a security audit program for cloud services could be a complex process; there are various aspects that a security professional must consider when choosing what areas to incorporate in an in-house framework or what industry standard to use to assess particular cloud infrastructures. A cloud provider must take into account various aspects related to the core of the service, such as API security, code reviews, infrastructure, and others; on the other hand. A cloud customer must consider other areas specific to the use of the service, such as vendor lock-in, contractual agreements, and others. This article contemplates a holistic view of the various frameworks and tools available to companies and security professionals building a comprehensive audit program; it covers the cloud provider and the cloud customer perspectives and expands on what tools could be applied depending on the security audit’s angle.
A Reversible Data Hiding Method in Encrypted Images for Controlling Trade-Off between Hiding Capacity and Compression Efficiency

In this paper, we propose a new framework for reversible data hiding in encrypted images, where both the hiding capacity and lossless compression efficiency are flexibly controlled. There exist two main purposes; one is to provide highly efficient lossless compression under a required hiding capacity, while the other is to enable us to extract an embedded payload from a decrypted image. The proposed method can decrypt marked encrypted images without data extraction and derive marked images. An original image is arbitrarily divided into two regions. Two different methods for reversible data hiding in encrypted images (RDH-EI) are used in our method, and each one is used for either region. Consequently, one region can be decrypted without data extraction and also losslessly compressed using image coding standards even after the processing. The other region possesses a significantly high hiding rate, around 1 bpp. Experimental results show the effectiveness of the proposed method in terms of hiding capacity and lossless compression efficiency.
Research on Parallel Support Vector Machine Based on Spark Big Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.
Performance comparison of programming interfaces on the example of REST API, GraphQL and gRPC

The article presents a comparison of the performance of three ways of implementing programming interfaces used in web applications - REST, GraphQL and gRPC. For the purposes of the research, three applications were developed, which were made in each of the indicated technologies and with the same functionalities. The applications were used for performance tests carried out with the use of the k6 tool. The applications are used to measure the execution time, performance and volume of processed data during display and adding operations. The obtained results allowed for the conclusion that the best interface in terms of performance (measured as the number of transactions per second) and server response time is REST. However, in terms of the smallest data volume, gRPC is the best choice.
Paddy Price Prediction in the South-Western Region of Bangladesh

In the current scenario, farmers are losing a lot of profit due to price fluctuations caused by climatic change and other price influencing factors. Farmers are affected emotionally and financially as a result of this. Price forecasting may aid the agriculture supply chain in making critical decisions to reduce and manage the risk of price fluctuations. Predictive analysis is supposed to solve the problems as a result of reduced agricultural productivity due to uncertain climatic conditions, global warming, and other factors. This research focuses on identifying appropriate data models that aid in achieving high price forecast accuracy and generality. Our dataset's class imbalance was reduced using SMOTE. However, SMOTE was not particularly beneficial because our dataset only comprised data from three districts in the Khulna division. We used Linear Regression, a Machine Learning Classification method, to predict the price of the crop. To compare the prediction results, we used a Neural Network. The data for forecasting paddy prices was originally collected by visiting local farmers in Bangladesh's Khulna Division. There are 154 instances in this dataset, each with its own set of 10 unique attributes, such as division name, district name, sub-district name, market name, pricing type, crop price, crop quantity, date, crop category, and crop name. The models we built have an RMSE value of 114.48 and MAE value of 80.08 for Linear Regression, while, for Neural Network we got the lowest RMSE value of 338.2241 and MAE value of 293.1295. Thus, it is concluded that the Linear Regression model performed better and there is still potential for improvement.
A New Pi-based Chaotic Map for Image Encryption

Novel chaotic map (CM) based on pi number socalled Pi-map is introduced for image encryption scheme (ImES). The chaotic behavior of the Pi-map is appreciated with respect to Lyapunov Exponent (LE) and bifurcation graphs. The LE which is stable and highest is also compared those reported elsewhere. The image encryption performance of Pi-map is corroborated on a simple ImES consisting of only two operations which are permutation and diffusion. The ImES with Pi-map is crypto-studied through various analysis and, they are even crosschecked with the others in the literature. It is shown that ImES is eminent and reliable thanks to the capability of the Pi-map regarding randomness and complexness.
Evaluation of Animation and Lip-Sync of Avatars, and User Interaction in Immersive Virtual Reality Learning Environments

Virtual Reality (VR) has been showing potential in new and diverse areas, notably in education. However, there is a lack of studies in the Foreign Language Teaching and Learning field, particularly in listening comprehension. Therefore, this study investigated the effects of avatar animations and lip synchronization, and user interaction; features deemed relevant in this broader area. A sociodemographic, a quick CEFR - Common European Framework of Reference for Languages - 15-minute English test, and questionnaire were used to evaluate the participants' Presence, Quality of Experience, Cybersickness and Knowledge Retention. Results show that, overall, the use of avatars with realistic animations and movements, and featuring lip synchronization have a positive influence on the users' sense of presence, knowledge retention and a more enjoyable overall quality of experience. The same can be said for the use of object interaction and navigation in the cultural representative environment, which had an overall positive impact.
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations

Prediction using the ground truth sounds like an oxymoron in machine learning. However, such an unrealistic setting was used in hundreds, if not thousands of papers in the area of finding graph representations. To evaluate the multi-label problem of node classification by using the obtained representations, many works assume that the number of labels of each test instance is known in the prediction stage. In practice such ground truth information is rarely available, but we point out that such an inappropriate setting is now ubiquitous in this research area. We detailedly investigate why the situation occurs. Our analysis indicates that with unrealistic information, the performance is likely over-estimated. To see why suitable predictions were not used, we identify difficulties in applying some multi-label techniques. For the use in future studies, we propose simple and effective settings without using practically unknown information. Finally, we take this chance to compare major graph representation learning methods on multi-label node classification.
A revisited representation of the red-black tree

Nowadays, the red-black tree is probably the most used balanced data structure. It searches, inserts and deletes keys in O log (n) time and it is highly recommended for applications with frequent updates. However this balanced binary tree is complicated in the implementation because of the different cases in both the insertion and deletion and the use of the red and black colours. That's why we propose in this paper a simpler representation of this structure. The new representation uses one extra bit in order to gain simplicity and efficiency. The red-black tree is seen as a partitioned binary tree and uses instead of colours three kinds of subtrees: 01, 10 and 11 subtrees. We show on one hand that we can simply express the insert algorithm and on the other hand that the performance of the algorithm is slightly better. Efficiency is due to the fact that the search branch used in the new representation is reduced by half. Therefore this representation can replace easily the standard algorithms of red-black tree in practice.
Quantum Entanglement of Monochromatic and Non-Monochromatic Photons on a Waveguide Beam Splitter

It is well known that the waveguide beam splitter can be used as a source for the quantum entanglement of photons. The analysis of such quantum entanglement is a difficult problem even for monochromatic photons, since the system under study is multiparametric. This paper will show that quantum entanglement can be represented in a simple form not only for monochromatic photons but also for non-monochromatic ones. It will be shown that quantum entanglement for non-monochromatic photons can be very different from monochromatic photons, which can be used to create large quantum entanglement.
Poverty, Cybercrime and National Security in Nigeria

The number of poor people in Nigeria continues to grow at an exponential rate, while Nigeria remains the poverty capital of the world. As a result, cybercrime has become a haven for youths. The festering of cybercrime increases the chances of cyberterrorism, hate speech proliferation, and radicalization – all of which pose a danger to national security in Nigeria. As a result, this study adopts an analytic approach to the interplay of poverty, cybercrime, and national security in Nigeria. The findings reveal that poverty is a major driver of cybercrime in Nigeria while cybercrime is a threat to national security. It shows that with the prevalence of poverty, deprivation, and inequality, many Nigerian youths turn to crime to realize the Nigeria of their dreams. In the same vein, cybercrime undermines national security by exposing various individuals to violence, cyberespionage, cyberstalking, and manipulation. The study, therefore, recommends that decisive policies be targeted at reducing poverty incidence in Nigeria and that youths should be regularly sensitized to the dangers of cybercrime and the incapacitating effect it has on national security. More so, cybersecurity should be given prominence by the Cyber Advisory Council while offenders of cybercrime should be dealt with using the full extent of the law, to serve as a deterrence to intending offenders.&#x0D;
Automatic Error Type Annotation for Arabic

We present ARETA, an automatic error type annotation system for Modern Standard Arabic.We design ARETA to address Arabic's morphological richness and orthographic ambiguity.We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications.ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC.We also demonstrate ARETA's usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction.The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M 2 scoring metrics used in the shared task.ARETA employs a large Arabic morphological analyzer, but is completely unsupervised otherwise.We make ARETA publicly available.
COMPUTER MODELING AS A METATECHNOLOGY FOR IMPLEMENTING INFORMATION AND DIGITAL TRAINING OF STUDENTS OF A VOCATIONAL AND PEDAGOGICAL UNIVERSITY

The introduction of digital technologies at a fundamentally new level, the transfer of social, economic and technical processes to the digital space, the formation of a digital ecosystem and, as a result, the formation of the digital economy had a powerful impact on the fi eld of vocational education, changing its goals and content, improving methods and technologies. Computer modeling is actively used in modern science and technology, so it is not necessary to talk about the relevance of its integration into the educational process. The use of computer modeling technologies, the transformation of the educational space through their active implementation provides new opportunities for the formation of information and digital training of a graduate of a vocational pedagogical university. The article presents the author’s understanding of embedding computer modeling technologies into vocational training, clarifi ed the defi nition of the term “readiness of a teacher of vocational training for computer modeling”, and also considered an interdisciplinary module as an eff ective means of forming readiness and organizing complex integrative training.
Formation of content modules for teaching artificial intelligence in the basic school

Problem and goal. The problem of insufficient substantive provision of training of primary school students in the field of artificial intelligence (AI) is considered. The goal is to identify and describe the basic didactic elements within the content modules, as well as to identify the intrasubject and intersubject connections that are realized in the course of teaching the elements of AI in the main school. Methodology. A set of methods was used: analysis of international experience (USA, China, England, Germany, Israel, Russia); analysis of documents defining requirements for the implementation of basic educational programs; analysis of textbooks; research of ready-made software solutions applicable in the process of teaching the basics of AI to primary school students; reflection of the content of the knowledge gained. Results. The study made it possible to identify learning opportunities in the field of AI for students starting from grade 5, regardless of the availability of teaching hours allocated for compulsory informatics education, due to the variability of general education. An analysis of informatics textbooks for basic school showed that teaching the basics of AI should be implemented taking into account intrasubject connections with informatics. This approach will make it possible to carry out a supporting, expanding or propaedeutic function of teaching AI elements in relation to the main course of informatics. In the process of forming meaningful modules for teaching the elements of AI to primary school students, starting from grade 5, didactic elements were identified, the development of which must be organized taking into account interdisciplinary connections with general education disciplines. The content of educational material in the field of AI should be mastered through the most expedient types of activity in the context of the system-activity approach, which will allow to form personally significant know- ledge and skills of students, to develop their creative abilities. Conclusion. The results of the study made it possible to substantiate the expediency of teaching the elements of AI to primary school students, starting from grade 5. Various options for including in the basic school curriculum issues related to the basics of AI, taking into account the existing experience of teaching schoolchildren in the field of informatics, were identified.
A Novel Method for Space Circular Target Detection in Machine Vision

Computer-vision-based space circular target detection has a wide range of applications in visual measurement, object detection, and other fields. The space circular target is projected into an ellipse in the camera for localization. Traditional methods based on monocular vision use a precise calculation model to calculate the center coordinate and normal vector of the space circular target according to the image's elliptic parameters. However, this accurate calculation method has the disadvantage of poor anti-interference ability in practical application. Aiming at the shortcomings of the above traditional calculation method, this paper proposes an optimization method for fitting the circular target in 3D space, where the image ellipse is projected back into 3D space and then detects the center coordinate and normal vector of the space circular target. Unlike the traditional method, this approach is not sensitive to the image's elliptic parameters; it has stronger noise resistance performance and notable application value. The feasibility and effectiveness of the proposed method were verified by both simulation and practical experimental results.