Reviewerly/exHarmony huggingface dataset:
Application of Video Processing Technology Based on Diffusion Equation Model in Basketball Analysis

Video event detection and annotation work is an important content of video analysis and the basis of video content retrieval. Basketball is one of the most popular types of sports. Event detection and labeling of basketball videos can help viewers quickly locate events of interest and meet retrieval needs. This paper studies the application of anisotropic diffusion in video image smoothing, denoising, and enhancement. An improved form of anisotropic diffusion that can be used for video image enhancement is analyzed. This paper studies the anisotropic diffusion method for coherent speckle noise removal and proposes a video image denoising method that combines anisotropic diffusion and stationary wavelet transform. This paper proposes an anisotropic diffusion method based on visual characteristics, which adds a factor of video image detail while smoothing, and improves the visual effect of diffusion. This article discusses how to apply anisotropic diffusion methods and ideas to video image segmentation. We introduced the classic watershed segmentation algorithm and used forward-backward diffusion to process video images to reduce oversegmentation, introduced the active contour model and its improved GVF Snake, and analyzed the idea of how to use anisotropic diffusion and improve the GVF Snake model to get a new GGVF Snake model. In the study of basketball segmentation of close-up shots, we propose an improved Hough transform method based on a variable direction filter, which can effectively extract the center and radius of the basketball. The algorithm has good robustness to basketball partial occlusion and motion blur. In the basketball segmentation research of the perspective shot, the commonly used object segmentation method based on the change area detection is very sensitive to noise and requires the object not to move too fast. In order to correct the basketball segmentation deviation caused by the video noise and the fast basketball movement, we make corrections based on the peak characteristics of the edge gradient. At the same time, the internal and external energy calculation methods of the traditional active contour model are improved, and the judgment standard of the regional optimal solution and segmentation validity is further established. In the basketball tracking research, an improved block matching method is proposed. On the one hand, in order to overcome the influence of basketball’s own rotation, this article establishes a matching criterion that has nothing to do with the location of the area. On the other hand, this article improves the diamond motion search path based on the basketball’s motion correlation and center offset characteristics to reduce the number of searches and improve the tracking speed.
Application of cloud matter element model in VTS radar station location

In order to balance the influence of subjective factors in the VTS radar station location and make the location more reasonable, an evaluation index system for the VTS radar station location has been constructed based on the principles of science, operability, and systemic. An evaluation model of VTS radar station location based on cloud matter element theory and combined weighting method is proposed. Analysis of the above problems in the location of the Yangshan VTS radar station shows that the evaluation index system for the VTS radar station location meets the basic requirements. The optimal location can be obtained from both qualitative and quantitative aspects, proving the feasibility and comprehensiveness of the proposed method.
Research on an Image Hiding Algorithm with Symmetric Means

Under the premise of comprehensively researching various image hiding algorithms, this paper proposes a new image hiding algorithm—the mean symmetric algorithm with lower distortion and higher security. This algorithm calculates the mean value and variance of the secret image block, then compresses the secret image block, uses the symmetry method to scramble the secret image block and convex combination to hide the secret image in the carrier image, making it a camouflage image. Through the analysis of experimental results, the method is simple to implement, which greatly improves the security of the image hiding system and the authenticity and robustness of the hidden image.
Human Emotion Detection Using Convolutional Neural Networks with Hyperparameter Tuning

Facial expression recognition (FER) is considered a primary cue for human emotion detection. Conventional and deep learning-based techniques are the two most commonly proposed techniques for addressing FER. Traditional approaches are less dependent on data and hardware but make end-to-end learning impossible as they require manual feature extraction. Although deep learning-based methods demand high computation power, they proved to show outstanding accuracy for FER. For the deep learning-based models, specifically convolutional neural networks (CNNs), performance highly depends on the configuration of hyperparameters. Studies have shown that the accuracy of CNN for image classification increased by 10% with hyperparameter tuning. Hence, it is possible to improve FER classifier performance further by hyperparameter optimization. Due to their stochastic nature, metaheuristic optimizers proved to offer better results over other trial and error optimizers. The present study used differential evolution (DE) for hyperparameter tuning. The proposed model is trained and tested on two benchmark datasets. The results obtained have shown a 4% improvement in classification accuracy.
Energy-Efficient Scheduling Algorithms with Reliability Goal on Heterogeneous Embedded Systems

In this paper, we are trying to find a algorithm for scheduling DAG (Directed Acyclic Graph) tasks in heterogeneous embedded systems to minimize energy consumption while meeting the reliability requirement. Like many traditional algorithms, we divide the task scheduling algorithm into two phases, the task priority calculation phase and the task allocation phase. In the task priority calculation phase, we proposes a priority calculation algorithm IOD based on the difference in task&#x2019;s input and output data. In the task allocation stage, we proposes a task allocation algorithm based on fault-tolerant technology of task replication and DVFS technology. Combining the two phase, we get three scheduling algorithms, IODS, IODQ and IODR. In the experimental part, we compare the performance of the algorithm proposed in this paper with existing research algorithms (EFSRG algorithm and HRRM algorithm). The analysis of experimental results shows that the IODS algorithm is a better choice.
Exponentially Handsome Proof Nets and Their Normalization

Handsome proof nets were introduced by Retor\'e as a syntax for multiplicative linear logic. These proof nets are defined by means of cographs (graphs representing formulas) equipped with a vertices partition satisfying simple topological conditions. In this paper we extend this syntax to multiplicative linear logic with units and exponentials. For this purpose we develop a new sound and complete sequent system for the logic, enforcing a stronger notion of proof equivalence with respect to the one usually considered in the literature. We then define combinatorial proofs, a graphical proof system able to capture syntactically the proof equivalence, for the cut-free fragment of the calculus. We conclude the paper by defining the exponentially handsome proof nets as combinatorial proofs with cuts and defining an internal normalization procedure for this syntax.
Graphic visualisation of hybrid learning contexts

The arrival of the pandemic has accelerated the need to transform learning environments and the way education is delivered.Universities have been faced with sudden changes.Teachers have been called upon to adapt their technological skills and redesign their courses to meet the new needs for interaction in new digital learning environments.With the possible return to normality, lecturers will need tools to help them re-contextualise their teaching activities and choose and relate appropriate technologies within physical (on-site) and digital (online) learning environments.Therefore, their task will be to design hybrid learning contexts.This paper discusses the results of performative experimentations carried out with teachers from the School of Design of the Politecnico di Milano to test the capabilities of an instructional design tool in creating graphic spatial visualisations of hybrid learning contexts.
A Highway Distance Posts Detection Method Based on S-YOLOv3

In GPS denial environments, the geographic information of the location of distance posts could be obtained through detecting these posts in images, and could be used for vehicle positioning. Resolution of vehicle-mounted camera imaging will be affected by complex environments, changing imaging scales and angles that bring difficulties to the detection. Although YOLOv3 is effective to solve this question, its architecture is too complex to calculate. Aiming at the requirements that there are only two types of distance posts to detect and fast operation speed in our task, we put forward a new form of YOLOv3, named S-YOLOv3 (simplified YOLOv3). Through video images acquired from the Daheng mercury camera mounted in the vehicle, we completed training and testing of the network. According to the results, our method has better detection robustness, whose mAP(mean Average Precision) reaches 85% and detection speed reaches 28FPS.
Continuous Course of Information Security at School in the System of Forming Digital Skills of the XXI Century

The normative and methodological support of a continuous course on information security is considered on the basis of an approximate program of the training course and textbooks "Information Security" from the Federal list of textbooks at the levels of general education.
Breast Cancer Classification Using Enhanced Binary Crow Search Optımızation With Iterative Flight Length

In this world, the increase in sophistication of human lives leads to a decrease in their life span. The mortality rate due to cancer is steadfastly raising every year. Among all types of cancer, breast cancer is a peculiar one that is responsible for more cancer deaths globally. With an eye to classify its severities, an appropriate and effective diagnostic methodology is therefore demanded. Accordingly, the work pitches light in designing a classification methodology that adopts an enhanced crow search algorithm for feature selection followed by a k-nearest neighbor classifier for classifying the breast cancer severities. The basic novel idea of this proposed methodology is that the flight length of the metaheuristic original crow search algorithm is made to be adaptive for feature selection. Then, the KNN algorithm is used as a classifier with grid search technique. In this regard, the paper exploits the standard benchmark breast cancer Wisconsin (diagnostic) dataset for its analysis.
Assessing the quality of distance learning in the discipline “physical culture and sports” in the educational process of the university

The pandemic announced in the world requires non-standard solutions from teachers to implement a full-fledged educational process in the higher education system of the Russian Federation. The academic discipline Physical Culture and Sports is no exception. The use of accessible and modern wellness programs in the classroom is necessary for the full maintenance and strengthening of students ' health, increasing their motor activity during the day. Special responsibility is assigned to teachers who are able to organize a competent educational process in the context of distance learning, while observing all the principles of teaching. Modern information technologies have made it possible to conduct training sessions online on the Mirapolis platform. To do this, we used selected wellness programs published on YouTube, the Fitify program and the Pacer pedometer. Students studied under the guidance of a teacher, using a video link. Thus, the teacher could not only control the correctness of the exercises, but also adjust the programs. At the end of the semester of distance learning in the discipline Physical Culture and Sports, a survey of students was conducted, the purpose of which was to analyze the degree of their satisfaction with the organization and methodology of classes. The greatest difficulties in physical education (6.3% of respondents) were caused by 1st year students, nevertheless, the motivation for physical education has not changed. More than 50% of respondents are satisfied with the received physical activity in distance classes. Among the positive aspects of distance learning were noted: the absence of the need to spend time on the way to and from the place of study and, as a result, the availability of more free time 79%, low risk of infection (71%), the opportunity to study at an individual pace (56%). For 40% of respondents, it is an opportunity to use modern technologies. Thus, the results showed that the majority of students adapted well to the organization of distance learning at SPbGUP, including in the discipline physical culture and sports.
Evaluation of the Effect of Blockchain Technology Application in the International Trade

Blockchain technology is widely used in many fields due to its non-tamper resistance, openness, transparency, and decentralization characteristics. The application of blockchain technology in international trade mainly includes five aspects: product traceability, trade payment, customs procedures, trade financing, and trade supply chain. Evaluation of the effect of blockchain technology application in the above aspects by a method of analytic hierarchy process (AHP) shows that the effect on trade payment and trade supply chain is better than others. At the same time, it has a less obvious effect on product traceability. Currently, there are still some barriers to applying blockchain technology in the field of international trade. Blockchain technology still has certain technical risks and lacks legal supervision. Better application of blockchain technology can be realized by cultivating high-quality blockchain technical talents and improving the supervision mechanism of blockchain technology.
Application of Allan variance for data control in sensor networks

This work proposes application of dynamic Allan variance for detection of failures or external interventions in data acquisition systems, cyber-physical systems, and Internet-of-Things (IoT). The work analyzed a number of sample data sequences to which a random noise of various spectra was added by means of simulation starting from some point of time. It was demonstrated that the dynamic Allan variance could detect the changes in the statistical characteristics of the random data sequence caused by the added noise. This can be one of the methods of automatic data control in sensor networks.
An Improved PersonalRank Recommendation Algorithm

In these years, recommendation systems are broadly researched as well as widely applied in our daily lives. The interaction between users and items is normally expressed by a matrix or graph. Many recommendation algorithms based on graph models have been developed, among which PersonalRank is an algorithm based on the theory of random walk, and NBI is the one based on bipartite network projection. In this paper, we demonstrate the characteristics they share, and fuse them to improve the former. The decay factor in NBI, which is used to inhibit the impact of vertices with high degrees in terms of the initial condition, is applied in PersonalRank in terms of the iteration process. Meanwhile, making use of the idea of the weighted matrix in NBI, we introduce diffusion parameters to generalize PersonalRank further. In addition, the termination condition of PersonalRank is discussed in our work. With the experimental framework shown, we test the algorithms in the benchmark datasets MovieLens. The simulation results show that after our modifications, the precision and coverage of PersonalRank are both significantly improved, outperforming a recent algorithm.
Continuous Human Action Detection Based on Wearable Inertial Data

Human action detection is a hot topic, which is widely used in video surveillance, human–machine interface, healthcare monitoring, gaming, dancing training and musical instrument teaching. As inertial sensors are low cost, portable, and having no operating space, it is suitable to detect human action. In real-world applications, actions that are of interest appear among actions of non-interest without pauses in between. Recognizing and detecting actions of interests from continuous action streams is more challenging and useful for real applications. Based on inertial sensor and C-MHAD smart TV gesture recognition dataset, this paper utilized different inertial sensor feature formats, then compared the performance with different deep neural network structures according to these feature formats. Experiment results show the best performance was achieved by image based inertial feature with convolution neural network, which got 51.1% F1 score.
Digital Humanities Strategy

This chapter outlines a strategy for digital humanities that can address some of the challenges that have arisen due to informing technologies. The chapter begins by giving a definition of humanism and by describing the nature of the humanistic attitude. The chapter next considers the role that mythology plays in humanism. After this, a matrix model of the humanities is presented that shows the role of the humanities in a computerized society. Then, a digital humanities research strategy is discussed with specific recommendations for implementing such research. This is followed by a discussion of the role that supercomputers play in research. Strategies for creating content in the digital humanities are then explored in disciplines such as philosophy, archeology, and history. Next, strategies for publishing and distributing information are described. The chapter concludes by considering strategies for teaching digital humanities and for developing digital social resonance.
Organizing programming classes in informatics courses and extracurricular activities using the trik studio programming environment

The article discusses the opportunities provided by the TRIK Studio programming and modeling environment in the process of organizing programming training in informatics course and in extracurricular activities with students of the 7th and 8th grades. The article provides a brief overview of TRIK studio, and suggests thematic planning of the lesson cycle using it. A fragment of a training session is presented, which discusses the basics of controlling a two-dimensional robot model and the implementation of linear algorithms for it. The materials of the article will introduce informatics teachers and teachers of additional education to the main methodological approaches to the use of the TRIK Studio programming environment in the educational process and will contribute to the organization of informatics lessons and extracurricular activities using this software.
Investigating Fast BiLSTM Neural Networks for Arabic Language Applications

Deep neural networks are providing efficient solutions for many natural language processing (NLP) applications. Recently, recurrent neural networks based on the long short-term memory (LSTM) cells are successfully used in various solutions for the Arabic language. However, these networks are complex and require long training times on large datasets. In this paper, we present an investigation for fast alternatives to the commonly used bidirectional LSTM (BiLSTM) layers. As case studies, we evaluate alternative networks for two Arabic applications: classifying poems and diacritizing text. We concentrate in this paper on networks based on the CUDA deep neural network (CuDNNLSTM) layer for its many advantages. We present thorough performance evaluation of BiLSTM and BiCuDNNLSTM networks including model complexity, training time, and accuracy for both applications. We recommend BiCuDNNLSTM networks as alternative for BiLSTM networks because they improve training time by 65 and 21 times for poem classification and diacritization, respectively. Moreover, the BiCuDNNLSTM networks provide similar or higher accuracies (99.16% vs. 98.41 % for diacritization). The advantage of this recommend layer originates from its efficient utilization of the GPU.
The Influence of New Means of Communication on the Translation of Scientific Knowledge

The article is devoted to the study of the influence of new means of communication, and especially the Internet, on the translation of scientific knowledge. It is emphasized that with the introduction of a new means of communication to the fore, the former ones are not &#x201C;abolished&#x201D;, but go into the background and acquire other characteristics. Under the influence of the Internet, a virtual (network) means of communication is formed, which is a dialectical synthesis of oral and written means of communication, giving them new functional characteristics.
An Improved Image Super-Resolution Reconstruction Method Based On LapSRN

With the gradual maturity of the traditional static image recognition field, super-resolution reconstruction based on deep neural networks is a research hotspot and difficulty in the field of computer vision, In particular, most single-frame image super-resolution methods have problems such as loss of high-frequency information, noise introduced in the up-sampling process, and difficulty in determining the interdependence between each channel of the feature map when reconstructing the predicted image. In order to solve the above problems, we introduce back projection mechanism into the LapSRN network in this paper. By introducing the back projection mechanism effectively improved the consistency between the extracted image feature data and the target feature data feature, and thereby improved the reconstructed image parameters. Experiments show that the improved network can achieve better performance than LapSRN.
Application of the Objects-Early Approach to the School Course on Computer Science

Raising the level of digital education is one of the main challenges facing the education system now and in the future. The use of modern technical means in the learning process is an important prerequisite for achieving this ambitious goal. However, this must go hand in hand with the introduction of new scientific methods and teaching approaches. This article presents different approaches which the author has applied in her practice with high school students. It proves the benefit of their implementation in the educational process.
Knowledge Graph for Aquaculture Recommendation System

Aquaculture is a growing industry. It would be beneficial to the fish farmers if the data about the fish, such as the ecosystem, food and related information are available to them to increase the fish yield. Most of the fish species data of the aquaculture domain are stored using relational databases. However, the relational tables work well only for structured data. It would help the fishermen if the data can be visualized and provided with a suitable recommendation system that recommends the best species and best ecosystem. In this paper, an approach to store the details of fish species of the brackish water of the west coast of Karnataka, India using Neo4j is presented. Further, a recommendation system to retrieve the best fish species for a particular ecosystem is proposed. The data relating to fish species, names, threatened status, taxonomy, fish species location, type of water they survive in are stored as a connected graph in the Neo4j graph database. This helps the aquatic scientists and aquaculture users visualize the relationship among the fish species and get suitable recommendations on fish species based on their interests. The proposed system is scalable and is capable of processing any complex relationship for providing recommendations.