Reviewerly/exHarmony huggingface dataset:
3D Sign language recognition based on multi-path hybrid residual neural network

Abstract: Sign language is an important communicating method for deaf-mute people. In recent years, the hybrid model between the Bi-directional Long-Short Term Memory (BiLSTM) and 3D convolutional network model makes full use of the feature extraction ability of convolutional neural networks and the advantages of time series classification of the recurrent neural network model to achieve more accurate recognition. However, high precision, scalability and robustness are still important challenges in future sign language recognition research. The main research direction and responding research methods aim to improve the accuracy and speed of 3D poses and continuous sentences sign language recognition based on hybrid models with the upgrading of computer hardware equipment and network. The paper improves a novel residual neural network and then engages it to extract features and build models with BiLSTM. The proposed hybrid model combines the improved neural network and Bi-directional Long-Short Term Memory (BiLSTM). In order to validate the proposed algorithm, we introduce the Chalearn dataset and Sports-1M dataset captured with depth, color and stereo-IR sensors. On the two challenging datasets, our multi-path hybrid residual neural network achieves an accuracy of 78.9% and 82.7%, outperforms other state-of-the-art algorithms, and is close to human accuracy of 88.4%.
IMU-Assisted Online Video Background Identification

Distinguishing between dynamic foreground objects and a mostly static background is a fundamental problem in many computer vision and computer graphics tasks. This paper presents a novel online video background identification method with the assistance of inertial measurement unit (IMU). Based on the fact that the background motion of a video essentially reflects the 3D camera motion, we leverage IMU data to realize a robust camera motion estimation for identifying background feature points by only investigating a few historical frames. We observe that the displacement of the 2D projection of a scene point caused by camera rotation is depth-invariant, and the rotation estimation by using IMU data can be quite accurate. We thus propose to analyze 2D feature points by decomposing the 2D motion into two components: rotation projection and translation projection. In our method, after establishing the 3D camera rotations, we generate the depth-relevant 2D feature point movement induced by the camera 3D translation. Then, by examining the disparity between inter-frame offset and the projection of estimated 3D camera motion, we can identify the background feature points. In the experiments, our online method is able to run at 30FPS with only 1 frame latency and outperforms state-of-the-art background identification and other relevant methods. Our method directly leads to a better camera motion estimation, which is beneficial to many applications like online video stabilization, SLAM, image stitching, etc.
Blockchain for the Healthcare Sector

Blockchain technology has gained a lot of recognition recently due to its application in the financial sector. Its applicability in the healthcare sector is fairly new and gaining the attention of several researchers. This chapter explores the concept of blockchain, its characteristics and types of blockchain networks. The chapter also includes how the technology of blockchain can be applied in the healthcare sector and how it can improve the existing healthcare management system. We further discuss the benefits of adopting blockchain in healthcare sector and the challenges that come its way.
Dense view synthesis for three-dimensional light-field display based on scene geometric reconstruction

The three-dimensional light-field display is one of the most promising display technologies and has achieved an impressive improvement in past years. To generate real-world dense views efficiently, many virtual view synthesis approaches have been proposed, which take sparse 2D images as input and synthesize dense virtual views. However, in the occlusion areas, the generated results of previous methods are not satisfactory, which usually contain errors and blurs because the occlusion relation cannot​ be recovered correctly. Here, a dense view synthesis method for three-dimensional light-field display based on scene geometric reconstruction is presented, which can synthesize high-quality arbitrary virtual views with correct occlusions. The scene geometric model is firstly reconstructed with several depth maps captured by RGB-D cameras. The geometric model can provide the correct occlusion relation and select the non-occlusion regions as the input data. To relieve the effect of the depth map noise and improve the quality further, the input views semantic feature maps are extracted and reprojected to the virtual view, which encodes the captured multi-view information and represents the virtual view implicitly. The virtual view image can be generated by rendering the reprojected encoded feature map. Compared with other view synthesis approaches, the quality of the synthesized view is significantly improved with our proposed method, especially in the occlusion areas. The experiments are carried out on a 27 inches 3D light-field display device, and the results demonstrate the effectiveness of our proposed method.
Recommendations for France Educational Institutions Digital Technologies Using in Native Distance Education

This paper provides an overview of the France educational system digital technologies experience using in context of the hybrid education implementation, as a country with high quality of education. On the generalized analysis basis recommendations and proposals for native education on the educational process realization with the use of distance learning technologies experience implementation are presented.
Based On Section Local Histogram Equalization Image Enhancement Algorithm

The implementation of aircraft docking auto-guide to improve airport the level of information and automation is essential, a method based on visual docking auto-guide because the information-rich, intuitive effects and low cost has been subject to the attention of scholars at home and abroad. During entry into the Stand, the aircraft geometry is being checked. The conducting depth studies of image preprocessing algorithms of image enhancement in special weather conditions. In order to better adjust the contrast of image, improved histogram equalization algorithm – section local histogram equalization algorithm is proposed, section transformation, the transformation function is different, become a fold line in the transform coordinate system, fold line discontinuities position according to the need to decide, the section is enhanced by improving the local histogram. Experimental results show that the proposed algorithm is simple, fast operation, to meet the real-time requirements of the system.
Towards an optimal right-turn assistant system to avoid accidents with vulnerable traffic participants

October 16, 2007, the German Road Safety Council (Deutscher Verkehrssicherheitsrat e. V.) decided to start basing their recommendation on the Vision Zero methodology. Although a lot has already been done to make the roads safer for unprotected traffic participants there is still a potential for improvement especially in avoiding accidents with cyclists. The European Commission has already decided that the obli-gation of having the blind-spot assistant systems will gradually include more and more vehicle types as well as retrofitting older trucks and buses. In this paper, we are evaluating how different sensors may contribute to an optimal turning assistant system besides the well-known existing solutions which typically rely on one kind of sensor. Moreover, we focused on a solution, which includes sensors, that have not yet been used for this purpose. In that sense, we are using the advantages of sensor-fusion techniques together with object classification by using machine learning algorithms. Our research spans from the more conventional choices like cameras and 77GHz radar to less apparent such as Real-Time Location System or bicycle bell sound recognition neural networks. Besides that, we decided to not only warn the truck driver but also the unprotected traffic participants about a possibly dangerous situation, which is as well a novelty to our best knowledge. Since our work is still in progress, especially due to the huge amount of training data, there are no final numbers regarding accuracy that could be given. Nevertheless, we would like to share what we have done thus far.
Model of Organizing Online Learning for Students in Agricultural Area

Currently, Uzbekistan is rapidly implementing measures to develop digital technologies in all spheres of the economy, as well as to widely introduce information and communication technologies in public administration, education, healthcare and agriculture and other areas of public life. The country needs specialists with systems thinking, who can make informed decisions, who are able to think outside the box and modern, who are able to search for and use useful information, and learn innovative technologies. Internet technologies play an important role in solving these problems in the educational system. In particular, the implementation of priority projects has begun, which provide for the improvement of the e-government system, the development of the market for software products and information technologies, the organization of IT parks in all regions of the republic, and the provision of this area with qualified personnel. The article presents a model for organizing students’ independent work using online technologies. Detailed instructions are given for completing a specific task in computer science using an electronic educational resource developed by the authors of this article
AIVMAF: Automatic Image Quality Estimation Based on Improved VMAF and YOLOv4

Abstract The current most widely used way of image quality estimation relies heavily on the subjective assessment, while majority of past objective estimation methods are not satisfactory on accuracy. To solve them and realize unsupervised image quality estimation with high precision, this paper creates a linear way with “Proportional Partition” controlled by horizontal and vertical rates of extracted pixel to get best representations of the image with patching, balance the uneven distribution of image quality in each source image, and offer dynamic compatibility to images with high resolution. Besides, it estimates the image quality automatically with a model trained by current best artificial intelligence (AI) algorithm for target detection YOLOv4 with 1000 images random selected from ImageNet2013 database. The proposal also uses the spirit of joint indices from the current widely used method named Video Multimethod Assessment Fusion (VMAF). But we replace its Visual Information Fidelity (VIF) with Visual Saliency-induced Index (VSI) and add VSI to our target function because of VIF’s dependence on subjective assessment, and also for VSI’s better performance surpassing most recent IQA estimators as TOP3 best model in recent world. Besides, contrast masking is also included by objective function for the KL-divergence to simulate the human visual perception better. A creative “Batch Learning” way is found to address patches for less calculation and faster speed. All source images are pretreated with colour space transformation and normalization to improve descriptiveness of images and reduce the redundant points, and a threshold is devised to formulate suppression mechanisms. The proposed solution is tested to be a good image quality assessor in many aspects such as correctness, consistency, linearity, monotonicity and speed, and performs well on even HD images.
Construction of Applied Undergraduate Course Evaluation System Based on BP Neural Network

The curriculum standard is a programmatic document that integrates the content of curriculum objectives, curriculum resource construction, students’ academic performance, and important reference and basis for teachers’ curriculum teaching and curriculum management evaluation. The purpose of this paper is to study how to analyze and study the evaluation of applied undergraduate courses based on the BP neural network and describe the construction of the system. This paper proposes the construction of the course evaluation system, which is based on the BP neural network and then elaborates on its concept and related algorithms and designs and analyzes the construction of the application-oriented undergraduate course evaluation system. The experimental results show that 90.74% of the currently used curriculum standards meet the requirements of applied talents training, 88.89% indicated that the course content was well aligned with professional standards, and 5.19% believed that the design of practical teaching links had a great effect on improving students’ skills. The curriculum is the basic way of education, teachers are the main body of curriculum implementation, and students are the carriers of curriculum effects. Taking teachers, students, and courses as evaluation elements in curriculum evaluation can highlight the core position of curriculum evaluation in curriculum construction.
IIPA-Net: Joint Illumination-Invariant and Pose-Aligned Feature Learning for Person Reidentification

Person reidentification (re-id) has gained significant progress and aroused great interest in computer vision. However, due to the effect of weak illumination and poor alignment, person re-id is still a challenging task. Many previous works focus on either illumination enhancement methods or pose estimation. However, those methods are difficult to apply in real-world scenarios, which usually contain various interference factors. To improve the performance of re-id, we propose an Illumination-Invariant and Pose-Aligned Network (IIPA-Net). The illumination change is handled by a retinex decompose network, and the pose variation problem is solved by a local feature matching method. Based on the multimodal nature of a person, we propose a part attention module to optimize the global feature. Finally, a data-driven training strategy is proposed to train the proposed architecture effectively. Experiments show that the proposed framework outperforms other state-of-the-art approaches on both normal- and low-light datasets.
Energy-efficient AI at the Edge

The advances in artificial intelligence, especially convolutional neural networks (CNNs), over the past few years resulted in state-of-the-art solutions for many tasks, e.g. computer vision. As more and more intelligent applications rely on these methods, there is a growing interest in processing the data locally, at the place of the generation: the rise of intelligent edge computing will enable new possibilities in many areas, like smart manufacturing, autonomous systems, remote control. To increase the performance of the applications, the complexity of the neural networks also increases, leading to growing computational costs. As typical edge computing systems are power constrained, power efficiency is a key factor in designing them. In this paper, we evaluate the performance and power efficiency of a GPU and an FPGA-based edge platform using a set of CNNs with different computing requirements.
Methodological Training of Future Teachers of Computer Science to Carry Out Their Activities in a Remote Form on the Basis of New Models

Methodological training of future teachers of computer science to carry out their activities in a remote form involves the implementation of new models of organization and implementation of the educational process. The article presents scientifically-based proposals for the implementation by future teachers of two models of teaching students computer science. The first model involves the use of digital devices of students and the Internet and direct full or partial interaction with the teacher. The second model is based on the predominant independent work of students. For each model, examples of the presentation of educational content using modern technologies, cloud services, and other communication tools are defined. On the basis of the Department of Computer Science and Methods of Teaching Computer Science of BSPU, the testing of these models was carried out for the implementation of methodological training of future teachers of computer science. The developed recommendations make it easy to implement the process of pre-paring educational content for students on all topics of the subject “Computer Science”.
Current Aspects of Training Digital Consultants in Online Communities of Schoolchildren

The article deals with a number of issues of training the digital consultants in the online communities of the schoolchildren. Specifically, there are described the modules of the training program, there is substantiated the efficiency of the collaborative educational activity in the training process in the context of the online communities of the students of the higher forms based on the integration of the Learning Management System (LMS) and the social services of the Internet.
Object detection based on multi-modal adaptive fusion using YOLOv3

Object detection is a technique used to localize and classify objects in an image or a video sequence. It is an emerging topic of research in the field of computer vision. However, detections in a video are affected by sensor-specific challenges. A convolutional neural network-based You Only Look Once, version 3 (YOLOv3) object detection algorithm was used to get optimized computation time and accuracy. In the proposed methodology, the YOLOv3 architecture extracted significant features from both the visible and thermal imaging domains, and an adaptive fusion of both domains was performed to determine the dominant imaging domain and provide robust detections. The resulting YOLOv3 detections included the bounding box coordinates, confidence score, and class from each imaging domain, which were fused implicitly into a single plane. The sensor domain having the maximum number of object detections was chosen as the reference to be compared with the other domain for the adaptive fusion process. After fusion, the algorithm removed redundancy using adaptive intersection over union thresholding. The mean average precision result obtained from the fusion algorithm was 44.25%. A comparative study was also carried out between pre-trained common objects in context weights and custom CAMEL dataset weights; it showed the significance of using adaptive fusion in challenging situations such as nighttime, shadow, varying illumination, moving camera, and crowd.
On the Issue of Creating an Course on Digital Educational Resources and Services for Organizing the Educational Process

The large-scale processes of transition to the use of new information technologies in all spheres of society, which are currently taking place, make the issue of digital transformation of education relevant and significant as the most important means for solving the problem of digital transformation of the economy. Building a digital educational environment is one of the main strategic directions of the state policy of the Russian Federation. This construction implies the modernization of educational programs, updating the content, methods and organizational forms of educational activities, taking into account the digitalization of education, the creation and development of public platforms for online learning, the design of modern digital educational resources, including online courses. As the past year has shown, in a pandemic, the task of creating mixed courses is especially important. One of the problems is the teacher’s inability to systematically use digital educational resources, lack of understanding of the basics of managing the educational process based on the capabilities provided by educational platforms using online and mixed courses. The article presents course for the preparation of masters, which will try to solve the problem of preparing a modern teacher competent in the field of digital technologies, as well as the systematic use of the possibilities of managing the educational process online based on a blended learning model.
Calculation of storm fuel supply of a seagoing vessel: solving professionally-oriented tasks using cloud services

Abstract The information and communicative competency today is an integral part of professional training in any sphere. Due to the fact that nowadays big amount of work is done or monitored in the remote mode the requirement is stipulated for every specialist to be able to use digital technologies professionally. For a future seafarer it means, first of all, the ability to use modern technologies as the information and data resource and also it presupposes the ability to work with software libraries, to create data bases, to perform necessary calculations by means of cloud services inclusive. This component is of extreme practical importance as it enables the satisfaction of the social mandate to prepare future seafarers for the living in the information environment. The task is quite challenging so it requires the adequate knowledge level. That is why the main objective of the course “Information technologies” together with other subjects within the curriculum for the professional training in maritime academy is to develop the reliable scientific and methodological grounds for the realization of any professionally related tasks of future merchant feet officers. Specially selected tasks acquaint cadets with information technology in terms of implementing the solution of applied professionally oriented tasks, such as navigating the course, determining the location of the vessel, calculating stocks for the trip, etc. Therefore, the obligatory elements of the tasks are: construction of mathematical (computer) models;analysis of possible solutions to the problem; of technological processes, taking into account all factors influencing the process and methods of decision-making;implementation of mathematical models of problems in spreadsheets. This article proposes a strategy for solving an applied problem using an information-modeling approach to the analysis and evaluation of the technological process, namely, the algorithm for calculating the required voyage and storm fuel reserves of a marine vessel. Both office spreadsheets and cloud services were used to implement the mathematical model for solving this problem. The use of “cloud computing” technologies in the training of seafarers is due to the fact that, firstly, while on board, the master or ship mechanic can perform some components of their work, especially joint projects, in modern services, and secondly, these technologies, supporting traditional forms of education, is a new stage in the development of education and a cost-effective, efficient and flexible way to meet the needs of students in acquiring new knowledge.