Reviewerly/exHarmony huggingface dataset:
3D Face Feature Processing and Recognition Technology

Abstract Nowadays, we frequently use a lot of biometric identification technology means, such as: iris recognition, voice recognition and face recognition, but compared with other human biometric features, face information is easier to collect and more easily accepted by the public. According to the survey, in interpersonal contact, most people get information based on the first impression, even if there are changes in external conditions such as age, expression, makeup, etc., but the brain can still recognize these information, this phenomenon shows that face does have more feature information. From the information of a person's facial features, we can effectively distinguish each person. Face recognition is a technology that analyzes the face data and then uses it as an authentication mechanism. Generally, cameras are used to track and collect human faces, model theory and then analyze the collected binary data stream to extract relevant feature data. The research of human face recognition technology began in the middle of 1960s. After 1980s, due to the development of computer technology and the improvement of optical image processing technology, it gradually matured. The real use stage of face recognition is in the late 1990s, with Germany, Japan and the United States as the main technologies.
Remote Sensing Image Object Detection Method Based On Improved YOLOv3

Abstract In order to solve the problem that irregular targets and dense targets are difficult to be detected in optical remote sensing images, this paper improved the YOLOV3 model Firstly, in order to further combine the feature information of different scales, the PaNet structure is introduced into the FPN part of the original YOLOv3, and the obtained effective feature layer is continued to be extracted for a round of feature. The feature is not only up-sampled to achieve feature fusion, but also down-sampled again to achieve enhanced feature fusion SimOTA method is introduced to dynamically match positive samples and set different positive sample numbers for different targets, which not only improves the speed of the algorithm, but also reduces the extra hyperparameters Experimental verification using richer DOIR data sets shows that the detection ability of the improved algorithm is significantly improved. Compared with the original YOLOv3, its mAP improves by 15.1 points, among which the detection accuracy of dense small targets is improved the most.
Improving Single-Trace Attacks on the Number-Theoretic Transform for Cortex-M4

The Number-Theoretic Transform (NTT) is a key feature for the efficiency of numerous lattice-based cryptographic schemes. The arithmetic structure of that operation makes it an important target for soft-analytical side-channel attacks, that are powerful single-trace side-channel attacks exploiting known arithmetic structure to improve noise tolerance. Among others, Pessl et al. used the belief-propagation technique to attack a software implementation of the Kyber key encapsulation mechanism for Arm Cortex-M4 microcontrollers. However, that implementation has since been thoroughly optimized, in particular through the use of an improved version of Plantard modular arithmetic. In this paper, we describe how we successfully attack the latest available version of this implementation. We show that precise knowledge of the implementation at hand allows for better performance of the belief-propagation technique. By modeling each individual arithmetic operation performed by the microcontroller, we are able to recover the secret values processed during the NTT, even with very noisy side-channel leakage. We also study some strategies for the attacker to either maximize the success rate, or minimize the runtime of the attack.
Problems and Opportunities for Blockchain and IoT-based Intelligent Medical Records

The Internet of Things (IoT) and blockchain technologies are being widely used in several industries, including e-healthcare. Internet of Things (IoT) technologies in the healthcare sector may transmit sensory data from patients in real time, allowing for immediate processing and analysis. The processing, computation, and storage of collected IoT data are centralized. Potential problems associated with such centralization include a reliance on a single point of failure, increased susceptibility to data manipulation and tampering, and a need to take extra precautions to protect sensitive information. Blockchain’s ability to store and interpret IoT data in a decentralized manner offers a promising solution to these systemic problems. Blockchain and Internet of Things (IoT) integration may be reasonable in the pursuit of a decentralized IoT-based electronic healthcare delivery system. To begin, we will provide you with some history on the blockchain. Second, we examine the most well-known blockchain consensus methods from the perspective of e-health. Finally, the viability of using blockchain technology in IoT-based e-healthcare is assessed. Finally, a few concrete examples of potential applications are shown to illustrate how the Internet of Things and blockchain technology might be used to improve healthcare systems and delivery. In addition, we suggest IoBHealth, a data infrastructure that combines IoT and blockchain to store, access, and manage e-healthcare data.
Super-resolution Reconstruction Based on Capsule Generative Adversarial Network

Abstract Using each part of the image's spatial information to generate better local details of the image is a key problem that super-resolution reconstruction has been facing. At present, mainstream super-resolution reconstruction networks are all built based on convolutional neural networks (CNN). Some of these methods based on Generative Adversarial Networks (GAN) have good performance in high-frequency details and visual effects. However, because CNN lacks the necessary attention to local spatial information, the reconstruction method is prone to problems such as excessive image brightness and unnatural pixel regions in the image. Therefore, using the capsule network's excellent perception of hierarchical spatial information and local feature relationships, the author proposes a super-resolution reconstruction based on capsule network CSRGAN. The experiment's final result shows that compared with the pure convolution method RDN, the PSNR value of CSRGAN is increased by 0.14, which is closer to the original image.
Research on Static Gesture Recognition Based on Deep Learning

Abstract With the continuous development and progress of the times, the ways of human-computer interaction have become more and more diverse. In order to reduce the spread of the new crown virus, gesture recognition has become a hot topic in the field of human-computer interaction in recent years. Traditional gesture recognition is affected by the environment and database, etc., with poor robustness and low accuracy. In order to improve the recognition rate of static gestures, this paper proposes to establish a deep learning model using CNN convolutional neural network, and a static gesture recognition method based on template matching. By establishing a palm template diagram, the gesture image to be recognized is matched with the template diagram based on the feature point, and the image is rotated after matching, and the template based on the grayscale value is matched again, so as to extract the gesture part. Through experimental proof, the algorithm can effectively improve the gesture recognition rate, the recognition accuracy rate reached 93.17%, and the recognition speed is faster.
A COMPARATIVE NEURAL RADIANCE FIELD (NERF) 3D ANALYSIS OF CAMERA POSES FROM HOLOLENS TRAJECTORIES AND STRUCTURE FROM MOTION

Abstract. Neural Radiance Fields (NeRFs) are trained using a set of camera poses and associated images as input to estimate density and color values for each position. The position-dependent density learning is of particular interest for photogrammetry, enabling 3D reconstruction by querying and filtering the NeRF coordinate system based on the object density. While traditional methods like Structure from Motion are commonly used for camera pose calculation in pre-processing for NeRFs, the HoloLens offers an interesting interface for extracting the required input data directly. We present a workflow for high-resolution 3D reconstructions almost directly from HoloLens data using NeRFs. Thereby, different investigations are considered: Internal camera poses from the HoloLens trajectory via a server application, and external camera poses from Structure from Motion, both with an enhanced variant applied through pose refinement. Results show that the internal camera poses lead to NeRF convergence with a PSNR of 25 dB with a simple rotation around the x-axis and enable a 3D reconstruction. Pose refinement enables comparable quality compared to external camera poses, resulting in improved training process with a PSNR of 27 dB and a better 3D reconstruction. Overall, NeRF reconstructions outperform the conventional photogrammetric dense reconstruction using Multi-View Stereo in terms of completeness and level of detail.
On the effectiveness of the combined managerial style of a modern general education school

The effectiveness of managing a general education school depends on the ability of the manager to use modern technologies in his activity skillfully. The article reflects the model of the management style of a modern school, which has both theoretical and concrete-practical content. The model is built on the selection of a number of effective management decisions in the management of a modern school, which is based on important management principles: systemacity, rationality, efficiency, human orientation, consistency, situationality, reflexivity. The use of such an approach will ensure the interaction of all subsystems of the school to increase the cognitive and motivational aspects of the educational process of knowers; will create conditions for achieving the necessary goal. Our studies have shown the dependence of the maturity indicator of the headmaster’s professional competence on the management style, the level of adequate self-assessment of his managerial qualities and abilities. During the research, we proposed a model for the combined style of modern general education school authorities that showed its effectiveness, developed the necess
Professional socialization: successful career orientation of students

The article discusses the features of building a successful career in the process of studying at a university. These features of overcoming crisis situations in the future profession determine the factors of the development of anti-crisis competencies for the growth of successful career aspirations. The research problem is caused by the role and essence of centered support of students at the stage of studying methods of overcoming crisis pedagogical situations, as well as conducting a detailed analysis of the psychological features of the interaction of emotion and intelligence as potential opportunities of pedagogical management at the stages of building career orientation. The relevance of the topic is determined by the fact that the future specialist does not have a clear understanding of the importance of the process of integrating human consciousness into the social system as part of a successful career and activity. Summarizing the results of the study allowed us to formulate the main conclusions and outline the prospects for further scientific search for changes in the main activities of universities and the reconstruction of provisions to increase the flexibility of general cultural, scientific and professional training of students, the introduction of special psychological conceptual foundations to stimulate their personal career growth.
Role of facilitators for successful implementation of massive open online courses (MOOC)

Dynamism of changes in the field of education raises the ‘level’ of requirements for the quality of training of a modern teacher. The development of open education systems through Distance Learning Technologies, Massive Open Online Courses (further MOOC), has become a priority and has reduced the need for teachers, ready to implement new role positions. The analysis of the literature shows the growing interest in teaching styles and role positions of the teacher, an important one for many students, is the teacher-facilitator. ‘However, in the scientific literature, the names and roles of ‘facilitator’ and ‘tutor’ are not mentioned.’ This article aims to model the main criteria for portraying a competent teacher-facilitator through the identification of educational and methodological and organizational problems by interviewing experience facilitators and teachers. The authors also study the role of the facilitator within the framework of teaching a number of disciplines in English language to 40 students in the traditional form of employment on recommended textbooks and teaching and methodological complexes, as well as using MOOC on various platforms and with the help of facilitation sessions. At the end, an opportunity was held to teach subjects in the course of conducting mass open online courses and determining the role of a teacher, which was provided by methodological support during the event of facilitation sessions. The results of the study make it possible to state that the role of the facilitator is preferable in the open (online) educational space.
A Novel Heuristic Fall-Detection Algorithm Based on Double Thresholding, Fuzzy Logic, and Wearable Motion Sensor Data

We present a novel heuristic fall-detection algorithm based on combining double thresholding of two simple features with fuzzy logic techniques. We extract the features from the acceleration and gyroscopic data recorded from a waist-worn motion sensor unit. We compare the proposed algorithm to 15 state-of-the-art heuristic fall-detection algorithms in terms of five performance metrics and runtime on a vast benchmarking fall data set that is publicly available. The data set comprises recordings from 2880 short experiments (1600 fall and 1280 non-fall trials) with 16 participants. The proposed algorithm exhibits superior average accuracy (98.45%), sensitivity (98.31%), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -measure (98.59%) performance metrics with a runtime that allows real-time operation. Besides proposing a novel heuristic fall-detection algorithm, this work has comparative value in that it provides a fair comparison on the relative performances of a considerably large number of existing heuristic algorithms with the proposed one, based on the same data set. The results of this research are encouraging in the development of fall-detection systems that can function in the real world for reliable and rapid fall detection.
Electronic Voting and Electoral System in Nigeria: An Assessment of the 2018 Local Government Elections in Kaduna State, Nigeria

Electronic voting is often seen as a tool for making the electoral process more efficient and for increasing trust in its management. However, the challenges are considerable. If not carefully planned and designed, e-voting may undermine the confidence in the whole electoral process. To this effect, the study, beyond the hype on electronic voting identifies the factors that necessitated the adoption of electronic voting in Nigeria. It also examines the effectiveness of electronic voting and its true impact on electoral transparency and credibility in the Nigeria electoral system. Furthermore, the study assesses the strengths and weaknesses of electronic voting in the electoral process with reference to the 2018 local government election in Kaduna State, Nigeria. Anchored on determinism theory, the study adopts descriptive research design and uses primary and secondary data sources. The primary data was sourced through an in-depth interview while, secondary data was sourced through internet and archive materials. Findings reveal that e-voting is fast, accurate, stress-free, and easy to use; nevertheless, it is highly prone to technical manipulations and human-induced errors. The study recommends that e-voting system should be adopted in Nigeria in order to foster electoral outcomes that are free, fair and credible as, this will in turn culminate into increased political participation and foster democratic growth in Nigeria. The study concludes that, there was a relative significant and positive influence of e-voting on electoral credibility all through the conduct of the 2018 Local Government elections in Kaduna State as, the party in government did not win in all the local government areas as was the norm in the previous elections where manual paper ballot system was used.
Intellectual Theory of Alexander Zinoviev

The article is devoted to A.A. Zinoviev’s contribution to the development of ideosphere and intelligence theories, as well as ideology as sphere of the future. The author of the article, who has been long engaged in the studies of intellectual activity diffi cult issues, compares his achievements in this fi eld with those of Zinoviev. He traces Zinoviev’s biography and his formation as philosopher and thinker, as well as major factors infl uencing his worldview formation and is creative and scientifi c achievements. The article testifi es to the great relevance and importance of his ideological legacy in contemporary world as a whole and in Russia in particular.
Versatile Video Coding (VVC) Standard: Overview and Applications

Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant Bit technique is used to encrypt each frame of the video file format to be able to increase security. The primary goals of this procedure are to safeguard the data by encrypting the frames and compressing the video file. Using PSNR to enhance process throughput would also enhance data transmission security while reducing data loss.
Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement

An algorithm for improving images was created in this procedure. It is founded on the linear domain simultaneous estimate of light and reflectance. For single picture low-light enhancement, many image priors have been used. However, the most effective technique is to evenly enhance the illumination directly. By extending the dynamic pixel intensity of a picture, histogram equalisation (HE) can solve the issue of lighting and make dark images visible. Instead than changing the lighting, HE seeks to improve the contrast. As a result, the findings are resilient to noise and improve image quality. The test findings demonstrate the good performance of the suggested approach to provide lighting and reflectance with increased visual outcomes and a promising convergence rate. The suggested technique produces equivalent or superior outcomes in subjective as well as objective evaluations when compared to previous testing methodologies. Image enhancement’s main goal is to treat an input image such that the final output is more suitable for a certain application than the original image. It draws attention to or emphasises visual elements like borders, limits, or contrast to make a graphic presentation more useful for study and display. The improvement enhances the dynamic range of the selected characteristics, making it easier to recognise them even while it does not increase the data's intrinsic information richness.
A Privacy-Preserving Medical Data Sharing Framework: Techniques, Applications, and Challenges

To improve patient outcomes and advance medical research, it is crucial that healthcare data are shared securely and effectively. It is difficult to communicate data while retaining confidentiality, though, due to the delicate nature of healthcare data and worries about patient privacy. Sensitive patient data must be safeguarded while facilitating secure data sharing, which calls for a framework that protects privacy. For implementing such a system, various methods like differential privacy, secure multi-party computation, and homomorphic encryption have been suggested. It is necessary to handle issues including interoperability problems, legal and ethical dilemmas, and technical difficulties. In order to create a framework for sharing medical data while protecting privacy, this study suggests a method that combines homomorphic encryption and blockchain technology. The suggested method offers a safe and effective means of exchanging and storing encrypted healthcare data while preserving data integrity and privacy. A multidisciplinary strategy combining cooperation between healthcare providers, data scientists, privacy experts, and regulatory agencies is necessary for the development and implementation of a medical data sharing framework that protects patient privacy. A privacy-preserving framework for medical data sharing can promote effective and secure data exchange for better healthcare outcomes by addressing the issues and utilizing the tools at hand.
Scalability Challenges and Opportunities in Blockchain-based Systems: A Systematic Review

The blockchain technology is quickly becoming the most disruptive technology of recent times. This technology has generated a lot of buzz and optimism, and it has gained a lot of attention from both the public and private sectors. Information can be transferred using blockchain technology in a manner that is confidential, safe, reliable, and open to scrutiny. This analysis focuses on the ways in which blockchain technology can be used to solve problems related to scalability and give solutions in the context of the healthcare industry. In light of this, the proposed solutions can be categorized into two primary categories: storage optimization and blockchain redesign. However, there are still several restrictions, such as the block size, the enormous volume of data and transactions, the number of nodes, and the obstacles posed by the protocol. It has seen widespread application in decentralized crypto currencies like Bitcoin and Ethereum, for example. An example of a public blockchain application that was successful, Bitcoin, was the impetus for a significant increase in research and development into blockchain technology. Nevertheless, scalability continues to be a significant obstacle. According to the facts that we've compiled, the primary causes of scalability issues appear to be bottlenecks in the transaction throughput, the network latency, and the consensus methods. On the other side, the potential for scalability includes the application of techniques like as sharding, hybrid systems, and off-chain computations. We also examine the consequences of these findings as well as prospective paths for future study to help solve the scalability problems that blockchain-based systems provide.