Reviewerly/exHarmony huggingface dataset:
Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results.
No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction

Accurately assessing image quality is a challenging task, especially without a reference image. Currently, most of the no-reference image quality assessment methods still require reference images in the training stage, but reference images are usually not available in real scenes. In this paper, we proposed a model named MSIQA inspired by biological vision and a convolution neural network (CNN), which does not require reference images in the training and testing phases. The model contains two modules, a multi-scale contour prediction network that simulates the contour response of the human optic nerve to images at different distances, and a central attention peripheral inhibition module inspired by the receptive field mechanism of retinal ganglion cells. There are two steps in the training stage. In the first step, the multi-scale contour prediction network learns to predict the contour features of images in different scales, and in the second step, the model combines the central attention peripheral inhibition module to learn to predict the quality score of the image. In the experiments, our method has achieved excellent performance. The Pearson linear correlation coefficient of the MSIQA model test on the LIVE database reached 0.988.
Digitalization of Higher Education During the Pandemic: Advantages and Risks

This research article presents the analysis and interpretation of the data of the authors’ study of the condition, problems and prospects of higher education digitalization at RSUH in comparison with the experience of leading foreign and Russian universities.The purpose of the article is to determine the level and features of the digitalization process of Russian higher education before, during and after the pandemic.The theoretical basis of the research is institutional and socio-cultural approaches that suggest considering the introduction of online education in the context of responding to the challenge of the formation of the digital economy and digital society. The empirical basis of the study is a survey conducted in April 2020 by the online questionnaire method, in which 327 people (representatives of the teaching staff, researchers, heads of educational departments (deans, heads of research centers), deputy heads of departments, as well as employees of deaneries, research centers and departments) took part in. Based on the generalization and interpretation of the data of the study, the authors showed that the digitalization of higher education at RSUH, as at other Russian universities, is based on the formation of an electronic information and educational environment. It appears that the evaluation of the level of access to this environment and the quality of services provided is high enough, while the effectiveness of the application needs to be improved. The introduction of electronic document management and meaningful communication with students remain the “pain points”.The causes and consequences of the impact of the pandemic on the digitalization of higher education are identified in the article. Among them: the need for new forms of student-teacher-administration communication; lack of specialized educational materials for online education; technical, methodological and psychological unpreparedness of teachers for distance education. The future belongs to mixed forms of equivalent and digital higher education. The scientific novelty of the article lies in the novelty of the comparative study of online education in Russian universities conducted by the authors (on the example of RSUH, MEPhI, NSU), as well as in the justification of the formation of hybrid (mixed) forms of higher education. Based on this study it is proposed to form a university management strategy and implement the restructuring of the educational process.
Physics-Guided Reflection Separation From a Pair of Unpolarized and Polarized Images

Undesirable reflections contained in photos taken in front of glass windows or doors often degrade visual quality of the image. Separating two layers apart benefits both human and machine perception. The polarization status of the light changes after refraction or reflection, providing more observations of the scene, which can benefit the reflection separation. Different from previous works that take three or more polarization images as input, we propose to exploit physical constraints from a pair of unpolarized and polarized images to separate reflection and transmission layers in this paper. Due to the simplified capturing setup, the system is more under-determined compared to the existing polarization-based works. In order to solve this problem, we propose to estimate the semi-reflector orientation first to make the physical image formation well-posed, and then learn to reliably separate two layers using additional networks based on both physical and numerical analysis. In addition, a motion estimation network is introduced to handle the misalignment of paired input. Quantitative and qualitative experimental results show our approach performs favorably over existing polarization and single image based solutions.
Solar Energy Assessment, Estimation, and Modelling using Climate Data and Local Environmental Conditions

On Renewable Energy (RE), this field covers the most significant share of the world energy demand and challenges on the expensive measurement and maintenance equipment to be used.In all studies and designs, global solar radiation (GSR) measurements require assessment, estimation, and models to be applied together with the environment and meteorological data on installing stations at the specific location.These meteorology stations provide measured data throughout the year/ annually or at specified periods, depending on the site of interest.This study includes assessment and estimations of the solar radiation at the Vhembe District using the geographical data measured daily, monthly, and throughout a year in the area.It provides variables such as the geographical maps of the solar availability at a minimum and maximum temperatures obtained during the annual analyses.Determining the solar radiation at a specific location for energy generation involves several procedures, estimations, and calculations using the climatological weather data measurements through MATLAB simulations.In addition, the Geographical Remote Sensing (RS) and Mappings, and Spreadsheet Graph Analytics, were applied to the measured data from the nine installed Weather Stations (WS) in the Vhembe District area was used.The analysis determines the minimum and maximum solar radiation equations associated with the local climate patterns in accommodating the theoretical bases and period changes.The paper contributes to the main project objectives on renewable energy assessment for potentials and generation at a micro/small scale in the district.These parameters are fundamental in estimating and determining the potential solar energy radiation using its extraterrestrial solar radiation per day/ weekly/ monthly.Annual periods towards methods to develop micro/small energy projects for rural and urban communities for domestic and commercial use.As a result, the meteorology analysis is being presented in this study.
Authenticity, Integrity, and Replay Protection in Quantum Data Communications and Networking

Quantum data communications and networking involve classical hardware and software. Quantum storage is sensitive to environmental disturbances that may have malicious origins. Teleportation and entanglement swapping, two building blocks for the future quantum Internet, rely on secure classical bit communications. When lack of authenticity, integrity, and replay protection may have a high impact, quantum data communications are at risk and need to be protected. Building upon quantum cryptography and random generation of quantum operators, we propose a solution to protect the authenticity, integrity, and replay of quantum data communications. Our solution includes a classical data interface to quantum data cryptography. We describe how classical keying material can be mapped to quantum operators. This enables classical key management techniques for secure quantum data communications.
Traffic Light and Crosswalk Detection and Localization Using Vehicular Camera

An improved convolutional neural network model for traffic light and crosswalk detection and localization using visual information from a vehicular camera is proposed. Yolov4 darknet and its pretrained model are used in transfer learning using our datasets of traffic lights and crosswalks; the trained model is supposed to be used for red-light running detection of the preceding vehicle. Experimental results, compared to the result of the pretrained model learned only from the Microsoft COCO dataset, showed an improved performance of traffic light detection on our test images which were taken under various lighting conditions and interferences; 36.91&#x0025; higher recall and 39.21&#x0025; less false positive rate. The crosswalk, which is incapable of detection in the COCO model, could be detected with 93.37&#x0025; recall and 7.74&#x0025; false-positive rate.
Hybrid dilated faster RCNN for object detection

Object detection is a very important part of computer vision, and the most common method of object detection is the Faster region convolutional neural network (RCNN), which uses CNN to extract image features. However, the parameters to be learned in CNN are enormous and may affecting the efficiency. In this paper, hybrid dilated Faster RCNN (HDF-RCNN) is proposed to solve this problem, and the main contributions are: 1) HDF-RCNN is built through replacing the VGG16 in Faster RCNN with HDC (hybrid dilated CNN) to achieve a fast and accurate object detection algorithm, and the LeakyReLU activation function is used to increase the ability of mapping input information; 2) the portability of HDC, namely, the possibility of embedding the HDC into object detection network with independent feature extraction part is verified. The Microsoft COCO data set is used to verify the performance of HDF-RCNN, and the experiments show that, compared with the traditional Faster RCNN, the testing accuracy of HDF-RCNN is averagely improved by 7.11%, the training loss and training time are averagely reduced by 40.06% and 34.29%, respectively. Therefore, the HDF-RCNN can significantly improve the efficiency of object detection and the HDC can be used as an independent feature extraction network to adapt to many different frameworks.
The presence and perceptibility of personal digital data: findings from a participant map drawing method

Personal digital data are often imagined and experienced as invisible and immaterial phenomena, albeit with increasingly powerful impacts on people’s lives. In this article we discuss findings from an ethnographic project involving 30 participants in Sydney, Australia, directed at identifying their practices and understandings concerning their home-based digital device use and the personal data generated with and through engagements with these technologies. As well as engaging in a video-recorded home tour, we asked participants to hand draw maps of the digital devices located within their homes and the flows of digital data emitting from the devices. These maps mark the presence, interconnections and mobilities of digital technologies and the digitised details generated by their sociomaterial entanglements. The maps were also used to spark further discussion with the participants about their devices and data, seeking to understand their sense-making practices. Working with our concept of ‘digital scaffolding’, we explore what these participant-generated maps can reveal and make visible about digital technologies and data in relation to the domestic environment as well as the world outside the home. We consider what the maps themselves show in terms of digital presence, and what the mapping activity made perceptible within the research encounter.
Hand Tracking in Extended Reality Educational Applications

Virtual, Augmented, and Mixed Reality (VR/AR/MR) or Extended Reality (XR) applications may be used as an innovative educational tool. Devices such as Oculus Quest 2 offer the possibility of using passthrough view and hand tracking which enables user to interact with virtual environment and see his surroundings. These functions enable the preparation of training applications, in which the user is able to simultaneously see his workplace and the number of generated job instructions or practice work-related activities without losing contact with the environment. This opens up a number of opportunities related to broadly understood education. The article briefly discusses the developed concept that allows for training in XR, in which user interactions with virtual elements are as close to the real ones as possible. The development of interactions using hand tracking and passthrough are described.
A Effective Encryption and Different Integrity Schemes to Improve the Performance of Cloud Services

Recent modern era becomes a multi-user environment. It&#x0027;s hard to store and retrieve data in secure manner at the end user side is a hectic challenge. Difference of Cloud computing compare to Network Computing can be accessed from multiple company servers. Cloud computing makes the users and organization to opt their services. Due to effective growth of the Cloud Technology. Data security, Data Privacy key validation and tracing of user are severe concern. It is hard to trace malicious users who misuse the secrecy. To reduce the rate of misuse in secrecy user revocation is used. Audit Log helps in Maintaining the history of malicious user also helps in maintaining the data integrity in cloud. Cloud Monitoring Metrics helps in the evaluation survey study of different Metrics. In this paper we give an in depth survey about Back-end of cloud services their concerns and the importance of privacy in cloud, Privacy Mechanism in cloud, Ways to Improve the Privacy in cloud, Hazards, Cloud Computing Issues and Challenges we discuss the need of cryptography and a survey of existing cryptographic algorithms. We discuss about the auditing and its classifications with respect to comparative study. In this paper analyzed various encryption schemes and auditing schemes with several existing algorithms which help in the improvement of cloud services.
Image Selection Method from Image Sequence to Improve Computational Efficiency of 3D Reconstruction: Analysis of Inter-image Displacement Based on Optical Flow for Evaluating 3D Reconstruction Performance

This paper describes the image selection method for an efficient three-dimensional (3D) reconstruction computation from an image sequence. Adequate images must be selected from the image sequence to improve the computational efficiency of the 3D reconstruction. Thus, we investigated a threshold based on the displacement among images obtained from a camera mounted on a remotely operated robot. The results confirmed that the proposed method can select adequate images for efficient 3D reconstruction by the threshold based on the optical flow from the image sequence. Therefore, the computational cost could be reduced by eliminating the duplicate and high-similarity images to perform the efficient 3D reconstruction.
SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities

Knowledge graph embedding acts as a pivotal role in predicting the missing information in knowledge graphs (KGs). Due to the evolving nature of real-world KGs, one requires the ability to make predictions for newly emerging entities besides those already observed at training time. Current studies have made great efforts to develop a neighborhood aggregator and embed out-of-knowledge-graph (OOKG) entities inductively, with less focus on exploiting the similarity between the existing and newly emerging entities. Attaching importance to such similarity helps facilitate semantic transfer. In this work, we propose a similarity-aware aggregation network for embedding out-of-knowledge-graph entities. Motivated by the fact that similar entities are likely to occur in common graph context, we skillfully design a similarity-aware function, which measures the distance of each entity pair based on the contextual gap. Moreover, we aggregate the neighborhood surrounding the target entity and its similarity information by query-specific attention weights, which are optimized during the learning process. Extensive experiments on knowledge graph completion task show that our method achieves substantial improvements over baselines.
Refinement of Inverse Depth Plane in Textureless and Occluded Regions in a Multiview Stereo Matching Scheme

In the multiview stereo (MVS) vision, it is difficult to estimate accurate depth in the textureless and occluded regions. To solve this problem, several MVS investigations employ the matching cost volume (MCV) approach to refine the cost in the textureless and occluded regions. Usually, the matching costs in the large textureless image regions are not reliable. In addition, if an occluded region is also textureless, the matching cost contains a significant error. The goal of the proposed MVS method is to reconstruct accurate depth maps in both large-textureless and occluded-textureless regions by iteratively updating the erroneous disparity. The erroneous disparity in the textureless region is updated by the 3D disparity plane of the region in the inverse-depth space. Then, the surface consensus is computed and used to run the two processes, the surface consensus refinement and the matching cost update. By the iterative update of the 3D inverse depth plane, surface consensus, and matching cost, the performance of the depth reconstruction in the large-textureless and occluded-textureless regions is greatly improved. The performance of the proposed method is analyzed using the Middlebury multiview stereo dataset. The depth reconstruction performance is also compared with several stereo vision methods.
An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

The handwritten character recognition process has gained significant attention among research communities due to its application in assistive technologies for visually impaired people, human–robot interaction, automated registry for business documents, and so on. Handwritten character recognition of the Telugu language is difficult owing to the absence of a massive dataset and a trained convolutional neural network (CNN). This article introduces an intelligent Telugu character recognition process using a multi-objective mayfly optimization with deep learning (MOMFO-DL) model. The proposed MOMFO-DL technique involves the DenseNet-169 model as a feature extractor to generate a useful set of feature vectors. A functional link neural network (FLNN) is used as a classification model to recognize and classify the printer characters. The design of the MOMFO technique for the parameter optimization of the DenseNet model and FLNN model shows the novelty of the work. The use of MOMFO technique helps to optimally tune the parameters in such a way that the overall performance can be improved. The extensive experimental analysis takes place on benchmark datasets and the outcomes are examined with respect to different measures. The experimental results pointed out the supremacy of the MOMFO technique over the recent state-of-the-art methods.
Solar PV Forecasting Using Machine Learning Models

Renewable energy is increasingly being used to mitigate the consequences of climate change and global warming. Various prediction strategies have been developed in attempt to increase renewable energy prediction ability. Solar Forecasting is an important procedure in PV systems to minimise safety and stability issues caused by its nature. The following are the objectives of this study, to give a quick overview of the various solar PV forecasting approaches. The significance of machine learning in forecasting is discussed, as well as the procedures required. This work aims to provide a review and evaluation of ML models used to forecast solar energy. It illustrates processes used in ML models for solar energy forecasts, such as data pre-processing approaches and parameter selection algorithms.
Regional attention reinforcement learning for rapid object detection

When people observe a picture, they first pay attention to local areas of the picture, rather than the whole areas, then combine them with previous experience in the brain, and finally make judgments through thinking. This is human visual logic. In this paper, we propose a regional attention reinforcement learning model for object detection. The proposed model uses human visual logical to solve the detection problem of small and complex targets in the picture. The model uses a recurrent network structure as the main framework to extract historical information, and fuse the historical information with the current concerned information. At each recurrent time step, it can pay attention to the fused information, especially pay more attention to the information that may have objects. Experimental results show that the proposed method has more than 5% improved in recognition accuracy to the conventional methods. In terms of FLOPs, the conventional methods normally require 170 M, while the proposed method only needs 25.4M This means that the proposed method has higher detection efficiency.