Reviewerly/exHarmony huggingface dataset:
Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types

This research poster outlines the initial development of a facial emotion recognition (FER) evaluation system based on RGB-D imagery captured via a mobile based device. The study outlined features control group of non-occluded facial types and a set of participants wearing a head mounted display (HMD) in order to demonstrate an occluded facial type. We explore an architecture to develop a FER system that is suitable for occluded facial analysis. This paper details the methodology, experimental design and future work to be carried out to deliver such a system.
B2GAN: Bidirectional-branch generative adversarial network for text image super-resolution with structure preservation

Text image super-resolution (SR) is one of the most hot topic research in computer vision. The traditional convolutional neural networks usually over-smooth fine structures of SR images while the popular generative adversarial network (GAN) based SR methods may produce undesirable SR results with distorted edges. To cope with these problems, this paper proposes a bidirectional-branch generative adversarial network (B2GAN) for text image SR. The proposed B2GAN consists of two parts: one is a generator, the other is a discriminator. In the generator, a forward branch is used for mapping the low-resolution (LR) image to its corresponding SR image to generate photo-realistic images, while a reverse branch is responsible for mapping the generated SR image to a low-resolution image which is viewed as its original LR image. A loss function is proposed to train a pleasing generator and guarantee a stable convergence to the original LR image. In the discriminator, The VGG16 and the adversarial loss are employed. In the experiments, we establish two text image datasets including Chinese and multi-language characters which are used to test the effectiveness of the proposed B2GAN method. Quantitative associated with qualitative experimental results validate that the proposed B2GAN method can achieve competitive performance and even outperform the state-of-the-arts.
Gesture recognition algorithm combining ResNet and ShuffleNet

Gesture is a form of non-verbal communication and has many applications, such as sign language communication between deaf and dumb people, robot control, human-computer interaction and medical applications. The commonly used acquisition equipment in gesture recognition is the visible light camera, but illumination has a great impact on the accuracy of the collected data classification processing. The whole project designed a complete end-to-end edge computing system design and deployment, the system can achieve from gesture image acquisition to gesture recognition. A dataset of 3600 thermal images was created, and each gesture had 1200 thermal images with only 4*4 resolution. These images were upsampled by bilinear interpolation and fed into a new lightweight deep learning model combining deep residual learning with ShuffleNet V2 for gesture classification. The system achieved 98.63% accuracy on the test data set. Another advantage is that it is based on thermal imaging, so the accuracy is not affected by background lighting conditions.
Modeling Memorization: A Data Collection and Mathematical Modeling Experience

This article presents a mathematical modeling activity for students related to the process of memorization in which students collect their own data to drive their model development, parameterization, and validation. Engaging in the data collection gives them insight to critique and evaluate various models. This task is a low-floor high ceiling problem that offers both a relatable context and a window to quantitative approaches in cognitive science. Experimental results of students participation in this activity are discussed. This article also includes pedagogical recommendations with a focus on fostering equitable teaching practices and a detailed analysis of the situation comprised of several mathematical approaches to model the memorization process that highlight the richness of the problem. Instructors can adapt and implement this modeling exploration for use in various undergraduate courses, from introductory to advanced, depending on the emphasis of the lesson.
Beyond Flicker, Beyond Blur: View-coherent Metameric Light Fields for Foveated Display

Ventral metamers, pairs of images which may differ substantially in the periphery, but are perceptually identical, offer exciting new possibilities in foveated rendering and image compression, as well as offering insights into the human visual system. However, existing lit-erature has mainly focused on creating metamers of static images. In this work, we develop a method for creating sequences of metameric frames, specifically light fields, with enforced consistency along the temporal, or angular, dimension. This greatly expands the potential applications for these metamers, and expanding metamers along the third dimension offers further new potential for compression.
Effects of Mirrors on User Behavior in Social Virtual Reality Environments

The authors have observed that users gather in front of mirrors on VRSNS such as VRChat. Based on these observations, we hypothesized that mirrors attracted users and conducted an experiment in a controlled environment. The participants were requested to converse in pairs in a VR space with mirrors and posters, and their behavior was recorded. Results showed that, although a certain number of users gathered in front of the mirror, it did not significantly increase their chance of staying. Conversely, we received comments such as &#x201C;I feel relaxed when I go in front of the mirror&#x201D;.
Modulation recognition method of wireless communication digital signal considering multi-dimensional characteristics

Modulation recognition is a key technology in the field of communication, especially in the field of non cooperative communication. It can realize the automatic recognition and classification of unknown signal modulation without prior knowledge. With the rapid development of modern communication technology, modulation methods become complex and diverse, and modulation recognition technology is also facing more challenges. Although there are relevant studies, there are still many problems and research work to be solved and improved. Based on this, a modulation recognition method of wireless communication digital signal considering multi-dimensional characteristics is proposed. By extracting multidimensional characteristic parameters, constructing multi-dimensional characteristic signal modulation decision tree model and wireless communication digital signal modulation recognition algorithm based on multi-dimensional characteristic model, the modulation mode recognition of digital signal is realized. The experiment shows that compared with the traditional methods, this recognition method has more advantages in the overall modulation recognition accuracy when the signal-to-noise ratio is rising.
Visual Servoing of manipulator with moving base

Manipulator systems are necessary part for unmanned autonomous systems (UAS) to achieve task executions and hence, they have been widely adopted in mobile platforms, such as UAV, UGV together with visual feedback to perform different tasks. Due to the unknown movements of the base, it is difficult to achieve the accurate manipulator visual servoing control since the depth information is continuously changed. In order to achieve the manipulator visual servoing with moving base, two solutions are presented in this paper. Firstly, a support vector regression based method is developed to predict the horizontal movements of the base. Thus, the position of the end-effector can be adjusted accordingly so as to reduce the influence of moving base on the end-effector. Then, an observer is proposed to estimate the depth change in presence of disturbance acted on the camera velocity. With the estimated depth, a visual servoing controller is developed. Simulation and experimental results are presented to illustrate the performance of the proposed solutions.
Algorithm for navigation of a mobile robot in energy-deficient environments

Exploring energy-deficient and remote environments where charging/refueling points are not available in a nearby location can be challenging for autonomous vehicles or rovers. This paper proposes an autonomous navigation algorithm that monitors the state of the battery simultaneously while exploring the surroundings and navigates the rover to a checkpoint to recharge upon reaching a critical battery level. The algorithm has been tested in ROS simulation environments. The results and implications of the same have also been discussed.
Fractional entropy and its applications in fuzzy c-means clustering

In this work, a novel fuzzy clustering method based on fuzzy c-means clustering is introduced. Indeed, in order to maximize the dissimilarity between clusters a fractional entropy, as a regularization function, is added to its objective function. Then, an explicit expression to update the partition matrix is presented.
To Lead Science and Understand People: To the 70th Birth Anniversary of Vladimir R. Firsov

The article analyses contribution of V.R. Firsov to the development of librarianship and library science. V.R. Firsov formed and continues to develop the new direction in library science — regulatory support for the activities of libraries; he is one of the main creators of the existing regulatory framework for the development of libraries in Russia. V.R. Firsov took an active part in the development of the most important legislative acts related to the library sphere: Federal Law of December 29, 1994 No. 78-FZ “On Librarianship”, the Concept of Part II of the “Model Library Code of the CIS Countries”, the Model Law “On Public Library”. Under the leadership of V.R. Firsov, the Russian Library Association (RLA) adopted many legal documents, including “Priorities for the Development of the Russian Library Association for 2011—2015”, “Code of Professional Ethics of the Russian Librarian”, “All-Russian Program for Preservation of Library Collections. The Second Stage: 2011—2020”, “Model Standard for Public Library Activity”, “Model Standard for the Activity of Special Library for the Blind of the Subjects of the Russian Federation”, “Guidelines for Public Libraries of Russia on Services for Young Adults”, etc. V.R. Firsov is the main developer and author-compiler of documents defining the status and development of the National Library of Russia (NLR): the Charter of the National Library of Russia, the Strategy for the Development of the National Library of Russia for the period up to 2020, the Concept for the Development of the National Library of Russia for 2018—2025. Thanks to his efforts, the role of the NLR as scientific and methodological centre of Russian libraries and modern scientific institution has increased; there have been created conditions for the development of the NLR as institution of additional professional education. V.R. Firsov is the author of more than 400 scientific publications (monographs, reference books, articles in the leading professional periodicals); a number of his collections on international acts in the field of librarianship, cultural policy in Europe and reading problems have been published. He took part in the creation of the “Library Encyclopaedia” (2007). V.R. Firsov is rightfully one of the leading scientists and organisers of library science, who has significant impact on the development of librarianship and library science in Russia and the CIS countries in the late 20th — early 21st century.
Smart Air Quality and Heat Control for Indoor Comfort

Day by day, smart devices are taking a huge part in our lives and making them easier than before, and as a vital part of our daily life, indoor comfort needs to become easier to manage since people can spend 90 percent of their life indoor [1]. Supervised learning is one of the methods to control heat and air quality for better indoor comfort in public facilities such as malls, hospitals, or metro stations and the smart system can help the user to predict the cost of different areas separately. The article focuses on the creation of the smart system and the financial effect of the smart system on businesses. As a result, the system will increase the efficiency of the existing systems of the infrastructure and will have a good financial impact on the budget of those facilities.
Ragdoll Recovery: Manipulating Virtual Mannequins to Aid Action Sequence Proficiency

In this paper, we present a Virtual Reality (VR) prototype to support the demonstration and practice of the First Aid recovery position. When someone is unconscious and awaiting medical attention, they are placed in the recovery position to keep their airways clear. The recovery position is a commonly taught action sequence for medical professionals and trained first-aiders across industries. VR is a potential pathway for recovery position training as it can deliver spatial information of a demonstrated action for a subsequent copy. However, due to limits of physical interaction with virtual avatars, the practice of this motor sequence is normally performed in the real world on training partners and body mannequins. This limits remote practice, a key strength of any digital, educational resource. We present Ragdoll Recovery (RR), a VR prototype designed to aid training of the recovery position through avatar demonstration and virtual practice mannequins. Users can view the recovery position sequence by walking around two demonstrator avatars. Observed motor skill sequence can then be practised on a virtual mannequin that uses ragdoll physics for realistic and real-time limb behaviour. RR enables remote access to motor skill training that bridges the gap between knowledge of a demonstrated action sequence and real-world performance. We aim to use this prototype to test the viability of action sequence training within a VR educational space.
Encrypted Cloud Data Mark and Group Search Method Based on Singular Value Decomposition

In order to improve the retrieval efficiency and security of the cloud server, an encrypted cloud data mark and group search method (MGSM) based on singular value decomposition is proposed in this paper. Firstly, all documents are clustered, then indexes and query marks are constructed according to classes, and then documents with low correlation are filtered according to their matching degree. Secondly, the reserved document index vector is expressed as an index matrix, and the singular value decomposition (SVD) algorithm is employed to reduce the dimensionality of the matrix. Then, the corresponding threshold is set to improve the search efficiency while ensuring accuracy. Thirdly, the reduced-dimensional indexes are grouped to reduce the high-dimensional encryption key to multiple low-dimensional keys, further reducing the index encryption time. Theoretical analysis and experimental results show that the proposed method is more feasible and more effective than the compared schemes.
Taboo Topics and Sexual Education in Indian Schools

Although India faces problems of the very high rate of population growth, sexual abuse, rape, teen pregnancy, even a high rate of sexually transmitted diseases like HIV, Indian schools are not yet ready to deliver on the topic to sensitize the young minds. There is no study on whether teachers are imparting this education and most importantly how they are communicating with the young individuals in the classroom. The chapter seeks to find how the taboo topics are discussed in the class and the communication strategies adopted by the teachers. For the study, teachers have been considered as respondents to examine their communication-related challenges and the strategies while communicating with the adolescent student. The study concludes that due to lack of any training, teachers do not know what and how to deliver the topics under sexuality education.
Why Bee colony is the most suitable with multi-query optimization?

This paper presents the comparison results on the performance of the swarm algorithms with Multi-Query Optimization (MQO). First the paper discusses query optimization process and the challenges appear when applied on multi query. Then the paper tackles swarm algorithms and how did they confront these challenges and achieve good results. Finally, proved that Artificial Bee Colony (ABC) algorithm achieves the best results in time and performance.
Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i.e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Model compression strategies, including weight quantization and pruning, are widely recognized as effective approaches to significantly reduce computation and memory intensities, and have been implemented in many DNNs on edge devices. However, most state-of-the-art works focus on ad hoc optimizations, and there lacks a thorough study to comprehensively reveal the potentials and constraints of different edge devices when considering different compression strategies. In this article, we qualitatively and quantitatively compare the energy efficiency of FPGA-based and mobile-based DNN executions using mobile GPU and provide a detailed analysis. Based on the observations obtained from the analysis, we propose a unified optimization framework using block-based pruning to reduce the weight storage and accelerate the inference speed on mobile devices and FPGAs, achieving high hardware performance and energy-efficiency gain while maintaining accuracy.
Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

Abstract With the advancements in 5G network and mobile edge computing technology, autonomous vehicle technology has gained new development opportunities. Multitarget tracking becomes the research hotspots in autonomous vehicles. Since many factors such as motion blur, partial occlusion, and illumination changes affect the performance of target tracking, the problem of target tracking is still an open topic. In this article, inspired by the strong discriminative ability of dictionary learning, the discriminative dictionary learning with embedded auto‐encoder (DDLEA) algorithm is developed for the multitarget tracking system. The DDLEA algorithm integrates the auto‐encoder into the dictionary learning framework and learns sparse representations while preserving the local structure and discriminative information of data. The learned dictionary model has the strong recognition ability. Further, a multitarget tracking system is developed based on the proposed DDLEA algorithm and the hierarchical data association scheme. Based on the target confidence in the STKSVD model, the hierarchical data association method first uses the Hungarian algorithm to complete the preliminary matching of high confidence targets, and then further tracks the low confidence targets to improve the tracking ability. Experiments are carried out the public MOT 2015 dataset. Compared with several popular multitarget tracking algorithms, the tracking performance of our system is satisfactory.
NSEDS: One Implementation of Lambda Architecture in Large-scale Seismic Exploration Scenes

The lambda architecture is a well-proven and successful system architecture approach in the field of big data, with the qualities of being resilient and fault-tolerant, scalable, and extensible, and many big data systems are built using this solution. Simultaneously, due to the seismic exploration industry being very specialized, the construction concept for its data collecting and processing system remained in the "stand-alone" system developing era. However, with the advancement of low-power integrated sensor technology such as MEMS, the sensor network of seismic exploration equipment is getting larger, the data collected (velocity, acceleration, electric field, magnetic field, and gravitational field) is getting much more accurate, and the system transmission and processing bandwidth required by the system is getting higher. The "stand-alone" system developing mode is obviously incapable of keeping up with the industry's development. As a result, we developed an experimental system for data acquisition and processing in large-scale seismic exploration based on the lambda system architecture, which is named NSEDS (Next Seismic Exploration Data System). The evaluation results demonstrate that NSEDS can achieve the design goals. This paper will have some reference values for seismic exploration and other IoT systems that have the same characteristics.
Are you Seeing what I'm Seeing?: Perceptual Issues with Digital Twins in Virtual Reality

With the availability of data and visualization capabilities, digital twins have grown in use cases and entered the virtual reality (VR) realm. Utilizing VR enhances the value of digital twins by enabling training, testing, and refinement of a system, through support of a user&#x0027;s workflow when interacting with the system. However, VR is not fully representative of real-world environments, particularly in areas such as visual perception, proprioception, vestibular response, and perceptions of information. The simulation and training domain has learned which ways VR can benefit users and which ways it falls short, allowing a rich domain of information for the digital twin world to leverage. While many of these differences can be mitigated through targeted environmental design or hardware selection, these differences in perceptual capabilities and feedback negatively impact the validity of VR digital twins. These impacts could range from user discomfort, injury, negative transfer to the real world, and poor design decisions based on unrepresentative digital twins. This paper will discuss the current use cases of digital twins in VR that center on human interaction, common perceptual concerns in VR uncovered in simulation research, and how these issues could impact a digital twin&#x0027;s representativeness. As the applicability of digital twins in VR expands, mitigations for perceptual issues in VR and open areas for improvement in VR for digital twins are presented.
Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network

Deep convolutional neural networks with strong expressive ability have achieved impressive performances in single-image super-resolution algorithms. However, excessive convolutions usually consume high computational cost, which limits the application of super-resolution technology in low computing power devices. Besides, super-resolution of arbitrary scale factor has been ignored for a long time. Most previous researchers have trained a specific network model separately for each factor, and taken the super-resolution of several integer scale factors into consideration. In this paper, we put forward a multi-scale factor network (MFN), which dynamically predicts the weights of the upscale filter by taking the scale factor as input, and generates HR images with corresponding scale factors from the weights. This method is suitable for arbitrary scale factors (integer or non-integer). In addition, we use an information distillation structure to gradually extract multi-scale spatial features. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, PSNR/SSIM evaluation indicators, and model parameters.