Reviewerly/exHarmony huggingface dataset:
A 3-D Ray Tracing Model for Short-Range Radar Sensing of Hand Gestures

In this paper, a three-dimensional (3-D) ray tracing model is presented for touchless sensing of hand gestures using a microwave or millimeter-wave radar. The model was built up based on radar range equation by analyzing the electromagnetic waves transmitted to the target hand and reflected from the numerous scattering points on the hand. The detailed mathematical approach for building up the 3-D ray tracing model was discussed, including both nonlinear phase modulation and linear phase demodulation of the gesture motions. Simulation results show that the proposed 3-D model can be used to accurately predict the complex motions of the action gestures.
Machine Translation Evaluation using Textual Entailment for Arabic

Textual entailment is a generic task aiming to capture semantic inference between two text fragments. It has been applied so far to improve many natural language applications such as Question Answering and Information Extraction for the English language. This work aims to exploit results obtained for Textual Entailment in machine translation evaluation to Arabic. We show that the output of a system built for textual entailment recognition for Arabic can be used to serve as a metric to evaluate machine translation into Arabic. Despite the simplicity of the concepts and techniques., this metric has realized competitive results in comparison to state-of-the-art tools in terms of correlation with human judgments.
The Machine Selection Reading Comprehension Based on Memory Correction

To solve the memory losses of raw information due to the deepening network layers, a model based on memory correction mechanism is adopted to address the attention bias and the raw information losses. By introducing memory correction mechanism to GA Reader, option coding module is applied to RACE dataset for solving the issue that datasets have fewer demands for deep reasoning ability and contain noises. According to the experiment and analysis, the optimized model has each layer fused with the raw document information in the process of network iterative learning to keep correcting attention for the iterating network. Meanwhile, the reasoning ability needed for the model has also been improved. The RACE machine selection reading comprehension model based on memory correction could effectively solve the issue of raw information losses and overly simplified datasets.
BLNet: Boundary Points Localization Network for Object Detection

Currently, almost all of the two-stage object detectors treat bounding box localization as an offset regression problem in the second stage. However, the spatial information in each Region of Interest (RoI) feature map is not considered in this pipeline. In this paper, we propose a novel boundary points localization network (BLNet) to predict the location of four boundary points (topmost, bottommost, leftmost, rightmost) of objects on RoI feature maps with a fully convolutional network. In addition, in order to compensate for the low resolution of the heatmaps, we use a differentiable operation called soft-argmax to convert the heatmaps into the numerical coordinates directly. Experiments on PASCAL VOC 2007 and 2012 datasets demonstrate that our BLNet significantly outperforms the traditional regression-based methods. Using ResNet-101 as the backbone, our method achieves 80.9% mAP on VOC 2007 and 78.7% mAP on VOC 2012 dataset.
Unified Approach to Augmented Reality

The authors present a new and holistic conceptualization on the feeling of presence. This consists of several components. The first two propose a new definition of presence and the identification of a fourth component of presence called presence of action. The third component consists of a unified approach to presence taking into account technological, psychological, and ecological considerations. They have developed a model that identifies the processes leading to the feeling of presence. According to the model, two unconscious phases of judgment condition the emergence of presence. The first judgment concerns the credibility of the environment, which depends on the satisfaction of the user's intellectual and perceptual expectations.
Improved Long-Short Term Memory U-Net for Image Segmentation

The use of convolutional neural networks (CNNs) to automate image segmentation is strived for improvement of microscopy imaging. Different networks have been proposed, with the U-Net currently the most popular in the field of medical image segmentation. This paper investigates different optimizers to a published long-short term memory (LSTM)-UNet to find the most efficient optimizer for this approach when applied to a dataset of simulated nuclei of HL30 cells stained with a particular fluorescent compound.
A Distance-Aware Technique for Object Detection Used in Self-Driving Vehicles

Object detection obtains huge improvement after adopting deep learning technique. However, deep learning technique requires extremely high computation complexity and heavy DRAM (Dynamic Random Access Memory) bandwidth requirements, which blocks the deployment over various kinds of platforms. This paper provides a distance-aware technique for object detection that can adjust the required computation complexity and DRAM access amount according to several different searching distances. According to our analysis, the perception system can save up to 34.2% and 21.5% of computation complexity and DRAM access amount, respectively, for detecting near field objects when compared to the full range detection, without sacrificing any detection accuracy.
Failure mechanisms of fiber composites

The raising use of fiber composites is leading to a growing demand for understanding and mastering the damages and failures during the in-service of composite components. This chapter aims to provide a relatively detailed description of defects and damages that occur in fiber composite structures especially the defects related to the fiber and the matrix. An understanding of the criticality of the defects and damages leads to determine the failure mechanisms and the failure modes that range from simple failure modes to complex failure modes occurring in bidimensional laminates. Conclusively, methods of detecting the defects and damages using nondestructive testing methods have also been described.
Non-rigid Joint Segmentation and Registration Using Variational Approach for Multi-modal Images

Segmentation and registration are two common problems in medical imaging in particular and computer vision in general. These two problems have been studied substantially in the past two decades and often required as simultaneous tasks. Combination of these tasks in a single framework has proven to yield better results in terms of accuracy. In this paper, a model for the joint segmentation and registration based on the variational approach is presented for non-rigid multi-modal. The model is based on an active contour without edges for the segmentation task, normalized gradient fields, and the linear curvature model for image registration. The discrete functional of the new joint model is optimized using coarse to fine registration. The model is evaluated on synthetic and medical images and compared with the existing model. The proposed model is comparable based on the two evaluation criteria used.
Practice of social education of children and teenagers by Stanislav Teofilovich Shatsky

Significant changes taking place in society in connection with the pandemic are radically changing the global educational space and social reality. The transition to the distance learning model makes us look for new learning technologies, with the widespread use of it-tools. To some extent, they provide the process of transmitting and mastering information, but they do not solve a set of educational tasks. Participants in the pedagogical process are separated from each other, which ultimately leads to the loss of communication skills or their deformation. This circumstance prompted the authors to analyze critically the legacy of Russian pedagogy, addressed to the problems of social education. The article presents the results of the analysis of the theoretical and practical heritage of social education of the prominent Russian teacher Stanislav Teofilovich Shatsky (1878 - 1934). Based on the study of the teacher’s works and archival materials, the authors identified and formulated the conceptual provisions of the theory of social education S. T. Shatsky and described the main events of his practice The originality of this research lies in the fact that the experience of S. T. Shatsky is presented as a pedagogical technology; the authors conclude that it is possible to use historical experience in modern conditions. According to the authors, the authenticity of historical experience and modern educational practice can be achieved thanks to the general principles of education that follow from the law of natural conformity of the pedagogical process. The authors used methods of historical analysis of pedagogical heritage, systematization of theoretical positions, conceptualization and construction of pedagogical technology.
Emerging Technologies and Universities

Emerging technologies such as machine learning, the cloud, the internet of things (IoT), social web, mobility, robotics, and blockchain, among others, are powering a technological revolution in such a way that are transforming all human activities. These new technologies have generated creative ways of offering goods and services. Today's consumers demand in addition to quality, innovation, a real-time and ubiquitous service. In this context, what is the challenge that academy faces? What is the effect of these new technologies on the universities mission? What are people's expectations about academy in this new era? This chapter tries to get answers to these questions and explain how these emerging technologies are converting universities to lead society transformation to the digital age. Under this new paradigm, there are only two roads: innovate or perish. As might be expected universities are embracing these technologies for innovating themselves.
Transformation of Contract Descriptions in a Domain Specific Language to Solidity Assembly

There are a variety of contracts being traded in the financial markets. To eliminate the ambiguities imposed by the financial contracts written in natural languages, Peyton Jones and co-researchers proposed a contract definition language for standard representation of the financial contracts and a combinator library embedded in Haskell programming language to define financial contracts. Further, a special purpose compiler which is an extension to this work has been already proposed by exploiting major advancements such as autonomous contract execution and elimination of central counterparty in contemporary smart contracts, to transform contracts written in Peyton Jones' Contract Descriptive Language to Solidity which is the scripting language used in Ethereum smart contract platform. However, we have noticed that the cost related to the execution of contracts in Ethereum platform curtails the benefits received through the transformation of those contracts. Hence, we propose a novel approach to reduce the cost using different optimization techniques and it involves the direct transformation of the Peyton Jones' Contract Descriptive language to Solidity (inline) Assembly language which enables the manipulation of data locations in the Ethereum Virtual Machine. A formal verification is provided by verifying the semantic equivalence between the Peyton Jones' Contract Descriptive language and the proposed solution to ensure the correctness of the proposed approach is preserved while it is being optimized.
Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.
Self-supervised hamiltonian mechanics neural networks

We developed a method to derive the hamiltonian of an unknown system by machine learning the motions of the system. We modified the training process of Greydanus et al.'s hamiltonian neural network to make it be capable of learning from a dataset without the change-of-state ground truth. In other word, the learning process is self-supervised. This improvement can extend the application of the hamiltonian neural network because it is sometimes difficult to accurately measure the change of state of the system. Our model can now be able to learn the free particle system and the harmonic oscillator system.
Video Reenactment as Inductive Bias for Content-Motion Disentanglement

Independent components within low-dimensional representations are essential inputs in several downstream tasks, and provide explanations over the observed data. Video-based disentangled factors of variation provide low-dimensional representations that can be identified and used to feed task-specific models. We introduce MTC-VAE, a self-supervised motion-transfer VAE model to disentangle motion and content from videos. Unlike previous work on video content-motion disentanglement, we adopt a chunk-wise modeling approach and take advantage of the motion information contained in spatiotemporal neighborhoods. Our model yields independent per-chunk representations that preserve temporal consistency. Hence, we reconstruct whole videos in a single forward-pass. We extend the ELBO's log-likelihood term and include a Blind Reenactment Loss as an inductive bias to leverage motion disentanglement, under the assumption that swapping motion features yields reenactment between two videos. We evaluate our model with recently-proposed disentanglement metrics and show that it outperforms a variety of methods for video motion-content disentanglement. Experiments on video reenactment show the effectiveness of our disentanglement in the input space where our model outperforms the baselines in reconstruction quality and motion alignment.
Mentoring as an effective way to train and develop postgraduates in engineering

Abstract The article defines the relevance of mentoring in the postgraduate stage of engineering education, the importance and necessity of mentoring for training and progress of young scientists and postgraduates in higher educational institutions and organizations of the Russian Academy of Sciences. The creation of the mentor register in institutions of higher education and research organizations in order to turn them further into scientific schools. The results of the studies conducted in management mentoring of promising young researchers both in Russia and abroad are considered. «The mentoring program» in the institution of higher education is proposed.
Trajectories of Knowledge Economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank’s Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.
Technical Theme Analysis of WeChat Graphic Based on Domain Science and Technology Information

In order to enhance the WeChat communication influence and effect in the field of scientific and technological information, this paper explores the relationship between the technical theme of WeChat public account and the WeChat communication in the field of scientific and technological information. Taking the “Advanced Energy Science and Technology Strategic Information Research Center” WeChat public account as an example, this paper uses the singular value decomposition algorithm to cluster the WeChat technical theme, and calculates the WeChat communication index of each technical theme. Based on the calculation results, this paper analyzes the overall of technical themes, the correlation of technical themes and the typical graphics of technical themes. This work has a certain guiding and reference significance to the operation of the WeChat public account in the field of scientific and technological information.
The Information Integration Platform for Satellite Earth Station based on The Dynamic Workflow Model

On the basis of analyzing of the current information system for satellite earth station, this paper proposes a dynamic workflow model in the web services framework and builds the frame of the information integration platform. The reliability and extendibility of the system are analyzed. This information platform can integrate existing information resources and improve the utilization rate of hardware and software resources to reach some new features, such as data sharing or tailor ability.
Intelligent Vehicle Environment Scene Parsing Method Based on Multi-tasking Convolutional Neural Network

An encoder-decoder convolutional neural network architecture is presented integrating multi-class semantic segmentation and multi-object detection to improve the efficiency and depth of scene parsing of intelligent vehicle. The encoder of the network is designed as a multi-scale structure to improve real-time performance while ensuring the accuracy. The decoders of the network comprise the semantic segmentation and object detection subnetworks, which share encoder feature maps to improve computational efficiency. During the training process, we use FPS (Frames Per Second) and MIoU (Mean Intersection over Union) as the evaluation metrics of semantic segmentation, while the mAP (mean Average Precision) and FPS are used as the performance evaluation indexes of object detection. We conduct separate and joint training on the network to evaluate its performance. Experimental results show that the proposed network can realize multi-class semantic segmentation and multi-object detection simultaneously with better real-time performance and richer feature information, making it highly possible for implementation on real vehicles.
Research on a novel gesture recognition device

Abstract In order to effectively detect the input gesture information, a new type of capacitive sensing technology, which is of a low power consumption, low cost but high resolution non-contact sensing technology, is adopted. When our finger gets close to the metal plate, it will cause the change of the capacitance value of the metal plate to the ground. That value can be converted into 28-bit binary data for output. The microprocessor is equipped with the IIC interface for acquisition and processing of the output data. Because a variety of gestures produce different capacitance values, their output varies. After many times of training and machine learning, different gestures can be identified. The test results show the rate of different gestures recognition is over 99%, which can be applicable in human-computer interaction equipment.
Build complementary models on human feedback for simulation to the real world

Using simulators is a cost-effective way to meet human needs. Nevertheless, inevitable errors derived from the gap between simulation and the real world sometimes cause great losses and must be taken seriously. This paper focuses on one cause of the gap, which is the incomplete state representation in simulation, and proposes a supervised learning approach, correcting human-unacceptable policies calculated by simulators, based on human feedback. The approach first detects the related blind spots by classifiers which are trained on data from aggregation of noisy human feedback. Then, it corrects the human-unacceptable policies through the complementary model presented based on linear function approximation (LFA) and a policy iteration algorithm FRU-SADPP that uses radial basis functions (RBFs). We evaluate our approach on two simulated domains and demonstrate its higher accuracy of policies than two baselines, in terms of three typical kinds of human suboptimality and human errors, and three types of human feedback. Experiments also show the scalability of our approach.
Application of Improved Genetic Algorithm Based on Lethal Chromosome in Fast Path Planning of Aircraft

Aiming at the problems of the uncontrollable randomness in the genetic algorithm, such as the number of iterations, the low efficiency of fitness evaluation, and the slow convergence speed, this paper proposes a modified genetic algorithm based on the lethal chromosome, a gene pool has been established by extracting the genetic information of the high-quality chromosomes and lethal chromosomes based on the characteristic information we focused on. The further filter of the established gene pool is conducted before the fitness evaluation of evolutionary individuals to guarantee that each participated individual is a “living body” which can productively reduce the amount of calculation and the number of iterations. The proposed modified algorithm is verified via the algorithm of aircraft path planning under multiple constraints, the results showing that our method can effectively boost the performance of the genetic algorithm.
Real-Time Biometric Verification and Management System Using Face Embeddings

With a recent increase in cases of cybercrime, an automated biometric verification system is all the more essential in different organizations for reliable and efficient monitoring. To this end, our paper introduces a scalable and interactive application for biometric authentication using face recognition technology. The application provides four top-level services: Biometric Verification, Show Attendance, User Registration, and User Deletion. We also provide tools for visualization and management of attendance records. The proposed end-to-end pipeline predicts an embedding for the detected face which is then used for face verification based on its Euclidean distance from other face embeddings. On the Face Recognition Data, University of Essex, UK, our proposed method obtains an overall accuracy of 96.76% in the Faces94 and Grimace category.