Reviewerly/exHarmony huggingface dataset:
Photoelasticity

PhotoelasticityPhotoelasticity is a simple full-field optical method of experimental mechanics. Its name comes from the Greek words “photo” which means light and “elasticity” which refers to the ability of an objectObject or material to resume its normal shape after being stretched or compressed. It is based on the phenomenon of temporaryTemporary double refraction or artificial double refractionArtificial double refraction or birefringence effect, first discovered by Sir David Brewster in 1816, according to which transparent materials that are optically isotropic when unstressed become optically anisotropic and behave like birefringent crystals when they are stressed. The birefringence of the material is proportional to the difference of the principal stresses and is retained only during the application of loads. It disappears when the loads are removed.
Simultaneous Learning of Relative and Absolute Spatial Concepts without Any Prior Distinction

This study proposes a learning method for relative and absolute spatial concepts using spoken user utterances. Service robots that assist humans in performing daily activities must be able to learn and understand spatial concepts and their linguistic representations. Spatial concepts are divided into two types: relative spatial concepts (e.g., front and right) and absolute spatial concepts (e.g., kitchen and corridor). Robots are required to learn both types of concepts. Therefore, we propose a method by which a robot can learn both relative and absolute spatial concepts without prior knowledge of words. The method is formulated using a probabilistic model to mutually complement the uncertainty of locations and spoken user utterances. The experimental results demonstrate that both relative and absolute spatial concepts can be learned under the condition that the robot does not know which type of concept is uttered. In addition, we show that spoken user utterances are segmented with high accuracy.
Neural Network-based Vocoders in Arabic Speech Synthesis

Text-to-speech (TTS) system has been used in many applications to generate natural speech that is intelligible for a given text. Thereby, TTS systems can be a very useful tool in many applications. A text-to-speech synthesis consists of a text analysis model, an acoustic model and a vocoder. The modern neural network-based model has significantly improved the speech synthesis quality. However, the English language is often the focus when developing state-of-the-art technologies. Even though Arabic is the formal language for 25 countries, there is no sufficient research or evaluation conducted to study the performance of state-of-the-art TTS approaches on Arabic speech synthesis, and only conventional TTS techniques have been adapted to the Arabic language. In this paper, the two neural-network vocoders Parallel WaveGan and Multi-Band MelGan are implemented. Investigation of the effectiveness of both vocoders for Arabic speech synthesis is carried out. This work involves reconstruction and examination of the selected models; and evaluating the performance and quality of the generated speech. It is found that the Parallel WaveGan outperformed Multi-Band MelGan in terms of Perceptual Evaluation of Speech Quality (PESQ), where the generated speech using Parallel WaveGan scored PESQ of 2.63, while Multi-Band MelGan scored 2.37.
ST-CFSFDP algorithm based on Euclidean distance constraint

The spatial-temporal clustering by fast search and find of density peaks (ST-CFSFDP) has a better clustering effect on the spatiotemporal data set in a small space. However, there are some deficiencies in the spatiotemporal dataset with large data volume and far interval between sample points, the clustering results showed great differences, too many interference points during visualization. Given the above deficiencies, this paper proposes a spatial-temporal clustering by fast search and find of density peak algorithm based on Euclidean distance constraint, by increasing the partition constraint of some sample points, the problems existing in the spatiotemporal clustering algorithm of ST-CFSFDP are improved. Experimental results show that the improved algorithm has a better clustering effect than the original algorithm.
Two-Dipole Model Based Coherent Radar Target Decomposition

A method for recovering objects parameters by experimental data based on the two-dipole model of target representing is discussed in the paper. The goal of the investigation is to develop the method of remote sensing objects parameters definition by measured data. A considered method is built on analysis of analytic expressions of PSM elements for a two-dipole mathematical model of object representation. The proposed method was applied to recover the parameters of different objects for which complex PSM’s were experimentally obtained. Some objects signatures were obtained and considered based on recovered parameters and provided their statistical analysis. Obtained results indicate the adequacy of the two-dipole model and appliance for modelling the real objects prototypes to study the process of transforming electromagnetic waves at reflecting from object also a possibility of radar targets imitating. Obtained results can be useful in solving radar objects recognition and classification problems.
Analysis of Ionospheric Disturbance on X-band SAR Image Registration

Abstract The atmosphere is a very important factor that affects the accuracy of X-band SAR image registration, and the ionosphere effect has the most intricate influence. In response to this problem, this paper introduces the mathematical model of ionospheric dispersion effect and scintillation effect. Then, echo simulation, imaging processing, and image registration are used to calculate the image offset caused by the ionosphere, which can determine whether the ionosphere effect needs to be compensated during image registration. Simulation experimental results show that in the X-band image registration, the dispersion effect needs to be compensated, and the impact of the scintillation effect can be ignored.
Super-resolution on Edge Computing for Improved Adaptive HTTP Live Streaming Delivery

According to a Cisco report, mobile network speeds will more than triple by 2023, from 13.2 Mbps in 2018 to 43.9 Mbps in 2023. The average 5G connection speed is forecasted to reach 575 Mbps by 2023. This increase in bandwidth on mobile networks, along with the growing demand for streaming video content, has imposed unprecedented challenges on the backhaul networks that interconnect mobile networks to the Internet core. A trend to mitigate this problem has been to bring the source of content closer to the users, bringing it from the cloud to multi-access edge computing (MEC), therefore shifting the traffic pattern from the Internet core to the edge. In this article, we propose a framework called live streaming with super-resolution (LiveSR) that uses deep neural network-based super-resolution. In the LiveSR, live video moves in low resolution down to MEC and upscales to high resolution before being delivered to viewers over high-bandwidth mobile networks. We evaluate the proposed framework in scenarios with real 5G network traces. When we compare the proposed framework and a cloud-based video delivery system in a network defined by congested backhaul links, results show that the LiveSR framework can increase the quality of experience (QoE) in adaptive live videos by 49%, 51%, and 58% for the LoL+, BOLA, and L2A-LL adaptive algorithms, respectively. A considerable reduction in traffic in the backhaul is also recorded, ranging from 97.36% to 98.18%.
A Co-Evolutionary Hybrid ACO for Solving Traveling Salesman Problem

Ant Colony Optimization (ACO) is an approximate method proposed recently. Many ACO based approaches and hybrid methods have been proposed for solving the traveling salesman problem (TSP); However, the balance between intensification and diversification is also difficult to solve. In this paper, we propose a co-evolutionary hybrid method (CEACO-GA) by adopting multiple colonies which perform ACO or GA algorithms, and a co-evolutionary strategy among colonies which is to enhance the interaction among colonies by communication between ACO and GA colonies, thereby to control the population diversity. The number of colonies that perform GA operations is used to adjust the balance between intensification and diversification. The CEACO-GA is tested on various problem instances in the TSPLIB standard library, and the results of numerical calculation show that the the CEACO-GA has more outstanding performance comparing to other algorithms.
Efficiency and problems of distance learning: Experience of Russia and Kazakhstan

The last decade has been marked by large-scale and rapid changes in education, taking place on the basis of modern infocommunication and digital technologies, new hardware and software, as well as teaching methods updated in accordance with this. The limited mobility of citizens due to the coronavirus pandemic further intensified these processes, when, even with a low readiness of educational institutions to switch to distance (electronic, remote) technologies, teachers and students had to quickly master these technologies and use them in practice. The resulting experience allows us to identify not only problematic, sometimes even negative, features of distance learning, but also to make sure of the undoubted advantages of these technologies. The article points out the need for a systematic analysis when deciding on the use of e-learning, taking into account both technical and methodological, as well as personnel readiness for the implementation of this technology. The article analyzes these problems using the example of teaching both technical and humanitarian disciplines. The experience of distance work is analyzed on the example of cooperation between the Moscow Technical University of Communications and Informatics (Russia) and the L. N. Gumilyov Eurasian National University (Kazakhstan), which in its practical activities purposefully invites foreign professors to deliver lectures, guide undergraduates, etc., including those based on distance technology. The data of an anonymous questionnaire survey of students of these universities are given, allowing to assess the effectiveness of the decisions made, as well as to clarify the existing problems.
Research on E-commerce Recommended Algorithm Based on Knowledge Graph

The traditional e-commerce recommendation algorithms are difficult to extract effective information from massive data to meet users’ needs. This paper proposes a new algorithm, which integrates user’s review with commodities knowledge graph, and constructs corresponding entities. It also extracts feature words and semantic relations to form a set, obtains product feature weights, and calculates the recommended value of commodities by using random walk algorithms. The experimental results show that the new algorithm has a certain degree of improvement in precision, recall rate, and F-value compared with the traditional recommendation algorithm.
Художня артикуляція проблеми постлюдства у п’єсі Карела Чапека “Р.У.Р. Россумові універсальні роботи”

In the article Karel Čapek’s play “R.U.R. Rossum’s universal robots” is read not only as an anti-utopia devoted to the confrontation between humans and robots, but also as a work of art that defines a number of humanitarian discourses of the twentieth and early twenty-first centuries. The play correlates with modern humanitarian studies of posthumanity, posthumanism and transhumanism, examines various projects of posthumanistic reduplication of man – the archetype of the homunculus, types of humanized robots, cyborgs, iron warriors, predicts the decline of masculine discourse and the origins of feminism. The impact factor of this dramatic text goes beyond fiction and theatre and extends to the whole culture, because the concept of “robot” introduced by Karel Čapek is a stable concept of culture, transformation and technology. At the conceptual level, the article deals with the ability of artistic thinking to forecast, identify promising general scientific issues and structure future humanitarian discourses.
AutoRoto: Automated Rotoscoping with Refined Deep Masks

The legendary movie Jurassic Park (1993) would have been nearly impossible without Computer Generated Imaginary(CGI). The movie's ground-breaking visual effects was able to smoothly integrate virtual characters with real-life footage. This rapidly increased the use of visual effects in modern movies. Creating high-end visual effects is a complex workflow where each shot is subjected to artistic operations. The key step in this workflow is the process called digital rotoscoping. It is the process of manually outlining and cutting out the foreground characters in a raw video frame via software. This is a time-consuming process and the average number of frames a professional artist can accomplished per day is 15. Due to this, it has become a temporal bottleneck of the VFX pipeline.In order to address the above mentioned issue, an automated rotoscoping approach with instance segmentation is presented in this paper. The novel method is efficient, accurate and will redeem the time of rotoscoping artists. Our model was able to extract foregrounds of multiple instances of classes and to the best of our knowledge our rotoscoping tool is the most generalized to extract foregrounds.
Unambiguous state discrimination and joint measurement attacks on passive side channel of the light source in BB84 decoy-state protocol

Abstract Quantum key distribution (QKD) has a promise of unconditionally secure communication between the remote sides. The real-world QKD implementations, however, have numerous loopholes, both of engeneering and physical origin, and compromise the security promise. In this work, we investigate two attack strategies on the passive side channel of the light source along with the optimal cloning attack on the BB84 protocol with decoy-states. We calculate an upper bound of a secret key rate for these situations and show that the joint measurement attack on the signal and side channel degree of freedom is more effective to the adversary.
Spatio-Temporal Graph Policy Gradients for Multi-Robot Formation Control

Formation control is crucial for multi-robot collaboration. The high dynamics of multi-robot environments make it challenging to learn interactions between robots. Although some progresses have been made, the existing methods cannot capture spatial and temporal dependencies. In this paper, we propose a novel spatio-temporal graph policy gradients (STGPG), to tackle the formation control problem in the multi-robot domain. We propose using spatio-temporal graph convolutional network (STGCN) to parametrize the formation policy. STGCN adapts to the dynamics of the underlying graph in the formation environment, and uses the pure convolution blocks to capture the overall spatio-temporal correlations for learning formation strategies. The gradient of the robot not only back-propagates to itself, but also to its neighbors to reinforce the learned formation policy. Experimental results indicate the effectiveness of the proposed STGPG.
Adaptable University‐Agency Early‐Career Fellowship Program Creates a Win‐Win‐Win for Wisconsin's Waters

Abstract Many of today's water resources challenges are wicked problems, demanding innovative solutions across the science‐policy‐management nexus. Simultaneously, early‐career researchers in water resources face a shifting professional landscape in which academic career paths are sparse but where versatile skill sets relevant to water resources issues in governments, non‐governmental organizations, and the private sector are in high demand. Here, we describe an adaptable fellowship model that has proven to be a “win‐win‐win” for early‐career researchers, government agencies, and universities tackling wicked water resources challenges in Wisconsin, USA. The fellowship program recruits post‐masters and post‐doctoral fellows to lead research on a water resources challenge identified as a high priority by a government agency partner. Fellows receive mentorship from both academic and agency mentors and co‐produce actionable knowledge. Costs and administrative responsibilities are shared by the university (Sea Grant/Water Resources Institute) and the host agency. Since its inception in 2015, this program has trained 24 fellows across 11 host programs on issues that range from highly quantitative water quality and hydrogeological questions to qualitative assessments of fisheries management and coastal hazards. In this arrangement, fellows receive collaborative and cross‐disciplinary training that prepares them well for diverse career paths, government agencies benefit from new knowledge targeted at pressing water resources management questions, and university institutions accomplish their missions of training researchers and developing actionable knowledge. We describe this model's applicability to other regions and institutions. Ultimately, this type of program benefits society by building long‐term capacity for collaboration which addresses wicked water problems.
Interpretation of the scientific picture of the world in the context of knowledge development

The scientific picture of the world enters into constant interaction with cognitive experience. And changes in the structure of informational space change behavioral patterns and criteria of influence of environment on individuals are determined. The purpose of this work is to review individual branches of scientific knowledge as a component of the scientific picture of the world and to identify characteristic figures of scientific research of the XV-XVI centuries as an epoch of "flight" of thought. The descriptive method of the investigated phenomenon is used in the work. The purpose of the research is to form a reliable and verified knowledge of the scientific picture of the world of the stated period, to identify the value potential of the representatives of different branches of knowledge, because it is impossible to relieve the phenomenon of current science and set the prospects for its further development without analysis and interpretation of the sources from the previous centuries. The scientific picture of the mentioned period shows the discordance of ideas and the scale of their theoretical and practical implementation (from philosophy to art). The author highlights the most characteristic features of the scientific picture, which contributed to the appearance of new lines of research, a new necklace base on which the scientific thinking of the next centuries was built.
Research on Non-pooling Convolutional Text Classification Technology Combined with Attention Mechanism

Text classification technology is a relatively basic and important application technology in natural language processing. Although the classification method based on traditional machine learning algorithms and manual annotation feature engineering has achieved certain results, the efficiency is not ideal. In recent years, the application of deep learning in natural language tasks processing has received extensive attention. In this paper, the structure of the traditional convolutional neural network model is modified, and an attention layer is introduced to complete the task of text classification. The feasibility of the model is analyzed, and a new research direction is provided for the application of neural networks in the field of natural language processing.
A new method for spinning projectile aerodynamic estimation: Extreme learning machine optimized by adaptive particle swarm

Aiming at the problem of aerodynamic parameter identification of a spinning projectile, an adaptive particle swarm optimization for the extreme learning machine algorithm is proposed in this paper. The algorithm uses the adaptive particle swarm optimization algorithm to optimize the hidden layer weight and threshold of the extreme learning machine to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The free flight data of the projectile are generated by numerical simulation, and the aerodynamic parameters of a projectile are identified by the proposed algorithm. Simulation results show that the proposed algorithm can effectively identify the aerodynamic parameters of the projectile, and it has high identification accuracy and fast convergence speed. The proposed algorithm is suitable for engineering applications.
Introducing the Networked Music Performance Library

In this article we present the Networked Music Performance Library, which documents research and practice in the field of networked music performance. The library was developed in response to the skyrocketing interest in networked music due to social distancing and travel restrictions during the COVID-19 pandemic in 2020. In this article, we discuss the library’s working environment and briefly describe several similar earlier projects by other researchers. The main goals we set for the library were to collect materials, organise accessible and straightforward ways of navigating the collection, including knowledge from a wider context of time-based arts (theatre, dance and other time-based art forms), and create a community of users and contributors. We describe the main methods we use to find the works for the library and examine the current state of our collection, which has grown in the last year. Through an analysis of the entries’ metadata, we provide several examples of professionals’ use of the library. Current library users include performing musicians, teachers and composers. As the project is ongoing, we outline the scope of our future work as well as analyse several possible post-COVID applications for networked music, which include assisting socially isolated musicians, teaching, travel and new pandemics. We also show how this library could contribute to the development of new electroacoustic approaches.
Virtual and Augmented Reality Awareness Tools for Universal Design

This chapter will be focused on contributing to the increase of universal design competencies of future engineers, educators, and designers through the use of mixed reality technologies, closing the gap between theory and field application of principles, towards a more inclusive world and promoting health and wellbeing for all. The experience of a situation where limitations arise in relation to what is taken for granted is an important experience that leads to a personal knowledge of the difficulties. By the use of simulators, especially virtual (VR) and mixed reality (MR) technologies, it is possible to create such experiences. Training based on MR can prepare future and current professionals for up-to-date requirements of the labor market. In addition, it can ensure that the standards such as barrier-free concepts, broader accessibility, adaptive and assistive technology will be familiar to trainees.