Reviewerly/exHarmony huggingface dataset:
Digital literacy of modern higher education teachers

The expansion of digital technologies in all spheres of human life determines the digital transformation of education. This is due to the fact that the digital transformation of education brings the educational system itself in line with the requirements of the technological revolution and allows students to obtain relevant digital competencies in order to be qualified personnel for the economic sector. In connection with the digitalization of education and the active use of information technologies in the educational process, the requirements to the faculty of higher education are also changing. This became especially noticeable over the past year, when universities worked in a remote format. Therefore, the authors of the article turn to the issue of digital literacy of modern higher education teachers. Based on the structurally functional theory of T. Parsons, the modernist concept of E. Giddens and the theory of determination of human behavior by A. Maslow, it is shown what competencies a modern higher education teacher has in the context of the digital transformation of education. Based on the analysis, it should be noted that digital competencies include competencies related to information literacy, skills for interaction in an electronic environment; the ability to generate digital content; ensuring the safety of the physical and psychological health of users; the ability to identify and eliminate technical problems when working with digital devices. Digital literacy of teachers is the mastery of a set of the listed competencies. In general, the digital literacy index of university teachers is quite high, but unlike young people, teachers trust technological innovations to a lesser extent, which reduces their willingness to actively work with the digital educational environment. In our opinion, modern teachers need to transform their attitude to digitalization with the help of high-quality training in working with a digital educational environment.
Water cycle algorithm: an approach for improvement of navigational strategy of multiple humanoid robots

Abstract This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.
Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.
On Double Full-Stack Communication-Enabled Architectures for Multicore Quantum Computers

Despite its tremendous potential, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Among other issues, there are hard limits to the number of qubits that can be integrated into a single chip. Multicore architectures are a firm candidate for unlocking the scalability of quantum processors. Nonetheless, the vulnerability and complexity of quantum communications make this a challenging approach. A comprehensive design should imply consolidating the communications stack in the quantum computer architecture. In this article, we explain how this vision, by entangling communications and computation in the core of the design, may help to solve the open challenges. We also summarize the first results of our application of structured design methodologies backing this vision. With our work, we hope to contribute with design guidelines that may help unleash the potential of quantum computing.
Design of a secure file storage and access protocol for cloud-enabled Internet of Things environment

The so-called cloud-enabled Internet of Things (CIoT) systems interconnect numerous devices to render robust data storage, efficient analysis, and message exchange features. However, it confronts the confidentiality of data from the owner as the access control of data is performed by honest-but-curious service providers. Although the encryption strategy ensures data security, it has adverse data deliverance due to its inherent all-or-nothing decryption. Thus, a robust protocol is proposed to achieve data sharing among geographically dispersed physical devices and efficient access to cloud data for clients. The proposed protocol resists the chosen-ciphertext attack (CCA) based on the hardness of the Decisional Diffie–Hellman Inversion (DDHI) assumption. The comprehensive analysis and simulation results show that our protocol is efficient in computation, communication, and storage costs.
High frequency patterns play a key role in the generation of adversarial examples

Deep neural networks (DNNs) have made significant progress in many fields, but the black-box nature hinders their application in critical areas. The emergence of adversarial examples raises serious questions about the safety of neural networks particularly. The interpretability of neural networks has become a research hotspot in the field of deep learning. It is significant to study what features DNNs have learned and how these features evolve within DNNs. Taking adversarial examples as the research object, we find that feature activations of all categories have relatively stable expectations at each layer, and adversarial examples have undergone a feature trajectory from origins to targets, generating the feature activations similar to those of the target category. Using image frequency spectrum, we prove that adversarial examples are mainly caused by the formation of high-frequency patterns widely present in the target category images. The conclusions of this paper contribute to a deeper understanding of feature learning and the generation of adversarial examples.
An efficient pruning scheme of deep neural networks for Internet of Things applications

Abstract Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.
DarkLight Networks for Action Recognition in the Dark

Human action recognition in the dark is a significant task with various applications, e.g., night surveillance and self-driving at night. However, the lack of video datasets for human actions in the dark hinders its development. Recently, a public dataset ARID has been introduced to stimulate progress for the task of human action recognition in dark videos. Currently, there are multiple models that perform well for action recognition in videos shot under normal illumination. However, research shows that these methods may not be effective in recognizing actions in dark videos. In this paper, we construct a novel neural network architecture: DarkLight Networks, which involves (i) a dual-pathway structure where both dark videos and its brightened counterpart are utilized for effective video representation; and (ii) a self-attention mechanism, which fuses and extracts corresponding and complementary features from the two pathways. Our approach achieves state-of-the-art results on ARID. Code is available at: https://github.com/Ticuby/Darklight-Pytorch
Resistance of the Montgomery Ladder Against Simple SCA: Theory and Practice

Abstract The Montgomery kP algorithm i.e. the Montgomery ladder is reported in literature as resistant against simple SCA due to the fact that the processing of each key bit value of the scalar k is done using the same sequence of operations. We implemented the Montgomery kP algorithm using Lopez-Dahab projective coordinates for the NIST elliptic curve B-233 . We instantiated the same VHDL code for a wide range of clock frequencies for the same target FPGA and using the same compiler options. We measured electromagnetic traces of the kP executions using the same input data, i.e. scalar k and elliptic curve point P , and measurement setup. Additionally, we synthesized the same VHDL code for two IHP CMOS technologies, for a broad spectrum of frequencies. We simulated the power consumption of each synthesized design during an execution of the kP operation, always using the same scalar k and elliptic curve point P as inputs. Our experiments clearly show that the success of simple electromagnetic analysis attacks against FPGA implementations as well as the one of simple power analysis attacks against synthesized ASIC designs depends on the target frequency for which the design was implemented and at which it is executed significantly. In our experiments the scalar k was successfully revealed via simple visual inspection of the electromagnetic traces of the FPGA for frequencies from 40 to 100 MHz when standard compile options were used as well as from 50 MHz up to 240 MHz when performance optimizing compile options were used. We obtained similar results attacking the power traces simulated for the ASIC. Despite the significant differences of the here investigated technologies the designs’ resistance against the attacks performed is similar: only a few points in the traces represent strong leakage sources allowing to reveal the key at very low and very high frequencies. For the “middle” frequencies the number of points which allow to successfully reveal the key increases when increasing the frequency.
Globalization, Governance, and the Green Economy in Sub-Saharan Africa: Policy Thresholds

This study assesses how globalization modulates the effect of governance on carbon dioxide (CO 2 ) emissions in sub‐Saharan African countries. The empirical evidence is based on Generalized Method of Moments. The minimum level (or negative threshold) of Foreign Direct Investment required for it to interact with political stability and contribute toward the green economy is 45 percent of gross domestic product (GDP), while 90 percent of GDP is the maximum level (or positive threshold) required for trade to complement “voice and accountability” in mitigating CO 2 emissions. Seventy‐six percent of GDP and 80 percent of GDP are, respectively, negative trade thresholds for government effectiveness and economic governance. The corresponding negative trade thresholds for the rule of law, corruption‐control, and institutional governance are, respectively, 230 percent of GDP, 63.5 percent of GDP, and 106.5 percent of GDP. Actionable openness policy thresholds are provided to inform policy makers on how governance interacts with globalization to promote the green economy.
Recognition of JSL fingerspelling using Deep Convolutional Neural Networks

In this paper, we present approach for recognition of static fingerspelling in Japanese Sign Language on RGB images. Two 3D articulated hand models have been developed to generate synthetic fingerspellings and to extend a dataset consisting of real hand gestures.In the first approach, advanced graphics techniques were employed to rasterize photorealistic gestures using a skinned hand model. In the second approach, gestures rendered using simpler lighting techniques were post-processed by a modified Generative Adversarial Network. In order to avoid generation of unrealistic fingerspellings a hand segmentation term has been added to the loss function of the GAN. The segmentation of the hand in images with complex background was done by proposed ResNet34-based segmentation network. The finger-spelled signs were recognized by an ensemble with both fine-tuned and trained from scratch neural networks. Experimental results demonstrate that owing to sufficient amount of training data a high recognition rate can be attained on RGB images. The JSL dataset with pixel-level hand segmentations is available for download.
Enhancing two-view correspondence learning by local-global self-attention

Seeking reliable correspondences is a fundamental and significant work in computer vision. Recent work has demonstrated that the task can be effectively accomplished by utilizing a deep learning network based on multi-layer perceptrons, which uses the context normalization to deal with the input. However, the context normalization treats each correspondence equally, which will reduce the representation capability of potential inliers. To solve this problem, we propose a novel and effective Local-Global Self-Attention (LAGA) layer based on the self-attention mechanism, to capture contextual information of potential inliers from coarse to fine, and suppress outliers at the same time in processing the input. The global self-attention module is able to capture abundant global contextual information in the whole image, and the local self-attention module is used to obtain rich local contextual information in the local region. After that, to obtain richer contextual information and feature maps with stronger representative capacity, we combine global and local contextual information. The extensive experiments have shown that the networks with our proposed LAGA layer perform better than the original and other comparative networks in outdoor and indoor scenes for outlier removal and camera pose estimation tasks.
Topic modeling for feature location in software models: Studying both code generation and interpreted models

In the last 20 years, the research community has increased its attention to the use of topic modeling for software maintenance and evolution tasks in code. Topic modeling is a popular and promising information retrieval technique that represents topics by word probabilities. Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling methods. However, the use of topic modeling in model-driven software development has been largely neglected. Since software models have less noise (implementation details) than software code, software models might be well-suited for topic modeling. This paper presents our LDA-guided evolutionary approach for feature location in software models. Specifically, we consider two types of software models: models for code generation and interpreted model. We evaluate our approach considering two real-world industrial case studies: code-generation models for train control software, and interpreted models for a commercial video game. To study the impact on the results, we compare our approach for feature location in models against random search and a baseline based on Latent Semantic Indexing, which is a popular information retrieval technique. In addition, we perform a statistical analysis of the results to show that this impact is significant. We also discuss the results in terms of the following aspects: data sparsity, implementation complexity, calibration, and stability. Our approach significantly outperforms the baseline in terms of recall, precision and F-measure when it comes to interpreted models. This is not the case for code-generation models. Our analysis of the results uncovers a recommendation towards results improvement. We also show that calibration approaches can be transferred from code to models. The findings of our work with regards to the compensation of instability have the potential to help not only feature location in models, but also in code.
Illusory Body Ownership Affects the Cortical Response to Vicarious Somatosensation

Fundamental human feelings such as body ownership ("this" body is "my" body) and vicariousness (first-person-like experience of events occurring to others) are based on multisensory integration. Behavioral links between body ownership and vicariousness have been shown, but the neural underpinnings remain largely unexplored. To fill this gap, we investigated the neural effects of altered body ownership on vicarious somatosensation. While recording functional brain imaging data, first, we altered participants' body ownership by robotically delivering tactile stimulations ("tactile" stroking) in synchrony or not with videos of a virtual hand being brushed ("visual" stroking). Then, we manipulated vicarious somatosensation by showing videos of the virtual hand being touched by a syringe's plunger (touch) or needle (pain). Only after the alteration of body ownership (synchronous visuo-tactile stroking) and specifically during late epochs of vicarious somatosensation, vicarious pain was associated with lower activation in premotor and anterior cingulate cortices with respect to vicarious touch. At the methodological level, the present study highlights the importance of the neural response's temporal evolution. At the theoretical level, it shows that the higher-level (cognitive) impact of a lower-level (sensory) body-related processing (visuo-tactile) is not limited to body ownership but also extends to other psychological body-related domains, such as vicarious somatosensation.
Which Heroes to Pick? Learning to Draft in MOBA Games With Neural Networks and Tree Search

Hero drafting is essential in multiplayer online battle arena (MOBA) game playing as it builds the team of each side and directly affects the match outcome. State-of-the-art drafting methods fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the multiround nature of a MOBA 5v5 match series, i.e., two teams play best-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> and the same hero is only allowed to be drafted once throughout the series. In this article, we formulate the drafting process as a multiround combinatorial game and propose a novel drafting algorithm based on neural networks and Monte Carlo tree search, named JueWuDraft. Specifically, we design a long-term value estimation mechanism to handle the best-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> drafting case. Taking <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Honor of Kings</i> , one of the most popular MOBA games at present, as a running case, we demonstrate the practicality and effectiveness of JueWuDraft when compared to state-of-the-art drafting methods.
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning

This paper investigates the problem of providing ultra-reliable and power-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users’ feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users’ head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users’ accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users’ tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solve a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% power-efficient than the benchmark algorithms.
Convolutional neural network architectures

Convolutional neural networks (CNNs) have been widely applied to many computer vision applications such as image classification, face recognition, object detection, and so on. This chapter introduces some of the most popular CNN architectures, including LeNet, AlexNet, VGG, GoogLeNet, and ResNet. Then, it presents two scenarios of implementing Inception-v3, a CNN for computer vision. In the first scenario, we create the Inception-v3 model by using Keras Applications. In the second scenario, we employ TensorFlow Hub to build the Inception-v3 model.