Reviewerly/exHarmony huggingface dataset:
China, the United States, and Central Bank Digital Currencies: How Important Is It to Be First?

In only a few years, central bank digital currencies (CBDC) have gone from a fringe idea promoted by cryptocurrency bloggers to an idea now seriously explored by 80% of the world’s major central banks, including the People’s Bank of China and the United States Federal Reserve. This paper gives an overview of the drive at the world’s central banks to evaluate CBDCs and examines the reasons behind the world’s two leading economies’ stark divergence in central bank digital currency development. China committed much earlier to launching a CBDC, announcing its intention to do so in 2016, and it has since then taken more concrete steps towards piloting and issuing a CBDC than the Federal Reserve, which has yet to commit to ever issuing one. This article then critiques the common framing of CBDC development as a race and explores the implications of China becoming a first mover for both countries and the global monetary system, including the role of the Dollar and RMB internationally. China has already begun retail pilots and is likely to be the first major economy to issue a CBDC to the general public. The analysis hypothesizes that a first mover advantage in CBDC will to be short lived due to the rapid changes in the technology and global marketplace for digital currencies, and that the Federal Reserve may not be as far “behind” China in understanding digital currency as it might appear from their different public response to CBDC.
Semantic Scene Graph Generation Using RDF Model and Deep Learning

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches.
TestDCat 3.0: catalog of test debt subtypes and management activities

When deadlines and resources of software projects become scarce, testing is usually in the first row to have its activities aborted or reduced; however, if defects cannot be found, product quality can be affected. In the software development process, aborted or reduced activities that can bring short-term benefits, but can be harmful to the project in the long run, are considered Technical Debt (TD) and, when the TDs impact testing activities, they are called Test Debt. Although there are several studies dealing with Test Debt, current solutions often deal with specific types of tests (e.g., exploratory and automated tests) and do not address the whole software testing process. Aiming to fill these gaps, this work then proposes a Test Debt Catalog, called TestDCat, with subtypes of Test Debts and Technical Debt management activities. This catalog is built based on the results of an empirical study, a literature review and semi-structured interviews conducted with practitioners who perform testing activities on five projects from industry. For the TestDCat evaluation, a case study is conducted in real projects in order to identify if the catalog is user-friendly and if its use helps the Test Debt management during the execution of test activities in these software development projects.
Video smoke removal based on low‐rank tensor completion via spatial‐temporal continuity constraint

Abstract Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low‐rank tensor completion via spatial‐temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real‐world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results.
Young Students’ Attitude Toward Expert Knowledge

The paper focuses on training professionals for active innovative work against the backdrop of continual development of knowledge-intensive technologies and expert systems. The authors address the characteristics of the expert knowledge functioning related to science development and forecasting the future of digital culture. Emphasis is placed on the expanding role of expert communities and expert system functions in the evolution of modern network society as a knowledge society. Trends in the youth's attitude to research work and the scientific expert status are analyzed. The empirical research is carried out by means of sociological survey of students. The sociological surveys conducted by the authors at the Peter the Great St. Petersburg Polytechnic University and the secondary data from other Russian universities show an increasing role of a student in the university scientific projects. It helps students gain the expertise needed to enter into a dialogue with the expert community. The research has shown that society demands a new interdisciplinary approach to training specialists that would be aimed at integrating the scientific knowledge and skills responsible not only for the role of a researcher but also for the role of an expert, capable of assessing innovation and competent prediction of its consequences for the social development in the digital era.
An Integrative Review of Image Captioning Research

Abstract In the field of computer vision, image captioning is a new frontier research. The basic task of image captioning is to generate a descriptive natural language for the input image. This paper investigates and analyzes the related research of image captioning. Firstly, the task and application scenarios of image captioning are introduced. Secondly, the image captioning algorithm based on template and the image captioning algorithm based on encoder-decoder structure are analyzed, and the advantages and limitations of each method are discussed. Then, the benchmark dataset and evaluation for image captioning are introduced. Finally, the future development of image captioning is prospected.
Multi-camera based full-field 3D displacement measurement using digital image correlation

In this work, a multi speckle matching method for full-field 3D measurement using multi-camera is proposed. The rotation matrix and translation matrix of different camera coordinate were calculated using multi speckle matching method. The measurement points of all cameras were converted to a same coordinate system and the 3D shape is construct. The full-field displacement is obtained by subtracting the coordinates before and after translation. The measurement of aluminum plate using multi-camera system is conducted. The results show that the converting error of displacement field is less than 0.1mm.
Machine Learning and Internet of Things for Smart Processing

Machine learning and the Internet of Things have gotten advancements in the everyday lives of a client, the eagerness and receptiveness of shoppers have fueled the producers to dole out new gadgets with more highlights and better style. While trying to stay aware of the challenge, the producers are not giving enough consideration to the digital security of these savvy gadgets. The gravity of security vulnerabilities is additionally bothered because of their associated nature. Subsequently, an undermined device would quit giving the planned help, yet could likewise go about as a host for malware presented by an assailant. Electrocardiogram information must appropriately be preprocessing. This chapter gives support to the research and relationship of the Internet of Things and machine learning for the smart impacts of three variables, including recurrence groups, spatial channels, and classifiers arrangement execution, to decide the best settings.
An Efficient Low Complexity Pruned DCT Approximation for Image Compression in Wireless Multimedia Sensor Networks

Prolonging the lifetime of wireless sensor networks (WSN) is the biggest challenging issue. In wireless multimedia sensor networks (WMSNs), sensor nodes have limited energy resource and to increase the lifetime of the network, it is necessary to design an effective fast algorithm that aims at reducing the consumed power. This paper proposes an energy-efficient discrete cosine transform (DCT) approximation requiring only 12 additions. Associated with a JPEG compression chain, this DCT approximation ensures a very good rate-distortion compromise, but above all, a very low computational complexity and significant compatibility with the exact DCT. Simulation results clearly show that the proposed fast transform algorithm achieves a better trade-off between image quality, computational complexity and energy consumption compared to any existing pruned DCT approximations. Furthermore, it is very suitable for the resource constrained wireless visual sensor networks (WVSNs) requiring low bitrates.
A Perceptual Quality-driven Video Surveillance System

Video-based surveillance systems often suffer from poor-quality video in an uncontrolled environment. This may strongly affect the performance of high-level tasks such as visual tracking, abnormal event detection or more generally scene understanding and interpretation. This work aims to demonstrate the impact and the importance of video quality in video surveillance systems. Here, we focus on the most important challenges and difficulties related to the perceptual quality of the acquired or transmitted images/videos in uncontrolled environments. In this paper, we propose an architecture of a smart surveillance system that incorporates the perceptual quality of acquired scenes. We study the behaviour of some state-of-the-art video quality metrics on some original and distorted sequences from a dedicated surveillance dataset. Through this study, it has been shown that some of the state-of-the-art image/video quality metrics do not work in the context of video-surveillance. This study opens a new research direction to develop the video quality metrics in the context of video surveillance and also to propose a new quality-driven framework of video surveillance system.
Fast Optimal Transport Artistic Style Transfer

Artistic style transfer plays an important role in the culture and entertainment industry. However, contemporary stylization approaches are suffering from two obstacles: 1) the low temporal efficiency and 2) the improper stylization metrics. To address these issues, we present a real-time style transfer framework optimized by the optimal transport theory. On the one hand, we design our learning scheme as a feed-forward network which can translate high-resolution images at the real-time speed; On the other hand, instead of learning the style manipulation unconstrained in the tensor space, we introduce the optimal transport optimization tool to ensure the stylization to be conducted along the style-manifold. Extensive experiments on Place365 and Wiki-art well demonstrate the excellent temporal efficiency as well as the convincing stylization effect of the proposed framework.
Constructive Modeling of the Software Development Process for Modern Code Review

The preeminent point of this work is to construct a model of the software development process for automatic analysis. Utilizing constructive-synthesizing modeling (CSM) tools, a code-writing history constructor was proposed. This research provides an innovative method for code and development process review in the preparation of teaching programs. This work aims to encourage the use of code review and monitoring the coding practice in educational institutions. The standard approach to evaluation, which takes into consideration only the result, no longer complies with the modern specifications for teaching programming. Practically, it is supposed to be utilized in programming training, particularly in initial stages.
Generative Image Inpainting by Hybrid Contextual Attention Network

Image inpainting is a challenging task due to the loss of the image information. Recently, GAN-based approaches have shown promising performance in the field of image inpainting. For this task, a superior similarity measurement of extracted patches from known and missing regions is important. Existing approaches usually adopt cosine distance to measure this similarity for missing region reconstruction. However, from the semantic-level perspective, these methods often generate content with inconsistent color and disorder structure due to the ignorance of the magnitude distance of the attended patches. To resolve this problem, we propose a Hybrid Contextual Attention Network (HCA-Net) with a novel attention module called hybrid contextual attention module (HCAM). HCAM takes account of both cosine distance and Euclidean distance as the measurement of the extracted patches and gives a better prediction of missing features. Besides, a Spectral-Normalization patch discriminator and the cosine loss are added into the model for patch-level and pixel-level consistency enhancement. Extensive results on three public datasets (Paris Street View, Celeba-HQ, and Places2), have both validated that our approach significantly outperforms the state-of-the-art approaches.
Comparative study of methods of restoring blurred and noisy images

Visual information faces several problems either during capture or during processing. In this paper, a comparative study of some deterministic methods of image restoration of visual data perturbed by blur and noise from a camera attached to quadcopter system. An estimate of blur and noise then considered and a comparative study between desired images obtained in order to prove the effectiveness of the proposed method, so as to reduce or remove blur noise for clarity of the image characteristics to facilitate the estimation of theft of our system.
A Human-Computer Interface For Smart Wheelchair Control Using Forearm EMG Signals

With the increase in the aging population and people with disabilities, caused by heredity, disease, or injury, the demand for assistive devices is growing. Considerable research has been devoted in recent times on developing assistive technologies based on mobile robots to create what is now known as “smart wheelchairs.” One of the main challenges is how the user interfaces with the smart wheelchair for its smooth control. These smart wheelchairs typically consist of a powered commodity wheelchair, a computer, and a network of sensors. The common interface of a joy stick is problematic because it requires certain motor skills, which may be lacking in elderly and in people with disabilities. We have been developing a new smart wheelchair that comprises of a standard power wheelchair as the testbed, a joystick interface for manual control, an on-board computer, a laser scanner, and an EMG signal reading device. Its goal is to offer the elderly and the physically handicapped, concentrating on the portion who have sufficient motor control skills in their forearm, another way to control a power wheelchair without the means of a joystick. This paper focuses on efficiently using electromyography (EMG) to measure and utilize muscle signals for control of the wheelchair. The proposed system aims to help the user navigate within their environment, indoor or outdoor, as well as provide a capable HMI. The paper focuses on the growth of a user-friendly HCI in its application of making customizable mobilization for users on any commercial powered wheelchair. The simulation results show the potential of the system's capabilities to recognize EMG-based signals for better mobility in an indoor, open office environment.
Novel technique to detect network error or modification of votes during transmission in online voting system

(2021). Novel technique to detect network error or modification of votes during transmission in online voting system. Journal of Discrete Mathematical Sciences and Cryptography: Vol. 24, No. 3, pp. 729-743.
Developing and Implementing Artificial Intelligence-Based Classifier for Requirements Engineering

Abstract In nuclear power plant (NPP) projects, requirements engineering manages the sheer volume of requirements, typically characterized by descriptive and nonharmonized requirements. Large projects may have tens of thousands to hundreds of thousands of requirements to be managed and fulfilled. Two main issues impede requirements analysis: tortuous requirements to be interpreted; and humans' very limited ability to concentrate on a specific task. It has therefore been recognized that artificial intelligence (AI) algorithms have the potential to support designers' decision making in classifying and allocating NPP requirements into predefined classes. This paper presents our work on developing an AI-based requirements classifier utilizing natural language processing (NLP) and supervised machine-learning (ML). In addition, the paper presents the integration of the classifier with the requirements management system. The focus is on the classification of nuclear power industry-specific requirements utilizing deep-learning-based NLP. Three classifiers are compared, and the corresponding results are presented. The results include predetermined requirement classes, manually gathered and classified data, a comparison of three models and their classification accuracies, microservice system architecture, and integration of the established classifier with the requirements management system. As the performance of the requirements classifier and related system has been successfully demonstrated, future AI-specific development and studies are suggested to focus on atomizing multiclass requirements, combining similar requirements into one, checking requirements syntax, and utilizing unsupervised learning for clustering. Furthermore, new and advantageous requirement classes and hierarchies are suggested for development while improving current datasets both quantitatively and qualitatively.
Minimum-Degree Distributed Graph Filter Design

Here we consider the problem of designing finite-impulse-response (FIR) graph filter (GF) in a fully distributed way. For a directed graph with N nodes, each node designs filter coefficients in a distributed manner, when the knowledge of the graph structure, recognized as global information, is unavailable to each node. By modeling graph signal shifting with observations at a node as a linear dynamical system, we establish fundamental connections between local response of shifting at anode, concerned in the graph signal processing (GSP) field, and the observability of the system, investigated in control theory. The observability, as a measure of how well internal states of a dynamical system can be learned from node's observations, is reflected by the minimal polynomial of a matrix pair related to the system. Specifically, by introducing a notion of observable graph frequencies to a node, we show that the output signals (observations) at a node only contain the spectral components of its so-called observable graph frequencies. Furthermore, we unveil that the observability of a node to the spectral components of a GS is related to the rank of its observability matrix from the perspective of control theory. Our work reveals that partial signal outputs at a node are sufficient to design the FIR GF locally in terms of node-variant (NV) GFs. These findings further enable us to characterize the minimum-degree NV GF, where a minimum number of its shifted GSs are involved in the filter's output, and devise a distributed GF design algorithm for it.
Reinforcement-Learning-Based Query Optimization in Differentially Private IoT Data Publishing

With the advancement of Internet of Things (IoT) and computing paradigms, massive data are collected and processed to enhance intelligent applications. However, by deliberately sending some queries, an attacker may be able to derive the sensitive information of IoT data owners. To prevent privacy leakage during IoT data query, differential privacy (DP) hides private information by introducing noise to the query results. As DP introduces randomized noise that will affect query accuracy (data utility), the tradeoff between privacy preservation and data utility is a challenge. In this article, we first propose a novel optimization framework for single query to minimize the privacy cost, while satisfying both personalized DP and customized data utility. We design a reinforcement learning-based algorithm for single query optimization framework (SQOF_RL) to solve the optimization problem efficiently. Then, we propose a SQOF_RL and SVT-based batch query optimization mechanism (S2BQOM) to answer more queries privately. The performance evaluation shows that SQOF_RL and S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BQOM can effectively optimize single query and batch queries in terms of privacy cost, data utility, personalized privacy, and query satisfaction. Finally, the performance analysis reveals that our work can be applied to multiple linear/nonlinear query functions instead of one particular query function.
A Probabilistic Method for Fractured Cultural Relics Automatic Reassembly

Masses of fragile cultural relics are dug out in fragments due to long-standing burying and their fragility, which must be reassembled to play a role in cultural heritage study. However, it is very challenging to automatically reassemble a large collection of fragments of unknown geometric shapes. In this article, a novel probabilistic method for fractured cultural relics automatic reassembly is proposed to solve the problem in terms of good accuracy, efficiency, and robustness. First, a set of matching units are detected and described by the 2D Link-Chain Descriptors (LCD) and the 3D Spatial-Distribution Descriptors (SDD). Second, the pairwise reassembly probability is calculated by combining the similarities of LCD and SDD descriptors, then the collision detection is conducted to eliminate the incorrect overlapping pairs. Finally, a global optimal reassembly solution is obtained by iterative graph optimization with the constrains of the loop-closures and overlap restrictions. Comprehensive experiments with eight challenging datasets demonstrate that the proposed method achieved good performance in terms of minor reassembly errors, efficiency and robustness to noise, varying point density and completeness.
Flappy Bird Automation using TensorFlow

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