Reviewerly/exHarmony huggingface dataset:
A reconfigurable and compact subpipelined architecture for AES encryption and decryption

Abstract AES has been used in many applications to provide the data confidentiality. A new 32-bit reconfigurable and compact architecture for AES encryption and decryption is presented and implemented in non-BRAM FPG in this paper. It can be reconfigured for the options of different key sizes which is very flexible for the users to apply AES for various application environments. The proposed design employs a single-round architecture and subpipeling to minimize the hardware cost. The fully composite field GF((2 4 ) 2 )-based encryption/decryption and keyschedule lead to the lower hardware complexity and efficient subpipelining for 32-bit data path. In addition, a new subpipelined on-the-fly keyschedule over composite field GF((2 4 ) 2 ) is proposed for all standard key sizes (128-, 192-, 256-bit) which generates the roundkeys simultaneously and efficiently. This feature is very useful and efficient when the main key has been changed since AES is a symmetric-key cryptography and the session key usually changes frequently. The proposed reconfigurable and compact design has higher throughput and lower hardware cost. It achieves throughputs of 375Mbits/s with 128-bit key, 318Mbits/s with 192-bit key and 275Mbits/s with 256-bit key on VIRTEX XC4VSX25-12, and the total number of slices is 1766. The proposed reconfigurable and compact AES architecture can be efficiently applied in computing-restricted environments such as wireless and embedded devices.
Blockchain Technology in Modern Power Systems: A Systematic Review

Blockchain is a distributed decentralized peer-to-peer network, which is used for sharing data across a large number of entities in a trusted and secure way. Blockchain utilizes different mechanisms, such as hash functions, consensus algorithms, etc., for data verification and validation. In modern power systems, blockchain technology is used for balancing supply and demand, contributing to the demand-side management programs, and mainly, transitioning consumers to prosumers to trade electricity and reduce operational costs. This article aims at providing an in-depth discussion on energy transition and digitalization in power systems and investigating the role of blockchain technology in modern power systems. In addition, opportunities, challenges, and limitations of blockchain technology in modern power systems are discussed.
Robust visual tracking using very deep generative model

Abstract Deep learning algorithms provide visual tracking robustness at an unprecedented level, but realizing an acceptable performance is still challenging because of the natural continuous changes in the features of foreground and background objects over videos. One of the factors that most affects the robustness of tracking algorithms is the choice of network architecture parameters, especially the depth. A robust visual tracking model using a very deep generator (RTDG) was proposed in this study. We constructed our model on an ordinary convolutional neural network (CNN), which consists of feature extraction and binary classifier networks. We integrated a generative adversarial network (GAN) into the CNN to enhance the tracking results through an adversarial learning process performed during the training phase. We used the discriminator as a classifier and the generator as a store that produces unlabeled feature-level data with different appearances by applying masks to the extracted features. In this study, we investigated the role of increasing the number of fully connected (FC) layers in adversarial generative networks and their impact on robustness. We used a very deep FC network with 22 layers as a high-performance generator for the first time. This generator is used via adversarial learning to augment the positive samples to reduce the gap between the hungry deep learning algorithm and the available training data to achieve robust visual tracking. The experiments showed that the proposed framework performed well against state-of-the-art trackers on OTB-100, VOT2019, LaSOT and UAVDT benchmark datasets.
Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design

In this work, we propose a sensitivity-based approach to construct reduced-order state estimators based on recurrent neural networks (RNN). It is assumed that a mechanistic model is available but is too computationally complex for estimator design and that only some target outputs are of interest and should be estimated. A reduced-order estimator that can estimate the target outputs is sufficient to address such a problem. We introduce an approach based on sensitivity analysis to determine how to select the appropriate inputs and outputs for data collection and data-driven model development to estimate the desired outputs accurately. Specifically, we consider the long short-term memory (LSTM) neural network, a type of RNN, as the tool to train the data-driven model. Based on it, an extended Kalman filter, a state estimator, is designed to estimate the target outputs. Simulations are carried out to illustrate the effectiveness and applicability of the proposed approach.
Requirements management in DevOps environments: a multivocal mapping study

Abstract Attention is currently being focused on DevOps, which aims to reduce software development time by means of close collaboration between the development and operations areas. However, little effort has been made to determine the role of requirements management in DevOps. The objective of this study is to help both researchers and practitioners by providing an overview of the best practices regarding requirements engineering in DevOps and identifying which areas still need to be investigated. A multivocal mapping study has, therefore, been carried out in order to study which methodologies, techniques and tools are used to support requirements management in DevOps environments. After applying the review protocol, 37 papers from formal literature and 14 references from grey literature were selected for analysis. The general conclusions obtained after analysing these papers were that, within DevOps, more attention should be paid to: (1) the reuse of requirements in order to identify systems and software artefacts that can serve as a basis for the specification of new projects; (2) the communication of requirements between the different areas of an organisation and the stakeholders of a project; (3) the traceability of requirements in order to identify the relationship with other requirements, artefacts, tasks and processes; (4) non-functional requirements in order to identify the requirements of the operations area in the early phases of a project; and finally (5) specific requirements tools that should be seamlessly integrated into the DevOps toolchain. All these issues must be considered without ignoring the agile and continuous practices of development, operations and business teams. More effort must also be made to validate new methodologies in industry so as to assess and determine their strengths and weaknesses.
CWAN: Covert Watermarking Attack Network

Digital watermarking technology is widely used in today’s copyright protection, data monitoring, and data tracking. Digital watermarking attack techniques are designed to corrupt the watermark information contained in the watermarked image (WMI) so that the watermark information cannot be extracted effectively or correctly. While traditional digital watermarking attack technology is more mature, it is capable of attacking the watermark information embedded in the WMI. However, it is also more damaging to its own visual quality, which is detrimental to the protection of the original carrier and defeats the purpose of the covert attack on WMI. To advance watermarking attack technology, we propose a new covert watermarking attack network (CWAN) based on a convolutional neural network (CNN) for removing low-frequency watermark information from WMI and minimizing the damage caused by WMI through the use of deep learning. We import the preprocessed WMI into the CWAN, obtain the residual feature images (RFI), and subtract the RFI from the WMI to attack image watermarks. At this point, the WMI’s watermark information is effectively removed, allowing for an attack on the watermark information while retaining the highest degree of image detail and other features. The experimental results indicate that the attack method is capable of effectively removing the watermark information while retaining the original image’s texture and details and that its ability to attack the watermark information is superior to that of most traditional watermarking attack methods. Compared with the neural network watermarking attack methods, it has better performance, and the attack performance metrics are improved by tens to hundreds of percent in varying degrees, indicating that it is a new covert watermarking attack method.
Measurement-based preparation of stable coherent states of a Kerr parametric oscillator

Kerr parametric oscillators (KPOs) have attracted increasing attention in terms of their application to quantum information processing and quantum simulations. The state preparation and measurement of KPOs are typical requirements when used as qubits. The methods previously proposed for state preparations of KPOs utilize modulation of external fields such as a pump and drive fields. We study the stochastic state preparation of stable coherent states of a KPO with homodyne detection, which does not require modulation of external fields, and thus can reduce experimental efforts and exclude unwanted effects of possible imperfection in control of external fields. We quantitatively show that the detection data, if averaged over an optimal averaging time to decrease the effect of measurement noise, has a strong correlation with the state of the KPO, and therefore can be used to estimate the state (stochastic state preparation). We examine the success probability of the state estimation taking into account the measurement noise and bit flips. Moreover, the proper range of the averaging time to realize a high success probability is obtained by developing a binomial-coherent-state model, which describes the stochastic dynamics of the KPO under homodyne detection.
Intelligent algorithms of construction of public transport routes

Today, in public transport planning systems, it is relevant to a search for a possible route with a minimum time. The aim of the work is the development of intelligent algorithms for constructing public transport routes, the development of programs, and the conduct of a computational experiment. Research methods are the theory of neural networks. The paper considers Hopfield neural networks and proposed recurrent neural networks. However, in Hopfield neural networks, the chances of solving this optimization problem decrease as the matrix size increases. A recurrent neural network is proposed, represented by a differential equation to solve this problem. As a result, the number of iterative computations can be reduced by n 2 times than in the Hopfield network.
The Changing Role of Marketing: Industry 5.0 - the Game Changer

The preceding revolution, Industry 4.0, surfaced by way of the influx of digital and automation technologies. Industry 5.0 followed the suit and the world is on the threshold of this new evolution. Industry 5.0 attempts to bring together the competency of smart machines and the exceptional ingenious potentials of the human workforce. Industry 5.0 is humanizing the digital and automated systems. It recognizes both automated technology and the human innovative skills on an equal platform. Marketing in its new role revolves on the axis of automation and cyber technology of Industry 5.0. This new makeover of marketing processes generates superior marketing actions, restructures marketing workflows, and assesses the results of marketing promotions. Industry 5.0 tools make available a fundamental marketing catalog for all marketing content and communications, thus assisting marketers to fashion a fragmented, customized, and favorable marketing experiences for prospective buyers. These systems and spaces offer automation attributes across numerous phases of marketing including videos, blogs, emails, social media, lead generation, direct mails, digital advertising, and more.This chapter aims to introduce the concept of Industry 5.0, where robots and machines are interweaved with the human intellect and labor as teammate instead of opponent. The objective of the chapter is to examine and explore the different facets of marketing in the face of Industry 5.0. The chapter describes the challenges and future trends and practices in marketing field in the wake of Industry 5.0 as the way forward for the companies for sustainability and resilience.
Classification of fracture characteristics and fracture mechanisms using deep learning and topography data

Abstract In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macroscopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research ( Industrielle Gemeinschaftsforschung , IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project.
Potential Applications of the Metaverse in Higher English Education

The metaverse, which is powered by blockchain, artificial intelligence, digital twins, and other cutting-edge intelligent technologies, is a three-dimensional digital space that combines the virtual and the real.It sparks a new technological trend across the globe with great potential in the education.The paper develops the basic framework of the education metaverse on the basis of the concept, technology and theory, investigates its potential application in the teaching of speaking, listening, and interpreting in higher English education, and analyzes its prospect from two dimensions of advantages and challenges.The education metaverse helps to foster the innovation of educational modes, ideas, and systems as compared to traditional courses.
Evaluation and analysis of teaching quality of university teachers using machine learning algorithms

Abstract In order to better improve the teaching quality of university teachers, an effective method should be adopted for evaluation and analysis. This work studied the machine learning algorithms and selected the support vector machine (SVM) algorithm to evaluate teaching quality. First, the principles of selecting evaluation indexes were briefly introduced, and 16 evaluation indexes were selected from different aspects. Then, the SVM algorithm was used for evaluation. A genetic algorithm (GA)-SVM algorithm was designed and experimentally analyzed. It was found that the training time and testing time of the GA-SVM algorithm were 23.21 and 7.25 ms, both of which were shorter than the SVM algorithm. In the evaluation of teaching quality, the evaluation value of the GA-SVM algorithm was closer to the actual value, indicating that the evaluation result was more accurate. The average accuracy of the GA-SVM algorithm was 11.64% higher than that of the SVM algorithm (98.36 vs 86.72%). The experimental results verify that the GA-SVM algorithm can have a good application in evaluating and analyzing teaching quality in universities with its advantages in efficiency and accuracy.
Integrating Aster Images Processing and Fieldwork for Identification of Hydrothermal Alteration Zones at the Oumjrane-Boukerzia District, Moroccan Anti-Atlas

Mapping from remote sensing has become more effective in the field of geology, mainly in lithological discrimination and identification of hydrothermal alteration zones.The use of this technique consists in obtaining information about the rock mass and the main ones existing in the inaccessible areas.Satellite data from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor represent a favorable potential for detecting the spectral signatures of mineral zones and identifying their nature.These data are more reliable in places where the climate is arid with less abundant vegetation, as at the Oumjrane-Boukerzia mining district.This region which is part of the Eastern Anti-Atlas, is composed of several mineralized veins which still require detailed studies and exploration by the technique of remote sensing.In this work we applied several processing techniques on ASTER imagery such as Colored Composition, Principal Component Analysis and Ratio Bands.The use of the reports of the specialized Bands makes it possible to identify some hydrothermal alteration minerals within the mining district of Oumjrane Boukerzia.These minerals are represented mainly by iron oxides and hydroxides (Hematite, jarosite, limonite and goethite), carbonate minerals (dolomite, calcite), clay minerals (Illite, kaolinite and chlorite) and quartz minerals.This work allows us to produce a map of hydrothermal alteration zones which can be used as a valuable reference in the strategy of mining exploration for the base metals (Cu, Pb, Zn and Ba), in the mining district of Oumjrane-Boukerzia and in the entire Eastern Anti-Atlas.
Obstacle-avoidance path planning based on the improved artificial potential field for a 5 degrees of freedom bending robot

Abstract. Path planning is a key technique used in the operation of bending robots. In this paper, an obstacle-avoidance path-planning method of a 5 degrees of freedom (5 DOF) bending robot based on improved artificial potential field is proposed. Firstly, a connecting-rod coordinate system of the 5 DOF Cartesian bending robot is established to determine an equation of motion trajectory of the bending robot. Secondly, in view of the problem of the local minimum in the artificial potential field (APF) method and the failure of path planning, an improved APF path-planning method based on a rapidly exploring random tree (RRT) algorithm is proposed, which reduces the length of the path and enhances path smoothness. Finally, through simulation and obstacle-avoidance experiments on the path of a mechanical arm, effective path planning based on the improved APF method is verified. The experimental results show that the proposed path-planning method can plan an optimal path and meet the technical requirements of bending robot operations.
DFAM-DETR: Deformable Feature Based Attention Mechanism DETR on Slender Object Detection

Object detection is one of the most important aspects of computer vision, and the use of CNNs for object detection has yielded substantial results in a variety of fields. However, due to the fixed sampling in standard convolution layers, it restricts receptive fields to fixed locations and limits CNNs in geometric transformations. This leads to poor performance of CNNs for slender object detection. In order to achieve better slender object detection accuracy and efficiency, this proposed detector DFAM-DETR not only can adjust the sampling points adaptively, but also enhance the ability to focus on slender object features and extract essential information from global to local on the image through an attention mechanism. This study uses slender objects images from MS-COCO dataset. The experimental results show that DFAM-DETR achieves excellent detection performance on slender objects compared to CNN and transformer-based detectors.
Scenario-based incident response training: lessons learnt from conducting an experiential learning virtual incident response tabletop exercise

Purpose This paper aims to discuss the experiences designing and conducting an experiential learning virtual incident response tabletop exercise (VIRTTX) to review a business's security posture as it adapts to remote working because of the Coronavirus 2019 (COVID-19). The pandemic forced businesses to move operations from offices to remote working. Given that this happened quickly for many, some firms had little time to factor in appropriate cyber-hygiene and incident prevention measures, thereby exposing themselves to vulnerabilities such as phishing and other scams. Design/methodology/approach The exercise was designed and facilitated through Microsoft Teams. The approach used included a literature review and an experiential learning method that used scenario-based, active pedagogical strategies such as case studies, simulations, role-playing and discussion-focused techniques to develop and evaluate processes and procedures used in preventing, detecting, mitigating, responding and recovering from cyber incidents. Findings The exercise highlighted the value of using scenario-based exercises in cyber security training. It elaborated that scenario-based incident response (IR) exercises are beneficial because well-crafted and well-executed exercises raise cyber security awareness among managers and IT professionals. Such activities with integrated operational and decision-making components enable businesses to evaluate IR and disaster recovery (DR) procedures, including communication flows, to improve decision-making at strategic levels and enhance the technical skills of cyber security personnel. Practical implications It maintained that the primary implication for practice is that they enhance security awareness through practical experiential, hands-on exercises such as this VIRTTX. These exercises bring together staff from across a business to evaluate existing IR/DR processes to determine if they are fit for purpose, establish existing gaps and identify strategies to prevent future threats, including during challenging circumstances such as the COVID-19 outbreak. Furthermore, the use of TTXs or TTEs for scenario-based incident response exercises was extremely useful for cyber security practice because well-crafted and well-executed exercises have been found to serve as valuable and effective tools for raising cyber security awareness among senior leadership, managers and IT professionals (Ulmanová, 2020). Originality/value This paper underlines the importance of practical, scenario-based cyber-IR training and reports on the experience of conducting a virtual IR/DR tabletop exercise within a large organisation.