Reviewerly/exHarmony huggingface dataset:
Holography and its applications for industry 4.0: An overview

Industry 4.0 is a new stage in the organisation and control of the industrial value chain, interchangeably with the fourth industrial revolution. It has a broad vision with well-defined frameworks and reference designs, focusing on bridging physical infrastructure and digital technology in so-called cyber-physical systems. Apart from the other essential technologies, Holography is considered a new innovative technology that can completely transform the vision of Industry 4.0. In industrial applications, holographic technology is used for quality control in manufacturing and fracture testing, such as holographic nondestructive testing. Holography has a wide range of applications in medicine, the military, weather forecasting, virtual reality, digital art, and security. The fourth industrial revolution aims to provide automated asset monitoring, decision-making for corporate operations, and real-time network connectivity. This paper explores holography and its significant benefits through various development processes, features, and applications, where the focus is on 'holography for Industry 4.0'. Hologram technology is a new industry trend and can impact multiple domains of Industry 4.0. Furthermore, the adoption of holographic technologies may improve the efficiency of existing products and services in other technology sectors such as architecture, 3D modelling, mechatronics, robotics, and healthcare and medical engineering.
Chart Question Answering: State of the Art and Future Directions

Abstract Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights. Often people analyze charts to answer questions that they have in mind. Answering such questions can be challenging as they often require a significant amount of perceptual and cognitive effort. Chart Question Answering (CQA) systems typically take a chart and a natural language question as input and automatically generate the answer to facilitate visual data analysis. Over the last few years, there has been a growing body of literature on the task of CQA. In this survey, we systematically review the current state‐of‐the‐art research focusing on the problem of chart question answering. We provide a taxonomy by identifying several important dimensions of the problem domain including possible inputs and outputs of the task and discuss the advantages and limitations of proposed solutions. We then summarize various evaluation techniques used in the surveyed papers. Finally, we outline the open challenges and future research opportunities related to chart question answering.
The Map of Competencies of a Foreign Language University Teacher in the Context of Digitalization of Education

Over the past twenty years, a large number of legislative documents, educational standards have been adopted, effective pedagogical models, methods and approaches to teaching foreign languages have been developed at various levels of education in our country and abroad. The article analyzes these documents to describe the current competencies of teachers of foreign languages of higher education. The developed map of competencies of foreign language teachers, which includes digital, professional communicative, methodological and universal competencies, is the result of theoretical research and analysis of the pedagogical experience of distance professional development programs for teachers of foreign languages, designed at the Faculty of Foreign Languages and Regional Studies of Moscow State University named after M.V. Lomonosov. This map can be used as a tool for professional development, since it will allow teachers of foreign languages: to reflect and identify some gaps in their professional development; set short-term and long-term goals; determine the best ways to boost their professional growth; to create innovative models of professional development of teachers, based on a combination of fundamental professional knowledge, professional competencies and skills and assuming the formation of innovative thinking of university employees.
RevEAL: Single-Trace Side-Channel Leakage of the SEAL Homomorphic Encryption Library

This paper demonstrates the first side-channel attack on homomorphic encryption (HE), which allows computing on encrypted data. We reveal a power-based side-channel leakage of Microsoft SEAL prior to v3.6 that implements the Brakerski/Fan-Vercauteren (BFV) protocol. Our proposed attack targets the Gaussian sampling in the SEAL's encryption phase and can extract the entire message with a single power measurement. Our attack works by (1) identifying each coefficient index being sampled, (2) extracting the sign value of the coefficients from control-flow variations, (3) recovering the coefficients with a high probability from data-flow variations, and (4) using a Blockwise Korkine-Zolotarev (BKZ) algorithm to efficiently explore and estimate the remaining search space. Using real power measurements, the results on a RISC-V FPGA implementation of the SEAL (v3.2) show that the proposed attack can reduce the plaintext encryption security level from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4.4</sup> . Therefore, as HE gears toward real-world applications, such attacks and related defenses should be considered.
Assessing the perceived usability of an intelligent contact tracing app to prevent the spread of COVID-19 using SUS and TAM: be aware Bahrain

Contact tracing apps are critical public health precaution and a key component of COVID-19 control initiatives. For a contact tracing app to be successful, it must be adopted and used by the public, and a low adaptation rate has become a key to preventing many apps from reaching their full potential. Bahrain, like many other countries, developed a contact tracing app named 'BeAware' Bahrain. This article presents a survey experiment (N= 405) Bahrain to investigate 'BeAware' Bahrain contact tracing app acceptanc through the concept of usability. The study utilizes HCI technique through the usage of the SUS and it also utilizes an IS technique through the usage of TAM to evaluate the 'BAware' Bahrain contact tracing app usability. The survey findings of citizens and residents of Bahrain demonstrate that both SUS and TAM techniques demonstrated similarity and equivalency, with the TAM's PEOU showing high similarity with SUS.
Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

Abstract The stochastic configuration network (SCN), a type of randomized learning algorithm, can solve the infeasible problem in random vector functional link (RVFL) by establishing a supervisory mechanism. The advantages of fast learning, convergence and not easily falling into local optima make SCN popular. However, the prediction effect of SCN is affected by the parameter settings and the nonstationarity of input data. In this paper, a hybrid model based on variational mode decomposition (VMD), improved whale optimization algorithm (IWOA), and SCN is proposed. The SCN will predict relatively stable data after decomposition by VMD, and parameters of SCN are optimized by IWOA. The IWOA diversifies the initial population by employing logistic chaotic map based on bit reversal and improves the search ability by using Lévy flight. The exploration and exploitation of IWOA are superior to those of other optimization algorithms in multiple benchmark functions and CEC2020. Moreover, the proposed model is applied to the prediction of the nonstationary wind speeds in four seasons. We evaluate the performance of the proposed model using four evaluation indicators. The results show that the R 2 of the proposed model under four seasons are more than 0.999, and the root mean square error, mean absolute error, and symmetric mean absolute percentage error are less than 0.3, 0.17, and 13%, respectively, which are almost 1/10, 1/10, and 1/4 those of SCN, respectively.
Accident responsibility identification model for Internet of Vehicles based on lightweight blockchain

Abstract The rapid development of autonomous vehicle technology has brought a new experience to people's daily travel. However, if a traffic accident involving autonomous vehicles occurs, it will face difficulties in vehicle accident forensic‐preservation, leakage of vehicle owner's privacy, and identifying legal liabilities. This article proposes an accident responsibility identification model for the Internet of Vehicles based on lightweight blockchain to solve the above problems. This model uses Car Forensics Master to collect evidence from the accident vehicle, and at the same time collects evidence from maintenance service providers, automobile manufacturers, transportation management departments, insurance companies, and other vehicle accident related parties and stores them in the preservation chain. We also use VPKI to protect the autonomous vehicle identity privacy. In order to improve the efficiency of the model and set up authorized access to related entities, the identification of accident liability is jointly completed by the preservation chain and the accident identification chain. In addition, we prove that the protocol proposed in the model has ideal security properties. Finally, we implement the smart contracts in the model through the Solidity language, and evaluate its performance.
Cyberattacks and Financial Stability: Evidence from a Natural Experiment

This paper studies the effects of a unique multi-day cyberattack on a technology service provider (TSP). Using several confidential daily datasets, we identify and quantify first- and second-round effects of the event. For banks using relevant services of the TSP, the attack impaired their ability to send payments over Fedwire, even though the Federal Reserve extended the time they had to submit payments. This impairment (first-round effect) caused other banks to receive fewer payments (second-round effect), leaving them at risk of having too few reserves to send their own payments (a potential third-round effect). These innocent-bystander banks responded differently depending on their size and reserve holdings. Those with sufficient reserves drew down their reserves. Of the others, smaller banks borrowed from the discount window, while larger banks borrowed in the federal funds market. These significant adjustments to operations and funding prevented the second-round effect from spilling over into third-round effect and broader financial instability. These findings highlight the important role for bank contingency planning, liquidity buffers, and the Federal Reserve in supporting the financial system’s recovery from a cyberattack.
Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Abstract Students of Software Engineering Capstone Courses face situations and challenges that grant them valuable experiences. However, as this knowledge is acquired through real‐life exposure, it is difficult to transmit it across different generations. To deal with this problem, it has been proposed that students record their learnings through a lesson learned model. But the question of how future students can best benefit from these records remains unanswered. This study addresses this problem through the use of Artificial Intelligence (AI) Virtual Assistant combined with a recommender system. Artificial Intelligence Virtual Assistants, as conversational chatbots, are a manifestation of AI via the simulation of conversation with human users. The technology has the potential to provide personalized service to a range of stakeholders. Thus, we seek to move this trend forward by now pushing these capabilities into the field of higher education. Consequently, we aim to assist software engineering students by leveraging collective knowledge to enhance learning experiences.
Effective Software Effort Estimation using Heterogenous Stacked Ensemble

Software Effort Estimation (SEE) estimates the effort required to complete the project successfully. The accuracy of estimation is directly proportional to the project success. This paper proposes a heterogenous stacked ensemble for effective effort estimation with Artificial Neural Network (ANN) and Support Vector Regressor (SVR) as base learners. Further, an empirical comparison is made among the proposed model and base learners to figure out the accuracy of proposed model. Five datasets from PROMISE repository are used and accuracy measures for comparison are MAR (Mean Absolute Error) and MMRE (Mean Magnitude of Relative Error). It is found that proposed model improves the performance by reducing the MAR by 50.4% and MMRE by 54.6% of base models respectively. It can be concluded from the experimental results that the proposed heterogenous stacked ensemble is best performer among the candidate models for SEE statistically.
An ensemble contrastive classification framework for imbalanced learning with sample-neighbors pair construction

While existing imbalanced classification methods have made great progress, there are still some challenges in the current imbalanced learning field: (1) How to achieve the balance between classes without introducing noise and losing information; (2) How to mine the differences between classes from datasets with a relatively small number of positive samples; (3) How to learn the distribution differences in overlapping areas efficiently. To address these problems, this paper proposes an ensemble contrastive classification framework with sample-neighbors pair construction. The traditional pointwise imbalanced classification is redefined as a pairwise label matching task between the sample to be classified (Target Sample, TS) and a group of neighbor samples (Contrastive Sample Group, CSG). For any TS, we can obtain multiple CSGs containing same/different class with the TS, and combine the TS with different CSGs into sample-neighbors pairs as positive/negative samples in the new task. In this way, the balance of classes and multiplied increase of sample scale are achieved without introducing any noise. Based on the obtained rich data, a robust classifier can be trained to mine the distribution differences in overlapping areas through the contrastive learning between TS and its CSGs. For a given test sample, we can obtain abundant sample-neighbors pairs and their corresponding classification results. Its label can be obtained by result integration and reverse reasoning. Extensive experimental evaluations on 48 KEEL and UCI public datasets show that the proposed method outperforms the existing state-of-the-art imbalanced classification methods in terms of F1-measure and G-mean.
An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems

Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.
The Application of 5G and Artificial Intelligence Technology in the Innovation and Reform of College English Education

Artificial intelligence and Fifth-Generation (5G) communication networks are examples of new information technologies that have aided and facilitated educational reform and progress. Built on the convincing requirements of college English education, with the advantage of artificial intelligence technology, we can enhance the teaching mode effectiveness, cultivate students' critical thinking ability, implement personalized wisdom and interactive learning, and thus realize artificial intelligence, and the effective use of 5G in English education is becoming a research hotspot area to be addressed. It is necessary to create a teaching aid system that uses a high-speed network and new type of technology. The specific techniques and measures for introducing 5G technology and artificial intelligence into higher education and English learning are discussed in this study. Students in higher education can benefit from the proposed technique by obtaining a better understanding of the subject and promoting their critical thinking skills. Finally, an experiment is performed to see how operative this technique is at teaching English when compared to traditional methods. The results are analyzed to determine that using this method is effective and boosts students' interest in learning as well as their hands-on abilities.
Blind image quality assessment of magnetic resonance images with statistics of local intensity extrema

Magnetic resonance (MR) imaging provides a large amount of data that requires a visual inspection before a diagnosis can be made. Since the exclusion of low-quality image sequences is performed manually and image processing methods are evaluated using techniques developed for natural images, automatic and reliable MR image quality assessment (IQA) approaches are desirable. Therefore, in this work, a new no-reference (NR) MR-IQA technique is proposed. The method uses introduced quality-aware features addressing characteristics of MR images. Specifically, in the method, an MR image is scaled, filtered with two gradient operators, and subjected to identification of the local intensity extrema. Then, the entropy and κ curvature are calculated to characterize extrema sequences and used as perceptual features to train a quality model with the Support Vector Regression (SVR) technique. In this paper, an extensive comparative evaluation of the method against recent NR approaches, including deep learning-based models, is conducted on two representative MR-IQA benchmarks. The results reveal the superiority of the introduced approach over competing methods as it obtained better overall Spearman and Pearson correlation coefficients by 5% and 3%, respectively.
Recruiting money mules on instagram: a qualitative examination of the online involvement mechanisms of cybercrime

There are reports that criminal networks advertise on social media to recruit new members. In this paper, we will focus on money mules and examine how they become involved in cybercrime on Instagram. Money mules are used to hide the financial trail of a crime and are thus indispensable for cybercriminal networks. Yet, there is a dearth of research on this group. Based on keywords derived from the literature, 43 Dutch Instagram accounts were identified that are most likely used to recruit money mules. We analyzed the content of said accounts in order to map the recruitment techniques used by criminals online. Insights from previous literature on offline recruitment were used to provide an initial framework for the analysis. Thereafter, results were considered in the light of situational crime prevention. In this article, we will argue that while Instagram indeed offers possibilities for criminals to recruit money mules, different situational strategies can be applied to prevent this. This study underlines the relevance of social media in criminological research and encourages future studies to explore this innovative method further.
Deep RF Device Fingerprinting by Semi-Supervised Learning with Meta Pseudo Time-Frequency Labels

With the ever-increasing growth of wireless communication technologies and the proliferation of the Internet of Things (IoT), intelligent authentication systems to distinguish legitimate devices are of vital importance. These years, deep learning based authentication algorithms have achieved considerable precision by leveraging radio frequency (RF) fingerprints. However, these methods depending on massive labeled data are difficult to apply on large-scale devices identification. In this paper, we propose a novel method using semi-supervised deep learning employing RF fingerprinting with meta pseudo time-frequency labels to improve identification performance in small-scale labeled datasets. We demonstrate how the scale of datasets and the proportion of labeled data influence the accuracy of identification by analyzing a dataset of 40 GB real Long-Term-Evolution (LTE) mobile phone&#x2019;s raw signals. Experimental results show that compared with the non-convergence of traditional supervised learning with 100 labeled data, our method can achieve the authentication accuracy of 99.86% with the same labeled data. Moreover, when using the same scale of training datasets and labeled half, our approach could obtain authentication accuracy higher than traditional supervised learning. And even 1% labeled data of 900 training data, this method can still obtain the accuracy of 91.25%.
ScalaGraph: A Scalable Accelerator for Massively Parallel Graph Processing

Graph processing is promising to extract valuable insights in graphs. Nowadays, emerging 3D-stacked memories and silicon technologies can provide over terabytes per second memory bandwidth and thousands of processing elements (PEs) to meet the high hardware demand of graph applications. However, this leap in hardware capability does not result in a huge increase but even a degradation sometimes in performance for graph processing. In this paper, we discover that the centralized on-chip memory hierarchy adopted in existing graph accelerators is the villain causing poor scalability due to its quadratic increase of hardware overheads with respect to the number of PEs.We present a novel distributed on-chip memory hierarchy by leveraging the network-on-chip (NoC) to enable massively parallel graph processing. We architect ScalaGraph, a brand new graph processing accelerator, to exploit this insight. ScalaGraph adopts a software-hardware co-design to minimize NoC communication overheads via an efficient row-oriented dataflow mapping and runtime aggregation. A specialized scheduling mechanism is also proposed to improve load imbalance. Our results on a Xilinx Alveo U280 FPGA card show that ScalaGraph on a modest configuration of 512 PEs achieves 2.2&#x00D7; and 3.2&#x00D7; speedups over a state-of-theart graph accelerator GraphDyns and a GPU-based graph system Gunrock, respectively. Moreover, ScalaGraph enables supporting at least 1,024 PEs with nearly linear performance scaling while GraphDyns fails to work.