Reviewerly/exHarmony huggingface dataset:
Key competencies for critical infrastructure cyber-security: a systematic literature review

Purpose The purpose of this paper can be encapsulated in the following points: identify the research papers published on the topic: competencies and skills necessary for critical infrastructure (CI) cyber-security (CS) protection; determine main focus areas within the identified literature and evaluate the dependency or lack thereof between them: make recommendations for future research. Design/methodology/approach This study is based on a systematic literature review conducted to identify scientific papers discussing and evaluating competencies, skills and essential attributes needed by the CI workforce for CS and preparedness to attacks and incidents. Findings After a comparative analysis of the articles reviewed in this study, a variety of skills and competencies was found to be necessary for CS assurance in CIs. These skills have been grouped into four categories, namely, technical, managerial, implementation and soft skills. Nonetheless, there is still a lack of agreement on which skills are the most critical and further research should be conducted on the relation between specific soft skills and CS assurance. Research limitations/implications Investigation of which skills are required by industry for specific CS roles, by conducting interviews and sending questionnaire\surveys, would allow consolidating whether literature and industry requirements are equivalent. Practical implications Findings from this literature review suggest that more effort should be taken to conciliate current CS curricula in academia with the skills and competencies required for CS roles in the industry. Originality/value This study provides a previously lacking current mapping and review of literature discussing skills and competencies evidenced as critical for CS assurance for CI. The findings of this research are useful for the development of comprehensive solutions for CS awareness and training.
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition

As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems that can run directly on-device; end-to-end (E2E) speech recognition models such as recurrent neural network transducers and their variants have recently emerged as prime candidates for this task. Apart from being accurate and compact, such systems need to decode speech with low user-perceived latency (UPL), producing words as soon as they are spoken. This work examines the impact of various techniques - model architectures, training criteria, decoding hyperparameters, and endpointer parameters - on UPL. Our analyses suggest that measures of model size (parameters, input chunk sizes), or measures of computation (e.g., FLOPS, RTF) that reflect the model's ability to process input frames are not always strongly correlated with observed UPL. Thus, conventional algorithmic latency measurements might be inadequate in accurately capturing latency observed when models are deployed on embedded devices. Instead, we find that factors affecting token emission latency, and endpointing behavior have a larger impact on UPL. We achieve the best trade-off between latency and word error rate when performing ASR jointly with endpointing, while utilizing the recently proposed alignment regularization mechanism.
ICDAR 2021 Competition on Document Visual Question Answering

In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented.
Image encryption algorithm by fractional based chaotic system and framelet transform

One of the most important tools used to create a secure environment for transmitting information is the use of chaotic systems. In the present work, chaotic systems have been improved by using differential equations based on fractional calculations. The structure of the improved system is studied by using various tests. And as an application of the new system, a cryptographic algorithm based on proposed system and framelet transmission is introduced and various types of security tests are examined on it.
Cross-Device Profiled Side-Channel Attack with Unsupervised Domain Adaptation

Deep learning (DL)-based techniques have recently proven to be very successful when applied to profiled side-channel attacks (SCA). In a real-world profiled SCA scenario, attackers gain knowledge about the target device by getting access to a similar device prior to the attack. However, most state-of-the-art literature performs only proof-of-concept attacks, where the traces intended for profiling and attacking are acquired consecutively on the same fully-controlled device. This paper reminds that even a small discrepancy between the profiling and attack traces (regarded as domain discrepancy) can cause a successful single-device attack to completely fail. To address the issue of domain discrepancy, we propose a Cross-Device Profiled Attack (CDPA), which introduces an additional fine-tuning phase after establishing a pretrained model. The fine-tuning phase is designed to adjust the pre-trained network, such that it can learn a hidden representation that is not only discriminative but also domain-invariant. In order to obtain domain-invariance, we adopt a maximum mean discrepancy (MMD) loss as a constraint term of the classic cross-entropy loss function. We show that the MMD loss can be easily calculated and embedded in a standard convolutional neural network. We evaluate our strategy on both publicly available datasets and multiple devices (eight Atmel XMEGA 8-bit microcontrollers and three SAKURA-G evaluation boards). The results demonstrate that CDPA can improve the performance of the classic DL-based SCA by orders of magnitude, which significantly eliminates the impact of domain discrepancy caused by different devices.
Factor Analysis of Financial and Economic Activities of Energy Enterprises of Ukraine

In modern conditions of the energy market crisis, the issues of increasing the efficiency of enterprises development in the energy sector of Ukraine have acquired particular relevance. The main task of energy enterprises is reliable and high-quality electricity supply to consumers at economically feasible tariffs. Energy has a number of its specific characteristics that fundamentally distinguish it from other types of economic activity: technological unity and coincidence in time of the processes of generation, transmission, distribution and consumption of energy; parallel operation of all stations for a combined daily load schedule; rapid development of accidents, which requires automatic control of modes. Energy is a specific commodity that distinguishes it from other types of commodities, namely: the impossibility of efficiently storing energy in large volumes; the impersonality of energy as a commodity, since all electricity is supplied to the general network; impossibility of energy rejection; energy demand is constant. For factor analysis, the principal component method was applied, which made it possible to reduce the dimension of the factor space and carry out its structuring and interpretation in the form of control factors. This approach makes it possible to carry out mathematical modeling of the dynamics of the main indicators of the activities of the enterprises under consideration in order to manage them.
Indeterminacy in Neutrosophic Theories and their Applications

Indeterminacy makes the main distinction between fuzzy/intuitionistic fuzzy (and other extensions of fuzzy) set/logic vs. neutrosophic set/logic, and between classical probability and neutrosophic probability. Also, between classical statistics vs. neutrosophic and plithogenic statistics, between classical algebraic structures vs. neutrosophic algebraic structures, between crisp numbers vs. neutrosophic numbers. We present a broad definition of indeterminacy, various types of indeterminacies, and many practical applications.
INTERVALES: INTERactive Virtual and Augmented framework for industriaL Environment and Scenarios

One of Industry 4.0’s greatest challenges for companies is the digitization of their processes and the integration of new related technologies such as virtual reality (VR) and augmented reality (AR), which can be used for training purposes, design, or assistance during industrial operations. Moreover, recent results and industrial proofs of concept show that these technologies demonstrate critical advantages in the industry. Nevertheless, the authoring and editing process of virtual and augmented content remains time-consuming, especially in complex industrial scenarios. While the use of interactive virtual environments through virtual and augmented reality presents new possibilities for many domains, a wider adoption of VR/AR is possible only if the authoring process is simplified, allowing for more rapid development and configuration without the need for advanced IT skills. To meet this goal, this study presents a new framework: INTERVALES. First, framework architecture is proposed, along with its different modules; this study then shows that the framework can be updated by not only IT workers, but also other job experts. The UML data model is presented to format and simplify the authoring processes for both VR and AR. This model takes into account virtual and augmented environments, the possible interactions, and ease operations orchestration. Finally, this paper presents the implementation of an industrial use case composed of collaborative robotic (cobotic) and manual assembly workstations in VR and AR based on INTERVALES data.
Constructions of Optimal Uniform Wide-Gap Frequency-Hopping Sequences

In frequency hopping (FH) communication systems, frequency hopping sequences (FHSs) are crucial in determining the system’s anti-jamming performance. If FHSs can ensure a wide-gap between two adjacent frequency points to avoid the frequency points with high interference probability, it will significantly improve the FH communication system’s anti-interference ability. Moreover, if each frequency point appears at the same number of times in a sequence period, the system’s anti-electromagnetic interference will be enhanced. Therefore, it is desirable to employ FHSs with low Hamming autocorrelation, wide frequency-hopping gap, and good uniformity in practical applications. However, to the best of our knowledge, no such infinite classes of FHSs have been reported in the literature to date. This paper aims to present two constructions of uniform wide-gap frequency-hopping sequences (WGFHSs) by concatenating two or three adequately designed sequences. For the first time, we obtain two infinite classes of WGFHSs, which are optimal with respect to the well-known Lempel-Greenberger bound.
AFDet: Toward More Accurate and Faster Object Detection in Remote Sensing Images

Object detection in remote sensing imagery usually suffers from inaccurate target localization and bounding box regression uncertainty, mainly due to the varying sizes of objects and the complexity of the background. Most detectors address these challenges by adding various feature extraction modules, which increases the size and computational burden of the network. In this paper, we propose a more accurate and faster detector named AFDet, which is composed of two parts: a backbone pretrained on ImageNet and a head that includes a center prediction branch (CPB), semantic supervision branch (SSB), and boundary estimation branch (BEB). CPB produces a keypoint heatmap using an elliptical Gaussian kernel to adapt to the ground truth with a large aspect ratio. SSB, which is used only during training, extracts extra keypoint features from boundary and interior points rather than only from the center point, thereby improving the quality of object localization. BEB predicts the distributions of the bounding box in four directions, which is further supervised by the focus loss, and the gather loss raises the box prediction accuracy. To verify the effectiveness and robustness of AFDet, we conduct extensive experiments on three widely used optical remote sensing object detection datasets, i.e., NWPU VHR-10, DIOR, and HRRSD, for which AFDet achieves state-of-the-art results.
Path Planning Algorithm Using the Hybridization of the Rapidly-Exploring Random Tree and Ant Colony Systems

This paper proposes a path planning algorithm using the hybridization of the rapidly-exploring random tree (RRT) and ant colony system (ACS) algorithms. The RRT algorithm can quickly generate paths. However, the resulting path is suboptimal. Meanwhile, the ACS algorithm can generate the optimal path from the suboptimal previous path information. Then, the proposed algorithm will combine the advantages of RRT with the ACS algorithm. Therefore, it can reach the optimal value with a good convergence speed. We call this proposed algorithm the RRT-ACS algorithm. This study developed a new method for hybridizing the RRT and ACS algorithms for path planning problems. This hybridization process is carried out using one of the ACS principles: the pseudorandom proportional rule. The performance of the proposed algorithm with the RRT*, informed RRT*, RRT*-connect, and informed RRT*-connect algorithms is tested with several benchmark cases. The test results from benchmark case tests with known optimal values indicate that the proposed algorithm has succeeded in achieving those optimal values. Furthermore, statistical tests have also been carried out to verify whether there is a significant difference in performance between the RRT-ACS algorithm and the existing algorithms. The test and statistical analysis results show that the RRT-ACS algorithm has good performance and convergence speed. We also discuss the stability, robustness, convergence, and rapidity of the RRT-ACS algorithm. The results indicates that the RRT-ACS algorithm may be used in applications that require fast and optimal path planning algorithms such as robots and autonomous vehicles.
Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F1&nbsp;-&nbsp;Score. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.
Critical barriers to augmented reality technology adoption in developing countries: a case study of Nigeria

Purpose The paper aims to identify the critical barriers to augmented reality technology (ART) in the Nigerian construction industry to allow wider adoption. The objectives are to reveal those hindrances to the wider usage of ART so that the obstacles can be tackled and overcome. Design/methodology/approach A survey of construction professionals involved in the built environment was sent well-structured questionnaires in the study area. The professionals involved are quantity surveyors, architects, engineers, project managers and builders. Convenience sampling techniques were used in selecting those professionals. Descriptive and inferential statistics were used in analyzing the retrieved data. Findings The most ranked obstacles in adopting augmented realities are lack of technological awareness, difficulty in ease of ART system set up, the unwillingness of government and private bodies to invest in augmented reality research, lack of repository database, lack of portability for ART equipment system. The findings also revealed the difference in the opinions of professionals among the variables. The results showed that there is a significant difference in the opinions of respondents regarding 6 of the identified 15 variables. Originality/value The study gives deep insight into possible hindrances in the use of emerging technologies such as ART in construction. This study also enumerated some ways how these challenges can be tackled and solved in the Nigerian construction industry for better adoption and performance.
Applicability of a digitalization model based on augmented reality for building construction education in architecture

Purpose The purpose of this paper is to introduce a digitalization model (DM) for building construction courses in architectural education as a response to the recent emerging technologies in the era of digital transformations. This DM is developed and applied through augmented reality (AR) technologies to boost perception, understandings and ability to solve building construction details. Design/methodology/approach Based on a thorough review of recent technologies like AR, virtual reality (VR), building information modelling (BIM) and their applications in architectural education, the methodology involves the generation of a model, its application and evaluation. The model is based on the integration of BIM and AR which is applied into a third year “Building Construction Project” course. Each student has designed a residential building and accordingly prepared the construction drawings by adapting the DM. An online survey –based on Technology Acceptance Model – is conducted to evaluate the DM by quantitative data analysis from SPSS and Excel. Findings The key findings of the study include the following items: determination of the proper digital tools, the definition of the steps and workflow based on the building project phases to develop construction drawings and define precise details effectively. By the help of this, the DM is generated and applied. According to the survey and results, the DM which involves BIM-based AR is considered as beneficial, highly motivating and providing better perception on construction details. Originality/value Implication of AR/VR technologies is frequently seen in design studios, whereas building construction courses state its traditional approach. However, there is a huge potential in the digitalization of building construction education by increasing the perception of students together with the increased level of communication. The study aims to close the gap of digitalization by proposing a DM, which brings a systematic approach considering each phase of building construction project as conceptual, schematic, design development and construction documents by using BIM integrated AR. Moreover, the novel model specifically brings a new approach by generation of QR codes for construction details to embed videos or simulations into the two-dimensional drawing sheets. Furthermore, the DM proposes a new approach to satisfy emerging needs and requirements of the Architecture Engineering Construction industry.
Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.
Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors' keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.