Reviewerly/exHarmony huggingface dataset:
Stable secure key distribution scheme via orthogonal polarizations and a joint source-channel model

Optical secure key distribution (SKD) based on reciprocity has been the subject of increasing discussion, for its inherent information-theoretic safety and because there is less occupation of fiber channels. The combination of reciprocal polarization and broadband entropy sources has proven effective in increasing the rate of SKD. However, the stabilization of such systems suffers from the limited span of polarization states and inconsistent polarization detection. The specific causes are analyzed in principle. To solve this issue, we propose a strategy for extracting secure keys from orthogonal polarizations. Optical carriers with orthogonal polarizations at interactive parties are modulated by external random signals using polarization division multiplexing dual-parallel Mach-Zehnder modulators. After bidirectional transmission through a 10-km fiber channel, error-free SKD with a rate of 2.07 Gbit/s is experimentally realized. The high correlation coefficient of the extracted analog vectors can be maintained for over 30 min. The proposed method is a step toward the development of secure communication with high speed and feasibility.
Framework for Vehicle Make and Model Recognition—A New Large-Scale Dataset and an Efficient Two-Branch–Two-Stage Deep Learning Architecture

In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a two-branch architecture performing make and model recognition respectively. A two-stage training procedure and a novel decision module are proposed to process the make and model predictions and compute the final model prediction. In addition, a novel metric based on the true positive rate is proposed to compare classification confusion of the proposed 2B–2S and the baseline methods. A complex experimental validation is carried out, demonstrating the generality, diversity, and practicality of the proposed DVMM dataset. The experimental results show that the proposed framework provides 93.95% accuracy over the more diverse DVMM dataset and 95.85% accuracy over traditional VMMR datasets. The proposed two-branch approach outperforms the conventional one-branch approach for VMMR over small-, medium-, and large-scale datasets by providing lower vehicle model confusion and reduced inter-make ambiguity. The paper demonstrates the advantages of the proposed two-branch VMMR paradigm in terms of robustness and lower confusion relative to single-branch designs.
A face mask detection system: An approach to fight with COVID‐19 scenario

Abstract A new coronavirus has caused a pandemic crisis around the globe. According to the WHO, this is an infectious illness that spreads from person to person. Therefore, the only way to avoid this infection is to take precautions. Wearing a mask is the most critical COVID‐19 protection method because it prevents the virus from spreading from an infected person to a healthy one. This study reflects a deep learning method to create a system for detecting Face Masks. The paper proposes a unique FMDRT (Face Mask Dataset in Real‐Time) dataset to determine whether a person is wearing a mask or not. The RFMD and Face Mask datasets are also taken from the internet to evaluate the performance of the proposed method. The CLAHE preprocessing method is employed to enhance the image quality, then resizing and Image augmentation techniques are used to convert it into a standard format and increase the size of the dataset, respectively. The pretrained Caffe face detector model is used to detect the faces, and then the lightweight transfer learning‐based Xception model is applied for the feature extraction process. This paper recommended a novel model that is, CL‐SSDXcept to distinguish the Face Mask or no mask images. However, accession with the MobileNetV2, VGG16, VGG19, and InceptionV3 models with different hyperparameter settings has been tested on the FMDRT dataset. We have also compared the results of the synthesized dataset FMDRT to the existing Face Mask datasets. The experimental results attained 98% test accuracy on the suggested dataset ‘FMDRT’ using the CL‐SSDXcept method. The empirical findings have been reported at 50 iterations with tuned hyperparameter values with an average accuracy 98% and a loss of 0.05.
Impact of Artisanal Crude Oil Refinery on Physicochemical and Microbiological Properties of Soil and Water in Igia-Ama, Tombia Kingdom, Rivers State, Nigeria

This study aimed to determine the impact of artisanal refinery operations on the physicochemical and microbiological properties of soil and water in Igia-Ama, Tombia Kingdom, Rivers State, Nigeria. Physicochemical parameters along with concentrations of heavy metals, polyaromatic hydrocarbons (PAHs), total petroleum hydrocarbon (TPH), benzene, toluene, ethylbenzene and xylene (BTEX), and polychlorinated biphenyls (PCB) were determined in soil and water samples using standard methods. Microbial populations were determined using standard plate count. Microbial isolates were identified based on their cultural, morphological and biochemical characteristics. Results show that concentrations of monitored physicochemical parameters differed significantly (p&lt;0.05) between impacted sites and control as well as between dry and wet season. Heavy metals, PAHs, TPH, BTEX and PCB concentrations were higher in the impacted sites than in the control (significant, p&lt;0.05), and also differed significantly between dry and wet season. Microbial counts varied between polluted samples and control as well as between dry and wet season, though not significantly different (p&gt;0.05). Bacterial isolates in polluted soil samples were identified as Enterobacter sp., Bacillus sp., Micrococcus sp., Staphylococcus sp. and Pseudomonas sp. Bacteria in polluted water samples were identified as Staphylococcus sp. and Escherichia coli. Fungi in polluted soil samples were identified as Aspergillus sp., Penicillium sp. Fusarium sp., Rhodotorula sp., Exophiala sp. and Crypotococcus sp. While only Penicillium sp. was isolated in polluted water sample. Artisanal crude oil refinery operations significantly impacted the physicochemical properties of soil and water in the study area but not the microbiological properties. The levels of heavy metals, PAHs, TPH and BTEX in the soil and water suggest the need for remediation of the impacted environment.
Digital Money and Central Banks Balance Sheet

Digital money is a logical step in a process of continuous technological advancement in payment systems.In response, central banks are reviewing their conduct of monetary operations in light of the new shape of financial markets and systems.The impact of digital money will depend on the type of money substitution by digital money.The paper straddles several cases where substitution of CiC (currency in circulation), and bank deposits may take place via digital money such as CBDC or other e-money, and how it would impact the central bank balance sheet.Remuneration of CBDC, if aligned to a new objective, could potentially amplify the effect on the interest rate channel of monetary policy.
Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions but are slow due to their computational complexity, and they require detailed environmental descriptions. While empirical models are less accurate, Machine Learning (ML) models provide fast predictions with accuracies comparable to that of deterministic models. Most Empirical models are versatile as they are valid for various values of frequencies, antenna heights, and sometimes environments, whereas most ML models are not. Therefore, developing a versatile ML model that will surpass empirical model accuracy entails collecting data from various scenarios with different environments and network parameters and using the data to develop the model. Combining datasets of different sizes could lead to lopsidedness in accuracy such that the model accuracy for a particular scenario is low due to data imbalance. This is because model accuracy varies at certain regions of the dataset and such variations are more intense when the dataset is generated from a fusion of datasets of different sizes. A Dynamic Regressor/Ensemble selection technique is proposed to address this problem. In the proposed method, a regressor/ensemble is selected to predict a sample point based on the sample’s proximity to a cluster assigned to the regressor/ensemble. K Means Clustering was used to form the clusters and the regressors considered are K Nearest Neighbor (KNN), Extreme Learning Trees (ET), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The ensembles are any combinations of two, three or four of the regressors. The sample points belonging to each cluster were selected from a validation set based on the regressor that made prediction with lowest absolute error per individual sample point. Implementation of the proposed technique resulted in accuracy improvements in a scenario described by a few sample points in the training data. Improvements in accuracy were also observed on datasets in other works compared to the accuracy reported in the works. The study also shows that using features extracted from satellite images to describe the environment was more appropriate than using a categorical clutter height value.
Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.
Beyond functionality: Building critical digital teaching competence among future primary education teachers

Digital literacy has moved away from its traditional instrumental conception, to be nourished by critical perspectives that have been increasingly adopted in all areas of the analysis of technology and education. The importance of generating educational models that contribute to the emancipation of people in a post-digital and highly complex world is an increasingly evident challenge. However, it is still difficult to find concrete examples of pedagogical strategies specifically devised to foster digital literacy in line with this much needed emerging critical lens. This paper presents a case study of a set of learning experiences integrated into a compulsory module for students in the 1st year of a degree in education sciences, which leads to the qualification required to teach at primary schools in Spain. The results highlight the importance of providing students with learning opportunities conceived to help them become future teachers ready to have a transversal impact on education for the emancipation of people in the post-digital world, rather than simply training them as operators who use technology to enhance skills.
Optimized separable convolution: Yet another efficient convolution operator

The convolution operation is the most critical component in recent surge of deep learning research. Conventional 2D convolution needs O(C2K2) parameters to represent, where C is the channel size and K is the kernel size. The amount of parameters has become really costly considering that these parameters increased tremendously recently to meet the needs of demanding applications. Among various implementations of the convolution, separable convolution has been proven to be more efficient in reducing the model size. For example, depth separable convolution reduces the complexity to O(C⋅(C+K2)) while spatial separable convolution reduces the complexity to O(C2K). However, these are considered ad hoc designs which cannot ensure that they can in general achieve optimal separation. In this research, we propose a novel and principled operator called optimized separable convolution by optimal design for the internal number of groups and kernel sizes for general separable convolutions can achieve the complexity of O(C32K). When the restriction in the number of separated convolutions can be lifted, an even lower complexity at O(C⋅log(CK2)) can be achieved. Experimental results demonstrate that the proposed optimized separable convolution is able to achieve an improved performance in terms of accuracy-#Params trade-offs over both conventional, depth-wise, and depth/spatial separable convolutions.
Cyber Trust Index: A Framework for Rating and Improving Cybersecurity Performance

Cybersecurity risk is among the top risks that every organization must consider and manage, especially during this time wherein technology has become an integral part of our lives; however, there is no efficient and simplified measurement method that organizations or regulators could use, as frequently as they need, to evaluate and compare the outcome of cybersecurity efforts that have been put in place. Consequently, this has resulted in an absence of critical data for cybersecurity improvement. This research proposes a Cyber Trust Index (CTI), a novel and simplified framework for evaluating, benchmarking, and improving organizations’ cybersecurity performance. Methods: The researchers analyzed prominent scientific research papers and widely used security standards to develop baseline security controls that serve as a measurement foundation. Then, they identified Control Enablers and Capability Tiers that were used as base measures and measurement methods. The CTI framework was evaluated by experts and tested with 35 organizations from the critical information infrastructure (CII) sector, as well as other generic sectors, in Thailand to confirm its validity and reliability in real organization settings and identify the priorities and factors that can contribute to better cybersecurity performance. Results: The CTI has two key elements: the baseline controls and rating methods. The baseline controls comprise 12 dimensions, 25 clusters, and 70 controls. The rating methods utilize five control enablers and five capability tiers to compute scores. A binary questionnaire is used to capture data for the rating process. Based on a statistical analysis of CTI results from 35 pilot organizations, 28.57% are in the beginner group with high-risk exposure, 31.43% are in the leader group with low-risk exposure, and 40% of organizations are in between (the intermediate and advanced groups). Two key factors distinguish between the beginner and leader groups: (1) an internal factor, which is the Control Enablers; and (2) an external factor, which is the influence of a cyber regulating body. Our study confirms that Control Enablers in higher Tiers will help organizations achieve better cybersecurity performance (R = 0.98021) and highlights the significance of cyber regulating bodies by showing a shear difference of 197.53% in cyber performance between highly regulated and low-regulated industries. Conclusions: This research reveals key insights into the importance of Control Enablers, which are the internal factors that organizations must leverage to drive better cybersecurity performance, and the positive return on enforcement, which emphasizes the need for cyber regulating bodies. The CTI framework has proven to be valid and efficient for measuring cybersecurity performance. At the very least, a step-wise roadmap is provided for organizations and regulators to adopt and adapt the CTI framework for their cybersecurity measurement and improvement mission.
An Extensive Study of Residential Proxies in China

We carry out the first in-depth characterization of residential proxies (RESIPs) in China, for which little is studied in previous works. Our study is made possible through a semantic-based classifier to automatically capture RESIP services. In addition to the classifier, new techniques have also been identified to capture RESIPs without interacting with and relaying traffic through RESIP services, which can significantly lower the cost and thus allow continuous monitoring of RESIPs. Our RESIP service classifier has achieved good performance with a recall of 99.7% and a precision of 97.6% in 10-fold cross validation. Applying the classifier has identified 399 RESIP services, a much larger set compared to 38 RESIP services collected in all previous works. Our effort of RESIP capturing leads to a collection of 9,077,278 RESIP IPs (51.36% are located in China), 96.70% of which are not covered in publicly available RESIP datasets. An extensive measurement on RESIPs and their services has uncovered a set of interesting findings as well as several security implications. Especially, 80.05% RESIP IPs located in China have sourced at least one malicious traffic flows during 2021, resulting in 52-million malicious traffic flows in total. And RESIPs have also been observed in corporation networks of 559 sensitive organizations including government agencies, education institutions and enterprises. Also, 3,232,698 China RESIP IPs have opened at least one TCP/UDP port for accepting relaying requests, which incurs non-negligible security risks to the local network of RESIPs. Besides, 91% China RESIP IPs are of a lifetime fewer than 10 days while most China RESIP services show up a crest-trough pattern in terms of the daily active RESIPs across time.
Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics

The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney is a fundamental step in the early and noninvasive detection of acute renal allograft rejection. In this paper, a new and accurate DCE-MRI kidney segmentation method is proposed. In this method, fuzzy c-means (FCM) clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution. Moreover, population-based shape (PB-shape) and subject-specific shape (SS-shape) statistics are both exploited. The PB-shape model is trained offline from ground-truth kidney segmentations of various subjects, whereas the SS-shape model is trained on the fly using the segmentation results that are obtained for a specific subject. The proposed method was evaluated on the real medical datasets of 45 subjects and reports a Dice similarity coefficient (DSC) of 0.953 ± 0.018, an intersection-over-union (IoU) of 0.91 ± 0.033, and 1.10 ± 1.4 in the 95-percentile of Hausdorff distance (HD95). Extensive experiments confirm the superiority of the proposed method over several state-of-the-art level set methods, with an average improvement of 0.7 in terms of HD95. It also offers an HD95 improvement of 9.5 and 3.8 over two deep neural networks based on the U-Net architecture. The accuracy improvements have been experimentally found to be more prominent on low-contrast and noisy images.
Technology Enabling the New Normal: How Students Respond to Classes

This cross-sectional study investigates the online education intention of undergraduate students in the largest and oldest public university in Bangladesh during the COVID-19 pandemic. Under convenient sampling, 843 undergraduate students with rural and urban backgrounds participated in an online self-administered questionnaire. Partial least squares structural equation modelling (PLS-SEM) was employed to examine the hypothesized relationships. We found that students’ online class intention is significantly influenced by their attitude towards online classes (AOC), perceived usefulness (PU), and facilitating conditions (FC). We further identified that external antecedents have significant indirect effects on the outcome variables. Our findings provide new insights and contribute to a learners’ community on online classes during the COVID-19 pandemic. This study extends the technology acceptance model (TAM) and the theory of planned behavior (TPB) to depict the factors influencing undergraduate students’ intention to attend online classes (IOC) during the COVID-19 pandemic.
Impact of Personalized Avatars and Motion Synchrony on Embodiment and Users’ Subjective Experience: Empirical Study

Background Embodiment through a virtual avatar is a key element for people to feel that they are in the virtual world. Objective This study aimed to elucidate the interaction between 2 methods of eliciting embodiment through a virtual avatar: motion synchronization and appearance similarity between a human and avatar, to understand embodiment (agency, body ownership, and self-location) and subjective experience (presence, simulator sickness, and emotion) in virtual reality. Methods Using a full-body motion capture system, 24 participants experienced their virtual avatars with a 3D-scanned face and size-matched body from a first-person perspective. This study used a 2 (motion; sync and async) × 2 (appearance; personalized and generic) within-subject design. Results The results indicated that agency and body ownership increased when motion and appearance were matched, whereas self-location, presence, and emotion were affected by motion only. Interestingly, if the avatar’s appearance was similar to the participants (personalized avatar), they formed an agency toward the avatar’s motion that was not performed by themselves. Conclusions Our findings would be applicable in the field of behavioral therapy, rehabilitation, and entertainment applications, by eliciting higher agency with a personalized avatar.