Reviewerly/exHarmony huggingface dataset:
Toward Secure and Resilient Networks: A Zero-Trust Security Framework with Quantum Fingerprinting for Devices Accessing Network

The importance of network security has increased with the emergence of networked systems in contemporary computing, making it an essential aspect of protecting digital assets and safeguarding against cyber threats. The current security mechanisms, which rely on cryptographic keys, may be susceptible to a number of attacks, such as media access control (MAC) spoofing, which might provide unauthorized users access to network resources. This study introduces a new approach, namely a zero-trust security framework with quantum fingerprinting for devices accessing a network, that utilizes quantum technology to protect networks from security threats and intruders. The proposed architecture relies on quantum fingerprinting to authenticate devices trying to access the network, and it is built on the zero-trust security concept. The framework is intended to offer a thorough, multi-layered approach to network security that may change in response to evolving security risks and specifications. By protecting against MAC spoofing and other types of device impersonation, the adoption of quantum fingerprinting adds another degree of protection. The proposed framework may be used to construct a reliable and scalable network security solution in different network environments.
HUMAN FACTORS SECURITY ENGINEERING: THE FUTURE OF CYBERSECURITY TEAMS

Human factors engineering has been a well-known practice since the early 1930s. This field of engineering is highly applicable and currently studied in relation to cybersecurity. Understanding the psychological components of human factors can help to resolve several concerns in the cybersecurity field. From employee recruitment and retention, to understanding burnout for SOC analysts, human factors are an emerging trend in cybersecurity. By researching and understanding the principles of human factors, cybersecurity professionals can improve security by design, reduce cognitive overload with security tooling, and improve awareness of behavioral analysis techniques. Human factors security has been increasingly gaining popularity in academic publications and will continue to trend upward as more researchers and technical professionals see the benefits of combining the two fields.
On causal structural learning algorithms: Oracles’ simulations and considerations

This work evaluates the performance of several causal structure learning algorithms, in terms of their effectiveness and efficiency in detecting true causal relations among variables. Constraint-based, score-based and hybrid algorithms are jointly compared and ranked according to the two criteria above and their performance is evaluated when used in either directed or undirected acyclic graphs. Fixing the number of variables considered, a Monte Carlo simulation is run for constructing linear causal effects among variables, both in small and large data samples with different causal network properties. Latent confounding variables are empirically demonstrated to be the main drawback of an algorithms' performance, independently of the size of the sample.
Topology- and Perception-Aware Image Vectorization

We propose a new color image vectorization method converting raster images to resolution-independent scalable vector graphics. Starting from a quantized raster image, the method builds a hierarchical structure to represent its discontinuity set. The lowest level elements, called curve-elements, separate exactly two colors and end at T-junctions or X-junctions. The middle-level objects, called curvebases, are concatenations of curve-elements following perceptual rules and representing the apparent contours of objects. On the highest level, the jump set coincides with the discontinuity set of the quantized image input. A geometric filtering method removes pixelization effects by affine shortening of the curvebases while resolving the induced topological changes. All junctions are preserved, thus maintaining the highest level of perceptual fidelity even on tiny pixel art images. A single parameter controls the simplification of curves between two junctions. Theoretical bounds are given to guarantee the method’s topological consistency. This allows the method to be iterated such that it yields a smoothing semigroup. In both qualitative and quantitative experiments, our method compares favorably to multiple state-of-the-art algorithms and software.
FFA-YOLOv7: Improved YOLOv7 Based on Feature Fusion and Attention Mechanism for Wearing Violation Detection in Substation Construction Safety

Ensuring compliance with safety regulations regarding wearing is essential for the safety and security of those working on substation construction sites. However, relying on supervisors to monitor workers in real time on the work site or through remote surveillance videos is both unreasonable and inefficient. A deep learning network approach named FFA-YOLOv7 is presented in this study that utilizes an improved version of YOLOv7 to detect violations of worker wearing in real time during power construction site surveillance. In YOLOv7, the feature pyramid network (FPN) of the neck stage is constructed through continuous upsampling and skip connections for feature fusion, after continuous downsampling of the backbone. However, this process can result in the loss of precise shallow position information. To tackle this issue, we have introduced a novel feature fusion pathway to the FPN architecture, enabling each layer not only to fuse feature maps from the same level during the downsampling course but also to fuse feature maps from shallower levels. This approach combines precise positional information from shallow layers with rich semantic information from deep layers. Additionally, we utilized attention after feature fusion in each layer to optimize the feature map fusion effect and achieve better detection accuracy performance. In order to conduct comparative experiments, we trained six variations of the YOLO model as detectors using a dataset gathered from realistic construction sites. The experimental results indicate that our proposed FFA-YOLOv7 attained a detection precision of 95.92% and a recall rate of 97.13%, demonstrating a high level of accuracy and a low rate of missed detections. These outcomes effectively satisfy the requirements for robust and accurate detection of real-world power construction violations.
Crowd Counting by Multi-Scale Dilated Convolution Networks

The number of people in a crowd is crucial information in public safety, intelligent monitoring, traffic management, architectural design, and other fields. At present, the counting accuracy in public spaces remains compromised by some unavoidable situations, such as the uneven distribution of a crowd and the difference in head scale caused by people’s differing distances from the camera. To solve these problems, we propose a deep learning crowd counting model, multi-scale dilated convolution networks (MSDCNet), based on crowd density map estimation. MSDCNet consists of three parts. The front-end network uses the truncated VGG16 to obtain preliminary features of the input image, with a proposed spatial pyramid pooling (SPP) module replacing the max-pooling layer to extract features with scale invariance. The core network is our proposed multi-scale feature extraction network (MFENet) for extracting features in three different scales. The back-end network consists of consecutive dilation convolution layers instead of traditional alternate convolution and pooling to expand the receptive field, extract high-level semantic information and avoid the spatial feature loss of small-scale heads. The experimental results on three public datasets show that the proposed model solved the above problems satisfactorily and obtained better counting accuracy than representative models in terms of mean absolute error (MAE) and mean square error (MSE).
Patient Identification Based on Deep Metric Learning for Preventing Human Errors in Follow-up X-Ray Examinations

Abstract Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture archiving and communication systems. However, such methods have not been incorporated into clinical use, and their performance can degrade with variability in the clinical images. Deep learning can be used to improve the performance of these methods. A novel method is proposed to automatically identify individuals among examined patients using posteroanterior (PA) and anteroposterior (AP) chest X-ray images. The proposed method uses deep metric learning based on a deep convolutional neural network (DCNN) to overcome the extreme classification requirements for patient validation and identification. It was trained on the NIH chest X-ray dataset (ChestX-ray8) in three steps: preprocessing, DCNN feature extraction with an EfficientNetV2-S backbone, and classification with deep metric learning. The proposed method was evaluated using two public datasets and two clinical chest X-ray image datasets containing data from patients undergoing screening and hospital care. A 1280-dimensional feature extractor pretrained for 300 epochs performed the best with an area under the receiver operating characteristic curve of 0.9894, an equal error rate of 0.0269, and a top-1 accuracy of 0.839 on the PadChest dataset containing both PA and AP view positions. The findings of this study provide considerable insights into the development of automated patient identification to reduce the possibility of medical malpractice due to human errors.
Exploring efficient and effective generative adversarial network for thermal infrared image colorization

Abstract Thermal infrared image colorization is very difficult, and colorized images suffer from poor texture detail recovery and low color matching. To solve the above problems, this paper proposes an Efficient and Effective Generative Adversarial Network (E2GAN). This paper proposes multi-level dense module, feature fusion module, and color-aware attention module in the improved generator. Adding multi-level dense module can enhance the feature extraction capability and the improve detail recovery capability Using the feature fusion module in the middle of the encoder–decoder reduces the information loss caused by encoder down-sampling and improves the prediction of fine color of the image. Using the color-aware attention module during up-sampling allows for capturing more semantic details, focusing on more key objects, and generating high-quality colorized images. And the proposed discriminator is the PatchGAN with color-aware attention module, which enhances its ability to discriminate between true and false colorized images. Meanwhile, this paper proposes a novel composite loss function that can improve the quality of colorized images, generate fine local details, and recover semantic and texture information. Extensive experiments demonstrate that the proposed E2GAN has significantly improved SSIM, PSNR, LPIPS, and NIQE on the KAIST dataset and the FLIR dataset compared to existing methods.
Feature Selection and Model Evaluation for Threat Detection in Smart Grids

The rising interest in the security of network infrastructure, including edge devices, the Internet of Things, and smart grids, has led to the development of numerous machine learning-based approaches that promise improvement to existing threat detection solutions. Among the popular methods to ensuring cybersecurity is the use of data science techniques and big data to analyse online threats and current trends. One important factor is that these techniques can identify trends, attacks, and events that are invisible or not easily detectable even to a network administrator. The goal of this paper is to suggest the optimal method for feature selection and to find the most suitable method to compare results between different studies in the context of imbalance datasets and threat detection in ICT. Furthermore, as part of this paper, the authors present the state of the data science discipline in the context of the ICT industry, in particular, its applications and the most frequently employed methods of data analysis. Based on these observations, the most common errors and shortcomings in adopting best practices in data analysis have been identified. The improper usage of imbalanced datasets is one of the most frequently occurring issues. This characteristic of data is an indispensable aspect in the case of the detection of infrequent events. The authors suggest several solutions that should be taken into account while conducting further studies related to the analysis of threats and trends in smart grids.
Neural network‐based optical flow versus traditional optical flow techniques with thermal aerial imaging in real‐world settings

Abstract The study explores the feasibility of optical flow‐based neural network from real‐world thermal aerial imagery. While traditional optical flow techniques have shown adequate performance, sparse techniques do not work well during cold‐soaked low‐contrast conditions, and dense algorithms are more accurate in low‐contrast conditions but suffer from the aperture problem in some scenes. On the other hand, optical flow from convolutional neural networks has demonstrated good performance with strong generalization from several synthetic public data set benchmarks. Ground truth was generated from real‐world thermal data estimated with traditional dense optical flow techniques. The state‐of‐the‐art Recurrent All‐Pairs Field Transform for the Optical Flow model was trained with both color synthetic data and the captured real‐world thermal data across various thermal contrast conditions. The results showed strong performance of the deep‐learning network against established sparse and dense optical flow techniques in various environments and weather conditions, at the cost of higher computational demand.
Which Stereotypes Are Moderated and Under-Moderated in Search Engine Autocompletion?

Warning: This paper contains content that may be offensive or upsetting.Language technologies that perpetuate stereotypes actively cement social hierarchies.This study enquires into the moderation of stereotypes in autocompletion results by Google, DuckDuckGo and Yahoo!We investigate the moderation of derogatory stereotypes for social groups, examining the content and sentiment of the autocompletions.We thereby demonstrate which categories are highly moderated (i.e., sexual orientation, religious affiliation, political groups and communities or peoples) and which less so (age and gender), both overall and per engine.We found that under-moderated categories contain results with negative sentiment and derogatory stereotypes.We also identify distinctive moderation strategies per engine, with Google and DuckDuckGo moderating greatly and Yahoo! being more permissive.The research has implications for both moderation of stereotypes in commercial autocompletion tools, as well as large language models in NLP, particularly the question of the content deserving of moderation.
An Improved User Requirements Notation (URN) Models’ Construction Approach

Semi-formal software techniques have been very successful in industry, government institutions and other areas such as academia. Arguably, they owe a large part of their success to their graphical notation, which is more human-oriented than their counterpart text-based and formal notation techniques. However, ensuring the consistency between two or more models is one of the known challenges of these techniques. This work looks closely at the specific case of the User Requirements Notation (URN) technique. Although the abstract model of URN provides for link elements to ensure the consistency between its two main components, namely, Goal-Oriented Requirement Language (GRL) and Use Case Maps (UCM), the effective implementation of such links is yet to be fully addressed. This paper performs a detailed analysis of the existing URN models construction process and proposes an improved process with some guidelines to ensure, by construction, the correctness and consistency of the GRL and UCM models. A case study is used throughout the paper to illustrate the suggested solution.
Underwater Image Enhancement Method Based on Improved Generative Adversarial Network

Aiming at the problems of noise, blurred edges and motion blur in the images obtained by direct underwater shooting, an underwater image enhancement method based on an improved generative adversarial network is proposed. The method used in the paper uses the structural reparameterization VGG (RepVGG) network structure in the original generator network model, which improves the edge details of the generated image and improves the quality of the generated image; modify the activation function in the generator network model, and use SeLU as the activation function to extract more features in the image and generate richer details; the gradient L1 distance between the generated image and the clear image is introduced as the regular term constraint of the generator loss, which enriches the edge information and structural information, and further improves the image quality improvement effect. Experimental results show that the quality of the enhanced image is greatly improved compared with the quality of the original degraded image, and the purpose of image enhancement is achieved.
Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods

Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field: related datasets and the problem of role overlapping between candidate entities, which has been overlooked in existing DEE (document-level event extraction) studies that predominantly employed sequence annotation for candidate entity extraction. To tackle these challenges, we constructed a Chinese document-level emergency extraction dataset (CDEEE) and provides annotations for argument scattering, multiple events, and role overlapping. Additionally, a model named RODEE is proposed to address the role overlapping problem in DEE tasks. RODEE employs two independent modules to represent the head and tail positions of candidate entities, and utilizes a multiplication attention mechanism to interact between the two, generating a scoring matrix. Subsequently, role-overlapping candidate entities are predicted to facilitate the completion of DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results show that RODEE effectively solves the problem of role overlapping among candidate entities, resulting in improved performance of the DEE model, with an F1 value of 77.7%.
LA-ICP-MS Trace Element Geochemistry of Sphalerite and Metallogenic Constraints: A Case Study from Nanmushu Zn–Pb Deposit in the Mayuan District, Shaanxi Province, China

The Nanmushu Zn–Pb deposit is a large-scale and representative deposit in the Mayuan ore field on the northern margin of the Yangtze Block. This study investigates the trace element geochemistry of sphalerite from this deposit using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The results show that the main trace elements in sphalerite include various trace elements, such as Mn, Fe, Cu, Ga, Ge, Ag, Cd, Pb, Co, Hg, Tl, In, Sn, and Sb. Among them, Ag, Ge, Cd, and Cu are valuable components that may be recovered during mineral processing or smelting techniques. The histograms, LA-ICP-MS time-resolved depth profiles, and linear scan profiles indicated that most trace elements occur in sphalerite as isomorphs, while partial Pb, Fe, and Ag occur as tiny mineral inclusions. The correlation diagrams of trace elements revealed that Fe2+, Mn2+, Pb2+, and Tl3+ can substitute Zn2+ in sphalerite through isomorphism. In sphalerite, Cd2+ and Hg2+ together or Mn2+, Pb2+, and Tl3+ together can replace Zn2+, i.e., ((3Mn, 3Pb, 2Tl)6+, 3(Cd, Hg)2+) ↔ 3Zn2+. Moreover, there is a mechanism of Ge4+ with Cu+ or Ga3+ with Cu+ replacing Zn2+ in the Nanmushu deposit, i.e., Ge4+ + 2Cu+ ↔ 3Zn2+ or 2Ga3+ + 2Cu+ ↔ 4Zn2+. Furthermore, the trace element compositions indicate that the Nanmushu Zn mineralization occurred under low-temperature conditions (&lt;200 °C) and should be classified as a Mississippi Valley-type (MVT) deposit. This study provides new insights into the occurrence and substitution mechanisms of trace elements in sphalerite and the metallogenic constraints of the Nanmushu deposit.
Rooftop Photovoltaic Energy Production Estimations in India Using Remotely Sensed Data and Methods

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective.