Reviewerly/exHarmony huggingface dataset:
Adverse Effects of Data Breach on Public Companies: A Study Based on Interpersonal Gossip Theory

ABSTRACTABSTRACTThe ability to effectively curb the adverse effects of data breach has become an urgent issue for enterprises. Employing interpersonal gossip theory, this study argues that the mechanism relating to information transparency and control provided by companies helps mitigate the adverse effects of data breach on these companies. Using listed companies in China with data breach in 2011–2020 as the treatment group and firms without data breach as the control group. We test the two-period PSM-DID model and find that transparency and control can effectively suppress the adverse effects of data breach on companies’ stock price and revenue. Specifically, the mechanism test found that: transparency and control inhibit the adverse effects of data breach by reducing negative rhetoric. The heterogeneity test found that firms with low institutional investor ownership significantly reduce the adverse effects of data breach by using transparency and control, while the dampening effect only occurs in state-owned enterprises with a high level of transparency. This study contributes to understanding the importance of establishing data security systems in firms and provides insights into the governance of data breach in listed firms.KEYWORDS: Data breachtransparencycontrolinterpersonal gossip theoryJEL: G14G32G34G35 Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis study is supported by the National Social Science Fund of China [22&ZD120].
Improvising grading of cervical cancer using quality assessment method in smart colposcopy images

Smart Colposcopy is a low-cost and highly effective screening device used to detect cervical cancer. However, the captured images can be significantly affected by specular reflection, resulting in reduced accuracy during the grading process. In this paper, proposed a novel approach to enhance the accuracy of cervical cancer grading by addressing the issue of specular reflection. Here the method employs binary masking to identify the glare region and fine-tuned U-Net model to perform segmentation. Partial convolutional inpainting is used to replace the segmented region with neighboring pixels, effectively removing the glare from the images. The resulting enhanced images are then fed into classification models, including DenseNet121, Vgg19, and Efficient Net, which are trained to accurately grade cervical cancer. Experimental results demonstrate that our proposed method significantly improves the accuracy of cervical cancer grading, achieving accuracy rates of 97.32%, 96.25%, and 96.75%, respectively.
CNN-Transformer for Microseismic Signal Classification

The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar. In order to identify the true microseismic signals with a microseismic monitoring system quickly and accurately, this paper proposes a lightweight network model that combines a convolutional neural network (CNN) and transformer, named CCViT. Of these, the CNN is used to extract shallow features locally, and the transformer is used to extract deep features globally. Moreover, a modified channel attention module provides important channel information for the model and suppresses useless information. The experimental results on the dataset used in this paper show that the proposed CCViT model has significant advantages for floating point operations (FLOPs), parameter quantity, and accuracy compared to many advanced network models.
Application of radial basis functions in strain analysis of digital image correlation

Abstract Digital image correlation systems provide the motion of material points on the surface of a specimen or a component. Commonly, local interpolation concepts are chosen to determine the in‐plane strains or principal stretches. However, this approach yields discontinuous fields. In this paper, a global interpolation concept, particular, radial basis functions is considered to determine the displacement and in‐plane strain field and to overcome the underlying problem of discontinuities. Since there might be boundary effects, i.e. there are larger errors in the approximation of the strains in comparison to analytical verification examples, multiquadric are applied as particular radial basis function. It turns out that the error becomes smaller at the edges of the domain of interest, although the condition number of the resulting systems of equations in the applied interpolation scheme, might be critical. However, the studies at additively manufactured parts lead to the conclusion that the approach is promising for real‐world applications.
A Parallel Corpus-Based Approach to the Crime Event Extraction for Low-Resource Languages

The information model and software of automatic search and analysis system of multilingual illegal
Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large.This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pretraining and improved TextGCN.On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features.On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied.Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of "Tangshan Barbecue Restaurant beating people" is obtained.In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government's public opinion warning and response ability to public opinion in sudden social security events.
An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services

Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with the risk of location privacy disclosure, which can infringe upon personal privacy and security. In this paper, a location privacy protection method based on differential privacy is proposed, which efficiently protects users' locations, without degrading the performance of LBS. First, a location-clustering (L-clustering) algorithm is proposed to divide the continuous locations into different clusters based on the distance and density relationships among multiple groups. Then, a differential privacy-based location privacy protection algorithm (DPLPA) is proposed to protect users' location privacy, where Laplace noise is added to the resident points and centroids within the cluster. The experimental results show that the DPLPA achieves a high level of data utility, with minimal time consumption, while effectively protecting the privacy of location information.
Linear optics and photodetection achieve near-optimal unambiguous coherent state discrimination

Coherent states of the quantum electromagnetic field, the quantum description of ideal laser light, are prime candidates as information carriers for optical communications. A large body of literature exists on their quantum-limited estimation and discrimination. However, very little is known about the practical realizations of receivers for unambiguous state discrimination (USD) of coherent states. Here we fill this gap and outline a theory of USD with receivers that are allowed to employ: passive multimode linear optics, phase-space displacements, auxiliary vacuum modes, and on-off photon detection. Our results indicate that, in some regimes, these currently-available optical components are typically sufficient to achieve near-optimal unambiguous discrimination of multiple, multimode coherent states.
Research on Super-resolution Image Based on Deep Learning

Abstract Image super-resolution is a kind of important image processing technology in computer vision and image processing. It refers to the process of recovering high-resolution image from low-resolution image. It has a wide range of real-world applications, such as medical imaging, security and others. In addition to improving image perception quality, it also helps improve other computer vision tasks. Compared with traditional methods, deep learning methods show better reconstruction results in the field of image super-resolution reconstruction, and have gradually developed into the mainstream technology. This article will study the depth in the super resolution direction is important method of types of introduction, combed the main image super-resolution reconstruction method, expounds the depth study of several important super-resolution network model, the advantages and disadvantages of different algorithms and adaptive application scenarios are analyzed and compared, this paper expounds the different ways in the super resolution to liquidate, Finally, the potential problems of current image super-resolution reconstruction techniques are discussed, and the future development direction is prospected.
Multi-key optical encryption based on two-channel incoherent scattering imaging

Optical encryption has been extensively researched in the field of information security due to its characteristics of being parallel and multi-dimensionsal. However, most of the proposed multiple-image encryption systems suffer from a cross-talk problem. Here, we propose a multi-key optical encryption method based on a two-channel incoherent scattering imaging. In the encryption process, plaintexts are coded by the random phase mask (RPM) in each channel and then coupled by an incoherent superposition to form the output ciphertexts. In the decryption process, the plaintexts, keys, and ciphertexts, are treated as a system of two linear equations with two unknowns. By utilizing the principles of linear equations, the issue of cross-talk can be mathematically resolved. The proposed method enhances the security of the cryptosystem through the quantity and order of the keys. Specifically, the key space is significantly expanded by removing the requirement of uncorrected keys. This approach provides a superior method that can be easily implemented in various application scenarios.
Energy-Efficient Virtual Machine Migration Algorithm in Data Centers

Abstract The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations.
Methods of Protection against Threat: Incorrect Ballot Filling by Voter in the Remote Electronic Voting System

A generalized scheme of remote electronic voice based on homomorphic encryption is considered. Two methods of protecting the voting system from the threat from the voter, consisting in incorrect filling of the ballot by the voter, are investigated. Both methods are based on the algorithms of “zero-knowledge proof”. Evaluations of the complexity of calculations in the formation of proof of the correctness of filling in the ballot by the voter and Evaluations of the complexity of verification of the proof by the controlling party are obtained. A comparative analysis of the complexity of the implementation of both methods has shown that the method based on the proof based on the equality of logarithms has less complexity of calculations on the voter's side compared to the method based on the mixing of votes. At the same time, the second method (the method of mixing votes) requires 1.67 times less calculations in the blockchain, which becomes a significant factor in choosing the second method in favor of a large number of voters.
Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network

Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive.
Gold–Sulfide Mineralization in the Manitanyrd Region, Polar Urals, Russia

This article describes the characteristics of gold–sulfide–quartz and gold–sulfide (gold–arsenic) ore occurrences in the Manitanyrd region of the Polar Urals. Ore occurrences are confined to NE-trending shear zones and have the common features of a geological structure. The host rocks are metamorphosed volcanic and volcanic–sedimentary rocks. We analyzed the mineral and chemical composition of the ore mineralization in all studied ore occurrences, showing that they belong to the same mineral type—pyrite–arsenopyrite, with a variable ratio of the main minerals. Arsenic pyrite is present in all ore occurrences. Two stages of ore formation were distinguished: early gold–pyrite–arsenopyrite with finely dispersed gold and late gold–galena–chalcopyrite–sphalerite with coarse gold, fahlore, and sulfosalts Pb, Cu, Bi, Sb. Native gold of the first generation, finely dispersed in arsenopyrite and pyrite, had an average to high fineness (800‰–1000‰) with a relatively low dispersion. Native gold of the second generation was larger, and its fineness in ore occurrences varied; in one of them, it varied from 300‰ to 950‰, while in others, it varied from 800‰ to 950‰. The isotope composition of sulfur in sulfides (δ34S) ranged between −0.2‰ and −8.0‰. δ34S values of sulfides in the range of −0.2‰ to −3.5‰ were similar to meteorite, indicating the participation of a single deep magmatic source of sulfur in the ore formation. According to the study of fluid inclusions, the formation of ore quartz veins occurs in the temperature range of 467–109 °C. The similarity of the geological–structural, mineralogical–geochemical, and isotope–geochemical features of the gold–sulfide–quartz and gold–sulfide occurrences in the area suggest their formation in a single hydrothermal system.
Haze removal using deep convolutional neural network for Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) multispectral remote sensing imagery

This paper presents a convolutional neural network to automatically remove the haze distribution using a single multispectral remote sensing image in the raw file format. To train the proposed dehazing network, we synthesized multispectral hazy images using the haze thickness map (HTM) and relative scattering model representing the wavelength-dependent scattering property of the haze distribution. Since the raw multispectral hazy images have a low dynamic range, we cannot accurately estimate the haze distribution directly from them. To differently impose a proper amount of attention to hazy and haze-free regions, we used the HTM from the contrast-enhanced version of the input hazy image. The proposed dehazing network consists of four sub-networks: (i) shallow feature extraction network (SFEN), (ii) cascaded residual dense block network (CRDBN), (iii) multiscale feature extraction network (MFEN), and (iv) refinement network (RN). The densely connected convolutional layers and local residual learning allow the residual dense block (RDB) to extract the abundant local features, and the cascaded architecture further improves the propagation of the local information and gradients. The MFEN is used to extract multiscale local features representing the hierarchical information for the haze distribution and haze-free region. Experimental results demonstrated that the proposed method can achieve improved dehazing performance on Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) multispectral remote sensing imagery without undesired artifacts. In the sense of quantitative assessment, the proposed method produced improved peak signal-to-noise ratio (PSNR) by 10%, structural similarity index measure (SSIM) by 1%, and spectral angle mapper (SAM) by 19% compared with the existing best method.
An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today’s network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model’s validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall.
Designing exceptional-point-based graphs yielding topologically guaranteed quantum search

Quantum walks underlie an important class of quantum computing algorithms, and represent promising approaches in various simulations and practical applications. Here we design stroboscopically monitored quantum walks and their subsequent graphs that can naturally boost target searches. We show how to construct walks with the property that all the eigenvalues of the non-Hermitian survival operator, describing the mixed effects of unitary dynamics and the back-action of measurement, coalesce to zero, corresponding to an exceptional point whose degree is the size of the system. Generally, the resulting search is guaranteed to succeed in a bounded time for any initial condition, which is faster than classical random walks or quantum walks on typical graphs. We then show how this efficient quantum search is related to a quantized topological winding number and further discuss the connection of the problem to an effective massless Dirac particle.
A robust image encryption scheme based on compressed sensing and novel 7D oscillato with complex dynamics.

The present work studies a novel efficient compressed based cryptosystem which combines a dynamical parameter coming from high dimensional system, a dynamics S-box and a 2D compress sensing (2D-CS). Firstly, a secret key depending on input image is gotten via the SHA-256 function. That input image, decomposed into four sub-images uses chaotic sequences of the novel 7D multistable system to perform 2D compress sensing. Afterward, the previous compressed image is substituted by the key-dependent Mordell elliptic curve based dynamic S-box. At the end, the diffusion of the substituted image is proceed by chaotic sequences coming from the novel 7D multistable system. The compressed based cryptosystem presented here is considerably dependent on the original image. Moreover, the 7D chaotic system exhibits phenomenon of transient chaos. Equally, we have found plan equilibria hidden attractors which is a good chaos based property in cryptography. Let's recall that up to date, phenomenon of transient chaos and plan equilibria hidden attractors are rarely reported in Josephson junction systems. This denothing the novelty of this article. Through well-known metrics, the results found are evaluated and validated as well as their robustness over brute force attacks. The obtained results are termed as good in accordances with the existing ones over the literature.
Dataset Bias Prediction for Few-Shot Image Classification

Dataset bias is a significant obstacle that negatively affects image classification performance, especially in few-shot learning, where datasets have limited samples per class. However, few studies have focused on this issue. To address this, we propose a bias prediction network that recovers biases such as color from the extracted features of image data, resulting in performance improvement in few-shot image classification. If the network can easily recover the bias, the extracted features may contain the bias. Therefore, the whole framework is trained to extract features that are difficult for the bias prediction network to recover. We evaluate our method by integrating it with several existing few-shot learning models across multiple benchmark datasets. The results show that the proposed network can improve the performance in different scenarios. The proposed approach effectively reduces the negative effect of the dataset bias, resulting in the performance improvements in few-shot image classification. The proposed bias prediction model is easily compatible with other few-shot learning models, and applicable to various real-world applications where biased samples are prevalent, such as VR/AR systems and computer vision applications.