Reviewerly/exHarmony huggingface dataset:
Efficient and Secure Algorithm: The Application and Improvement of ECDSA

As a currently widely used digital signature algorithm, ECDSA is more efficient than RSA. Specifically, ECDSA provides a shorter key than RSA in the same security level. This makes ECDSA signature verification more efficient than RSA. However, the application difficulty and security issues of ECDSA make the research to improve this signature algorithm very important. This article mainly investigates the improvement of ECDSA in efficiency, the safety of ECDSA, and the application of ECDSA. For example, the article introduces an approach that improves the efficiency by changing the verification algorithm, analyzing the solution of ECDSA being attacked by side-channel, and introducing the application of ECDSA in IoT From an analysis of the experimental results, it can be found that current attack methods and vulnerabilities require ECDSA to reduce efficiency to improve security, which decreases the application area of ECDSA algorithm. Furthermore, it should be noted that the ECDSA still cannot effectively defend against side channel attacks.
Cybersecurity Threat Assessment Integrating Qualitative Differential and Evolutionary Games

Most current game theory-based cybersecurity analysis methods use traditional game models, which do not meet realistic conditions of continuous dynamic changes in attack-defense behaviors and decision makers without perfect rationality, and therefore they adapt with difficulty to the practical requirements of cybersecurity threat assessment. This paper draws on infectious disease dynamics methods to describe the cybersecurity threat propagation process. It constructs a dynamic game model of a cybersecurity threat based on continuous attack-defense confrontation and boundedly rational decision makers, combining qualitative differential and evolutionary game theories. Qualitative differential games are used to analyze the confrontation process of security threats, calculate attack-defense barriers, and construct a benchmark to measure the degree of a security threat. Evolutionary games are used to analyze the dynamic change of attack-defense strategy-selection probabilities based on replicator dynamics, and to deduce the evolutionary trajectory of the network security state. We then calculate the multidimensional Euclidean distance between the evolutionary trajectory and the attack-defense barrier metric benchmark, and use it as the basis for a dynamic threat assessment algorithm to improve the timeliness and objectivity of threat assessment. Simulation experiments show that the model and algorithm are effective and feasible.
Blockchain-Based Offline Auditing for the Cloud in Vehicular Networks

The rapid growth of various vehicular apps such as automotive navigation and in-car entertainment has brought the explosion of vehicular data. Such a growth has given rise to a huge challenge of maintaining the quality of cloud storage services for the whole period of storage in vehicular networks. As a result, poor quality of services easily causes data corruption problems and thereby threats vehicular data integrity. Blockchain, a tamper-proofing technique, is considered a promising approach for mitigating data integrity risks in cloud storage. However, existing blockchain-based schemes for auditing long-term cloud data integrity suffer from poor communication performance in a vehicular network. In this study, a blockchain-based offline auditing scheme for cloud storage in the vehicular network is proposed to improve auditing performance. Inspired by the data structure of blockchain, we design an evidence chain to achieve offline auditing, which allows the cloud to spontaneously generate data integrity evidence without communicating with auditors during the evidence generation phase. Furthermore, we extend our scheme to support public and automatic validation based on the smart contract. We prove the security of the proposed scheme under the random oracle model and further provide the performance evaluation by comparing with the state-of-the-art approaches.
Impact of Box-Cox Transformation on Machine-Learning Algorithms

This paper studied the effects of applying the Box-Cox transformation for classification tasks. Different optimization strategies were evaluated, and the results were promising on four synthetic datasets and two real-world datasets. A consistent improvement in accuracy was demonstrated using a grid exploration with cross-validation. In conclusion, applying the Box-Cox transformation could drastically improve the performance by up to a 12% accuracy increase. Moreover, the Box-Cox parameter choice was dependent on the data and the used classifier.
Fast transformation of discriminators into encoders using pre-trained GANs

A typical generative adversarial network (GAN) consists of a generator and a discriminator. Currently, finely tuned deep GANs can synthesize high-quality (HQ) images via their generators. However, the discriminator in typical GANs is only able to distinguish true or fake images in the training process. Moreover, some synthesized images from GANs are imperfect, and we can not reconstruct images via GANs. In this paper, we revisit pre-trained GANs and offer a self-supervised method to quickly transform GAN’s discriminators into encoders. We reuse parameters of the GAN’s discriminator and replace its output layer, so it can be transformed into an encoder and output reformed latent vectors. The transformation makes GAN architecture more symmetrical and allows for better performance. Based on the method, GANs can be made to reconstruct synthesized images via GAN encoders. Compared to synthesized images, these reconstructions can maintain or even attain higher quality. The code and pre-trained models are available at https://github.com/disanda/GAN-Encoder-Sym.
Post-Editese in Literary Translations

In the present study, we investigated the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We used two literary texts, namely, the English children’s novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins’ popular book The Girl on the Train (TGOTT). Both literary texts were Google translated from English into Brazilian Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examined how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts.
Deep LSAC for Fine-Grained Recognition

Fine-grained recognition emphasizes the identification of subtle differences among object categories given objects that appear in different shapes and poses. These variances should be reduced for reliable recognition. We propose a fine-grained recognition system that incorporates localization, segmentation, alignment, and classification in a unified deep neural network. The input to the classification module includes functions that enable backward-propagation (BP) in constructing the solver. Our major contribution is to propose a valve linkage function (VLF) for BP chaining and form our deep localization, segmentation, alignment, and classification (LSAC) system. The VLF can adaptively compromise errors of classification and alignment when training the LSAC model. It in turn helps to update the localization and segmentation. We evaluate our framework on two widely used fine-grained object data sets. The performance confirms the effectiveness of our LSAC system.
GEOPHYSICAL EXPLORATIONS BY RESISTIVITY AND INDUCED POLARIZATION METHODS FOR THE COPPER DEPOSIT, SOUTH KHORASAN, IRAN

Link for citation: Adel Shirazy, Ardeshir Hezarkhani, Aref Shirazi, Timkin T.V., Voroshilov V.G. Geophysical explorations by resistivity and induced polarization methods for the copper deposit, South Khorasan, Iran. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2022, vol. 333, no. 3, рр. 99-110. In Rus&#x0D; The relevance. These are the first geophysics studies in the Mesgaran exploration area. Based on geological studies in the mineral zone, copper mineralization was proven. The mineralization type in this area was identified as Cyprus-type massive sulfide. The complexity of the geological structure determines the need to use geophysical research and forecasting methods for planning mining and drilling operations. The main aim of this study is the application of geophysical methods in the search for mineral deposits and modeling of the geological environment. Object: Mesgaran exploration area, South Khorasan province, Iran. Methods. In order to obtain more subsurface information, geophysical methods IP and Rs were used. Five profiles were designed and implemented for geoelectric surveys. Overall, five profiles (P1 to P5) were read as dipole-dipole arrays with 784 points. The profiles were taken east-west and north-south along parallel to the mineralization zones. The dipole-dipole array is designed with the parameters AB=MN=20 m, jump=20 m and up to 6 jumps for MN. Results. In general, four types of anomalies were identified in these investigations. Which to some extent revealed the association of anomalies with the types of rocks and mineralization of the study area: 1) anomalies with low electrical resistivity, as well as low polarity, often correspond to quaternary alluvial deposits; 2) abnormalities with high electrical resistance, and low polarity, which are consistent with limestone’s and are often superficial; 3) anomalies with high polarity and high resistance, which in the south of the profiles form altitude and correspond to the agglomerates; 4) anomalies with high polarity and higher relative resistance are associated with igneous masses, which are often composed of andesitic-basaltic rocks. Based on the position of the identified anomalies in the 5 profiles, the optimal location of the drills was determined.
Cybersecurity and Financial Stability

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Behavior-Based Early Cervical Cancer Risk Detection Using Artificial Neural Networks

In a worldwide perspective of the most common cancer diseases, cervical cancer is ranked fourth most frequent whereas the worldwide mortality rate is at 54.56%. In the Philippines, the second leading site among women is cervical cancer next to breast cancer. Research shows that cervical cancer is one of the most treatable cancer forms if detected and managed early. Currently, the most reliable diagnosis and prevention method of cervical cancer is thru a regular testing via Pap Smear test and HPV vaccination being performed in hospitals worldwide. However, according to the Centers for Disease Control and Prevention in California, the cervical cancer screening rate of regular testing in hospitals went down significantly during the stay-at-home order by the government due to the COVID-19 pandemic. Also, there are limited research based on the behavior information in relation to cervical cancer risk prediction, but existing studies proves the possibility of the risk prediction based on behavior information. This paper presents an Artificial Neural Network-based model for early cervical cancer risk detection based on behavior information. The neural network was trained using scaled conjugate gradient back propagation. The system showed 98% overall correctness in early cervical cancer risk prediction.
Face Recognition Based Automated Attendance Management System

At the beginning and end of each session, attendance is an important aspect of the daily classroom evaluation. When using traditional methods such as calling out roll calls or taking a student's signature, managing attendance can be a time-consuming task. The teacher normally checks it, although it's possible that a teacher will miss someone or some students' answers many times. Face recognition-based attendance system is a solution to the problem of recognizing faces for the purpose of collecting attendance by utilizing face recognition technology based on high-definition monitor video and other information technology. Instead of depending on time-consuming approaches, we present a real-time Face Recognition System for tracking student attendance in class in this work. The suggested method included identifying human faces from a webcam using the Viola-Jones technique, resizing the identified face to the desired size, and then processing the resized face using a basic Local Binary Patterns Histogram algorithm. After the recognition is completed, the attendance will be immediately updated in a SQLite database with the relevant information. Many institutions will profit greatly from this endeavor. As a result, the amount of time it takes and the number of human errors it makes are minimized, making it more efficient.
Intelligent Sleep Monitoring System Based on Microservices and Event-Driven Architecture

Sleep monitoring using polysomnography (PSG) in hospitals can be considered expensive, so the preferable way is to use contactless and wearable sensors to monitor sleep daily by patients at home. In this study, the Internet-of-Things (IoT) platform was utilized for sleep monitoring with contactless or wearable sensors as an integrated system developed based on an event-driven and microservice architecture. Multiple services that respond to events are provided within the system. Electrocardiogram (ECG) data were used as the input in the sleep monitoring system. The combination of the weighted extreme learning machine (WELM) algorithm with particle swarm optimization (PSO) was used to process the ECG data, followed by fuzzy logic to measure sleep quality, then display the data on the dashboard. Based on the experimental results, the proposed architecture increased throughput by 34.76%, decreased response time by 55.85%, and reduced memory consumption by 37.26% per instance replication compared to the non-event-driven architecture. The accuracies of the sleep stage classification were 78.78% and 73.09% for the three and four classes, respectively, and the area under a receiver operating characteristic (ROC) curve (AUC) reached 0.89 for both the three and four class classifications.
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition

Knowledge distillation has been widely used to compress existing deep learning models while preserving the performance on a wide range of applications. In the specific context of Automatic Speech Recognition (ASR), distillation from ensembles of acoustic models has recently shown promising results in increasing recognition performance. In this paper, we propose an extension of multi-teacher distillation methods to joint CTC-attention end-to-end ASR systems. We also introduce three novel distillation strategies. The core intuition behind them is to integrate the error rate metric to the teacher selection rather than solely focusing on the observed losses. In this way, we directly distill and optimize the student toward the relevant metric for speech recognition. We evaluate these strategies under a selection of training procedures on different datasets (TIMIT, Librispeech, Common Voice) and various languages (English, French, Italian). In particular, state-of-the-art error rates are reported on the Common Voice French, Italian and TIMIT datasets.
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio

Speaker-attributed automatic speech recognition (SA-ASR) is a task to recognize “who spoke what” from multi-talker recordings. An SA-ASR system usually consists of multiple modules such as speech separation, speaker diarization and ASR. On the other hand, considering the joint optimization, an end-to-end (E2E) SA-ASR model has recently been proposed with promising results on simulation data. In this paper, we present our recent study on the comparison of such modular and joint approaches towards SA-ASR on real monaural recordings. We develop state-of-the-art SA-ASR systems for both modular and joint approaches by leveraging large-scale training data, including 75 thousand hours of ASR training data and the VoxCeleb corpus for speaker representation learning. We also propose a new pipeline that performs the E2E SA-ASR model after speaker clustering. Our evaluation on the AMI meeting corpus reveals that after fine-tuning with a small real data, the joint system performs 8.9-29.9% better in accuracy compared to the best modular system while the modular system performs better before such fine-tuning. We also conduct various error analyses to show the remaining issues for the monaural SA-ASR.
Robust and accuracy calibration method for a binocular camera using a coding planar target

High-accuracy binocular camera calibration is a vital basis of precise binocular vision 3D measurement. In this work, a high-precision and robust binocular camera calibration method based on a coding target is proposed. First, a coding target with the simple patterns is designed. Every corner on the coding target has a unique code number, which can make the identification of homonymous corners easier and more valuable, even if the target is partially occluded. The decoding of the coding target is rapid, robust, and accurate at a complex background. Subsequently, the zenith and azimuth angles are introduced in the proposed calibration method to study the effect of the orientation of the coding target on the stability of calibration results and improve the robustness of the calibration results. Finally, to fully utilize the 3D information of the calibration corners on the coding target, we combine the reprojection and 3D geometric constraints to propose a multi-constraint optimization method for refining the parameters of binocular camera and improving the accuracy of binocular camera calibration. The comparison experiments have been done to verify the performance of the proposed calibration method. The standard deviations of the intrinsic and extrinsic parameters are greatly decreased, compared with Zhang's method. The mean reprojection and 3D geometric errors calculated by the proposed method have a large reduction. And the application experiment furtherly validates the effectiveness of the proposed method.
Federated horizontally partitioned principal component analysis for biomedical applications

Federated learning enables privacy-preserving machine learning in the medical domain because the sensitive patient data remain with the owner and only parameters are exchanged between the data holders. The federated scenario introduces specific challenges related to the decentralized nature of the data, such as batch effects and differences in study population between the sites. Here, we investigate the challenges of moving classical analysis methods to the federated domain, specifically principal component analysis (PCA), a versatile and widely used tool, often serving as an initial step in machine learning and visualization workflows. We provide implementations of different federated PCA algorithms and evaluate them regarding their accuracy for high-dimensional biological data using realistic sample distributions over multiple data sites, and their ability to preserve downstream analyses.Federated subspace iteration converges to the centralized solution even for unfavorable data distributions, while approximate methods introduce error. Larger sample sizes at the study sites lead to better accuracy of the approximate methods. Approximate methods may be sufficient for coarse data visualization, but are vulnerable to outliers and batch effects. Before the analysis, the PCA algorithm, as well as the number of eigenvectors should be considered carefully to avoid unnecessary communication overhead.Simulation code and notebooks for federated PCA can be found at https://gitlab.com/roettgerlab/federatedPCA; the code for the federated app is available at https://github.com/AnneHartebrodt/fc-federated-pca.Supplementary data are available at Bioinformatics Advances online.
ARFNet: Attention-Oriented Refinement and Fusion Network for Light Field Salient Object Detection

Light field salient object detection plays an important role in computer vision tasks because of the excellent performance on challenging scenes. However, the imprecise extraction of saliency features from light field imaging puts a negative impact on the accuracy of saliency results, and the invalid fusion of these extracted features makes saliency results lose details, further cutting down their accuracy. In this article, to effectively extract and fuse saliency features of light field imaging, we propose an attention-oriented refinement and fusion network to solve these problems, which mainly includes the attention-oriented refinement module (ARM) and the attention-oriented fusion module (AFM). Specifically, ARM can precisely refine similar features between focal slices and all-focus image, and fully extract abundant structural characteristics of the light field. Subsequently, AFM can efficiently fuse those features extracted by ARM to obtain saliency results with detail information. Furthermore, different dilated convolution layers embedded with ARM and AFM are leveraged to further capture complete saliency results with detail information. Experimental results demonstrate that our proposed method outperforms other 20 state-of-the-art methods and achieves Top-1 accuracies on three light field datasets.
Community Inference From Partially Observed Graph Signals: Algorithms and Analysis

This paper considers community inference methods for finding communities on a graph. We treat the setting where the edges are not fully observed. Instead, inference is based on partially observed filtered graph signals where observations from some nodes are missing. Under this setup, we treat two related tasks: <inline-formula><tex-math notation="LaTeX">$\mathsf{A}$</tex-math></inline-formula>) <i>blind</i> inference which recovers the inherited communities on the sub-graph; <inline-formula><tex-math notation="LaTeX">$\mathsf{B}$</tex-math></inline-formula>) <i>semi-blind</i> inference which recovers communities on the full graph with additional partial topology information. For task <inline-formula><tex-math notation="LaTeX">$\mathsf{A}$</tex-math></inline-formula>, we suggest a spectral method which analyzes the principal components of the data covariance matrix. We prove that it succeeds in finding the &#x2018;true&#x2019; communities if the graph filter is low-pass and the nodes are uniformly sampled. For task <inline-formula><tex-math notation="LaTeX">$\mathsf{B}$</tex-math></inline-formula>, we propose a method using spectral interpolation with a Nystr&#x00F6;m extension. The latter approach is proven to succeed in finding the &#x2018;true&#x2019; communities for modular graphs and low-pass graph filters. Numerical experiments on synthetic and real data corroborate our results.