Reviewerly/exHarmony huggingface dataset:
Ternary perfect sequences with three-valued cross-correlation

<p style='text-indent:20px;'>The calculation of the cross-correlation between a sequence with good autocorrelation and its decimated sequence has been a longstanding research problem in the field of sequence design. The objective of this paper is to determine the cross-correlation between a class of well-known ternary sequences with perfect autocorrelation and its <inline-formula><tex-math id="M1">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>-decimation. Based on the theory of quadratic forms and exponential sums over finite fields, it is shown that the cross-correlation function takes on three low values.
Lost in digitization – A systematic review about the diagnostic test accuracy of digital pathology solutions

Introduction: Digital pathology solutions are increasingly implemented for primary diagnostics in departments of pathology around the world. This has sparked a growing engagement on validation studies to evaluate the diagnostic performance of whole slide imaging (WSI) regarding safety, reliability and accuracy. The aim of this review was to evaluate the performance of digital pathology for diagnostic purposes compared to light microscopy (LM) in human pathology, based on validation studies designed to assess such technologies. Methods: In this systematic review based on PRISMA guidelines, we analyzed validation studies of WSI compared with LM. We included studies of diagnostic performance of WSI regarding diagnostic test accuracy (DTA) indicators, degree of overdiagnosis, diagnostic concordance, and observer variability as a secondary outcome. Overdiagnosis is (for example) detecting a pathological condition that will either not progress or progress very slowly. Thus, the patient will never get symptoms from this condition and the pathological condition will never be the cause of death. From a search comprising four databases PubMed, EMBASE, Cochrane Library, and Web of Science, encompassing the period 2010-2021, we selected and screened 12 peer-reviewed articles that fulfilled our selection criteria. Risk of bias was conducted through QUADAS-2 tool, and data analysis and synthesis were performed in a qualitative format. Results: We found that diagnostic performance of WSI was not inferior to LM for DTA indicators, concordance, and observer variability. The degree of overdiagnosis was not explicitly reported in any of the studies, while the term itself was used in one study and could be implicitly calculated in another. Conclusion: WSI had an overall high diagnostic accuracy based on traditional accuracy measurements; however, the degree of overdiagnosis is unknown.
Unsupervised Outlier Detection Mechanism for Tea Traceability Data

The presence of outliers in tea traceability data can mislead customers and have a significant impact on the reputation and profits of tea companies.To solve this problem, an unsupervised outlier detection mechanism for tea traceability data is proposed.Firstly, tea traceability data is uploaded to the MySQL database, and then the data is preprocessed to aggregate features based on relevance, which makes it easier to identify abnormal features.Secondly, the LOKI algorithm based on Local Outlier Factor (LOF), Isolation Forest (IForest), and K-Nearest Neighbors (KNN) algorithms is used to achieve unsupervised outlier detection of tea traceability data.In addition, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN-based) tuning method for unsupervised outlier detection algorithms is also provided.Finally, the types of anomalies among the identified outliers are identified to investigate the causes of the anomalies in order to develop remedial procedures to eliminate the anomalies, and the analysis results are fed back to the tea companies.Experiments on real datasets show that the DBSCAN-based tuning method can effectively help the unsupervised outlier detection algorithm optimize the parameters, and that the LOF-KNN-IForest (LOKI) algorithm can effectively identify the outliers in tea traceability data.This proves that the unsupervised outlier detection mechanism for tea traceability data can effectively guarantee the quality of tea traceability data.
Multi-Class Lane Semantic Segmentation of Expressway Dataset Based on Aerial View

Multi-Class Lane Semantic Segmentation (MCLSS) is a hot topic in the computer vision research, which is of great significance for detecting violations of vehicles on expressway. At present, there is a lack of public dataset for semantic division of complete road areas. This paper completes the collection, cleaning, analysis, classification and labeling of expressway data on aerial view and proposes a semantic segmentation model “Deeplab-ERFC” (DeepLab with Erosion Loss and a Fully-Connected Conditional Random Field). This model extends DeepLabv3+ by adding a Erosion Loss (ER Loss) that can improve boundary prediction performance using corrosion operation to estimate Hausdorff Distance (HD) and a Fully-Connected Conditional Random Field (Fully-Connected CRF) that will reduce the generation of cavities through two gaussian kernel functions considering the color intensity and position relationship between pixels. Based on mean Intersection over Union (mIoU), our proposed Deeplab-ERFC achieves the best semantic segmentation performance on our Expressway Dataset, reaching 83.9% mIoU in the test set.
State-of-the-art techniques for passive image forgery detection: a brief review

Images are major information carriers in the digital era. Along with the benefits, there are many drawbacks of digital visual media. The digital multimedia editing tools like Adobe Photoshop, CorelDRAW, Affinity, Freehand, GNU Image Manipulation Program (GIMP), etc. are being used to tamper or manipulate the images for malicious purposes. Image forgery is the process of manipulating a digital image by adding some content or hiding some content such that the integrity of the image is lost. So, it becomes important to check the credibility and integrity of the images. In order to detect the image manipulation, various active and passive techniques have been put forward. The recent methods make use of deep learning techniques to detect image tampering. This manuscript attempts to review state-of-the-art approaches in the discipline of passive image forgery detection, and presents a comparative performance analysis. Also, the publicly available benchmarking databases for image forgery detection and performance evaluation parameters are elucidated.
Multi-scale progressive blind face deblurring

Abstract Blind face deblurring aims to recover a sharper face from its unknown degraded version (i.e., different motion blur, noise). However, most previous works typically rely on degradation facial priors extracted from low-quality inputs, which generally leads to unlifelike deblurring results. In this paper, we propose a multi-scale progressive face-deblurring generative adversarial network (MPFD-GAN) that requires no facial priors to generate more realistic multi-scale deblurring results by one feed-forward process. Specifically, MPFD-GAN mainly includes two core modules: the feature retention module and the texture reconstruction module (TRM). The former can capture non-local similar features by full advantage of the different receptive fields, which facilitates the network to recover the complete structure. The latter adopts a supervisory attention mechanism that fully utilizes the recovered low-scale face to refine incoming features at every scale before propagating them further. Moreover, TRM extracts the high-frequency texture information from the recovered low-scale face by the Laplace operator, which guides subsequent steps to progressively recover faithful face texture details. Experimental results on the CelebA, UTKFace and CelebA-HQ datasets demonstrate the effectiveness of the proposed network, which achieves better accuracy and visual quality against state-of-the-art methods.
Structured negativity: A physically realizable measure of entanglement based on structural physical approximation

Quantification of entanglement is one of the most important problem in quantum information theory. In this work, we will study this problem by defining a physically realizable measure of entanglement for any arbitrary dimensional bipartite system ρ, which we named as structured negativity (NS(ρ)). We have shown that the introduced measure satisfies the properties of a valid entanglement monotone. We also have established an inequality that relate negativity and the structured negativity. For d⊗d dimensional state, we conjecture from the result obtained in this work that negativity coincide with the structured negativity when the number of negative eigenvalues of the partially transposed matrix is equal to d(d−1)2. Moreover, we proved that the structured negativity is not only implementable in the laboratory but also a better measure of entanglement in comparison to negativity. In few cases, we obtain that structure negativity gives better result than the lower bound of the concurrence obtained by Chen et al. (2005).
An Image Tampering Detection Algorithm of Qualification Certificate Based on CNN and SVM

With the high speed development of digital image processing technology, the phenomenon of malicious tampering with certificate images is rampant.Facing the problems of difficult human eye recognition of tampering traces and poor detection and classification effect of CNN model, a tampering detection algorithm based on CNN combined with SVM is proposed.The algorithm first performs data preprocessing of image compression, difference, resise, and normalization on the image.Then it uses the CNN model to extract the tamper feature, finally completes the calculation and classification of the fully connected layer feature data through SVM, and builds a certified image CNN+SVM model with tamper detection capability.Experimental results show that comparing with the traditional human eye recognition method and the existing deep learning CNN model, the algorithm can reach a detection accuracy of 48.09%, 91.79 %, and 98.67% under the self-built data set of tampering with certificate images.The detection accuracy rate is higher than the previous two detection accuracy rates.
S2-Net: Self-Supervision Guided Feature Representation Learning for Cross-Modality Images

Dear Editor, This letter focuses on combining the respective advantages of cross-modality images which can compensate for the lack of information in the single modality. Meanwhile, due to the great appearance differences between cross-modality image pairs, it often fails to make the feature representations of correspondences as close as possible. In this letter, we design a cross-modality feature representation learning network, S2-Net, which is based on the recently successful detect-and-describe pipeline, originally proposed for visible images but adapted to work with cross-modality image pairs. Extensive experiments show that our elegant formulation of combined optimization of supervised and self-supervised learning outperforms state-of-the-arts on three cross-modal datasets.
Comparison of SNR gain between quantum illumination radar and classical radar

It has been proved that quantum illumination (QI) radar has the quantum advantages in error-probability exponent. However, the error-probability exponent is not a recognized figure of merit in the radar literature, nor does it correspond in a straightforward manner to any such figure of merit. Signal to noise ratio (SNR) gain is an important criterion in radar theory. While, the theoretical analysis of quantum enhancement in SNR gain of QI radar has not been reported. In this paper, we compare the physical fundamental of matched filter (MF), which can achieve the optimal SNR gain under white noise in classical radar theory, and phase conjugation (PC) receiver. Furthermore, the quantum enhancement of SNR gain in QI radar is studied. It is shown that QI radar with practical receivers can achieve about 3dB quantum advantage in SNR gain. In addition, in the case of extremely weak signal, it can potentially achieve tens of dB enhancement in SNR gain compared with the MF based classical radar.
Designing lawful machine behaviour: Roboticists’ legal concerns

Regulatory requirements weighing on roboticists are becoming heavier, and at the same time, the activity of producing robots is theorized as creating new types of legal risks. Roboticists become responsible not only for compliance to a set of regulatory objectives, but they also become responsible for giving machines an ability to make decisions in uncertain environments. All of this gives roboticists’ legal literacy a newfound importance, and reveals the lack of data on that population from a socio-legal angle. While we have some knowledge of lawyers’ and policy makers’ concerns for robots, we do not know how roboticists understand their relationship to law and regulation, or the legal value they attribute to various engineering processes. This article offers a preliminary exploration of that population and of their legal concerns, through interviews of ten engineers from Danish robotics companies. The analysis shows that they perceive legal concerns as dependant and integrated into their concerns for safety, which are themselves inferior to functionality and economy concerns. The article further highlights differences in understanding and perception of various legal matters by our informants, and from our practitioner perspective. Hopefully, highlighting these differences will by itself help close the gap between the two professions.
Test Time Transform Prediction for Open Set Histopathological Image Recognition

Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to address this problem by applying Open Set Recognition techniques to the task of jointly classifying tissue that belongs to a set of annotated classes, e.g. clinically relevant tissue categories, while rejecting in test time Open Set samples, i.e. images that belong to categories not present in the training set. To this end, we introduce a new approach for Open Set histopathological image recognition based on training a model to accurately identify image categories and simultaneously predict which data augmentation transform has been applied. In test time, we measure model confidence in predicting this transform, which we expect to be lower for images in the Open Set. We carry out comprehensive experiments in the context of colorectal cancer assessment from histological images, which provide evidence on the strengths of our approach to automatically identify samples from unknown categories. Code is released at https://github.com/agaldran/t3po .
A Robust VideoSAR Single Target Tracker by Integrating Correlation Filter and IMM-PDAF

Video synthetic aperture radar (VideoSAR) provides the potential of high-resolution target images with also high frame rate. Different from the target signal in VideoSAR, target shadow straightforwardly indicates its location and track without suffering from the Doppler shift and smearing induced by the motion modulation. The target shadow can be exploited to realize the real-time tracking of the moving target with VideoSAR. In this letter, we propose a robust single target shadow tracker, which integrates the discriminative correlation filter(DCF) and interacting multiple model probabilistic data association filter(IMM-PDAF) to simultaneously overcome the interference of around clutter and target maneuvers. Considering the dependence between target shadow appearance and its dynamic motion, a filtering template updating strategy based on the estimated mode probability was proposed. VideoSAR tracking experiment shows that the proposed algorithm outperforms conventional visual tracking techniques in the robustness aspects.
On Comparison of Phonetic Representations for Czech Neural Speech Synthesis

In this paper, we investigate two research questions related to the phonetic representation of input text in Czech neural speech synthesis: 1) whether we can afford to reduce the phonetic alphabet, and 2) whether we can remove pauses from phonetic transcription and let the speech synthesis model predict the pause positions itself. In our experiments, three different modern speech synthesis models (FastSpeech 2 + Multi-band MelGAN, Glow-TTS + UnivNet, and VITS) were employed. We have found that the reduced phonetic alphabet outperforms the traditionally used full phonetic alphabet. On the other hand, removing pauses does not help. The presence of pauses (predicted by an external pause prediction tool) in phonetic transcription leads to a slightly better quality of synthetic speech.
The Reformation of College English Teaching under the Background of Smart Education

In order to reform the college English teaching, teachers can use smart education to release teaching information on the education platform, analyze students' data and intervene through Big Data, and teachers and students and can interact through the education platform.Based on the characteristics of college English curriculum, it is feasible to apply smart education to college English teaching and realize the personalized mode of teaching and learning.
Two-step orthogonalization phase demodulation method based on a single differential interferogram

To reduce the acquisition time of interferogram and provide a dynamic phase retrieval method with arbitrary phase shift using a dual-channel simultaneous polarization phase-shifting system, a two-step orthogonalization phase demodulation method (TOPD) based on a single differential interferogram is proposed in this paper. In this method, the differential interferogram obtained by subtracting two phase-shifting interferograms and one of the Gaussian filtered based-interferograms are used to normalize and orthogonalize, and then the phase related parameters are solved by the Lissajous ellipse fitting method. Finally, the measured phase is obtained with high accuracy. The proposed method further reduces the deviation caused by the filtering operation performed in the two-step phase demodulation method. At the same time, combined it with the Lissajou ellipse fitting method reduces the limitation associated with the approximation conditions of the orthogonalization and normalization method. The experimental and simulation results demonstrate that this method provides a solution with high accuracy, high stability, strong practicability, and few restrictions for phase extraction in quantitative phase imaging.
Geochemical and mineralogical soil survey of the conterminous USA: a project retrospective

From 2007 to 2010, the United States Geological Survey (USGS) conducted a low-density (one site per 1600 km 2 ), geochemical and mineralogical soil survey of the conterminous USA ( c . 8 × 10 6 km 2 ). This project was initiated to address the lack of a national soil geochemical database that was a critical need for state and federal environmental agencies, public health specialists and those engaged in risk assessment of contaminated land. Sampling and analytical protocols were developed in consultation with stakeholders at a 2003 workshop, and pilot studies were carried out from 2004 to 2007. Sampling began in 2007 and concluded in 2010. Chemical and mineralogical analyses were completed in 2013, and the datasets were released to the public that same year. Geochemical and mineralogical maps were published in 2014, and an interactive website was released in 2019. The author was Project Chief for this effort throughout the lifetime of the study. The evolution of the project is discussed from its inception through to the publication of results and its impact. The lessons learned during the project are reviewed in the hope that applied geochemists who undertake such broad-scale geochemical mapping projects in the future will find them useful. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping
Colorization of Digital Images: An Automatic and Efficient Approach through Deep learning

Colorization is not a guaranteed, but a feasible mapping between intensity and chrominance values. This paper presents a colorization system that draws inspiration from recent developments in deep learning and makes use of both locally and globally relevant data. One such property is the rarity of each color category on the quantized plane. The denoising model contains hybrid approach with cluster normalization through U-Net deep learning construction of framework. These are built on the basic U-Net design for segmentation. To eliminate gaussian noise in digital images, this article has developed and tested a generic deep learning denoising model. PSNR and MSE are used as performance measures for comparison purposes.
Chosen Plaintext Combined Attack against SM4 Algorithm

The SM4 algorithm is widely used to ensure the security of data transmission. The traditional chosen plaintext power attacks against SM4 usually need to analyze four rounds power traces in turn to recover the secret key. In this paper, we propose a new combined chosen plaintext power analysis, which combines the chosen plaintext power attack and the differential characteristics of the substitution box (S-box) in SM4. In our attack, only the second and fourth round S-box outputs of SM4 algorithm are used as attack points, and some sensitive fixed intermediate values are obtained by power analysis when inputting specific plaintext. Then the differential analysis of these sensitive intermediate values is carried out to calculate the difference between the input and output of the S-box, and the key can be recovered from the differential characteristics of S-box. Compared with the traditional chosen plaintext power analysis, which requires four rounds of analysis, our analysis reduces the number of attack rounds into two rounds, and adopts the nonlinear S-box with obvious leakage information as the attack intermediate value, which effectively improves the feasibility of attack. Finally, a practical attack experiment is carried out on a Field Programmable Gate Array (FPGA) based implementation of SM4 algorithm, and the results show that our method is feasible and effective for real experiments.
Combining statistical, structural, and linguistic features for keyword extraction from web pages

&lt;abstract&gt; &lt;p&gt;Keywords are commonly used to summarize text documents. In this paper, we perform a systematic comparison of methods for automatic keyword extraction from web pages. The methods are based on three different types of features: statistical, structural and linguistic. Statistical features are the most common, but there are other clues in web documents that can also be used. Structural features utilize styling codes like header tags and links, but also the structure of the web page. Linguistic features can be based on detecting synonyms, semantic similarity of the words and part-of-speech tagging, but also concept hierarchy or a concept graph derived from Wikipedia. We compare different types of features to find out the importance of each of them. One of the key results is that stop word removal and other pre-processing steps are the most critical. The most successful linguistic feature was a pre-constructed list of words that had no synonyms in &lt;italic&gt;WordNet&lt;/italic&gt;. A new method called &lt;italic&gt;ACI‑rank&lt;/italic&gt; is also compiled from the best working combination.&lt;/p&gt; &lt;/abstract&gt;