Reviewerly/exHarmony huggingface dataset:
Learning Correlated Noise in a 39-Qubit Quantum Processor

Building error-corrected quantum computers relies crucially on measuring and modeling noise on candidate devices. In particular, optimal error correction requires knowing the noise that occurs in the device as it executes the circuits required for error correction. As devices increase in size, we will become more reliant on efficient models of this noise. However, such models must still retain the information required to optimize the algorithms used for error correction. Here, we propose a method of extracting detailed information of the noise in a device running syndrome extraction circuits. We introduce and execute an experiment on a superconducting device using 39 of its qubits in a surface code doing repeated rounds of syndrome extraction but omitting the midcircuit measurement and reset. We show how to extract from the 20 data qubits the information needed to build noise models of various sophistication in the form of graphical models. These models give efficient descriptions of noise in large-scale devices and are designed to illuminate the effectiveness of error correction against correlated noise. Our estimates are furthermore precise: we learn a consistent global distribution where all one- and two-qubit error rates are known to a relative error of 0.1%. By extrapolating our experimentally learned noise models toward lower error rates, we demonstrate that accurate correlated noise models are increasingly important for successfully predicting subthreshold behavior in quantum error-correction experiments.4 MoreReceived 18 April 2023Accepted 29 August 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040311Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum circuitsQuantum correlations in quantum informationQuantum error correctionQuantum information theoryQuantum Information, Science & Technology
Counterfeits on dark markets: a measurement between Jan-2014 and Sep-2015

Abstract Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. Estimations in the literature are mostly based on border seizures, but in this paper, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types to those found in seizures but that the share of watches is higher while the share of electronics, clothes, shoes, and Tobacco is lower on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the equivalent products on the surface web appear to be advertised for a multiple of the prices found for darknet markets. We provide some suggestions on how information about darknet market counterfeits could be used by companies and authorities for preventative purposes, showing that insight gathering from the dark web is valuable and could be a cost-effective alternative (or compliment) to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better.
Leveraging Feature Extraction and Context Information for Image Relighting

Example-based image relighting aims to relight an input image to follow the lighting settings of another target example image. Deep learning-based methods for such tasks have become highly popular. However, they are often limited by the geometric priors or suffer from shadow reconstruction and lack of texture details. In this paper, we propose an image-to-image translation network called DGATRN to tackle this problem by enhancing feature extraction and unveiling context information to achieve visually plausible example-based image relighting. Specifically, the proposed DGATRN consists of a scene extraction, a shadow calibration, and a rendering network, and our key contribution lies in the first two networks. We propose an up- and downsampling approach to improve the feature extraction capability to capture scene and texture details better. We also introduce a feature attention downsampling block and a knowledge transfer to utilize the attention impact and underlying knowledge connection between scene and shadow. Experiments were conducted to evaluate the usefulness and effectiveness of the proposed method.
Improving Person Re-Identification with Distance Metric and Attention Mechanism of Evaluation Features

In the present study, we developed a person re-identification network called the Multiple Granularity Attention Cosine Network (MGAC). MGAC utilizes the Multiple Granularity Network (MGN), which combines global and local features and constructs an attention mechanism to add to MGN to form a Multiple Granularity Attention Network (MGA). With the attention mechanism, which focuses on important features, MGA assesses the importance of learned features, resulting in higher scores for important features and lower scores for distracting features. Thus, identification accuracy is increased by enhancing important features and ignoring distracting features. We performed experiments involving several classical distance metrics and selected cosine distance as the distance metric for MGA to form the MGAC re-identification network. In experiments on the Market-1501 mainstream dataset, MGAC exhibited high identification accuracies of 96.2% and 94.9% for top-1 and mAP, respectively. The results indicate that MGAC is an effective person re-identification network and that the attention mechanisms and cosine distance can significantly increase the person re-identification accuracy.
A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

In industrial applications, thermal infrared images, which are commonly used, often suffer from issues such as low contrast and blurred details. Traditional image enhancement algorithms are limited in their effectiveness in improving the visual quality of thermal infrared images due to the specific nature of the application. Therefore, we propose a dual Convolutional Neural Network (CNN) combined with an attention mechanism to address the challenges of enhancing low-quality thermal infrared images and improving their visual quality. Firstly, we employ two parallel sub-networks to extract both global and local features. In one sub-network, we utilize a sparse mechanism incorporating dilated convolutions, while the other sub-network employs Feature Attention (FA) blocks based on channel attention and pixel attention. This architecture significantly enhances the feature extraction capability. The use of attention mechanisms allows the network to filter out irrelevant background information, enabling more flexible feature extraction. Finally, through a simple yet effective fusion block, we thoroughly integrate the extracted features to achieve an optimal fusion strategy, ensuring the highest quality enhancement of the final image. Extensive experiments on benchmark datasets and real images demonstrate that our proposed method outperforms other state-of-the-art models in terms of objective evaluation metrics and subjective assessments. The generated images also exhibit superior visual quality.
Medical Specialty Classification Based on Semiadversarial Data Augmentation

Rapidly increasing adoption of electronic health record (EHR) systems has caused automated medical specialty classification to become an important research field. Medical specialty classification not only improves EHR system retrieval efficiency and helps general practitioners identify urgent patient issues but also is useful in studying the practice and validity of clinical referral patterns. However, currently available medical note data are imbalanced and insufficient. In addition, medical specialty classification is a multicategory problem, and it is not easy to remove sensitive information from numerous medical notes and tag them. To solve those problems, we propose a data augmentation method based on adversarial attacks. The semiadversarial examples generated during the dynamic process of adversarial attacking are added to the training set as augmented examples, which can effectively expand the coverage of the training data on the decision space. Besides, as nouns in medical notes are critical information, we design a classification framework incorporating probabilistic information of nouns, with confidence recalculation after the softmax layer. We validate our proposed method on an 18-class dataset with extremely unbalanced data, and comparison experiments with four benchmarks show that our method improves accuracy and F1 score to the optimal level, by an average of 14.9%.
Blockchain Within the Insurance Industry: A Bibliometric Analysis

Blockchain is a distributed database of records. Although blockchain is mostly known for its insinuation with Bitcoin—it has surely come a long way since then. Nowadays, blockchain is gaining tremendous attention in a wide variety of applications and industries such as health care, finance, and, of course, insurance. The paper presents a bibliometric analysis of the applications of blockchain within the Insurance Industry.
Securing Transmission of Medical Images Using Cryptography Steganography and Watermarking Technique

A healthcare rival has a data breach aimed at critical medical data. Healthcare data breaches affect patients, doctors, and hospitals in addition to being a problem and challenge for security specialists. Despite the variety of healthcare data breaches, they virtually always have the same effect. This study offers information on the numerous types of healthcare data breaches that various firms have to deal with. The major goal is to conduct a thorough analysis of medical data and data breaches. The survey discovered that network server hacking, EMR hacking, and unauthorized internal disclosures are the most frequent modes of assault responsible for healthcare data breaches. The process of hiding data and quality that may be a medical image inside a hidden image is widely used in medical images. Hybrid CSW (Cryptography, Steganography, and Watermarking) technique has recently drawn more attention because it has become a potent tool for many applications, including medical images. Further, explain cryptography, steganography, watermarking transmission domain techniques, and medical image analysis which are images X-ray, Ultrasound, CT scan, Microscopy, MRI, Elastography, Echocardiography, Nuclear Medicine, and PET. Healthcare data breaches report for the last 10 years (2013–2022) with location network servers, electronic medical records (EMR), and 4 years (2018–2022) of more healthcare data breaches. After that, several issues identifies have emerged, which are network server, privacy, security, and transmission of the medical data reduction.
Implementation and Analysis of Different Visual Cryptographic Schemes

Nowadays images became ubiquitous on Internet and mobile applications. Image secret sharing is a subset of secret sharing scheme. Visual Cryptography (VC) is one of the secure ways to transfer images over network. Visual cryptography is a special approach to the general secret sharing problem. In (k, n) image secret sharing, the image that carries the secret is broken into n pieces (called as shares) and decryption is totally impossible unless at least k pieces are collected and superimposed. The biggest advantage of this technique is decryption that is done via naked eye. There is no need of any mathematical computation. Here, exploration and exploitation of various visual cryptographic algorithms for grayscale, black and white (binary) as well as coloured images are given. This paper gives a comprehensive analysis of various image encryption algorithm based on metric such as Peak Signal to Noise Ratio (PSNR) and Mean Normalized Cross-Correlation. Here, results for implementation of various (2, 2) VC scheme image encryption are given. Most of these schemes can be extended to (n, n) VC scheme.
BBIWMS: A Secure Blockchain-Based Framework for Integrated Water Management System for Smart City

In the two-thirds of the earth that is covered by water, only less than 1% of the water is fit for human use. A natural resource and prerequisite for human survival is water. Due to the lack of water becoming a global crisis, it affects agricultural activities by reducing crop levels as well as the availability of drinking water for people, domestic animals, and other essential water-dependent activities. This creates a risk for future water usage, and less rain also exacerbates the water crisis. To overcome these difficulties, there are some existing research work conducted using technologies called IoT, machine learning algorithms, wireless sensor networks, cloud computing, etc. We have been able to identify some gaps using this existing research, including difficulties with safely updating the firmware of sensors that have already been deployed in the field and physical attacks on sensor devices situated in remote field locations. We have also been able to identify network hacking and jamming, device cloning, and eavesdropping as other issues. There is a problem with centralized traditional architecture used in water management systems that prevents safe and reliable communication between equipment. We have developed a technology known as Blockchain as a solution after consulting prior research. Blockchain is a distributed, decentralized ledger system that aids in the management of India's water shortage issue. In this research work, we will discuss opportunities and challenges in water management systems using Blockchain technology with a proposed framework model called Blockchain-based integrated water management system (BBIWMS). In future research work, we will implement the proposed framework model with good results.
Two-Level Security of Color Image Using 9D-Hyperchaotic System and DWT

This research paper proposes a novel technique to secure the color image using a 9D-hyperchaotic system and Discrete Wavelet Transform (DWT). The proposed scheme uses a 9D-hyperchaotic system for encrypting the color image and DWT is used for embedding this encrypted image into another color image. For extracting secret image, the reverse process of the scheme is used. Different types of security analysis tests such as key sensitivity, entropy, and histogram analysis are performed to check the robustness of the image being embedded. Experimental results show that that proposed scheme can face many attacks like brute-force attack, cropping attack, etc. Experimental results prove that the proposed technique is much more robust and efficient to secure digital images.
A Recent Survey of Reversible Data Hiding Techniques for 2D and 3D Object Models

In recent years, humans have gradually begun experiencing the world in 3D object models, and the vast use of 3D object models has made them an ideal information hiding platform. The most common uses of these models are displaying virtual surfaces and volumes. The use of 3D object models in industrial, medical, and entertainment fields has increased substantially over the last decade, giving immense value to research in 3D object models. Information hiding is one of the most important techniques in terms of privacy protection for multimedia content. Additionally, reversible data hiding (RDH) offers impressive performance in data security and authentication. Thus, to explore more, the RDH in encrypted images technique has been extended to 3D mesh models. It gives better performance in comparison to existing 2D models. This paper presents a review with the aim to show the development of emerging methodologies in the field of RDH on 3D object models. In this paper, the existing RDH techniques in the spatial domain and encrypted domain on 2D and 3D object models have been compared. This paper also spots the areas where RDH on 3D object models may be explored more.
Adaptive protocols for SU(1,1) interferometers to achieve ab initio phase estimation at the Heisenberg limit

Abstract The precision of phase estimation with interferometers can be greatly enhanced using non-classical quantum states, and the SU(1,1) interferometer is an elegant scheme, which generates two-mode squeezed state internally and also amplifies the signal. It has been shown in Anderson et al (2017 Phys. Rev. A 95 063843) that the photon-number measurement can achieve the Heisenberg limit, but only for estimating a small phase shift. We relax the constraint on the range of phase by considering two adaptive protocols: one also uses the photon-number measurement with a specially tuned sequence of feedback phase; the other implements the yet-to-be-realized optimal measurement but without fine tuning.
Blockchain-Based NFT for Evidence System

The non-fungible tokens (NFTs), metaverse, and Blockchain have the potential to transform the digital world in the future. This paper introduces a Blockchain-based NFT for evidence system. Records and evidence related to crime, property, or forgery are sensitive and easy targets for tampering. Evidence is also important in the courts of law, where sometimes verdicts are completely based on evidence. Hence, it becomes necessary to safeguard them against any unwanted access. This work utilizes the NFTs and interplanetary file system (IPFS) to store and secure records such as warrants issued on behalf of complaints as first information reports (FIRs), evidence, and warrants, etc. in police stations. NFTs have a unique value and identity, which allows a piece of evidence to remain immutable and also track changes to it through consensus. IPFS, on the other hand, allows decentralized storage of files, making it a robust system to handle evidence records. Moreover, this paper also presents the Ethereum Blockchain, the application of NFTs, with the issues and challenges of using them. Results show that the NFT is a viable solution for an evidence system.
Time series forecasting with multilayer perceptrons

An implementation of a week-ahead air temperature and atmospheric pressure forecast using a multilayer perceptron is presented (MLP). According to the specified meteorological parameters, data preparation, implementation and performance evaluation were performed for two MLP models. The MLP architecture was a s upervised feed -forward neural network with five hidden nodes and twenty iterations (repetitions). The obtained values of the ris k function (in this case, the standard deviation of the MSE) in both implementations are quite large.
Application of the Lagrangian stochastic dispersion model for urban air pollution simulation

In the presented work the Lagrangian stochastic dispersion model is applied to simulate the emission of pollutants (contaminant gas) from a point source above the ground. The obtained values of concentration in the region are presented graphically. Comparison with the analytical solution was carried out. Dispersion modeling in the convective boundary layer is performed, and sufficient grounds for using the model for the city of Tomsk are obtained. The considered Lagrangian stochastic dispersion model was applied to study the pollutants spread from high-altitude point sources in the city of Tomsk.
Hypothetical scenarios of microplastics propagation in Lake Baikal

This work is a continuation of the study on modeling the propagation of microplastic (MP) in Lake Baikal. A three-dimensional Eulerian type model, represented by the advection-diffusion-reaction equation, is used to simulate the behavior of MP in the lake. To set the hydrodynamic background, a three-dimensional model of the hydrothermodynamics of the lake in a non-hydrostatic approximation is used. Some "climatic" scenarios of atmospheric influence are compiled to be used for MP propagation modeling. In addition to the traditional approach based on direct (forward in time) modeling, we consider its combination with inverse modeling methods, assuming to solve the actual problems of sensitivity, optimization and control, which are important in environmental protection and design. Based on hydrodynamic scenarios, we formulate some inverse problems for assessing the fate of MP in the lake. In particular, two hypothetical scenarios are considered to assess the informational significance of the virtual monitoring networks that "observe" the spread of MP in the lake.
A Novel Approach for Block Chain Technology based Cyber Security in Cloud Storage Using Hash Function

Block chain is generic name to describe the technology used by Bitcoin and other digital currency to record and secure transaction. This technology enables a highly accessible ledger with greatly reduced risk for tampering. The dynamic immutable, data ledger makes ideal for real time monitoring of the shipment of goods. Cloud is an important of distributed storage system of networking. Cloud system need for security, storage management, minimize the cloud cost and fast storage could be improved. The security using new most development security system of block chain technology is used to improve the cloud security. The data owner to be uploads the data on web page and access the folder. The user has been accessing the data on cloud storage using encryption and decryption using block chain based cyber security system. the problem for security in cloud storage because data transmission and data sharing, the alternate for security solution using Post Quantum-proof cryptography algorithm is used to improve the encryption and decryption process and more tight security for block chain technology for the cloud system. The SHA-3 512 Hash function algorithm automatically generate the key for data security enhancements of cloud networks. Post Quantum-proof cryptography algorithm has been improved the encryption Performance and reduced the power consumption, and increase the Latency performance, and boost up the security performances. Finally Post Quantum-proof cryptography algorithm for well support for security system of the cloud networks. Even some passwords, which are often cited as the weakest link in cyber security, may not be necessary.
DEVELOPMENT OF A SURROUNDING ENVIRONMENT BASED LEARNING BOOK FOR STUDENTS AT SDN PURWOREJO 03 SANANKULON

The objectives to be achieved in this research are to analyze the Feasibility of the Environment-Based Kewan Lan Wit-Witan Javanese Language Support Book at SDN Purworejo 03 Sanankulon Blitar and to analyze the Effectiveness of the Environment-Based Kewan Lan Wit-Witan Javanese Language Support Book at SDN Purworejo 03 Sanankulon Blitar. This research is included in development research which uses a Research and Development (R&amp;D) approach. This development research is longitudinal (gradually) to develop a new product or improve an existing product and test its effectiveness so that it can function in the wider community. The results of the research are environment-based interactive learning media about animals and plants (Kelantan) by material experts considered appropriate with a percentage of 86.25% media experts rated "appropriate" with a percentage of 85%, and linguists rated "appropriate" with a percentage of 88.60%. Then The level of readability of interactive learning media based on the surrounding environment about animals and plants (Kelantan) by material students is considered very feasible with a percentage of 90%.&#x0D; &#x0D; Keywords: Javanese; Kewan; Wit-Witan; Supporting Books, Environment&#x0D;