Reviewerly/exHarmony huggingface dataset:
Quantum Metrology of Noisy Spreading Channels

We provide the optimal measurement strategy for a class of noisy channels that reduce to the identity channel for a specific value of a parameter (spreading channels). We provide an example that is physically relevant: the estimation of the absolute value of the displacement in the presence of phase randomizing noise. Surprisingly, this noise does not affect the effectiveness of the optimal measurement. We show that, for small displacement, a squeezed vacuum probe field is optimal among strategies with same average energy. A squeezer followed by photodetection is the optimal detection strategy that attains the quantum Fisher information, whereas the customarily used homodyne detection becomes useless in the limit of small displacements, due to the same effect that gives Rayleigh's curse in optical superresolution. There is a quantum advantage: a squeezed or a Fock state with $N$ average photons allow to asymptotically estimate the parameter with a $\sqrt{N}$ better precision than classical states with same energy.
Ultrafast quantum key distribution using fully parallelized quantum channels

The field of quantum information processing offers secure communication protected by the laws of quantum mechanics and is on the verge of finding wider application for the information transfer of sensitive data. To improve cost-efficiency, extensive research is being carried out on the various components required for high data throughput using quantum key distribution (QKD). Aiming for an application-oriented solution, we report the realization of a multichannel QKD system for plug-and-play high-bandwidth secure communication at telecom wavelengths. We designed a rack-sized multichannel superconducting nanowire single photon detector (SNSPD) system, as well as a highly parallelized time-correlated single photon counting (TCSPC) unit. Our system is linked to an FPGA-controlled QKD evaluation setup for continuous operation, allowing us to achieve high secret key rates using a coherent-one-way protocol.
A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it's important to assess software maintainability. Many software maintainability prediction models' compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms.
Learning Invariance From Generated Variance for Unsupervised Person Re-Identification

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this article, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.
Shape Matters: Deformable Patch Attack

Though deep neural networks (DNNs) have demonstrated excellent performance in computer vision, they are susceptible and vulnerable to carefully crafted adversarial examples which can mislead DNNs to incorrect outputs. Patch attack is one of the most threatening forms, which has the potential to threaten the security of real-world systems. Previous work always assumes patches to have fixed shapes, such as circles or rectangles, and it does not consider the shape of patches as a factor in patch attacks. To explore this issue, we propose a novel Deformable Patch Representation (DPR) that can harness the geometric structure of triangles to support the differentiable mapping between contour modeling and masks. Moreover, we introduce a joint optimization algorithm, named Deformable Adversarial Patch (DAPatch), which allows simultaneous and efficient optimization of shape and texture to enhance attack performance. We show that even with a small area, a particular shape can improve attack performance. Therefore, DAPatch achieves state-of-the-art attack performance by deforming shapes on GTSRB and ILSVRC2012 across various network architectures, and the generated patches can be threatening in the real world.
PPT: Token-Pruned Pose Transformer for Monocular and Multi-view Human Pose Estimation

Recently, the vision transformer and its variants have played an increasingly important role in both monocular and multi-view human pose estimation. Considering image patches as tokens, transformers can model the global dependencies within the entire image or across images from other views. However, global attention is computationally expensive. As a consequence, it is difficult to scale up these transformer-based methods to high-resolution features and many views. In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens. Furthermore, we extend our PPT to multi-view human pose estimation. Built upon PPT, we propose a new cross-view fusion strategy, called human area fusion, which considers all human foreground pixels as corresponding candidates. Experimental results on COCO and MPII demonstrate that our PPT can match the accuracy of previous pose transformer methods while reducing the computation. Moreover, experiments on Human 3.6M and Ski-Pose demonstrate that our Multi-view PPT can efficiently fuse cues from multiple views and achieve new state-of-the-art results. Source code and trained model can be found at https://github.com/HowieMa/PPT .
Education Management using Blockchain as Future Application Innovation

This paper means to give a precise writing audit on blockchain innovation in training to offer a definite comprehension of the current situation as far as advantages, boundaries, current uses of blockchain innovation and future regions where blockchain innovation can be carried out in different spaces of schooling. A bibliometric investigation was performed for information in distributions, diaries, creators and gathered references, and checked by applying bibliometric measures. The investigation shows that blockchain innovation in schooling is as yet a youthful discipline, yet has a great deal of potential to help the training area on the loose. This examination gives an establishment to instructive organizations, strategy creators and specialists to investigate different regions where blockchain innovation can be executed, albeit this exploration additionally recommends some potential employments of blockchain innovation in different elements of the training framework, more applications can be brought into the schooling framework to exploit the capability of blockchain innovation. This paper examines the use of blockchain in innovation in schooling with the assistance of bibliometric examination. This is one of the main realized investigations to survey blockchain innovation by distinguishing the current advantages, boundaries, and utilizations of blockchain innovation.
Improving Test-Time Adaptation Via Shift-Agnostic Weight Regularization and Nearest Source Prototypes

This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to alleviate the performance degradation due to the distribution shift between the source and target domains. Adapting the entire model parameters using the unlabeled online data may be detrimental due to the erroneous signals from an unsupervised objective. To mitigate this problem, we propose a shift-agnostic weight regularization that encourages largely updating the model parameters sensitive to distribution shift while slightly updating those insensitive to the shift, during test-time adaptation. This regularization enables the model to quickly adapt to the target domain without performance degradation by utilizing the benefit of a high learning rate. In addition, we present an auxiliary task based on nearest source prototypes to align the source and target features, which helps reduce the distribution shift and leads to further performance improvement. We show that our method exhibits state-of-the-art performance on various standard benchmarks and even outperforms its supervised counterpart.
Cyberbullying Detection across Social Media Platforms via Platform-Aware Adversarial Encoding

Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms has received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from three different platforms through six cross-platform configurations, showing its effectiveness with both BERT and RoBERTa as the underlying Transformer models.
Novel Key Indicators Selection Method of Financial Fraud Prediction Model Based on Machine Learning Hybrid Mode

In recent years, the financial fraud event of listed companies has occurred continuously. The financial fraud has brought huge losses to the capital market and investors, hindering the investment allocation mechanism of the capital market. The current financial fraud prediction model can judge the company that may conduct financial fraud in advance. So, this can reduce economic losses. The key factor to construct the financial fraud prediction model is how to select the evaluation indicators. This paper analyzes the existing indicators selection method and finds the problem of low prediction accuracy. A key indicators selection method of prediction model based on machine learning hybrid mode is proposed. First, the contribution degree of the selected algorithm and model is ranked according to the features. The support vector machine with good classification effect and heterogeneity with other models is used as the intermediate evaluation model. A variety of selected indicators from machine learning are tested for AUC on the intermediate model. Well-performing machine learning models are selected and combined into multiple hybrid modes. These hybrid models are tested for AUC again. Experiments demonstrate that the hybrid mode of Lasso method and random forest performed best in the AUC test. The repetition indicators of the hybrid model are then selected as important indicators of the prediction model. Finally, the correlation of the indicators is tested, and the indicators beyond the threshold are removed. The selected key indicators effectively improve the accuracy of financial fraud prediction.
User experience (UX) in metaverse: realities and challenges

User experience (UX) design is an interdisciplinary field that combines elements of psychology, computer science, and design. It is concerned with how a product or service is perceived by its users and how it meets their needs. UX designers work to ensure that a product or service is easy to use, efficient, and satisfying for the person using it. The purpose of this article is to describe some implications of User Experience (UX) in the metaverse. UX is relevant in the metaverse because it determines how people interact with and perceive virtual environments. This will involve working with a variety of stakeholders, including developers, designers, and users, to understand the goals and needs of the virtual environment and to create user experiences that are intuitive, engaging, and satisfying. The role of the UX professional in the metaverse will involve applying traditional UX design principles and techniques to the unique challenges and opportunities presented by virtual environments. There are several ways in which UX professionals can insert themselves into working in the metaverse. First of all, work with virtual reality (VR) and augmented reality (AR) companies, VR and AR companies are at the forefront of developing metaverse technologies, and they often hire UX professionals to help design and improve their products. UX professionals in the metaverse must be able to design for immersive and interactive environments that can be difficult to predict and control.
Smart Healthcare Monitoring System Using IoT

The progress in communication and information technology has contributed to the creation of the Internet of Thing (IoT). Internet of Things today plays a crucial role in tracking, documenting, storing, presentation and communication in variety of fields like healthcare, smart cities, engineering, and others. Using an IoT-based monitoring system, the critical parameters of health are tracked and data transmitted via network. Data will be accessed for the benefit of the patient's current condition. This research paper focused on remote monitoring of body temperature (DS18B20), heart rate and SPO2(MAX30100), additionally the position of patient can be obtained on demand using SIM7600E GSM and GNSS HAT (Hardware attached on top) Module. Raspberry Pi 4B used as microcontroller to gather data from health parameter sensors. The data from sensors transmitted into cloud storage through a network. The proposed system uses last version of IoT microcontroller and devices were used which significantly affected the precision and speed of the whole system. A GUI cross platform mobile application was developed for presenting real-time data to both doctors and patients. The system is beneficial for synchronous monitoring of patient’s health status hence allowing urgent medical decision by doctors at right time.
Uncertainty-guided hierarchical frequency domain Transformer for image restoration

Existing convolutional neural network (CNN)-based and vision Transformer (ViT)-based image restoration methods are usually explored in the spatial domain. However, we employ Fourier analysis to show that these spatial domain models cannot perceive the entire frequency spectrum of images, i.e., mainly focus on either high-frequency (CNN-based models) or low-frequency components (ViT-based models). This intrinsic limitation results in the partial missing of semantic information and the appearance of artifacts. To address this limitation, we propose a novel uncertainty-guided hierarchical frequency domain Transformer named HFDT to effectively learn both high and low-frequency information while perceiving local and global features. Specifically, to aggregate semantic information from various frequency levels, we propose a dual-domain feature interaction mechanism, in which the global frequency information and local spatial features are extracted by corresponding branches. The frequency domain branch adopts the Fast Fourier Transform (FFT) to convert the features from the spatial domain to the frequency domain, where the global low and high-frequency components are learned with Log-linear complexity. Complementarily, an efficient convolution group is employed in the spatial domain branch to capture local high-frequency details. Moreover, we introduce an uncertainty degradation-guided strategy to efficiently represent degraded prior information, rather than simply distinguishing degraded/non-degraded regions in binary form. Our approach achieves competitive results in several degraded scenarios, including rain streaks, raindrops, motion blur, and defocus blur.
Quantum Advantage in Cryptography

Ever since its inception, cryptography has been caught in a vicious circle: Cryptographers keep inventing methods to hide information, and cryptanalysts break them, prompting cryptographers to invent even more sophisticated encryption schemes, and so on. But could it be that quantum information technology breaks this circle? At first sight, it looks as if it just lifts the competition between cryptographers and cryptanalysts to the next level. Indeed, quantum computers will render most of today’s public key cryptosystems insecure. Nonetheless, there are good reasons to believe that cryptographers will ultimately prevail over cryptanalysts. Quantum cryptography allows us to build communication schemes whose secrecy relies only on the laws of physics and some minimum assumptions about the cryptographic hardware—leaving basically no room for an attack. While we are not yet there, this paper provides an overview of the principles and state-of-the-art of quantum cryptography, as well as an assessment of current challenges and prospects for overcoming them.
Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform

Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthcare, sports, and human activity monitoring. Convolutional neural networks (CNN) are becoming a popular approach for addressing HAR problems. However, this method requires extensive training datasets to perform adequately on new data. This paper proposes a novel deep learning model pre-trained on scalograms generated using the continuous wavelet transform (CWT). Nine popular CNN architectures and different CWT configurations were considered to select the best performing combination, resulting in the training and evaluation of more than 300 deep learning models. On the source KU-HAR dataset, the selected model achieved classification accuracy and an F1 score of 97.48% and 97.52%, respectively, which outperformed contemporary state-of-the-art works where this dataset was employed. On the target UCI-HAPT dataset, the proposed model resulted in a maximum accuracy and F1-score increase of 0.21% and 0.33%, respectively, on the whole UCI-HAPT dataset and of 2.82% and 2.89%, respectively, on the UCI-HAPT subset. It was concluded that the usage of the proposed model, particularly with frozen layers, results in improved performance, faster training, and smoother gradient descent on small HAR datasets. However, the use of the pre-trained model on sufficiently large datasets may lead to negative transfer and accuracy degradation.
Shapley values to explain machine learning models of school student’s academic performance during COVID-19

In this work we perform an analysis of distance learning format influence, caused by COVID-19 pandemic on school students’ academic performance. This study is based on a large dataset consisting of school students grades for 2020 academic year taken from “Electronic education in Tatarstan Republic” system. The analysis is based on the use of machine learning methods and feature importance technique realized by using Python programming language. One of the priorities of this work is to identify the academic factors causing the most sensitive impact on school students’ performance. In this work we used the Shapley values method for solving this task. This method is widely used for the feature importance estimation task and can evaluate impact of every studied feature on the output of machine learning models. The study-related conditional factors include characteristics of teachers, types and kinds of educational organization, area of their location and subjects for which marks were obtained.
Image Quality Assessment for Magnetic Resonance Imaging

Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality.The growing popularity of image enhancement, generation, and recovery models instigated the development of many methods to assess their performance.However, most IQA solutions are designed to predict image quality in the general domain, with the applicability to specific areas, such as medical imaging, remaining questionable.Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models.In this work, we aspire to fill these gaps by carrying out the most extensive IQA evaluation study for Magnetic Resonance Imaging (MRI) to date (14,700 subjective scores).We use outputs of neural network models trained to solve problems relevant to MRI, including image reconstruction in the scan acceleration, motion correction, and denoising.Our emphasis is on reflecting the radiologist's perception of the reconstructed images, gauging the most diagnostically influential criteria for the quality of MRI scans: signal-to-noise ratio, contrast-to-noise ratio, and the presence of artifacts.Seven trained radiologists assess these distorted images, with their verdicts then correlated with 35 different image quality metrics (full-reference, no-reference, and distribution-based metrics considered).The top performers -DISTS, HaarPSI, VSI, and FID VGG16 -are found to be efficient across three proposed quality criteria, for all considered anatomies and the target tasks.