Reviewerly/exHarmony huggingface dataset:
Large-alphabet time-bin quantum key distribution and Einstein–Podolsky–Rosen steering via dispersive optics

Abstract Quantum key distribution (QKD) has established itself as a groundbreaking technology, showcasing inherent security features that are fundamentally proven. Qubit-based QKD protocols that rely on binary encoding encounter an inherent constraint related to the secret key capacity. This limitation restricts the maximum secret key capacity to one bit per photon. On the other hand, qudit-based QKD protocols have their advantages in scenarios where photons are scarce and noise is present, as they enable the transmission of more than one secret bit per photon. While proof-of-principle entangled-based qudit QKD systems have been successfully demonstrated over the years, the current limitation lies in the maximum distribution distance, which remains at 20 km fiber distance. Moreover, in these entangled high-dimensional QKD systems, the witness and distribution of quantum steering have not been shown before. Here we present a high-dimensional time-bin QKD protocol based on energy-time entanglement that generates a secure finite-length key capacity of 2.39 bit/coincidences and secure cryptographic finite-length keys at 0.24 Mbits s −1 in a 50 km optical fiber link. Our system is built entirely using readily available commercial off-the-shelf components, and secured by nonlocal dispersion cancellation technique against collective Gaussian attacks. Furthermore, we set new records for witnessing both energy-time entanglement and quantum steering over different fiber distances. When operating with a quantum channel loss of 39 dB, our system retains its inherent characteristic of utilizing large-alphabet. This enables us to achieve a secure key rate of 0.30 kbits s −1 and a secure key capacity of 1.10 bit/coincidences, considering finite-key effects. Our experimental results closely match the theoretical upper bound limit of secure cryptographic keys in high-dimensional time-bin QKD protocols (Mower et al 2013 Phys. Rev. A 87 062322; Zhang et al 2014 Phys. Rev. Lett. 112 120506), and outperform recent state-of-the-art qubit-based QKD protocols in terms of secure key throughput using commercial single-photon detectors (Wengerowsky et al 2019 Proc. Natl Acad. Sci. 116 6684; Wengerowsky et al 2020 npj Quantum Inf. 6 5; Zhang et al 2014 Phys. Rev. Lett. 112 120506; Zhang et al 2019 Nat. Photon. 13 839; Liu et al 2019 Phys. Rev. Lett. 122 160501; Zhang et al 2020 Phys. Rev. Lett. 125 010502; Wei et al 2020 Phys. Rev. X 10 031030). The simple and robust entanglement-based high-dimensional time-bin protocol presented here provides potential for practical long-distance quantum steering and QKD with multiple secure bits-per-coincidence, and higher secure cryptographic keys compared to mature qubit-based QKD protocols.
Harnessing virtual reality and simulation technologies in education for sustainable development

Sustainable development is one of the key topics for modern society. Based on existing global challenges such as climate change and biodiversity loss, it is necessary to look for new ways of sustainable development that take into account economic, social and environmental aspects. The purpose of this study is to analyze the role of education in shaping sustainable development and assess the impact of new educational technologies on achieving sustainability. This study examines the importance of introducing virtual reality and simulation into the educational process in Russia. The authors discuss the benefits of using these technologies, such as increased student engagement, more visual learning, and professional skill development. Particular attention is paid to the ability of virtual reality to create a realistic learning environment that allows students to interact with the virtual world and put the concepts they are learning into practice.
Data Augmentation Using Transformers and Similarity Measures for Improving Arabic Text Classification

The performance of learning models heavily relies on the availability and adequacy of training data. To address the dataset adequacy issue, researchers have extensively explored data augmentation (DA) as a promising approach. DA generates new data instances through transformations applied to the available data, thereby increasing dataset size and variability. This approach has enhanced model performance and accuracy, particularly in addressing class imbalance problems in classification tasks. However, few studies have explored DA for the Arabic language, relying on traditional approaches such as paraphrasing or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets: AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 7% in AraSarcasm, 8% in ASTD, 11% in ATT, and 13% in MOVIE.
The societal impact of Deepfakes: Advances in Detection and Mitigation

This comprehensive survey paper examines the current state of deepfake detection techniques, analyzing their strengths, weaknesses, and limitations, as well as their effectiveness in detecting different types of deepfakes. We also analyze the evolution of deepfake detection techniques over time and discuss future research directions. By providing a thorough examination of the existing literature, this paper aims to assist researchers, practitioners, and policymakers in understanding the current landscape of deepfake detection and identifying areas for future development.
Semantic Communications Based on Adaptive Generative Models and Information Bottleneck

Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequence of symbols, rather than the symbols themselves. In semantic communications, the scope of the destination is not to recover a list of symbols identical to the transmitted ones, but rather to recover a message that is semantically equivalent to the message emitted by the source. This paradigm shift introduces many degrees of freedom to the encoding and decoding rules that can be exploited to make communication systems much more efficient. In this article, we present an approach to semantic communication, building on three fundamental ideas: represent data over a topological space as a formal way to capture semantics as expressed through relations; use the information bottleneck principle as a way to identify relevant information and adapt the information bottleneck online, as a function of the wireless channel state, in order to strike an optimal trade-off between transmit power, reconstruction accuracy, and delay; and exploit probabilistic generative models as a general tool to adapt the transmission rate to the wireless channel state and make possible the regeneration of the transmitted images, or run classification tasks at the receiver side.
A Distributed Learning Architecture for Semantic Communication in Autonomous Driving Networks for Task Offloading

Semantic communication based on machine learning (ML) techniques emerged as a new transmission paradigm that can significantly improve spectrum efficiency. It looks promising for improving the task of offloading quality of service (QoS) for autonomous driving networks (ADNs) where autonomous vehicles require a significant amount of communication with the vehicle edge clouds (VECs). However, in practical ADNs, updating the ML-based semantic communication coder model is affected by various unique factors such as mobility and privacy considerations. Therefore, in ADNs, the conventional ML frameworks are not directly applicable to updating semantic communication coders. In this article, we discuss the unique challenges faced by updating the semantic communication coder in ADNs, and review the existing ML frameworks. To address these challenges, we further propose a privacy-preserving personalized federated learning (3PFL) framework for updating the semantic communication coder in ADNs. Simulation results confirm the effectiveness of 3PFL for this process.
Fault Tolerance of Deadline Constrained Tasks based on Load Balancing in Cloud Computing

Although cloud computing has been extensively studied in the past, there are still serious problems with task balancing in cloud-based systems. Effective load allocation is one of the most importance challenge in cloud computing as the available resources and virtual machines are limited. Excessive load on virtual machine may leads to failure of deadline constrained tasks as the task fails to meet deadline. In this paper, we proposed a load balancing technique for deadline constrained tasks which balance the workload among virtual machine to minimize the task rejection ratio. The proposed algorithm uses double threshold policy to measure the current load of node after allocating load which reduces the chance of task failure due to deadline miss. Tasks are distributed to underloaded nodes to balance the load and the algorithm dynamically check the status of nodes after every allocation. The proposed algorithm eliminates workload imbalance, maximize resource utilization, and minimize task rejection ratio. The proposed algorithm is implemented using Python platform. The result shows that the proposed load-balancing algorithm outperforms the existing algorithm.
Cryptocurrency Price Prediction and Analysis

Cryptocurrencies have become a major element in enterprises and financial market showing promising potential during the past ten years. Predictions that are accurate can help cryptocurrencies investors to make the best decisions and possibly enhance their earnings. For this work 4 cryptocurrencies namely Bitcoin, Binance Coin, Ethereum and Tether (USDT) are considered. The considered dataset is for around 5 years, which has a total of 9 columns, One Date Column ranging from year 2017 to 2022 and for each of the Coin there is the Closing price and the Volume. The Dataset is collected from Kaggle. Attempts are made to build the models in various ways, by dealing with certain features or by taking a subset of the dataset. All the models, according to the way the dataset was dealt were evaluated based on certain evaluation metrics like R-squared, mean square error and root mean square error. The models used are KNN, Decision Tree, Random Forest, XGBoost and CatBoost. Visualized and understood how the prices of those cryptocurrencies were impacted by other features of the dataset. The models that outperformed are Random Forest and CatBoost. Achieved RMSE was 365, 2.99, 29.99 and 0.0023 for Bitcoin, Binance, Ethereum and USDT respectively.
Improved Pin Entry Method to Prevent Shoulder Surfing Attacks

Remarkable recognizable proof numbers or (PINs) and passwords are striking and notable check methodologies utilized in different gadgets, for example, ATMs, cell phones, electronic passage locks, and that's just the beginning. Sadly, his traditional PIN passage method is irredeemably helpless against his shoulder riding assaults. Nonetheless, the security concentrates on used to help these proposed frameworks center around quantitative investigations, yet on the aftereffects of analyses and tests performed on the proposed frameworks. We propose another hypothesis based thought and technique for examination of quantitative security examination of PIN passage plans. This paper initially presents the guidelines of his safe new strategy for PIN section by inspecting another security idea known as framework based validation frameworks and normal stretch schedules as for new design for current schedules. Thusly, keep existing framework rules. We try to lay out another her PIN passage strategy that dependably sidesteps a human shoulder his riding assault by enormously expanding the computational intricacy an assailant needs to break a safe framework.
SignBD-Word: Video-Based Bangla Word-Level Sign Language and Pose Translation

Bangla sign language (BdSL) is a complete and independent natural sign language with its own linguistic characteristics. While there exists video datasets for well-known sign languages, there is currently no available dataset for word-level BdSL. In this study, we present a video-based word-level dataset for Bangla sign language, called SignBD-Word, consisting of 6000 sign videos representing 200 unique words. The dataset includes full and upper-body views of the signers, along with 2D body pose information. To evaluate the dataset, a number of deep learning-based algorithms for video classification and pose translation are used. The experimental results reveal that the recently developed SlowFast and I3D methods achieve higher accuracy for RGB and body pose data respectively, compared to the traditional attention-based approach. Furthermore, the experiments indicate that pix2pixHD outperforms CycleGAN and SPADE for human- to-human sign pose translation from bodypose data.
Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a type of deep learning techniques that have shown remarkable success in generating realistic images, videos, and other types of data. This paper provides a comprehensive guide to GANs, covering their architecture, loss functions, training methods, applications, evaluation metrics, challenges, and future directions. We begin with an introduction to GANs and their historical development, followed by a review of the background and related work. We then provide a detailed overview of the GAN architecture, including the generator and discriminator networks, and discuss the key design choices and variations. Next, we review the loss functions utilized in GANs, including the original minimax objective, as well as more recent approaches s.a. Wasserstein distance and gradient penalty. We then delve into the training of GANs, discussing common techniques s.a. alternating optimization, minibatch discrimination, and spectral normalization. We also provide a survey of the various applications of GANs across domains. In addition, we review the evaluation metrics utilized to assess the diversity and quality of GAN-produced data. Furthermore, we discuss the challenges and open issues in GANs, including mode collapse, training instability, and ethical considerations. Finally, we provide a glimpse into the future directions of GAN research, including improving scalability, developing new architectures, incorporating domain knowledge, and exploring new applications. Overall, this paper serves as a comprehensive guide to GANs, providing both theoretical and practical insights for researchers and practitioners in the field.
Image augmentation with conformal mappings for a convolutional neural network

Abstract For augmentation of the square-shaped image data of a convolutional neural network (CNN), we introduce a new method, in which the original images are mapped onto a disk with a conformal mapping, rotated around the center of this disk and mapped under such a Möbius transformation that preserves the disk, and then mapped back onto their original square shape. This process does not result the loss of information caused by removing areas from near the edges of the original images unlike the typical transformations used in the data augmentation for a CNN. We offer here the formulas of all the mappings needed together with detailed instructions how to write a code for transforming the images. The new method is also tested with simulated data and, according the results, using this method to augment the training data of 10 images into 40 images decreases the amount of the error in the predictions by a CNN for a test set of 160 images in a statistically significant way ( $$p=0.0360$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.0360</mml:mn> </mml:mrow> </mml:math> ).
YouTube Transcript Summarizer

The YouTube Transcript Summarizer is a web application that creates a succinct summary of a YouTube video's transcript using Natural Language Processing and the Flask framework. NLP techniques like text lemmatization, portion labeling, and named entity identification are used to extract essential information. The application is built with the Flask platform and set up on a web server to allow users easy access. With the help of the YouTube Transcript Summarizer, viewers may quickly and simply extract the most crucial information from lengthy movies.
Saliency of breast lesions in breast cancer detection using artificial intelligence

The analysis of mammograms using artificial intelligence (AI) has shown great potential for assisting breast cancer screening. We use saliency maps to study the role of breast lesions in the decision-making process of AI systems for breast cancer detection in screening mammograms. We retrospectively collected mammograms from 191 women with screen-detected breast cancer and 191 healthy controls matched by age and mammographic system. Two radiologists manually segmented the breast lesions in the mammograms from CC and MLO views. We estimated the detection performance of four deep learning-based AI systems using the area under the ROC curve (AUC) with a 95% confidence interval (CI). We used automatic thresholding on saliency maps from the AI systems to identify the areas of interest on the mammograms. Finally, we measured the overlap between these areas of interest and the segmented breast lesions using Dice's similarity coefficient (DSC). The detection performance of the AI systems ranged from low to moderate (AUCs from 0.525 to 0.694). The overlap between the areas of interest and the breast lesions was low for all the studied methods (median DSC from 4.2% to 38.0%). The AI system with the highest cancer detection performance (AUC = 0.694, CI 0.662-0.726) showed the lowest overlap (DSC = 4.2%) with breast lesions. The areas of interest found by saliency analysis of the AI systems showed poor overlap with breast lesions. These results suggest that AI systems with the highest performance do not solely rely on localized breast lesions for their decision-making in cancer detection; rather, they incorporate information from large image regions. This work contributes to the understanding of the role of breast lesions in cancer detection using AI.
Mass Formula for Self-Orthogonal and Self-Dual Codes over Non-Unital Rings of Order Four

We study the structure of self-orthogonal and self-dual codes over two non-unital rings of order four, namely, the commutative ring I=a,b|2a=2b=0,a2=b,ab=0 and the noncommutative ring E=a,b|2a=2b=0,a2=a,b2=b,ab=a,ba=b. We use these structures to give mass formulas for self-orthogonal and self-dual codes over these two rings, that is, we give the formulas for the number of inequivalent self-orthogonal and self-dual codes, of a given type, over the said rings. Finally, using the mass formulas, we classify self-orthogonal and self-dual codes over each ring, for small lengths and types.
The supply chain information sharing path based on the internet of things

Abstract In the context of globalization and digitization, supply chain information sharing and circulation have become important links in supply chain management. The current difficulty in sharing supply chain information is significant, and there are issues of information asymmetry and incompleteness among co participants. In order to improve the integrity and efficiency of supply chain information sharing and enhance the competitiveness of the high supply chain, this article conducts in‐depth research on the path of supply chain information sharing using Internet of Things technology. This article first analyzes the level of information sharing, influencing factors, and existing problems, then explores the Internet of Things technology, and finally establishes specific methods and means for the supply chain information sharing path through the Internet of Things technology. To verify the effectiveness of the supply chain information sharing path based on the Internet of Things, in this article, a comparison was made between the development effects of enterprise supply chains before and after applying the supply chain information sharing path based on the Internet of Things. The results show that after applying the IoT based supply chain information sharing path, the market price, production capacity, and market forecasting ability of enterprises have all increased by more than 20%. The conclusion indicates that the Internet of Things can help optimize supply chain information sharing and provide a new perspective for enterprise supply chain management and development.
Blind image watermark decoder in NSST-FPCET domain using Weibull Mixtures-HMT

Balancing imperceptibility, robustness, and data payload is a key topic in digital watermarking technology. Many contributions point to statistical modeling as an effective solution to this problem. On the basis of this, in this paper, we devise a hybrid domain image watermark decoder based on Weibull mixtures based hidden Markov tree (Weibull Mixtures-HMT) model. In the embedding phase, Otsu-Canny edge detection method is used to map the positions of high entropy blocks to the target subband obtained by non-subsampled shearlet transform (NSST), and fast polar complex exponential transform (FPCET) is computed in the target blocks to obtain the NSST-FPCET domain. The watermark signals are embedded into NSST-FPCET magnitudes by a linear method. In the extraction phase, NSST-FPCET magnitude coefficients are modeled by Weibull Mixtures-HMT model, which describes the distribution characteristics as well as the dependencies of NSST-FPCET magnitudes. The efficient variance reduced stochastic expectation maximization method is employed for estimating the parameters of Weibull Mixtures-HMT model. A statistical image watermark decoder in NSST-FPCET domain is finally achieved by the maximum likelihood decision. Massive experiments are performed so as to confirm the superiority of the designed scheme in trade-off among the data payload, imperceptibility, and robustness.
SoK: Privacy-preserving smart contract

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.