Reviewerly/exHarmony huggingface dataset:
Communication With Unreliable Entanglement Assistance

Entanglement resources can increase transmission rates substantially. Unfortunately, entanglement is a fragile resource that is quickly degraded by decoherence effects. In order to generate entanglement for optical communication, the transmitter and the receiver first prepare entangled spin-photon pairs locally, and then the photon at the transmitter is sent to the receiver through an optical fiber or free space. Without feedback, the transmitter does not know whether the entangled photon has reached the receiver. The present work introduces a new model of unreliable entanglement assistance, whereby the communication system operates whether entanglement assistance is present or not. While the sender is ignorant, the receiver knows whether the entanglement generation was successful. In the case of a failure, the receiver decodes less information. In this manner, the effective transmission rate is adapted according to the assistance status. Regularized formulas are derived for the classical and quantum capacity regions with unreliable entanglement assistance, characterizing the tradeoff between the unassisted rate and the excess rate that can be obtained from entanglement assistance. It is further established that time division between entanglement-assisted and unassisted coding strategies is optimal for the noiseless qubit channel, but can be strictly suboptimal for a noisy channel.
<i>D</i>-Semifaithful Codes That are Universal Over Both Memoryless Sources and Distortion Measures

We prove the existence of codebooks for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -semifaithful lossy compression that are simultaneously universal with respect to both the class of finite-alphabet memoryless sources and the class of all bounded additive rational distortion measures. By applying independent random selection of the codewords according to a mixture of all memoryless sources, we achieve redundancy rates that are within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\log n/n)$ </tex-math></inline-formula> close to the empirical rate-distortion function of every given source vector with respect to every bounded, rational distortion measure.
SoK: Managing risks of linkage attacks on data privacy

Novel attacks on dataset privacy are usually met with the same range of responses: surprise that a route to information gain exists from information previously thought to be safe; disputes around the viability or validity of the attack in real-world contexts; and, in the case of the computer science community, a drive to produce techniques that provably protect against the new class of attack. The result is a disjointed landscape with no shared approach to modelling threats to dataset privacy, and a toolbox of technically complex systems whose guarantees come with narrow assumptions and whose application in real-world contexts is hard to achieve. In this paper we aim to understand these issues by charting the history of dataset privacy attacks and systematising breaches through the lens of data linkage. We show how identification or information gain on a dataset's subjects can be expressed as data linkage, and use this to present a taxonomy of threat models which we apply to ninety-four attacks from across the literature. Our work demonstrates that dataset privacy must be approached first as a risk management problem, rather than one of strict guarantees, an approach which aligns well with law and practice. Our taxonomy of attacker intents provides a coherent language for expressing the wide variety of threat models in dataset privacy, and a framework for understanding how risks identified under one model can be understood within another. We also present insights around the factors that affect the feasibility and severity of attacks, and proposals for practical techniques that can be used for risk appraisal and management by practitioners, researchers, and regulators alike.
A Quantum Image Watermarking Scheme Based on Quantum Hilbert Scrambling and Steganography about the Moiré Fringe

In order to boost the security and confidentiality of information in quantum images, on the foundation of the NEQR model, a novel quantum watermarking scheme combining quantum Hilbert scrambling with steganography based on the Moiré fringe is designed in this paper. First of all, for carrier image, and watermark image, the color information and position information are denoted, respectively, by the NEQR model. Next, the watermark image is converted to a disordered image by quantum Hilbert scrambling, and the message of the original watermark image cannot be gained from the disordered image. At last, the watermark image after scrambling is embedded into the carrier image through the steganography of the Moiré fringe, obtaining the watermarked image. Due to the unitary image of the quantum gate, quantum Hilbert inverse scrambling is the opposite process of quantum Hilbert scrambling. In addition, the watermark image can be completely extracted from the watermarked image. What’s more, the experimental simulation and performance analysis of the scheme are done. The experimental simulation proves the feasibility of this algorithm. Visually, there is no difference between the carrier image and the watermarked image. The PSNR between the watermarked image and the carrier image is measured, which quantitatively shows the high similarity. In addition, the time complexity of the quantum circuit is lower than some other quantum image watermarking schemes, which proves the simplicity of this scheme.
Patch loss: A generic multi-scale perceptual loss for single image super-resolution

In single image super-resolution (SISR), although PSNR is a key metric for signal fidelity, images with high PSNR do not necessarily render high visual quality. As a result, current perception-driven SISR methods employ perceptual metrics close to the human eye to measure the quality of the generated images. Unfortunately, the perceptual loss and adversarial loss, widely used by the perception-driven SISR methods, still underperform on these non-differentiable perceptual metrics. To this end, we propose a generic multi-scale perceptual loss, i.e., the patch loss, which can be easily plugged into off-the-shelf SISR methods to improve a broad range of perceptual metrics. Specifically, the proposed patch loss minimizes the multi-scale similarity of image patches and enhances the restoration of regions with complex textures and sharp edges via parameter-free adaptive patch-wise attention. Our proposed patch loss introduces more realistic details compared to the perceptual loss and fewer artifacts compared to the adversarial loss.
A meta-framework for multi-label active learning based on deep reinforcement learning

Multi-label Active Learning (MLAL) is an effective method to improve the performance of the classifier on multi-label problems with less annotation effort by allowing the learning system to actively select high-quality examples (example-label pairs) for labeling. Existing MLAL algorithms mainly focus on designing reasonable algorithms to evaluate the potential values (as previously mentioned quality) of the unlabeled data. These manually designed methods may show totally different results on various types of datasets due to the defect of the methods or the particularity of the datasets. In this paper, instead of manually designing an evaluation method, we propose a deep reinforcement learning (DRL) model to explore a general evaluation method on several seen datasets and eventually apply it to unseen datasets based on a meta framework. In addition, a self-attention mechanism along with a reward function is integrated into the DRL structure to address the label correlation and data imbalanced problems in MLAL. Comprehensive experiments show that our proposed DRL-based MLAL method is able to produce comparable results as compared with other methods reported in the literature.
Information security for big data using the NTRUEncrypt method

Cloud computing processes vast quantities of data and offers a variety of flexible, secure, on-demand, and cost-effective collaboration options for consumers. Due to the increasing prevalence of hosted services, data security has become an increasingly critical concern. Hadoop, the engine at the heart of cloud computing, causes serious problems for the cloud. Any public, private, or hybrid cloud environment can use this security solution without any hassle (IaaS). Furthermore, it is compatible with the vast majority of Cloud computing's capabilities. Increase cloud security using NTRU encryption. This study made advantage of the (NTRUEncrypt) algorithms residing in Hadoop to speed up the file encryption and decryption processes. If HDFS is engaged in the Map Task, then HDFS will take care of both the encryption and decryption processes. Data on the cloud can be kept private and secure thanks to the proposed protection technique, which makes use of cryptography. Combining the proposed technique with preexisting infrastructure and web-based.
Interval-Valued Hesitant Fuzzy DEMATEL-Based Blockchain Technology Adoption Barriers Evaluation Methodology in Agricultural Supply Chain Management

Blockchain technology is emerging and has high potential to improve and transform the agricultural supply chain. This study investigates the critical barriers to blockchain technology adoption in the Vietnamese agricultural supply chain using a novel interval-valued hesitant fuzzy Decision-Making Trial and Evaluation Laboratory (IVHF-DEMATEL) approach. The IVHF-DEMATEL technique is applied to identify cause-and-effect relationships and draw the influence-relations map of the barriers. In contrast to prior work, which converts fuzzy sets into crisp sets and then uses crisp set operations, this study is the first study to investigate the Vietnamese agricultural supply chain that uses fully hesitant fuzzy operations representing experts’ assessment without information loss during the conversion. Our results show that ‘lack of government regulation’, ‘lack of scalability and system speed’, ‘a large amount of resource and capital requirements’, and ‘lack of trust among agro-stakeholder or public perception’ are the main barriers. Consistent with previous studies, ‘lack of government regulation’ is the most significant barrier. The results also indicate the hesitant degree of each barrier and better inform decision-makers about uncertain situations. Moreover, a priority order for tackling barriers is proposed to accelerate blockchain adoption in the Vietnamese agricultural supply chain.
Noise simulation in classification with the noisemodel R package: Applications analyzing the impact of errors with chemical data

Abstract Classification datasets created from chemical processes can be affected by errors, which impair the accuracy of the models built. This fact highlights the importance of analyzing the robustness of classifiers against different types and levels of noise to know their behavior against potential errors. In this context, noise models have been proposed to study noise‐related phenomenology in a controlled environment, allowing errors to be introduced into the data in a supervised manner. This paper introduces the noisemodel R package, which contains the first extensive implementation of noise models for classification datasets, proposing it as support tool to analyze the impact of errors related to chemical data. It provides 72 noise models found in the specialized literature that allow errors to be introduced in different ways in classes and attributes. Each of them is properly documented and referenced, unifying their results through a specific S3 class, which benefits from customized print, summary and plot methods. The usage of the package is illustrated through four application examples considering real‐world chemical datasets, where errors are prone to occur. The software presented will help to deepen the understanding of the problem of noisy chemical data, as well as to develop new robust algorithms and noise preprocessing methods properly adapted to different types of errors in this scenario.
A similarity-enhanced hybrid group recommendation approach in cloud manufacturing systems

With the development of cloud manufacturing (CMfg), a huge amount of services appears on the Internet, which makes recommender systems in CMfg service a promising research field. To this end, recent studies mainly focus on solving individual recommendation to meet the requirements of every user. However, due to the time complexity problem, ‘Many-to-Many’ recommendation mode is increasing in real applications. To implement such a group recommendation is very challenging, because the system not only needs to achieve high recommendation quality but also satisfies user clusters in an even way. Therefore, we propose a similarity-enhanced hybrid group recommendation approach named HGRA for cloud manufacturing. Specifically, we implement the HGRA system by three main components. Firstly, an enhanced user similarity measuring approach is designed to identify a similar user group based on the non-linear model Proximity-Significance-Singularity (PSS) and Kullback-Leibler (KL) distance algorithms. Secondly, a set of user subgroups are further clustered using K-medoids algorithm, in which additional information similarity is calculated by making full use of functional information about the users. Thirdly, a weighted ranking aggregation model is established to generate a recommendation list according to the representative user of each subgroup. The performance of our system is tested by two data sets from real-world cloud manufacturing systems. The experimental results demonstrate the feasible and effectiveness of our approach, compared with some state-of-the-art benchmark solutions, especially in CMfg systems.
Multi-occupant dynamic thermal comfort monitoring robot system

Nowadays occupant thermal comfort has been a research focus for people’s increasing indoor time and its potential influence on productivity. Efforts have been paid to develop thermal comfort monitoring systems for building environments. Fixed sensors might be less efficient for non-uniform environment distribution. To solve this problem, mobile robots are introduced for occupant thermal monitoring. However, there are still some challenges for thermal comfort monitoring systems, which include multi-view extraction and fusion of human features, dynamic recognition and real-time estimation for multiple occupants, and multi-occupant localization. To fulfill these research gaps, we propose a novel mobile robot based thermal comfort monitoring system, which collects occupant thermal information from RGB-D and thermal images, locates occupants and estimates their thermal comfort in real time. An autonomous robot is designed to automatically recognize occupants, their appearance, wearing cloth, and body temperature from collected images. A machine learning method is trained to estimate each occupant’s thermal comfort. Moreover, the robot can re-identify the occupant in different views, mark their positions and track their trajectory in a real-time map. We conducted an experiment on 20 occupants in 80 h in an office building, and the results showed that our system can estimate the occupant’s thermal comfort in real time with a high ROC-AUC score of 0.84.
“Everything will be all right (?)”: Discourses on COVID-19 in the Italian linguistic landscape

The study of the linguistic landscape (LL) focuses on the representations of languages on signs placed in the public space and on the ways in which individuals interact with these elements. Regulatory, infrastructural, commercial, and transgressive discourses, among others, emerge in these spaces, overlapping, complementing, or opposing each other, reflecting changes taking place and, in turn, influencing them. The COVID-19 pandemic has affected all aspects of life, including cities, neighborhoods, and spaces in general. Against this background, the study of the LL is fundamental not only to better understand the ways in which places have changed and how people are interpreting and experiencing them but also to analyze the evolution of COVID-19 discourses since the pandemic broke out. This contribution aims to investigate how and in what terms the COVID-19 pandemic has had an impact on the Italian LL, considered both in its entirety, as a single body that, regardless of local specificities, responded to and jointly reflected on the shared shock, and specifically, assuming the city of Florence as a case study. The data collected in the three main phases of the pandemic include photographs of virtual and urban LL signs and interviews, which were analyzed through qualitative content analysis with the aim of exploring citizens' perceptions and awareness of changes in the LL of their city. The results obtained offer a photograph of complex landscapes and ecologies, which are multimodal, multi-layered, and interactive, with public and private discourses that are strongly intertwined and often complementary. Furthermore, the diachronic analysis made it possible to identify, on the one hand, points in common with the communication strategies in the different phases, both at a commercial and regulatory level. On the other hand, strong differences emerged in the bottom-up representations, characterized in the first phase by discourses of resilience, tolerance, hope, solidarity, and patriotism, and in the second and third phases by disillusionment, despair, and protest.
A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography

In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator's experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge.
Bitstream-Based Perceptual Quality Assessment of Compressed 3D Point Clouds

With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model. We then construct a texture distortion assessment model upon texture complexity and quantization parameters. By combining this texture distortion model with a geometric distortion model derived from Trisoup geometry encoding parameters, we obtain an overall bitstream-based NR point cloud quality model named streamPCQ. Experimental results show that the proposed streamPCQ model demonstrates highly competitive performance when compared with existing classic full-reference (FR) and reduced-reference (RR) point cloud quality assessment methods with a fraction of computational cost.
Speeding at the Edge: An Efficient and Secure Redactable Blockchain for IoT-Based Smart Grid Systems

As a promising approach to extending cloud resources and services, blockchain-enabled Internet of Things (IoT)-based smart grid edge computing has attracted much attention. However, the edge node’s resource-constraint nature makes it difficult to store the entire chain as the sensing IoT data volume increases. To address this issue, we propose an FS scheme, a fast and secure multithreshold trapdoor Chameleon hash scheme which serves as the basis for block substitution at the edge nodes to solve the storage limitation problem. The FS scheme is used to achieve a consensus-based block substitution, which allows <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -out-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> edge nodes to compute a hash collision collaboratively to reliably substitute a historical block without leaking the randomness <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> . Also, inspired by the rationale of fast polynomial interpolation, we optimize the FS scheme to FS-I to reduce the time complexity from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(nt)$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(t{\mathrm{ log}}^{2}t)$ </tex-math></inline-formula> . In addition, we further optimize FS-I to FS-II by using a fast Fourier transform (FFT) to dramatically improve the computational efficiency of Lagrange interpolation, which leads to a significant improvement in terms of block substitution performance. Finally, We provide security analysis and evaluate the performance through comprehensive experiments and the results show that FS can achieve up to several magnitudes better than DTTCH. The results also demonstrate that the FS scheme can provide high service quality for large-scale IoT-based smart grid systems.
Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows

With the increasing deployment of small unmanned aerial systems (sUASs) on various tasks, it becomes crucial to analyze and detect anomalies from their flight logs. To support research in this area, we curate Drone Log Anomaly (DLA), the first real-world time series anomaly detection dataset in the domain of sUASs, which contains 41 sUAS flight logs annotated with various types of anomalies. As anomalies tend to occur in low-density areas within a distribution, we propose graphical normalizing flows (GNF), a graph-based autoregressive deep learning model, to perform anomaly detection through density estimation. GNF contains 1) a temporal encoding module using a transformer to capture the temporal dynamics, 2) an interfeature encoding module leveraging graph representation learning on a Bayesian network to model the statistical dependencies among time series features, and 3) a density-estimation module with normalizing flows. Extensive experiments have demonstrated GNF’s superior anomaly detection power on DLA compared with state-of-the-art baselines.