Reviewerly/exHarmony huggingface dataset:
On the Security of Quantum Key Distribution Networks

The main purpose of a quantum key distribution network is to provide secret keys to any users or applications requiring a high level of security, ideally such as to offer the best protection against any computational attack, even of a quantum nature. The keys shared through a point-to-point link between a source and a detector using a quantum key distribution protocol can be proven information-theoretically secure based on the quantum information theory. However, evaluating the security of a quantum key distribution network, especially if it is based on relay nodes, goes far beyond the quantum security of its single quantum links, involving aspects of conventional security for devices and their communication channels. In this contribution, we perform a rigorous threat analysis based on the most recent recommendations and practical network deployment security issues. We show that, at least in the current state of our understanding of quantum cryptography, quantum key distribution networks can only offer computational security and that their security in practical implementations in the shorter term requires resorting to post-quantum cryptography.
Validating a Task Complexity Framework for Studies of Uncertainty Visualization

In this study, we validate and extend previous theoretical and empirical work on the design of visualizations to support decision makers using interfaces with high levels of information density and evolving constraints. Prior research proposed and validated a theoretical framework of task complexity to facilitate the systematic evaluation of uncertainty visualizations for these contexts. We adopted this approach to the evaluation of visualizations of uncertainty, displaying hurricane visualizations on a map rather than abstract symbols on a grid, manipulating the dispersion of visualizations, and varying an additional visuospatial element––visualization orientation––to improve the ecological validity of the experimental task. The results of this study provide evidence for the utility of a task complexity framework for research on the visualization efficacy.
Road-Side Unit Anomaly Detection

Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs’ behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required.
Blockchain Application to Financial Market Clearing and Settlement Systems

Blockchain technology has emerged as a transformative force in the financial industry, offering the potential to streamline and enhance financial markets’ clearing and settlement processes. This paper explores the application of blockchain technology in these critical areas. We examine traditional clearing and settlement procedures, the challenges they pose, and how blockchain can address these issues. Through case studies and technical insights, we illustrate the benefits and limitations of implementing blockchain solutions. This paper utilizes the PRISMA method to survey papers related to blockchain-based clearing and settlement systems, while using Science Direct to identify papers that have been published in this area. These papers were reviewed to identify themes that relate to extending blockchain development for clearing and settlement system in financial markets. As a result, this paper also shows how the Layer One X (L1X) blockchain can be applied to develop financial markets clearing and settlement systems.
The interaction between online and offline Islamophobia and anti‐mosque campaigns: The literature review with a case study from an anti‐mosque social media page

Abstract In the aftermath of the war on terror, mosques have become targets for hate groups, leveraging online platforms to amplify global anti‐mosque campaigns. These groups link local protestors with international hate networks, fuelling both online and offline (i.e., onsite) anti‐mosque campaigns. Thoroughly reviewing the literature addressing the interaction between online and offline Islamophobia and introducing an anti‐mosque social media page instilling the public with online and offline anti‐mosque hate, this article suggests a strong interaction between online and offline Islamophobia. In the provided case study from the Stop Mosque Bendigo (SMB), purposeful sampling was used to collect postings before and after the Christchurch Mosque attacks to analyse the evolution of online anti‐mosque campaigns in tandem with real‐life hate cases. The literature and the case study reveal the interaction between local and global, digital, and physical realms, as well as the convergence of everyday racism with extremist far‐right ideologies like the Great Replacement theory. Relying on the present literature and indicative findings, the article advocates for systematic investigations to uncover the direct connection between online hate and physical attacks and urges closer monitoring and accountability for those online platforms and social media pages apparently contributing to onsite hate‐driven actions.
The Identity Construction of Microblog Users from the Perspective of Impression Management Theory

In recent years, the emergence of mobile social media and platforms has made the communication between people more and more convenient. As a social media platform based on user relations, microblog users can realize the real-time sharing and interaction of information in the form of multimedia such as text, pictures and videos. Therefore, it is of certain theoretical and practical significance to study how microblog users use this new social stage for impression management and identity construction. Based on impression management theory, this study adopts the method of combining qualitative and quantitative to analyze and study the motivation and strategy of ordinary microblog users (different from celebrities) when using microblog and their construction identities. The research shows that when ordinary microblog users use Weibo for impression management, most users prefer to use acquired impression management strategies, publishing positive, healthy and upward content, and through these they construct positive personal identities. In addition, the main motivation of these users for using microblog is to win likes and attention from others, which is beneficial for users to manage their image and better promote the healthy development of social network.
A fault diagnosis deep learning model based on feature extraction with good anti-noise ability

This paper proposes a fault diagnosis model based on the combination of continuous wavelet transform and improved first-layer wide convolutional neural network (CWT-IWDCNN). The model first performs continuous wavelet transform on the data containing a lot of noise to extract its characteristic data, and converts the original data into a time-frequency map. Then, the time-frequency diagram is fed into the IWDCNN model to obtain the diagnosis result. The proposed CWT-IWDCNN has the following advantages. 1) The continuous wavelet transform can effectively extract fault features; 2) The first layer of the convolutional neural network uses a wide convolution kernel to suppress high-frequency noise, and other layers use small the convolution kernel improves the domain adaptive ability of the model through nonlinear mapping to improve the accuracy of diagnosis; Finally, in order to test the performance of this model, the CWRU dataset is used for experimental verification. The experiments show that the proposed fault diagnosis method is better than WDCNN in most cases in terms of accuracy and robustness.
Vehicle pedestrian detection method based on improved YOLOv5 algorithm

Vehicle pedestrian detection is a key aspect in driver assistance systems, which need to accurately detect all vehicle pedestrian targets on the roadway in order to ensure driving safety. To solve the problem of low accuracy in vehicle pedestrian target detection, this paper proposes a vehicle pedestrian detection method based on the improved YOLOv5 algorithm. In this paper, the initial anchor boxes of the dataset are re-clustered by the K-means clustering algorithm, and the CIOU loss function and DIOU_nms, are applied to the YOLOv5 algorithm to improve the target recognition effect and reduce the false and missed detection rate of small targets. The experimental results show that the mAP@0.5 of the improved YOLOv5 algorithm is improved by 1.85%.
Blind watermarking algorithm based on redistributed invariant integer wavelet transform and BP network

In this paper, a blind watermarking algorithm based on redistributed invariant integer wavelet transform (RI-IWT) and BP network is proposed. The host image is processed by RI-DWT and QR decomposition, and the watermark is embedded in the low frequency and high frequency sub-band of the host image, so as to increase the embedding capacity. Moreover, we realize the blind extraction of watermark through BP network. In the extraction stage, the host image information and part of the watermark information are no longer required, thus improving the security of the algorithm. Finally, we simulate the algorithm and compare it with the relative algorithms. Simulation results show that the proposed algorithm has high invisibility and good robustness against JPEG attacks, rotation attacks, noise attacks.
CPCL: Conceptual prototypical contrastive learning for Few-Shot text classification

As prototype-based Few-Shot Learning methods, Prototypical Network generates prototypes for each class in a low-resource state and classify by a metric module. Therefore, the quality of prototypes matters but they are inaccurate from the few support instances, and the domain-specific information of training data are harmful to the generalizability of prototypes. We propose a Conceptual Prototype (CP), which contains both rich instance and concept features. The numerous query data can inspire the few support instances. An interactive network is designed to leverage the interrelation between support set and query-detached set to acquire a rich Instance Prototype which is typical on the whole data. Besides, class labels are introduced to prototype by prompt engineering, which makes it more conceptual. The label-only concept makes prototype immune to domain-specific information in training phase to improve its generalizability. Based on CP, Conceptual Prototypical Contrastive Learning (CPCL) is proposed where PCL brings instances closer to its corresponding prototype and pushes away from other prototypes. “2-way 5-shot” experiments show that CPCL achieves 92.41% accuracy on ARSC dataset, 2.30% higher than other prototype-based models. Meanwhile, the 0-shot performance of CPCL is comparable to Induction Network in the 5-shot way, indicating that our model is adequate for 0-shot tasks.
M-channel filter banks for digraph signals

This paper presents a method for multichannel filter banks on directed graph signals. The multichannel filter banks on directed graph are constructed with oversampling structure, and the analysis and synthesis filters are designed in the form of directed graph filters. In this paper, the spectral folding properties of the directed Hermitian Laplacian basic matrix on the directed bipartite graph are derived and analyzed, and the method on the undirected graph is extended to the directed graph by combining the signal representation on the directed graph, and the rotation parameters and the values of the matrix weights are also discussed. The results of the multiresolution analysis on the signals on different directed graphs verify the perfect reconstruction properties of the designed directed graph filter banks with the help of Chebyshev polynomial approximation algorithm.
Design of binocular vision system based on Zynq

Binocular vision is an important branch of computer vision that processes images captured from two different cameras with different perspectives to obtain depth information of a scene. However, the stereo matching algorithm is complex and traditional platforms often struggle to meet real-time and accuracy requirements simultaneously. In this article, we implement a binocular vision system based on the idea of software-hardware co-design on the Zynq platform. According to the characteristics of the Zynq platform, the system's software and hardware functions are divided. The processing system (PS) is mainly responsible for flow control and binocular correction, while the programmable logic (PL) is responsible for parallel acceleration of the stereo matching algorithm. In the image acquisition part, we configure the format and output image timing using a designed camera acquisition module. In the stereo matching part, we design a semi-global stereo matching algorithm using Verilog language. Finally, we build a binocular vision system on the Zynq platform for testing. The experimental results show that the frame rate can reach 30fps when the input image resolution is 640×480, which satisfies the requirements of real-time and accuracy of the output disparity map.
Revised elliptic curve cryptography multi-signature scheme (RECC-MSS) for enhancing security in electronic health record (EHR) system

Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient’s health record as a centralized network. So, “blockchain technology” has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the “Revised Elliptic Curve Cryptography Multi-Signature Scheme” (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as “SHA224”, “SHA256”, “SHA384”, “SHA512”, “SHA3-224”, “SHA3-256”, “SHA3-384”, “SHA3-512”, and “Message Digest5” (MD5) by “One-way ANOVA” test in terms of “accuracy”, “throughput” and “time complexity”. The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms.
Auditable anonymous authentication scheme for cross-chain data transmission

Blockchain, as a distributed database, has been wildly used in real-world applications. Different systems based on blockchain form data silos. Therefore, cross-chain data transmission is required to realize value transfer in different systems. In the process of cross-chain data transmission, authentication is important for data security. In this work, we design an auditable anonymous authentication scheme for cross-chain data transmission. In our scheme, the sender can generate anonymous authentication message on the data. Then the receiver of the target blockchain knows the data is sent by a legal user of the original blockchain, but cannot know the real identity of the sender. By using Trusted Execution Environment (TEE) and pseudo identity methods, our scheme is more efficient than the similar anonymous authentication schemes in blockchain.
Improved AKAZE feature matching algorithm based on grid motion statistics

Aiming at the problem that AKAZE algorithm has slow feature extraction speed and low accuracy in the feature matching process, this paper proposes to improve AKAZE's feature matching algorithm based on grid statistical motion. Firstly, in the feature extraction stage, the proposed algorithm uses the oFAST algorithm instead of constructing scale space to extract feature points. Then, the M-LDB descriptor is used for feature point description. Finally, the BF algorithm is used to perform coarse matching of features, and the grid motion statistics (GMS) algorithm is added to achieve the purification of matching point pairs and complete the matching. The performance of the proposed algorithm was compared with the AKAZE and ORB algorithms in the experimental fields and grayscale graph groups. The results show that the improved algorithm not only improves the matching speed, which is more than 2 times faster than the AKAZE algorithm, but also maintains a high matching accuracy, which is similar to the AKAZE algorithm.
Blockchain-based ciphertext access control for data sharing

Currently, with the continuous in-depth research and application of blockchain access control, security issues on the blockchain have become a focus of attention. Based on CPABE, this paper proposes a trusted and secure blockchain access control scheme based on ciphertext policy. Firstly, a decentralized attribute-based encryption algorithm (DABE) is adopted to achieve distributed calculation of user attribute private keys, effectively solving the problems of high trust cost and single point of failure caused by the key center generating private keys in traditional CPABE. At the same time, a private key consensus verification protocol based on zero-knowledge proof is designed to ensure the correctness and security of user attribute private keys without leaking private key information. Through the analysis of on-chain security and experimental simulation, the results show that this scheme has better performance while maintaining high security and is more suitable for distributed access control with large attribute scales.
Research on intrapulse modulation mode recognition based on multilayer convolution neural network

With the development of technology, the intrapulse modulation technology of radar signal is becoming more and more complex. In this paper, a method based on multilayer convolutional neural network (MCNN) is proposed to identify the intrapulse modulation mode. Firstly, five signal modulation modes (conventional pulse signal, linear frequency modulation signal, sinusoidal frequency modulation signal, phase shift keying signal and frequency shift keying signal) are established. By changing the values of carrier frequency, pulse width and repetition frequency, five kinds of training data are generated with a certain signal-to-noise ratio (SNR). Then, five trained MCNNs are obtained by training five kinds of training data with a MCNN. Further, the recognition performance of different trained MCNNs is studied with the test data generated under different SNRs. Finally, the simulation shows that the mode with parameter variations has the best recognition performance.
Results of Monitoring the Integration of Academic Disciplines in the Context of Digital Education l

The authors of the article analyze the results of monitoring career guidance work in educational organizations of the Stavropol Territory. The head of educational organizations took part in monitoring. Based on the analysis of monitoring results, effective tools for career guidance work in an educational organization are described.
Evaluation of oral English teaching quality based on BP neural network optimized by improved crow search algorithm

Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance.
A vibe algorithm based on fused edge information optimization

Foreground detection is a significant area of study within the realm of computer vision and plays a crucial role in video-based applications. The Vibe algorithm is an efficient foreground detection method, and this paper proposes an optimized Vibe algorithm that incorporates fused edge information to address the issues of "ghosting" and noise. Initially, the edge information derived from the three-frame method is enhanced by the Canny operator. Subsequently, the OTSU algorithm is employed to filter out additional noise in the image, thereby enhancing the robustness of the algorithm. Finally, the optimized three-frame difference method is fused with the Vibe algorithm to eliminate the "ghosting" and achieve a complete foreground target. Experimental results demonstrate that the proposed algorithm effectively removes "ghosting" and noise, providing good real-time performance and robustness, thereby meeting the application requirements of real-time detection.