Reviewerly/exHarmony huggingface dataset:
Private Federated Submodel Learning with Sparsification

We investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification. In FSL, a machine learning model is divided into multiple submodels, where each user updates only the submodel that is relevant to the user’s local data. PRUW is the process of privately performing FSL by reading from and writing to the required submodel without revealing the submodel index or the values of updates to the databases. Sparsification is a widely used concept in learning, where the users update only a small fraction of parameters to reduce the communication cost. Revealing the coordinates of these selected (sparse) updates leaks privacy of the user. We show how PRUW in FSL can be performed with sparsification. We propose a novel scheme which privately reads from and writes to arbitrary parameters of any given submodel, without revealing the submodel index, values of the updates, or the coordinates of the sparse updates, to databases. The proposed scheme achieves significantly lower reading and writing costs compared to what is achieved without sparsification.
Cross-scale feature fusion-based JND estimation for robust image watermarking in quaternion DWT domain

Owing to the rapid development of just-noticeable difference (JND) and the high robustness of discrete wavelet transform (DWT) based watermarking, robust watermarking methods based on JND models have been extensively adopted in the area of image copyright protection. In general, in existing JND model-based methods, transform domain-based features play an important role during the quantization strength measurement. However, most existing methods only use the subband-specific features from the final multi-scale wavelet transform domain, ignoring the feature relevance among subband-specific features with different scales among RGB channels. To address this issue, in this study, we put forward a JND estimation based on cross-scale feature fusion (CFJnd) mechanism in quaternionic DWT domain for robust image watermarking. For the proposed CFJnd, we first design different quaternionic wavelet scales for the JND modeling, which can be used to measure the minimum distortion level of the embedding-specific subband relevance. Furthermore, the cross-scale features among different subbands are adopted to jointly generate the pattern, texture and color measurement, making the final perceptual masking more representative. In addition, we further develop a CFJnd-based robust image watermarking, and the watermark is embedded by quantifying the candidate quaternionic DWT coefficients of the embedding-specific subband on a level which is adaptively defined according to the proposed CFJnd. Experimental results based on the publicly available image database show that the proposed method can produce reliable and promising results, compared to other state-of-the-art image watermarking methods.
Low Computational Vehicle Re-Identification for Unlabeled Drone Flight Images

Recently advanced vehicle re-identification frameworks are mainly based on convolutional neural networks (CNN) and labeled information. Previous frameworks face two difficulties. First CNN includes complicated architectures, which require expensive GPU devices to perform computation. The second difficulty is that annotating vehicle identities for every frame is expensive and time-consuming. To tackle these two difficulties, this study proposes a simple but effective method to perform re-ID without CNN and labeled identities. The proposed method has two streams of vehicle re-identification. The object detector takes charge of detecting vehicles on the road. With the position of vehicles in the image, the condition module extracts the vehicle movement information and sets the condition to match the same vehicle between current and subsequent frames. To train the object detector and test the proposed algorithm, a set of drone flight images collect and annotate for studying the traffic road. It contains 9,776 train images and 2,200 test images for object detection. In the experiments, three different traffic video clips were applied for testing the proposed method.
Chain-Net: An Internet-inspired Framework for Interoperable Blockchains

Blockchain has introduced new opportunities with the potential to enhance systems and services across diverse application domains. Fundamental characteristics of blockchains such as immutability, decentralisation, transparency and traceability have a profound role in this. However, integration with contemporary systems and among disparate blockchain-based applications is a non-trivial challenge primarily due to differences with respect to platforms, consensus mechanism, and governance. Although this challenge has received some attention from the research community, it requires careful analysis to analyse existing work and ascertain gaps to achieve effective and efficient solution to this challenge. This article presents a thorough systematic review of existing research within blockchain interoperability highlighting significant contributions. Leveraging this analysis, the article presents an internet-inspired framework (Chain-Net) to facilitate interoperability within blockchain-based systems whereby two systems within independent Blockchain networks can securely exchange data with each other. This is achieved by using gateway module at each network. This module is a lightweight node registered by the Blockchain network, equipped with discovery service to lookup a target blockchain, and is responsible for forwarding cross-chain transactions to gateway module at the target blockchain. Gateway module plays a vital role in the Chain-Net model, as it holds a cross-chain transaction in a pending state until a confirmation is received from the target blockchain, thus maintaining the record integrity between the two chains. The article presents our efforts to evaluate the proposed blockchain interoperability framework against success criteria based on our analysis of the blockchain interoperability challenge.
Edge AI: Addressing the Efficiency Paradigm

Recent years have seen a growing trend towards massive deep learning neural network algorithms. This movement is further perpetuated by the rapid growth in available computation. While these giant models attain remarkable performance, the required computational cost is proportionally huge. There is a resulting necessity for efficient and intelligent algorithm design that can achieve similar high performance to current state-of the-art.
Embedding Learning in Hybrid Quantum-Classical Neural Networks

Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters do not utilize the Hilbert space efficiently. We observe that the few-shot learned embeddings generalize to unseen classes and suffer less from the circuit bypass problem compared with other approaches.
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video

This paper aims to tackle the challenge of efficiently producing interactive free-viewpoint videos. Some recent works equip neural radiance fields with image encoders, enabling them to generalize across scenes. When processing dynamic scenes, they can simply treat each video frame as an individual scene and perform novel view synthesis to generate free-viewpoint videos. However, their rendering process is slow and cannot support interactive applications. A major factor is that they sample lots of points in empty space when inferring radiance fields. We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos. Specifically, given multi-view images at one frame, we first build the cascade cost volume to predict the coarse geometry of the scene. The coarse geometry allows us to sample few points near the scene surface, thereby significantly improving the rendering speed. This process is fully differentiable, enabling us to jointly learn the depth prediction and radiance field networks from RGB images. Experiments on multiple benchmarks show that our approach exhibits competitive performance while being at least 60 times faster than previous generalizable radiance field methods.
A novel deterministic substitution box generator over elliptic curves for real-time applications

Recently, highly dynamic substitution box (S-box) generators are widely used in cryptosystems to improve the security against modern computational attacks. However, generating S-boxes is computationally costly, and frequently generating them slows down the encryption process and hence limits the encryption throughput. This fact necessitates developing new S-box generators that can provide optimal security with minimum computation cost. We propose a novel S-box generator based on a deterministic algorithm over elliptic curves to generate highly dynamic and secure S-boxes with low computation cost. Experimental results show that our generator is i times, i∈[6.720,1.3×105] faster than the fastest available S-box generator to generate S-boxes of an input size 2m,m=4,6,8,10,12,14,16 over large ECs. This fact allows us to attain significantly higher throughput as compared to the state-of-the-art schemes. Furthermore, our generator can generate highly dynamic, key-sensitive, and cryptographically secure S-boxes. As a sample application, we used our generator in an image encryption scheme. Experimental results reveal that our encryption scheme has high resistance against differential and statistical attacks, and key attacks. Furthermore, with the new S-box generator, the running time of the modified scheme reduced by two orders of magnitude.
Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder

Graph-based recommender system has attracted widespread attention and produced a series of research results. Because of the powerful high-order connection modeling capabilities of the Graph Neural Network, the performance of these graph-based recommender systems are far superior to those of traditional neural network-based collaborative filtering models. However, from both analytical and empirical perspectives, the apparent performance improvement is accompanied with a significant time overhead, which is noticeable in large-scale graph topologies. More importantly, the intrinsic data-sparsity problem substantially limits the performance of graph-based recommender systems, which compelled us to revisit graph-based recommendation from a novel perspective. In this article, we focus on analyzing the time complexity of graph-based recommender systems to make it more suitable for real large-scale application scenarios. We propose a novel end-to-end graph recommendation model called the Collaborative Variational Graph Auto-Encoder (CVGA), which uses the information propagation and aggregation paradigms to encode user–item collaborative relationships on the user–item interaction bipartite graph. These relationships are utilized to infer the probability distribution of user behavior for parameter estimation rather than learning user or item embeddings. By doing so, we reconstruct the whole user–item interaction graph according to the known probability distribution in a feasible and elegant manner. From the perspective of the graph auto-encoder, we convert the graph recommendation task into a graph generation problem and are able to do it with approximately linear time complexity. Extensive experiments on four real-world benchmark datasets demonstrate that CVGA can be trained at a faster speed while maintaining comparable performance over state-of-the-art baselines for graph-based recommendation tasks. Further analysis shows that CVGA can effectively mitigate the data sparsity problem and performs equally well on large-scale datasets.
An Exploration Analysis of Social Media Security

Social media security is a rising concern among today’s generation, as when the pandemic started, a lot more people than before have begun using social media due to lack of entertainment or other reasons. There is a rise of 62% in Ransomware since 2019 (pre-pandemic), as mentioned by the Cyber Threat Report by SonicWall. As cybersecurity attacks are becoming more severe, this number of attacks is still set to rise. So in order to investigate the possible security issues, this paper digs deep into the concepts of social media security, potential threats and feasible solutions. The importance of having users’ data secure and protected from various threats such as malware attacks, identity theft, cyberbullying and so on, is addressed so that neither the user nor the developers suffer from any loss. Organizations may do more effective patch management to prioritize security-related patching and update their software in accordance with the solutions discussed in this paper.
TOW-IDS: Intrusion Detection System Based on Three Overlapped Wavelets for Automotive Ethernet

Devices that ensure vehicle and driver safety or provide services to drivers generate a substantial amount of network traffic. The traffic is transmitted to the In-Vehicle Network (IVN) depending on the defined function. Consequently, to quickly process a lot of traffic transmitted to the IVN, an advanced network protocol such as Automotive Ethernet is necessary. However, owing to the connectivity reinforcement between devices inside a vehicle and external networks, attack vectors and vulnerabilities can be easily inherited from an established Ethernet to Automotive Ethernet. The present study proposes a method for detecting and identifying abnormalities in Automotive Ethernet based on wavelet transform and deep convolutional neural network. First, we define attack scenarios and extract normal and abnormal data corresponding to these scenarios. Second, we conduct several preprocesses, such as fixing the packet size and normalizing the network image data. Finally, we conduct extensive evaluations of the proposed method’s performance, considering the size of network image data and multi-resolution levels. The results demonstrate that the proposed method can effectively detect an abnormality. Furthermore, the results suggest that the our method is more effective in terms of time-cost compared to default ResNet and EfficientNet methods.
Variable-Modulation Specific Emitter Identification With Domain Adaptation

Specific emitter identification (SEI) is a technique of identifying individual emitters via unique characteristics of different emitters. In this paper, we consider a SEI problem with transmitter changing modulations scenario. There have been few previous studies on this type of scenario. To cope with the daunting challenge, a variable-modulation SEI framework with domain adaptation is proposed. The components characteristics of transmitter are analyzed and the distortion models are established for simulation dataset generation. The received in-phase/quadrature (I/Q) signals are demodulated and reconstructed to obtain baseband ideal modulation signals. The received signals and the ideal modulation signals corresponding to demodulation and reconstruction are merged and embedded into the feature extraction network. Domain adversarial neural network (DANN) is added into the SEI framework to generate domain-invariant fingerprint features, thus realizing variable-modulation SEI. To better align the distortion features of emitters with variable modulations, Gaussian Encoder is designed to project fingerprint features into Gaussian distribution space. Numerous experiments show that the proposed SEI framework can improve recognition accuracy of individual emitter for single modulation and variable transfer greatly, and outperform the existing transfer learning methods. The ablation study demonstrates the components of framework are complementary. The complexity of framework is acceptable and it can extend to large-scale use. The robustness of framework is verified through modulation transfer among PSK and QAM.
Understanding and combating robust overfitting via input loss landscape analysis and regularization

Adversarial training is widely used to improve the robustness of deep neural networks to adversarial attack. However, adversarial training is prone to overfitting, and the cause is far from clear. This work sheds light on the mechanisms underlying overfitting through analyzing the loss landscape w.r.t. the input. We find that robust overfitting results from standard training, specifically the minimization of the clean loss, and can be mitigated by regularization of the loss gradients. Moreover, we find that robust overfitting turns severer during adversarial training partially because the gradient regularization effect of adversarial training becomes weaker due to the increase in the loss landscapes curvature. To improve robust generalization, we propose a new regularizer to smooth the loss landscape by penalizing the weighted logits variation along the adversarial direction. Our method significantly mitigates robust overfitting and achieves the highest robustness and efficiency compared to similar previous methods. Code is available at https://github.com/TreeLLi/Combating-RO-AdvLC.
A hybrid optimization algorithm for GWO Fine-tuning GRU-Aided AKF during GPS outage

In order to improve the positioning accuracy of the combined navigation system when GPS outages, a hybrid model using Grey Wolf Optimizer to fine-tune Gated Recurrent Unit (GWO-GRU) aided adaptive Kalman filter (AKF) is proposed. Firstly, it is verified that the introduction of GWO plays a crucial role in the prediction performance of GRU, and effectively solves the problem of difficult selection of hyperparameters in GRU networks. Secondly, the trajectory curves with different complexity are predicted by three simulated GPS outage experiments, which verifies that the proposed model has strong error suppression ability and solves the problem that complex trajectory is difficult to predict accurately. In general, this paper demonstrates the prediction ability of GWO-GRU + AKF model when GPS is outang for a long time and complex trajectory.
Medical Image Compression Based on Variational Autoencoder

With the rapid growth of medical image data, it has become a current research hotspot that how to realize the large amounts of the real-time upload and storage of medical images with limited network bandwidth and storage space. However, currently, medical image compression technology cannot perform joint optimization of rate (the degree of compression) and distortion (reconstruction effect). Therefore, this study proposed a medical image compression algorithm based on a variational autoencoder. This algorithm takes rate and distortion as the common optimization goal and uses the residual network module to directly transmit information, which alleviates the contradiction between improving the degree of compression and optimizing the reconstruction effect. At the same time, the algorithm also reduces image loss in the medical image compression process by adding the residual network. The experimental results show that, compared with the traditional medical image compression algorithm and the deep learning compression algorithm, the algorithm in this study has smaller distortion, better reconstruction effect, and can obtain higher quality medical images at the same compression rate.
Towards data augmentation in graph neural network: An overview and evaluation

Many studies on Graph Data Augmentation (GDA) approaches have emerged. The techniques have rapidly improved performance for various graph neural network (GNN) models, increasing the current state-of-the-art accuracy by absolute values of 4.20%, 5.50%, and 4.40% on Cora, Citeseer, and PubMed, respectively. The success is attributed to two integral properties of relational approaches: topology-level and feature-level augmentation. This work provides an overview of some GDA algorithms which are reasonably categorized based on these integral properties. Next, we engage the three most widely used GNN backbones (GCN, GAT, and GraphSAGE) as plug-and-play methods for conducting experiments. We conclude by evaluating the algorithm’s effectiveness to demonstrate significant differences among various GDA techniques based on accuracy and time complexity with additional datasets different from those used in the original works. While discussing practical and theoretical motivations, considerations, and strategies for GDA, this work comprehensively investigates the challenges and future direction by pinpointing several open conceivable issues that may require further study based on far-reaching literature interpretation and empirical outcomes.
Managing the Inevitable – A Maturity Model to Establish Incident Response Management Capabilities

Although the ongoing digital transformation offers new opportunities for organizations, more emphasis on information security is needed due to the evolving cyber-threat landscape. Despite all preventive measures, security incidents cannot entirely be mitigated. Organizations must establish incident response management to treat inevitable incidents in a structured manner and under considerable time pressure. If not handled, incidents can result in reputational or financial losses and disrupt business continuity. Especially organizations that have not addressed incident response management extensively need to understand which capabilities are required to develop their incident response management. However, research still lacks a practice-grounded and socio-technical conceptualization of those capabilities and their development. For such challenges, maturity models have proven valuable in practice and research. This paper follows a design science research approach to develop an incident response management maturity model (IRM3) closely aligned with practice requirements under a socio-technical lens. Iteratively applying and evaluating the IRM3 with seven real-world organizations leverages its comprehensive view based on four focus areas and 29 capability dimensions to understand which capabilities organizations need to approach incident response management. Building on existing research, this work provides a comprehensive perspective on incident response management and its associated capabilities. For practitioners, especially in organizations with initial incident response maturity, the IRM3 offers descriptive value when used as a status quo assessment tool and prescriptive value by outlining capabilities for successful incident response management.
A hands-on gaze on HTTP/3 security through the lens of HTTP/2 and a public dataset

Following QUIC protocol ratification on May 2021, the third major version of the Hypertext Transfer Protocol, namely HTTP/3, was published around one year later in RFC 9114. In light of these consequential advancements, the current work aspires to provide a full-blown coverage of the following issues, which to our knowledge have received feeble or no attention in the literature so far. First, we provide a complete review of attacks against HTTP/2, and elaborate on if and in which way they can be migrated to HTTP/3. Second, through the creation of a testbed comprising the at present six most popular HTTP/3-enabled servers, we examine the effectiveness of a quartet of attacks, either stemming directly from the HTTP/2 relevant literature or being entirely new. This scrutiny led to the assignment of at least one CVE ID with a critical base score by MITRE. No less important, by capitalizing on a realistic, abundant in devices testbed, we compiled a voluminous, labeled corpus containing traces of ten diverse attacks against HTTP and QUIC services. An initial evaluation of the dataset mainly by means of machine learning techniques is included as well. Given that the 30 GB dataset is made available in both pcap and CSV formats, forthcoming research can easily take advantage of any subset of features, contingent upon the specific network topology and configuration.