Reviewerly/exHarmony huggingface dataset:
Deep Wavelet Residual Attention Network for Radio Frequency Fingerprint Recognition under Low SNR

Traditional communication security means need to encrypt data in high-level protocols, which cannot prevent hacker attacks in low-level protocols. Meanwhile, those methods cannot meet the cost and efficiency requirements of a large number of edge devices. Radio frequency fingerprint (RFF) recognition technology can recognize and classify source signals by using data of physical layer protocol. At present, most researches in this field do not consider that low signal-to-noise ratio (SNR) environment affected by noise in real environment and the classification accuracy of mainstream recognition models decreases significantly under the condition of low SNR. In order to solve this problem, the deep wavelet residual attention network (DWRAN) model is proposed in this paper, and the experiment is carried out using real WiFi dataset ORACLE RF. The accuracy of DWRAN model can reach 97% at 10dB and 92% even at 0dB, which is higher than convolutional neural network (CNN), Transformer and long short-term memory (LSTM) models. Experiments show that the DWRAN model has high accuracy and reliability under the condition of SNR from 30dB to 0dB.
Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic

The availability of ubiquitous and heterogeneous Internet-of-Things (IoT) devices in smart homes and their interactions with users provide a unique opportunity to monitor, understand, recognize, learn, and infer user activities for safety monitoring, connected health, energy saving as well as other disruptive services. Our analysis on IoT network traffic from smart homes with a variety of IoT devices has discovered that user activities often trigger overlapping traffic waves from multiple IoT devices that are deployed near the activities. This insight leads us to adopt wavelet analysis to decompose IoT network traffic in smart homes into low, middle, and high frequency bands that distinguish IoT traffic waves triggered by user activities from background noises such as heartbeat signals between IoT devices and cloud servers. Subsequently, we extract a broad range of traffic features from these IoT traffic waves and explore supervised machine learning (ML) algorithms to classify various user activities with these features. Based on the labelled user activities and IoT network traffic data collected from real smart home environments, our experiments have demonstrated that the ML-based algorithms are able to use IoT network traffic to accurately infer various user activities in smart homes.
Joint Focal Loss and Dominant Gradient Correction for Gradient Conflict in Federated Learning

Federated learning faces significant challenges of data heterogeneity among clients. In this heterogeneous data scenario, the preference of local models can result in gradient conflict during the global aggregation phase. This can significantly lead to a decrease in the global accuracy. In this paper, we present a novel Federated Learning Mitigating Gradient Conflict method named FedMGC that aims to mitigate gradient conflict. FedMGC replaces the cross-entropy loss function with the focal loss function. This balances the proportion of each class in the loss and reduces the preference of the local models for majority classes. In the global aggregation phase, we design the dominant gradient correction method called DGC to improve the global accuracy. Specifically, we select some of the gradients with small outliers to form the dominant gradients. And then we use dominant gradients to adjust the local gradients and alleviate the gradient conflict. In the evaluation experiments on heterogeneous datasets, FedMGC achieves higher test accuracy compared to baselines. In particular, over the CIFAR-10 dataset, FedMGC achieves 3.88%, 1.7% and 1.31% higher test accuracy than those of FedAvg, FedProx and FedNova, respectively.
d-BAEV: Distributed Blockchain-Based Anonymous Electronic Voting

Electronic voting (e-voting) presents a convenient and cost-effective alternative to current paper ballot-based voting. It provides many benefits such as increased voter turnout and accuracy in the decision-making process. While presenting many improvements, e-voting still faces serious security challenges that hinder its adoption, especially when designed to be run over mobile devices. In this paper, we propose d-BAEV, a novel remote e-voting model for large-scale elections by introducing a novel distributed trust model through participation of (at least) two conflicting parties to ensure election integrity and accountability. d-BAEV is secure and preserves voter privacy through secure multi-party computations performed by parties of differing allegiances. It also leverages a blockchain running smart contracts as a publicly accessible and tamper-resistant bulletin board to permanently store votes and prevent double-voting. Our performance analysis and simulation results show that the proposed d-BAEV scheme is practical for large-scale elections in smartphone devices.
GAN-inspired Defense Against Backdoor Attack on Federated Learning Systems

Federated Learning (FL) provides an opportunity for clients with limited data resources to combine and build better Machine Learning models without compromising their privacy. But aggregating contributions from various clients implies that the errors present in some clients’ resources will also get propagated to all the clients through the combined model. Malicious entities leverage this negative factor to disrupt the normal functioning of the FL system for their gain. A backdoor attack is one such attack where the malicious entities act as clients and implant a small trigger into the global model. Once implanted, the model performs the attacker desired task in the presence of the trigger but acts benignly otherwise. In this paper, we build a GAN-inspired defense mechanism that can detect and defend against the presence of such backdoor triggers. The unavailability of labeled benign and backdoored models has prevented researchers from building detection classifiers. We tackle this problem by utilizing the clients as Generators to construct the required dataset. We place the Discriminator on the server-side, which acts as a backdoored model detecting binary classifier. We experimentally prove the proficiency of our approach with the image-based non-IID datasets, CIFAR10 and CelebA. Our prediction probability-based defense mechanism successfully removes all the influence of backdoors from the global model.
Reinforcement Learning for Node Selection in Mixed Integer Programming

Mixed Integer Linear Programming (MILP) optimization is a critical NP-hard problem applied across various sectors. Although machine-learning-based strategies have used historical data and expert strategies to effectively expedite optimal solution identification, they often neglect the importance of early search tree exploration and the cost associated with path backtracking. In this paper, we propose an innovative reinforcement learning (RL) approach for the node selection in the Branch-and-Bound (B&B) process. Our solution includes a novel reward function, an epsilon-greedy strategy for balancing exploitation and exploration, and a unique RL technique adaptable to the dynamic nature of the state and action spaces. Our empirical results show that our approach achieves an average of 10% acceleration compared to traditional SCIP and significantly outperforms other AI-based methods, underscoring the immense potential of RL in MILP optimization.
A compact system for accurate measurement of true stress-strain curves in transparent materials subject to extensive deformation

Accurate quantification of the true stress-strain curve over an extensive strain range is pivotal for elucidating material mechanical properties and simulating non-linear plastic deformation. A novel experimental approach was developed to directly measure true stress-strain behavior in transparent materials. Our setup featured a single 3CCD camera and mirrors, capturing images of both specimen surfaces with bicolored fluorescent speckle patterns. Initially, we determined and corrected the specimen’s refractive index using a point-by-point least squares method. Subsequently, we used multispectral dual 3D digital image correlation (DIC) to reconstruct surface profiles and correct refractive distortions. This allowed precise computation of the deformed cross-sectional area over an expansive strain range. True stress was calculated from the measured engineering stress and computed deformed area. Validity was confirmed by deforming a fluorescent elastomer, and results aligned with the theoretical predictions. This compact experimental setup effectively evaluates true stress-strain relationships in transparent materials under extensive deformation.
A Continual Learning Framework for Event Prediction with Temporal Knowledge Graphs

Events such as crises, public opinion issues, and social hotspots usually follow certain patterns. From a large amount of historical data, we can extract these patterns to predict future events. This valuable task can be viewed as the Temporal Knowledge Graph (TKG) inference problem, as TKGs are widely employed to sketch ongoing events. However, most of the traditional TKG inference methods mainly focus only on entity prediction and do not take into consideration the variable length of information summarized from events at different periods. To address these challenges, we propose a new collaborative entity- relation prediction method called Predicting the Future Without Forgetting (PFWF). PFWF introduces historical representation to deal with the issue posed by the variability of information length. We also treat the TKG prediction task as a continual learning problem that prevents training new models from scratch when new data are added, as real-world knowledge graphs are constantly evolving. We validated the effectiveness of PFWF on four public TKG datasets related to crisis events in offline and online continual learning settings.
An Approach for Evaluation of Cloud Outage Risk based on FAIR Model

This research investigates the impact of network disruptions on cloud applications and proposes strategies to mitigate associated risks for enterprise adoption. The study utilizes the Factor Analysis of Information Risk (FAIR) model and data on server outages from Panopta's monitoring service. Through Monte Carlo simulations, the research quantifies the risks of network disruptions. Furthermore, this study extends prior evaluations of the risks associated with server disconnections, not only providing a general understanding of the overall impact across server service providers, but also making a novel contribution by introducing a geographically oriented analysis. This method enables a more effective risk control by examining server services and downtime durations for different server service providers in various regions. In our analysis of the same server service providers, a remarkable disparity was identified, with maximum risk values varying by as much as 3.03 times across different geographical locations. This innovative methodology has potential to substantially mitigate these risks, presenting a significant improvement over traditional analyses which mainly focused on provider's effects rather than region-specific impacts.
Local Differentially Private Clustered Federated Learning on Heterogeneous Data

Training federated learning models on personal data has raised increasing privacy concerns in many applications. One popular way to preserve data privacy is to apply local differential privacy (LDP) into federated learning. However, existing work does not provide a practical solution in achieving privacy-utility tradeoff due to three issues. First, the magnitudes of model parameters in federated learning may vary drastically across layers as well as training epochs. Thus, clipping gradients based on fixed clipping range may degrade the model utility seriously, when applying the LDP mechanism. Second, if LDP is applied at each iteration along the iterative training process, the privacy budget will accumulate, which causes the explosion of the total privacy budget. Last, since the training data and computing nodes are distributed, how to train federated learning models on heterogeneous data also becomes an intriguing problem. In this paper, we proposed a local differentially private clustered federated learning model, which is expected to improve privacy-utility trade-off in clustered federated learning and mitigate the privacy degradation caused when applying the LDP mechanism in multiple training iterations. Empirical evaluations on MNIST dataset demonstrate that the proposed model achieves a superior performance in balancing the tradeoff between privacy and utility.
The UAV Image Matching System Based on Multi-Feature Fusion Representation

Image matching, a process that identifies and aligns pixel-level attributes shared or similar between two images, is a critical component in the perception and navigation capabilities of unmanned aerial vehicle (UAV) systems. Current research methodologies, which predominantly employ either feature-based or deep learning-based methods, present significant challenges. Feature-based methods often lack robustness, while deep learning-based methods necessitate the sacrifice of substantial specific samples and computational resources. To address these issues, we propose a novel UAV image matching system predicated on multi-feature fusion representation. The results indicate that our proposed system enhances robustness and augments overall matching accuracy by a factor of 2.5 to 6 when juxtaposed with existing methods. Code is made publicly available at https://github.com/songxf1024/DeFusion.
Poisoning the Competition: Fake Gradient Attacks on Distributed Generative Adversarial Networks

Generative Adversarial Networks (GANs) have gained significant attentions due to their powerful generative abilities. However, the requirements for data privacy protection and data decentralization have led to the emergence of Distributed GANs (DGANs), where multiple discriminators collaboratively train the central generator model. However, the mode of distributed training poses the risk of fake gradient attacks where the discriminator forges the gradient to destroy or induce the training process of the generator. In this paper, we first explore the impact of gradient attacks on generator models in DGANs, with two types of fake gradient attacks proposed. The qualitative experiments on two simulated datasets demonstrate that the distribution characteristics of the generated data can be seriously affected by the two attacks. The attack reduces the similarity between the generated and real data and yields higher FID values than the most commonly used models. Especially, in the context of DGANs utilizing the Wasserstein GAN, our data-free attack increases the FID value of the generated images from 18.15% to 54.08%, and from 15.50% to 37.33% on the MNIST and CIFAR 10 datasets, respectively.
FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients’ data, posing a threat to clients’ privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively.
Improving Computational Efficiency of Voice Anti-Spoofing Models

With the proliferation of smart voice-controlled systems, the vulnerability exhibited by voice-based IOT applications necessitates diligent attention. Voice anti-spoofing provides effective countermeasures by detecting spoofed speech utterances using machine learning (ML). Compared to conventional ML, deep neural networks (DNNs) show significantly higher effectiveness for anti-spoofing, albeit usually requiring extremely powerful computational resources not suitable for deployment on resource-constrained systems. Thus, new techniques are needed to accelerate and compress models while maintaining inference effectiveness. In this work, we investigate existing general-purpose compression methods and novel task-specific compression methods for voice anti-spoofing. We evaluate models on efficiency and effectiveness across various platforms including low-resource devices and high-performance computing machines. In our evaluation, the best general-purpose compression shows an 80.55% inference efficiency improvement with an increase in EER of about 10%, while the best method of task-specific compression yields a 96.63% inference efficiency improvement with an EER increase of 5.4%. Our code is open-source at github.com/yuangongnd/efficient-voice-antispoof.
Secure Decentralized Learning with Blockchain

Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure problem in FL, decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation, which has been considered an attractive solution for machine learning tasks on distributed personal devices. However, this process is vulnerable to attackers who share false models and data. If there exists a group of malicious clients, they might harm the performance of the model by carrying out a poisoning attack. In addition, in DFL, clients often lack the incentives to contribute their computing powers to do model training. In this paper, we proposed Blockchain-based Decentralized Federated Learning (BDFL), which leverages a blockchain for decentralized model verification and auditing. BDFL includes an auditor committee for model verification, an incentive mechanism to encourage the participation of clients, a reputation model to evaluate the trustworthiness of clients, and a protocol suite for dynamic network updates. Evaluation results show that, with the reputation mechanism, BDFL achieves fast model convergence and high accuracy on real datasets even if there exist 30% malicious clients in the system.
Container Restart Reduction Technique in Kubernetes Using Memory Oversubscription

This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, the similar effect as pausing them can be achieved. The suspension of the identified containers is released as soon as it is determined that the corresponding node’s memory usage has been reduced. The proposed method reduces the average number of container restarts by a maximum of 58% and reduces the total run time by an average of 7% and a maximum of 13% for high memory volatile workflows in Kubernetes environments.
Resource and Bandwidth-Aware Video Analytics with Adaptive Offloading

To enable computation-intensive video analytics, streaming video data and offloading computation from the source to the inference server running deep neural networks has now become a mainstream approach. Existing works adapt to either computing or network resources, and fail to consider the adaptation for both computation and network resources, which is a practical issue in real-world scenarios. To this end, we propose a video analytics pipeline that jointly considers the computing resource on both sides as well as the available bandwidth to achieve high inference accuracy with guaranteed inference latency and alleviate server-side workloads. We adopt model partitioning to allocate the computation on both sides and a hybrid compression method to adjust the data transmission. We further propose an offloading adaptation algorithm to make proper decisions on the partitioning point and the compression ratio to achieve good performance under limited source-side capabilities and varying bandwidth. Evaluation results show that our resource and bandwidth-aware adaptive offloading approach can maintain almost lossless inference accuracy with low latency while reducing server-side resource usage by up to 30% compared to the baseline approaches.
A Fully Decentralized Homomorphic Federated Learning Framework

The convergence of Big Data, Metaverse, and Large Language Models (LLM) has presented a new horizon for Artificial Intelligence (AI). However, due to legal and security constraints in data sourcing from multiple channels, it has become challenging to realize this potential. This paper proposes a possible solution to this problem using a Homomorphic Encrypted Federated Learning technique that is verifiable and decentralized. Unlike existing Encrypted Federated Learning systems, our method does not require local model updates to be decrypted during aggregation by the server. This results in reduced network overhead, computation time, and memory usage without compromising the convergence rate of the system. Furthermore, our approach provides a verifiable proof that the model update remains untampered.
Hierarchical Model for SAR Image Despeckling VIA Low-Rank Representation and Prior Knowledge Regulation

Synthetic Aperture Radar (SAR) images often suffer from inherent speckle noise that can significantly degrades their quality, and makes it difficult to identify important targets or extract useful information from them. To solve this problem, a novel SAR image despeckling model called HM-SIDLR is proposed, which can suppress noise and preserve image edges from low-rank residues simultaneously. Specifically, our method considers that there is some structural information discarded as noise in residues. Therefore, we construct a hierarchical model to extract more edge details which can compensate for the over-smoothing problem caused by removing most of the speckle noise in the low-rank part. So as to learn the information from the edge subspace, a tunable prior knowledge matrix is designed, allowing for differentiation between clean and noisy pixels. Experimental results with the Virtual SAR dataset indicate that HM-SIDLR can achieve comparable despeckling performance to the state-of-the-art (SOTA) results.