In massive multiple-input and multiple-output (mMIMO) systems, compressed sensing (CS)-based channel state information (CSI) feedback methods still face significant challenges, such as unknown channel sparsity, high computational complexity, and unavoidable channel estimation (CE) errors at the user equipment (UE). These factors degrade the accuracy of downlink CSI reconstruction at the base station (BS). To tackle these challenges, inspired by the work of perception-assisted communication, a perception-assisted CS-based CSI feedback method is proposed in this paper. In this method, an active perception scheme is developed to extract the support set of downlink CSI from the echo signals, addressing the issue of unknown channel sparsity in CS-based methods. With the perceived support set, the perception-assisted reconstruction without perception errors (PaRwoPE) method is proposed to achieve a lower bound of CSI recovery accuracy. This method develops a perception-assisted suppression scheme to reduce CE errors and transceiver noise and uses the perceived support set to avoid iterative reconstruction, thereby significantly improving the recovery accuracy of downlink CSI with markedly reduced computational complexity. To suppress the inevitable perception errors, a perception-assisted reconstruction with perception errors (PaRPE) method is developed. This method utilizes path correlation to eliminate false paths in the downlink CSI, while also developing a low-complexity iterative scheme to recover missed paths. The computational complexity analysis shows that the proposed method notably reduces computational complexity. Furthermore, simulation results demonstrate its effectiveness in improving normalized mean squared error (NMSE) performance and exhibit robustness against parameter variations.

Accurate acquisition of downlink channel state information (CSI) at the base station (BS) remains a critical challenge in frequency division duplex (FDD) millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) systems. Although compressive sensing (CS) and deep learning (DL)-based CSI feedback methods demonstrate their advantages, the recovery accuracy of downlink CSI in FDD mmWave mMIMO systems still faces severe challenges due to the facts of significant user equipment (UE) estimation errors, and typical compression requirements. To tackle these challenges, this paper proposes a novel echo sensing-aided CSI feedback framework designed to enhance downlink CSI recovery accuracy. In the proposed method, the communication echo signals observed at the BS are utilized to extract the dedicated sensing prior information for the downlink CSI recovery. With the extracted sensing prior information, a CSI denoising method is developed to suppress the non-path entries of the downlink CSI matrix in the angular-delay domain, thereby improving the recovery accuracy of downlink CSI at the BS. The framework operates as a plug-in module at the BS receiver, requiring no modifications to existing UE hardware. This work establishes a new paradigm for integrating sensing-assisted enhancements into FDD mmWave mMIMO systems without compromising compatibility with standardized UE operations. Simulations validate that the proposed method outperforms conventional CS and DL-based methods in terms of CSI reconstruction accuracy while demonstrating robustness to parameter variations.

With the rapid expansion of blockchain applications, smart contracts are becoming increasingly complex, making the automated detection of contract vulnerabilities more critical than ever. Large language models, due to their advanced code comprehensive ability, are considered to have the potential to undertake the task of automated software vulnerability discovery. Although there have been empirical studies on ChatGPT's automated discovery of contract vulnerabilities, the current empirical research has not addressed how well ChatGPT can detect logical vulnerabilities in smart contracts or whether ChatGPT's detection performance for logical vulnerabilities can be improved. To fill this gap, this study collected and organized seven types of logical vulnerability source codes from 6165 real smart contract audit reports and three datasets, such as Web3Bugs, and used this database to validate ChatGPT's detection capability for logical vulnerabilities. To improve ChatGPT's accuracy in detecting logical vulnerabilities, we fine-tuned ChatGPT with a dataset marked with a specific method, achieving an average accuracy rate of 95% for single vulnerability detection per training session. We improved the original marking method to increase further the number of vulnerabilities that a single model can detect. We used a specific completion marking format, ultimately enabling ChatGPT to detect various logical vulnerabilities. In terms of enhancing model scalability, we found a special training set marking method that allows for the addition of detectable vulnerability types through secondary training.

Ultra-wideband (UWB) ranging systems are increasingly deployed in critical, security-sensitive applications due to their precise positioning and secure ranging capabilities. In this work, we introduce a practical DoS attack via reactive jamming, referred to as UWBAD+, which targets commercial UWB ranging systems by exploiting the vulnerabilities of the normalized cross-correlation process. This allows UWBAD+ to selectively and effectively disrupt ranging sessions without requiring prior knowledge of the victim devices' configurations, leading to potentially severe consequences such as property loss, unauthorized access, or vehicle theft. The enhanced effectiveness and low detectability of UWBAD+ stem from the following: (i) it can rapidly sniff the physical layer structures of unknown UWB systems, even in the presence of multiple UWB devices operating simultaneously; (ii) it blocks each ranging session efficiently by employing field-level jamming, thus exerting a significant impact on commercial UWB ranging systems; and (iii) its compact, reactive, and selective design based on COTS UWB chips, which makes it both affordable and less noticeable. We successfully executed real-world attacks on commercial UWB ranging systems produced by the three largest UWB chip vendors in the market, including Apple, NXP, and Qorvo. We disclosed our findings to Apple, relevant Original Equipment Manufacturers (OEMs), and the Automotive Security Research Group. As of the time of writing, the involved OEM has acknowledged this vulnerability in their automotive systems and has issued a${\$} 5,000$bounty as a reward.

UWB ranging systems have been adopted in many critical and security sensitive applications due to its precise positioning and secure ranging capabilities. We present a practical jamming attack, namely UWBAD, against commercial UWB ranging systems, which exploits the vulnerability of the adoption of the normalized cross-correlation process in UWB ranging and can selectively and quickly block ranging sessions without prior knowledge of the configurations of the victim devices, potentially leading to severe consequences such as property loss, unauthorized access, or vehicle theft. UWBAD achieves more effective and less imperceptible jamming due to: (i) it efficiently blocks every ranging session by leveraging the field-level jamming, thereby exerting a tangible impact on commercial UWB ranging systems, and (ii) the compact, reactive, and selective system design based on COTS UWB chips, making it affordable and less imperceptible. We successfully conducted real attacks against commercial UWB ranging systems from the three largest UWB chip vendors on the market, e.g., Apple, NXP, and Qorvo. We reported our findings to Apple, related Original Equipment Manufacturers (OEM), and the Automotive Security Research Group. As of the writing of this paper, the related OEM has acknowledged this vulnerability in their automotive systems and has offered a 5, 000 reward as a bounty.

Federated Learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Most of the current FL research focuses on perspectives such as communication efficiency, privacy protection, and personalization. Almost all work assumed that the data of FL are already ideally collected. However, in medical image analysis scenarios, data annotation demands both expertise and tedious labor, which means it is a critical problem that cannot be neglected in FL. In this study, we proposed a federated active learning (FedAL) framework that can decrease the annotation workload while maintaining the performance of FL. To the best of our knowledge, this is the first federated active learning framework working on medical images. Using only up to 50% of samples, our FedAL was able to achieve state-of-the-art performance on the real-world dermoscopic task. Our FedAL outperformed active learning methods under FL and achieved the performance comparable to full data FL.

In this paper, we explore to learn representations of legislation and legislator for the prediction of roll call results. The most popular approach for this topic is named the ideal point model that relies on historical voting information for representation learning of legislators. It largely ignores the context information of the legislative data. We, therefore, propose to incorporate context information to learn dense representations for both legislators and legislation. For legislators, we incorporate relations among them via graph convolutional neural networks (GCN) for their representation learning. For legislation, we utilize its narrative description via recurrent neural networks (RNN) for representation learning. In order to align two kinds of representations in the same vector space, we introduce a triplet loss for the joint training. Experimental results on a self-constructed dataset show the effectiveness of our model for roll call results prediction compared to some state-of-the-art baselines.