To address the issues of high computational overhead, vulnerability to physical capture attacks, and insufficient in offering quantum resistance in traditional blockchain-based cross-domain authentication protocols for collaborative unmanned aerial vehicles (UAVs) missions, we propose APQE-CDA, a PUF-based post-quantum end-to-end cross-domain authentication protocol. The proposed protocol employs a lightweight bucket shifter PUF (BS-PUF) to reduce the computational overhead of UAVs while providing resilience against physical capture attacks. Static Random-Access Memory (SRAM) PUF-enhanced Kyber post-quantum cryptography is utilized to ensure secure quantum resistance. In APQE-CDA, we exploit the reversibility of BS-PUF to facilitate secure Challenge Response Pair (CRP) storage on the blockchain while achieving lightweight identity authentication through commutativity. We integrate SRAM PUF randomness into the Kyber key generation mechanism to eliminate key storage on UAVs while ensuring quantum-resistant security in the authentication interactions. Finally, Burrows Abadi Needham (BAN) logic, Real-or-Random (ROR), and informal security analysis are adopted to demonstrate the security of the proposed scheme. Experiments conducted on the Raspberry Pi 5 and FPGA platforms show superior computational efficiency, lower power consumption, and enhanced quantum-resistant security compared to existing solutions. Furthermore, extensive validation through the NS3 network simulator and Hyperledger Fabric framework substantiates the protocol’s authentication efficiency and practical viability in real environment conditions.

The era of big data has made vast amounts of clinical data readily available, particularly in the form of electronic health records (EHRs), which provides unprecedented opportunities for developing data-driven diagnostic tools to enhance clinical decision making. However, data-driven modeling of EHRs faces challenges such as irregularly spaced time series, issues of incompleteness, and data imbalance. Realizing the full data potential of EHRs hinges on the development of advanced analytical models. In this paper, we propose a novel Missingness-aware mUlti-branching Self-Attention Encoder (MUSE-Net) to cope with the challenges in modeling longitudinal EHRs for data-driven disease prediction. The proposed MUSE-Net is composed by four novel modules including: (1) a multi-task Gaussian process (MGP) with missing value masks for data imputation; (2) a multi-branching architecture to address the data imbalance problem; (3) a time-aware self-attention encoder to account for the irregularly spaced time interval in longitudinal EHRs; (4) interpretable multi-head attention mechanism that provides insights into the importance of different time points in disease prediction, allowing clinicians to trace model decisions. We evaluate the proposed MUSE-Net using both synthetic and real-world datasets. Experimental results show that our MUSE-Net outperforms existing methods that are widely used to investigate longitudinal signals. Note to Practitioners—: This article is motivated by the growing need for robust machine learning models capable of handling the complexities of real-world EHRs, including irregular time intervals, missing data, and class imbalance. The proposed MUSE-Net model integrates advanced imputation via MGP with missingness masks, a time-aware self-attention encoder, and a multi-branching framework to enhance predictive robustness. Additionally, MUSE-Net leverages an interpretable multi-head attention mechanism to provide transparent decision-making, allowing clinicians to trace model predictions back to key time points. This framework offers a practical and trustworthy solution for disease prediction and clinical decision support.

The TLS protocol is one of the most crucial foundations for ensuring internet security. Consequently, vulnerabilities within the TLS protocol have a significant impact on the Internet security. This paper aims to explore more efficient methods of discovering vulnerabilities in the TLS protocol. Fuzzing stands out as one of the most important techniques for vulnerability discovery in the TLS protocol. To tackle the high complexity of the TLS protocol, stateful greybox fuzzers such as AFLnet have been introduced to enable stateful fuzzing of TLS servers. However, these mutation-based fuzzers often encounter chal-lenges in preserving the message format information during the mutation process, which can undermine the testing results. As a result, this paper proposes a novel approach that incorporates a formatted mutation strategy into the stateful greybox fuzzing process, with the aim of achieving more efficient mutation results. The evaluation process involves four mainstream fuzzers, with OpenSSL's TLS server serving as the target. The results demonstrate that the proposed method significantly enhances the quality of generated seeds, code coverage, and state coverage across all four fuzzers.

Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods. Specifically, our MB-TCN-TC model provides AUROC and AUPRC scores of 0.949 and 0.793 respectively, achieving an improvement of 6.27% on AUROC, 11.85% on AUPRC, and 19.3% on F1 score compared with the traditional TCN model.

Source code analysis has many application scenarios, such as code plagiarism detection and software vulnerability search. Source code analysis can benefit from machine learning , but it typically requires a standard vector representation and cannot be directly applied to the source code. Thus, we are required to embed source code into vector representation while maintaining the semantics of the code as much as possible. Code2vec proposes a code embedding method that converts source code into code vector through Abstract Syntax Tree(AST). However, we found that code2vec uses a hashing algorithm to generate the identifier for the path in the path context, which leads to the loss of node information in the path and also causes the model training parameters to be very large. Therefore, we present a new path representation which utilizes RNN to generate vectors for paths. We also proposed alternative model designs and evaluated their impact on the model in the experiments. The results we obtained in a challenging source code classification task suggest that, compared to code2vec, the RNN-based paths representation can produce a better embedding model with fewer training parameters.

Abstract In the Pickup‐and‐Delivery Traveling Salesman Problem with Handling Costs (PDTSPH), a single vehicle has to satisfy multiple customer requests, each defined by a pickup location and a delivery location. Cargo handling is performed at the rear end of the vehicle, in a Last‐In‐First‐Out (LIFO) order for PDTSPH. However, additional handling operations are permitted with a penalty if other loads that block the access to the delivery have to be unloaded and reloaded. The objective of PDTSPH is to minimize the total transportation and handling cost. In this paper, we present a new Mixed Integer Programming (MIP) model and a branch‐and‐cut algorithm to solve PDTSPH. We also present new integral separation procedures to effectively handle the exponential number of constraints in our MIP model. A family of inequalities are introduced to enhance the scalability of our implementation. The performance of our approach is compared with a compact formulation from the literature (Veenstra et al. [21]) in instances ranging from 9 to 21 customer requests. Computational results show our algorithm outperforming the compact formulation in 69% of instances with an average runtime improvement of 57%.