Currently, FPGA-based processing systems in cloud environments are gaining popularity due to the growing demand for flexible and scalable hardware acceleration in cloud-based services. To reduce infrastructure costs and enhance resource utilization, multi-tenant sharing of computing resources has become a viable option for cloud service providers (CSPs). However, several attacks can occur during the deployment of heterogeneous applications, potentially leading to privacy leaks or even system crashes. In this paper, we propose SecTEND, a comprehensive solution for FPGA-SoCs that includes both a multi-party protocol for the secure delivery and loading of heterogeneous applications from tenants to remote devices of CSPs, and a security framework designed to ensure the protection and isolation of these applications. Within the framework, a multi-key protection mode and a hardware-accelerated cryptographic pathway are proposed and implemented. Meanwhile, several security-related functionalities, such as secure communication among parties, bitstream validation and loading are provided. Finally, we perform a security analysis, discuss possible countermeasures to further enhance the security of SecTEND, and evaluate our solution on a Xilinx UltraScale+ FPGA-SoC platform, demonstrating its security with acceptable timing overhead. Additionally, the hardware-accelerated cryptographic operations provided by the framework achieve higher throughput compared to software solutions in most cases.

High-quality sense-annotated datasets are vital for evaluating and comparing WSD systems. We present a novel approach to creating parallel sense-annotated datasets, which can be applied to any language that English can be translated into. The method incorporates machine translation, word alignment, sense projection, and sense filtering to produce silver annotations, which can then be revised manually to obtain gold datasets. By applying our method to Farsi, Chinese, and Bengali, we produce new parallel benchmark datasets, which are vetted by native speakers of each language. Our automatically-generated silver datasets are of higher quality than the annotations obtained with recent multilingual WSD systems, particularly on non-European languages.

Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The evolution of malware has led to the development of increasingly sophisticated evasion techniques, significantly escalating the challenges for researchers in obtaining and labeling new instances for analysis. Conventional deep learning detection approaches struggle to identify new malware variants with limited sample availability. Recently, researchers have proposed few‐shot detection models to address the above issues. However, existing studies predominantly focus on model‐level improvements, overlooking the potential of domain adaptation to leverage the unique characteristics of malware. Motivated by these challenges, we propose a few‐shot learning‐based malware family detection framework (MalFSLDF). We introduce a novel method for malware representation using structural features and a feature fusion strategy. Specifically, our framework employs contrastive learning to capture the unique textural features of malware families, enhancing the identification capability for novel malware variants. In addition, we integrate entropy graphs (EGs) and gray‐level co‐occurrence matrices (GLCMs) into the feature fusion strategy to enrich sample representations and mitigate information loss. Furthermore, a domain alignment strategy is proposed to adjust the feature distribution of samples from new classes, enhancing the model’s generalization performance. Finally, comprehensive evaluations of the MaleVis and BIG‐2015 datasets show significant performance improvements in both 5‐way 1‐shot and 5‐way 5‐shot scenarios, demonstrating the effectiveness of the proposed framework.

With the deepening militarization of global cyberspace, cyber threats have evolved into advanced persistent threats (APTs), characterized by high targeting, persistence, and destructiveness, rendering traditional perimeter-based defenses ineffective. In response, researchers have proposed the zero-trust architecture, which enforces strict identity verification for all access requests, whether external or internal, to reduce the attack surface and mitigate APTs lateral movement. However, zero-trust remains largely a conceptual framework rather than a standardized technical solution, with existing approaches primarily integrating conventional security mechanisms under zero-trust principles without systematically deconstructing threats from an APT countermeasure perspective. Consequently, these methods struggle to identify APTs at the tactical and technical level or accurately assess and mitigate APT risks. To address the above problems, this article proposes a access control method within zero-trust network for APT mitigation. First, this article identifies APT tactics, techniques, and procedure that threaten zero-trust by leveraging MITRE ATT&CK mitigations and zero trust maturity model. Next, this article designs an attack detection algorithm using Sigma rules, correlating historical entity behavior with security alerts to uncover APT indicators. Finally, this article establishes a risk assessment framework for network entities based on APT behavioral patterns, devises a trust computation model tailored to APTs, and implements dynamic access control policies weighted by entity trust levels. The experimental results demonstrate the method’s feasibility and effectiveness, achieving 93.1% APT attack detection rate, offering a new approach for mitigating APT attacks.

Healthcare 5.0 delivers high-quality healthcare by promoting collaboration among multiple trusted domains to effectively integrate resources. In this process, cross-domain authentication is a critical issue that must be addressed to ensure secure communication and resource sharing. Common unified authentication frameworks rely on consistent and coordinated authentication policies across different domains, which may lead to challenges in accommodating diverse policies and increase the risk of privacy breaches. To address these issues, this paper proposes a privacy-preserving blockchain-based cross-domain dynamic authentication scheme, referred to as CDDA, for Healthcare 5.0. CDDA enables cross-domain authentication across various healthcare domains, each with its own distinct authentication policies. To protect domain privacy, it anonymizes identity credentials and policy structures. Additionally, it implements a blockchain-based decentralized approach integrated with threshold verification mechanisms to enhance security. Finally, we conduct a security analysis to demonstrate that CDDA satisfies the essential security and privacy criteria. We further validate its effectiveness and efficiency through thorough experimental evaluations. The results indicate that the most complex phase of the cross-domain resource request operation takes an average of 1.38 seconds, which is acceptable given its significant contribution to enhancing cross-domain privacy and flexibility.

Constructing lexicons with explicitly identified lexical gaps is a vital part of building multilingual lexical resources.Prior work has leveraged bilingual dictionaries and linguistic typologies for semi-automatic identification of lexical gaps.Instead, we propose a generallyapplicable algorithmic method to automatically generate concept lexicalizations, which is based on machine translation and hypernymy relations between concepts.The absence of a lexicalization implies a lexical gap.We apply our method to kinship terms, which make a suitable case study because of their explicit definitions and regular structure.Empirical evaluations demonstrate that our approach yields higher accuracy than BabelNet and ChatGPT.Our error analysis indicates that enhancing the quality of translations can further improve the accuracy of our method.

Convolutional Neural Network (CNN) and Transformer architectures have been extensively applied in the domain of remote sensing image super-resolution. However, to achieve optimal performance, many existing methods are designed with a large number of parameters, thereby increasing the complexity of the model and hindering practical deployment. To address this issue, we propose an innovative model that synergizes CNN and Transformer architectures while employing a minimal number of their respective modules. This approach significantly reduces the parameter count while maintaining superior performance. Firstly, by constructing additional shallow feature representations as input, we enhance the feature extraction capabilities for individual images. Secondly, we utilize residual connections between various modules to integrate multi-scale, high-dimensional feature information, thus ensuring efficient transmission. Finally, the image reconstruction module is employed to restore the high-resolution image. Experimental results show that FCTNet significantly outperforms existing methods while maintaining a substantially lower parameter count, as demonstrated through evaluations on two public datasets.

As the minimum semantic units of natural languages, sememes can provide interpretable representations of concepts.Despite the widespread utilization of lexical resources for semantic tasks, the use of sememes is limited by a lack of available sememe knowledge bases.Recent efforts have been made to connect Ba-belNet with HowNet by automating sememe prediction.However, these methods depend on large manually annotated datasets.Instead, we propose to use sense alignment via a novel unsupervised and explainable method.Our method consists of four stages, each relaxing predefined constraints until a complete alignment of BabelNet synsets to HowNet senses is achieved.Experimental results demonstrate the superiority of our unsupervised method over previous supervised ones by an improvement of 12% overall F1 score, setting a new state of the art.Our work is grounded in an interpretable propagation of sememe information between lexical resources, and may benefit downstream applications which can incorporate sememe information.

Large language models (LLMs) have demonstrated exceptional natural language understanding abilities, and have excelled in a variety of natural language processing (NLP) tasks.Despite the fact that most LLMs are trained predominantly on English, multiple studies have demonstrated their capabilities in a variety of languages.However, fundamental questions persist regarding how LLMs acquire their multilingual abilities and how performance varies across different languages.These inquiries are crucial for the study of LLMs since users and researchers often come from diverse language backgrounds, potentially influencing how they use LLMs and interpret their output.In this work, we propose a systematic way of qualitatively and quantitatively evaluating the multilingual capabilities of LLMs.We investigate the phenomenon of cross-language generalization in LLMs, wherein limited multilingual training data leads to advanced multilingual capabilities.To accomplish this, we employ a novel prompt back-translation method.The results demonstrate that LLMs, such as GPT, can effectively transfer learned knowledge across different languages, yielding relatively consistent results in translation-equivariant tasks, in which the correct output does not depend on the language of the input.However, LLMs struggle to provide accurate results in translation-variant tasks, which lack this property, requiring careful user judgment to evaluate the answers.

With advancements in cellular vehicle-to-everything (C- V2X) and drone manufacturing technologies, integrating drone-small-cells (DSCs) into terrestrial cellular networks is a promising solution to enabling diversified vehicle applications. In this paper, a multi-DSC-assisted dynamic spectrum management framework is presented to maximize the network utility under quality-of-service (QoS) constraints in 5G and beyond cellular vehicular networks. The network utility maximization problem is formulated as mixed-integer nonlinear programming regarding association patterns between vehicles and base stations (BSs) and spectrum partitioning among heterogeneous BSs. For mathe-matical tractability, the joint optimization problem for spectrum partitioning and vehicle- DSC associations is transformed as a biconcave optimization problem. An alternate search algorithm is then designed to determine vehicle association patterns and spec-trum slicing ratios. Our simulation demonstrates that compared with state-of-the-art methods, the proposed scheme achieves a significant performance improvement in network throughput and spectrum utilization.

The inference of Boolean networks is crucial for analyzing the topology and dynamics of gene regulatory networks. Many data-driven approaches using evolutionary algorithms have been proposed based on time-series data. However, the ability to infer both network topology and dynamics is restricted by their inflexible encoding schemes. To address this problem, we propose a novel Boolean network inference algorithm for inferring both network topology and dynamics simultaneously. The main idea is that, we use a marker-based genetic algorithm to encode both regulatory nodes and logical operators in a chromosome. By using the markers and introducing more logical operators, the proposed algorithm can infer more diverse candidate Boolean functions. The proposed algorithm is applied to five networks, including two artificial Boolean networks and three real-world gene regulatory networks. Compared with other algorithms, the experimental results demonstrate that our proposed algorithm infers more accurate topology and dynamics.

Punctuation restoration is an important post-processing step in automatic speech recognition. Among other kinds of external information, part-of-speech (POS) taggers provide informative tags, suggesting each input token's syntactic role, which has been shown to be beneficial for the punctuation restoration task. In this work, we incorporate an external POS tagger and fuse its predicted labels into the existing language model to provide syntactic information. Besides, we propose sequence boundary sampling (SBS) to learn punctuation positions more efficiently as a sequence tagging task. Experimental results show that our methods can consistently obtain performance gains and achieve a new state-of-the-art on the common IWSLT benchmark. Further ablation studies illustrate that both large pre-trained language models and the external POS tagger take essential parts to improve the model's performance.

MOTIVATION: Inferring gene regulatory networks from gene expression time series data is important for gaining insights into the complex processes of cell life. A popular approach is to infer Boolean networks. However, it is still a pressing open problem to infer accurate Boolean networks from experimental data that are typically short and noisy. RESULTS: To address the problem, we propose a Boolean network inference algorithm which is able to infer accurate Boolean network topology and dynamics from short and noisy time series data. The main idea is that, for each target gene, we use an And/Or tree ensemble algorithm to select prime implicants of which each is a conjunction of a set of input genes. The selected prime implicants are important features for predicting the states of the target gene. Using these important features we then infer the Boolean function of the target gene. Finally, the Boolean functions of all target genes are combined as a Boolean network. Using the data generated from artificial and real-world gene regulatory networks, we show that our algorithm can infer more accurate Boolean network topology and dynamics from short and noisy time series data than other algorithms. Our algorithm enables us to gain better insights into complex regulatory mechanisms of cell life. AVAILABILITY AND IMPLEMENTATION: Package ATEN is freely available at https://github.com/ningshi/ATEN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivated by a real project for a sophisticated automated storage and retrieval system (AS/RS), we study the problem of generating K shortest paths that are required to satisfy a set of constraints. We propose a structural branching procedure that decomposes the problem into at most K|N| subproblems, where |N| is the number of nodes in the network. By using a Network Modification procedure, each subproblem can be transformed into a constrained shortest path problem (CSP). When these constraints satisfy a so called separable property, the subproblem can be further simplified. Based on this branching procedure, we propose a specific algorithm for an application where resource and loopless constraints have to be respected. Numerical results show that our algorithm is very efficient and robust.

Abstract We study the new problem of stochastic programs over trees with dependent random arc capacities. This problem can be used as a subproblem in decomposition methods that solve multi‐stage networks with independent random arc capacities and random travel times. An efficient algorithm is provided to compute the expected total cost. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(2), 157–163 2007