Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging. To address these issues, we introduce an expert-based online HD map method, termed MapExpert. MapExpert utilizes sparse experts, distributed by our routers, to describe various non-cubic map elements accurately. Additionally, we propose an auxiliary balance loss function to distribute the load evenly across experts. Furthermore, we theoretically analyze the limitations of prevalent bird's-eye view (BEV) feature temporal fusion methods and introduce an efficient temporal fusion module called Learnable Weighted Moving Descentage. This module effectively integrates relevant historical information into the final BEV features. Combined with an enhanced slice head branch, the proposed MapExpert achieves state-of-the-art performance and maintains good efficiency on both nuScenes and Argoverse2 datasets.

The longest match strategy in LZ77, a major bottleneck in the compression process, is accelerated in enhanced algorithms such as LZ4 and ZSTD by using a hash table. However, it may results in numerous random memory accesses, which modern CPUs handle inefficiently, thus reducing the compression speed. In this paper, we introduce the LZHV algorithm, which significantly reduces unnecessary memory accesses by over 99% through the implementation of hash verification within the hash table. By integrating LZHV into LZ4 at its default compression level and ZSTD at levels 3 and 4, we achieve a compression speed improvement of over 10% across various platforms.

The SZZ method and its variants are widely employed to identify vulnerability-affected ranges by analyzing vulnerability-fixing commits to trace back vulnerability-inducing commits. However, these methods generally suffer from low precision due to several key factors: 1) Current static method-based variants often incorrectly consider too many irrelevant lines and files in a commit. While methods that extract file references from vulnerability discussions can help narrow down relevant files, obtaining bug discussions for every CVE is often difficult. 2) Learning-based approaches focus exclusively on code to capture semantic relationships for identifying root cause lines. However, these models utilize limited information and demonstrate insufficient capacity for effective capture. 3) The reliance on line mapping algorithms results in inadequate tracing capabilities for complex vulnerabilities, especially when vulnerability-inducing commits are obscured in earlier software versions. To address these issues, this paper innovatively incorporates semantic information from descriptive text and the nature of CVEs derived from vulnerability-fixing commit diffs. By leveraging large language models (LLMs), this approach aims to capture the true root cause lines of vulnerabilities more accurately and enhance the tracing capabilities of the SZZ method, thereby achieving precise localization of the vulnerability impact range. Experimental results indicate that our proposed LLM-SZZ method outperforms existing state-of-the-art approaches, achieving over a 18 % increase in precision across datasets in various programming languages, demonstrating a significant performance advantage.

Open-source software (OSS) is widely reused in Internet of Things (IoT) devices, leading to widespread N-Day vulnerabilities when outdated components remain unpatched. Existing methods typically encode features of different Common Vulnerabilities and Exposures (CVEs) within a shared representation space. However, the model’s limited capacity, combined with the new vulnerability features, can disrupt previously learned patterns. Minimal code modifications in tiny-patch vulnerabilities are often overshadowed by variations introduced by different compilation settings, making it more difficult to distinguish vulnerable functions from their patched counterparts. This paper introduces ISGraphVD, a novel graph-based and function-level vulnerability detection approach that supports cross-compilation settings and enhances detection accuracy. By modeling each CVE independently through a one-model-per-CVE strategy, ISGraphVD reduces feature interference and improves detection accuracy across diverse CVEs. To better detect tinypatch vulnerability, we propose ISGraph, a fine-grained graph representation that models variable dependencies within and across basic blocks by integrating control flow analysis. Then, ISGraphVD utilizes a Graph Matching Network (GMN) with a cross-graph attention mechanism to identify critical vulnerability patterns. Experiments on IoT OSS projects show that ISGraphVD outperforms state-of-the-art methods, achieving a 6.3 percentage-point (pp) accuracy improvement over the strongest baseline, and real-world tests further validate its effectiveness in IoT supply chains.

The application of deep learning in voice cloning has significantly enhanced the quality of cloned voices. While advanced voice cloning technologies are widely applied across various domains, they also pose serious security challenges such as producing natural Deepfakes. In response, numerous studies have focused on detecting fake voices, with many reporting outstanding performance. However, is the issue truly resolved? This paper introduces Adversarial Neural Mimicry Attack (ANMA) which leverages a specialized model to predict the behavior of other similar models, transforming black-box attacks into white-box scenarios indirectly. Based on ANMA and Speaker-irrelative Features (SiFs), we propose a novel black-box attack framework called SiFMimicEvader, designed to evade fake voice detectors with high success rates and minimal query requirements. The framework utilizes speech representation models as the breakthrough to predict the behaviors of fake voice detectors and employs a series of SiFs editing operations as perturbations to deceive these detectors. Experimental results demonstrate the effectiveness of SiFMimicEvader, achieving an average attack success rate exceeding 50% across various detectors, significantly outperforming other attack methods, while also showing great performance in audio quality and query scale, indicating its high availability in real-world scenarios.

Autonomous driving represents a prominent application of artificial intelligence. Recent approaches have shifted from focusing solely on common scenarios to addressing complex, long-tail situations such as subtle human behaviors, traffic accidents, and non-compliant driving patterns. Given the demonstrated capabilities of large language models (LLMs) in understanding visual and natural language inputs and following instructions, recent methods have integrated LLMs into autonomous driving systems to enhance reasoning, interpretability, and performance across diverse scenarios. However, existing methods typically rely either on real-world data, which is suitable for industrial deployment, or on simulation data tailored to rare or hard case scenarios. Few approaches effectively integrate the complementary advantages of both data sources. To address this limitation, we propose a novel VLM-guided, end-to-end adversarial transfer framework for autonomous driving that transfers long-tail handling capabilities from simulation to real-world deployment, named CoC-VLA. The framework comprises a teacher VLM model, a student VLM model, and a discriminator. Both the teacher and student VLM models utilize a shared base architecture, termed the Chain-of-Causality Visual–Language Model (CoC VLM), which integrates temporal information via an end-to-end text adapter. This architecture supports chain-of-thought reasoning to infer complex driving logic. The teacher and student VLM models are pre-trained separately on simulated and real-world datasets. The discriminator is trained adversarially to facilitate the transfer of long-tail handling capabilities from simulated to real-world environments by the student VLM model, using a novel backpropagation strategy. Experimental results show that our method effectively bridges the gap between simulation and real-world autonomous driving, indicating a promising direction for future research.

Robotic navigation in unknown environments is challenging due to the lack of high-definition maps. Building maps in real time requires significant computational resources. Nevertheless, sensor data can provide sufficient environmental context for robots’ navigation. This paper presents an interpretable and mapless navigation method using only two-dimensional (2D) light detection and ranging (LiDAR), mimicking human strategies to escape from dead ends. Unlike traditional planners, which depend on global paths or vision-based and learning-based methods, requiring heavy data and hardware, our approach is lightweight and robust, and it requires no prior map. It effectively suppresses oscillations and enables autonomous recovery from local minimum traps. Experiments across diverse environments and routes, including ablation studies and comparisons with existing frameworks, show that the proposed method achieves map-like performance without a map—reducing the average path length by 50.51% when compared to the classical mapless Bug2 algorithm and increasing it by only 17.57% when compared to map-based navigation.

Recent speech synthesis technology can generate high-quality speech indistinguishable from human speech, thus introducing various security and privacy risks. Numerous recent studies have focused on fake voice detection to address these risks, with many claiming to achieve ideal performance. However, is this really the case? A recent research work introduced Speaker-Irrelative-Features (SiFs), unrelated to the information in speech files but capable of influencing fake detectors. This means that existing detectors may rely on SiFs to a certain extent to distinguish real and fake speech. In this paper, we introduce an evaluation framework to evaluate the influence of SiFs in existing fake voice detectors in depth. We evaluate three SiFs which include background noise, the mute parts before and after voice, and the sampling rate on ASVspoof2019 and FoR. Our results confirm the substantial influence of SiFs on fake voice detection performance, and we delve into the analysis of the underlying mechanisms.

Open-source software has emerged as a pivotal force in the advancement of information technology. Robust open-source compliance governance is essential for the sustainable and healthy growth of both open-source software and its communities. License incompatibility analysis, in particular, represents a critical challenge hindering the progress of open-source software. Traditional methods of incompatibility analysis often fail to account for diverse usage scenarios or are tailored to a limited subset of scenarios. This limitation obstructing their ability to handle the intricate compatibility arising from varied programming language interactions, leading to a high false positives. Our study embarks from an examination of license exceptions, delving into the incompatibility analysis challenges through extensive empirical research on these exceptions. We discovered that the majority of exceptions are, in fact, detectable. Leveraging this empirical insight, our research further develops the license compatibility analysis model by introducing a new, refined legal terminology representation alongside a novel method for license compatibility reasoning. This approach begins with modeling different scenarios to represent license compatibility variably. Furthermore, based on these modeling outcomes, we have designed and implemented LiScopeLens, a tool capable of discerning dependency behaviors for granular compatibility assessment, starting with binary dependencies. Our experimental findings affirm that LiScopeLens proficiently determines the license compatibility status of open-source software across various usage scenarios, demonstrating its significant practical utility.

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks.

The canonical video action recognition methods usually label categories with numbers or one-hot vectors and train neural networks to classify a fixed set of predefined categories, thereby constraining their ability to recognise complex actions and transferable ability to unseen concepts.In contrast, cross-modal learning can improve the performance of individual modalities.Based on the facts that a better action recogniser can be built by reading the statements used to describe actions, we exploited the recent multimodal foundation model CLIP for action recognition.In this study, an effective Vision-Language action recognition adaptation was implemented based on few-shot examples spanning different modalities.We added semantic information to action categories by treating textual and visual label as training examples for action classifier construction rather than simply labelling them with numbers.Due to the different importance of words in text and video frames, simply averaging all sequential features may result in ignoring keywords or key video frames.To capture sequential and hierarchical representation, a weighted token-wise interaction mechanism was employed to exploit the pair-wise correlations adaptively.Extensive experiments with public datasets show that cross-modal action recognition learning helps for downstream action images classification, in other words, the proposed method can train better action classifiers by reading the sentences describing action itself.The method proposed in this study not only reaches good generalisation and zero-shot/few-shot transfer ability on Out of Distribution (OOD) test sets, but also performs lower computational complexity due to the lightweight interaction mechanism with 84.15% Top-1 accuracy on the Kinetics-400.

Random number generation is essential for applications in simulation, numerical analysis, and data encryption. The ubiquitous presence of system-on-chip (SoC) field-programmable gate array (FPGA) embedded devices in critical sectors necessitates robust random number generators (RNGs) that operate within these specialized environments. Traditional RNGs in GNU/Linux systems derive entropy from peripheral hardware events, which are scarce in SoC FPGA platforms lacking standard PC peripherals. Addressing this challenge, this paper proposes a novel random number generator named NDSTRNG that leverages the unique hardware structure of the SoC FPGA and the inherent randomness of GNU/Linux. The proposed generator employs a non-deterministic sampling model to circumvent reliance on various peripherals while ensuring unbiased output via a linear feedback shift register (LFSR)-based post-processing method. We implement this random number generator in SoC FPGA GNU/Linux using minimal FPGA resources and only one Linux task for sampling. NDSTRNG achieved a throughput exceeding 700 Kbps. Moreover, the entropy source of the generator is evaluated using NIST SP 800-90B, while the quality of the generated random numbers is assessed through ENT, NIST SP 800-22, and DIEHARDER. The results confirm that NDSTRNG meets the stringent criteria for both high-quality and high-speed random number generation, making it suitable for deployment in communication, defense, and medical domains where reliable RNGs are indispensable.

Pulmonary arterial hypertension (PAH) is considered the third most common cardiovascular disease after coronary heart disease and hypertension. The diagnosis of PAH is mainly based on the comprehensive judgment of computed tomography and other medical image examinations. Medical image processing based on deep learning has achieved significant success. However, the data belongs to the patient's privacy; therefore, the medical institutions as data custodians have the responsibility to protect the security of their data privacy. This situation makes medical institutions face a dilemma when building data-driven deep learning-assisted medical diagnosis methods. On the one hand, they need to pursue more high-quality data based on Big Data architecture for deep learning; on the other hand, they need to protect patient privacy to avoid data leakage. In response to the above challenges, we propose a hierarchical hybrid automatic segmentation model for pulmonary blood vessels based on local learning and federated learning approaches for segmenting the pulmonary blood vessels. The experiments prove the proposal could automatically segment the vessels from the original CT. It also indicates that the model based on a federated learning approach can achieve impressive performance under the premise of protecting data privacy for Big Data.

Pre-copy-based Virtual Machine (VM) live migration seamlessly migrates the running VM to the target physical server by pre-copying memory pages and realizing updates through loop iterations. This method, which has high reliability and robustness, can effectively achieve load balancing and reduce energy consumption. It is widely used in the industry to manage server cluster resources. However, it also involves many problems, such as many dirty memory pages resulting from repeated transmission and convergence failure of iterative transmission. Hence, pre-copy live migration cannot efficiently allocate server cluster resources. To resolve these problems, a VM pre-copy live migration technology based on the similarity of dirty memory pages is proposed in this paper. The access priority of historical dirty memory pages was determined by calculating the similarity weight based on the Hamming distance. A priority-based delay transmission scheme for high dirty pages and low dirty pages was used to decrease the frequent transmission of high dirty memory pages, increase the convergence speed of the live-migration iterative copy process, and reduce the overall migration time of VMs. A comparative analysis of experimental results based on six dimensions showed that the proposed method achieved better migration efficiency than the conventional live migration strategy.

As an essential intermediate courier station on the expressway, the service area plays an important role in regulating expressway traffic, ensuring expressway safety, and alleviating driver fatigue. With the rapid development of information technology, Expressway Service Areas (ESA) are developing in digitization and intelligence. This paper conducts exploratory research on Expressway Intelligent Service Area (EISA), sum-marizes the definition and characteristics of EISA, analyzes the current situation and existing problems of EISA construction, and elaborates the key technologies that the construction of EISA relies on. The research of this paper is in an effort to provide ideas for the subsequent reconstruction and expansion planning and design of the EISA.

Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing computing resources onto edge servers that are attached to base stations or access points at the edge of the cloud in close proximity with end-users. Due to edge servers’ geographic distribution, the EC paradigm is challenged by many new security threats, including the notorious distributed Denial-of-Service (DDoS) attack. In the EC environment, edge servers usually have constrained processing capacities due to their limited sizes. Thus, they are particularly vulnerable to DDoS attacks. DDoS attacks in the EC environment render existing DDoS mitigation approaches obsolete with its new characteristics. In this article, we make the first attempt to tackle the edge DDoS mitigation (EDM) problem. We model it as a constraint optimization problem and prove its$\mathcal {NP}$-hardness. To solve this problem, we propose an optimal approach named EDMOpti and a novel game-theoretical approach named EDMGame for mitigating edge DDoS attacks. EDMGame formulates the EDM problem as a potential EDM Game that admits a Nash equilibrium and employs a decentralized algorithm to find the Nash equilibrium as the solution to the EDM problem. Through theoretical analysis and experimental evaluation, we demonstrate that our approaches can solve the EDM problem effectively and efficiently.

Clothing is one of the symbols of human civilisation. Clothing design is an art form that combines practicality and artistry. The Dunhuang clothes culture has a long history which represents ancient Chinese aesthetics. Artificial intelligence (AI) technology has been recently applied to multiple areas, which is also drawing increasing attention in fashion. However, little research has been done on the usage of AI for the creation of clothing, especially in traditional culture. It is challenging that the exploration of computer science and Dunhuang clothing design, which is a cross-history interaction between AI and Chinese classical culture. In this paper, we propose ClothGAN, which is an innovative framework for “designing” new patterns and styles of clothes based on generative adversarial network (GAN) and style transfer algorithm. Besides, we built the Dunhuang clothes dataset and conducted experiments to generate new patterns and styles of clothes with Dunhuang elements. We evaluated these clothing works generated from different models by computing inception score (IS), human prefer score (HPS) and generated score (IS and HPS). The results show that our framework outperformed others in these designing works.

Proview is probably the first Open Source system based on Linux for process control. It is modern, powerful and general. It contains all functions normally required for successful sequential control, adjustment, data acquisition, supervision, communication, etc. But it is not used widely yet. This paper proposes a new Soft PLC (Programmable Logic Controller) solution of automatic burdening system based on Proview. The system adopts advanced DCS (Distributed Control System) structure and PID control to get high precision. The focus is on the principle of process control and system structure in Proview, providing I/O configuration, hardware design circuit, and part of ladder program. Finally, verification is carried out with the performance test of the overall machine.

Fault-tolerance has huge impact on embedded safety-critical systems. As technology that assists to the development of such improvement, Safe Node Sequence Protocol (SNSP) is designed to make part of such impact. In this paper, we present a mechanism for fault-tolerance and recovery based on the Safe Node Sequence Protocol (SNSP) to strengthen the system robustness, from which the correctness of a fault-tolerant prototype system is analyzed and verified. In order to verify the correctness of more than thirty failure modes, we have partitioned the complete protocol state machine into several subsystems, followed to the injection of corresponding fault classes into dedicated independent models. Experiments demonstrate that this method effectively reduces the size of overall state space, and verification results indicate that the protocol is able to recover from the fault model in a fault-tolerant system and continue to operate as errors occur.

The emergence of Cloud Services and Mobile Internet has influenced the society a lot, including the tourism industry. This paper proposes a design of backpacking service, not only aimed at the travelling routines, but also focus on the low-cost airlines. This kind of service aids backpackers with an effective travelling and satisfy the price requirement. With this low-cost airlines system, the backpackers can experience real e-Tourism and enjoy a better travel aided by the real-time information. According to the basic principles of low-cost airlines, this paper provides the backpackers with a definitely efficient way to check out a suitable flight itinerary under an acceptable price.

In most cases of safety-related systems, the network is an indispensable part. At this point, the system reliability is as important as the system communication quality. With the emergence of multi-core architectures, the first generation usually aims to provide reliable and deterministic computing resources. Therefore, with the boost requirement of reliability and throughput that cannot be satisfied by general single-core processors, the deployment of safety-related systems is transferred and processed multi-core environments. In this paper, we propose Reliable Communication Server on SPU (RCSoS), which is a server model for reliable communication utilizing SPU (Synergistic Processor Unit) in Cell/B.E (Cell Broadband Engine). It simulates SPU as a communication server and guarantees the reliability and determinacy by the isolation mode of SPU and contract model. We have implemented RCSoS in PlayStation 3, which dynamically adjust parameters, and inform applications on contract violations. Experiments show the performance of this model.

We designed a grid computing model in math based on current network computing technologies. MICE is an emerging technology to provide uniform programming, task submission, and management specifications across the large scale distributed computing nodes which deployed some famous mathematical software. MICE utilizes a three-level architecture that shields users from low-level computing resource discovery and provides globe uniform view for users. We extended MathML to solve the mathematical semantic objects' expression. CSP (computing service platform) servers are adopted in MICE to provide uniform task access, transfer and management of heterogeneous distributed resources across multiple administrative domains. This architecture enables the mathematical software resources to be deployed as services on the Internet. MICE can achieve good scalability, reliability and can be flexibly deployed and configured