Dynamic Random Access Memory (DRAM) remains the most widely used memory in computer architecture. This paper explores identifying a device using DRAM for creating Physically Unclonable Functions (PUFs) from Rowhammer-...
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Virtual reality technology offers a cost effective means to train emergency rescuers, which is an urgent task on account of the increasing terrorist activities. An immersive virtual reality system called ERT-VR is int...
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Variational Autoencoder (VAE), as one of the main generative models, has a powerful representation learning capability. However, the hidden space representation learned by VAE is a high-dimensional and complex vector ...
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Inspired by early research on exploring naturally annotated data for Chinese Word Segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to ...
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Precise object counting is crucial in practical applications, finding extensive utility across numerous societal domains. In the context of few-shot object counting, variations in object angles can significantly alter...
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Precise object counting is crucial in practical applications, finding extensive utility across numerous societal domains. In the context of few-shot object counting, variations in object angles can significantly alter the distribution and distinguishability of object feature points, thereby increasing the difficulty of feature extraction. To address these challenges, a spatial and channel similarity-aware attention-enhancement network for few-shot scenarios is introduced. The network employs slice convolution and attention mechanisms within the spatial and channel dimensions, respectively. Through such means, it effectively extracts object features from diverse angles, adeptly surmounting the challenges in feature extraction engendered by angular diversity. Specifically, the network introduces a spatial similarity-aware module, which performs multi-angle matching of support and query features in the spatial dimension to enhance the model's feature extraction capabilities. Additionally, a channel attention enhancement module is incorporated to weight and enhance critical channel features, improving the network's ability to handle diverse object angles. Comprehensive experiments carried out on TRANCOS, CARPK, PUCPR+ and UCSD datasets demonstrate that the proposed network exhibits excellent counting performance and robust generalization capabilities. It offers an effective approach for precise object counting tasks.
The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is chal...
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Mobile Edge Computing (MEC) pushes computing resources from the network center to the network edge to provide efficient and reliable computing services. However, due to the mobility and diversity of mobile users (MUs)...
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Context: The advent of Autonomous Driving Systems (ADS) has marked a significant shift towards intelligent transportation, with implications for public safety and traffic efficiency. While these systems integrate a va...
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Context: The advent of Autonomous Driving Systems (ADS) has marked a significant shift towards intelligent transportation, with implications for public safety and traffic efficiency. While these systems integrate a variety of technologies and offer numerous benefits, their security is paramount, as vulnerabilities can have severe consequences for safety and trust. Objective: This study aims to systematically investigate potential security weaknesses in the codebases of prominent open-source ADS projects using CodeQL, a static code analysis tool. The goal is to identify common vulnerabilities, their distribution and persistence across versions to enhance the security of ADS. Methods: We selected three representative open-source ADS projects, Autoware, AirSim, and Apollo, based on their high GitHub star counts and Level 4 autonomous driving capabilities. Using CodeQL, we analyzed multiple versions of these projects to identify vulnerabilities, focusing on CWE categories such as CWE-190 (Integer Overflow or Wraparound) and CWE-20 (Improper Input Validation). We also tracked the lifecycle of these vulnerabilities across software versions. This approach allows us to systematically analyze vulnerabilities in projects, which has not been extensively explored in previous ADS research. Results: Our analysis revealed that specific CWE categories, particularly CWE-190 (59.6%) and CWE-20 (16.1%), were prevalent across the selected ADS projects. These vulnerabilities often persisted for over six months, spanning multiple version iterations. The empirical assessment showed a direct link between the severity of these vulnerabilities and their tangible effects on ADS performance. Conclusions: These security issues among ADS still remain to be resolved. Our findings highlight the need for integrating static code analysis into ADS development to detect and mitigate common vulnerabilities. Meanwhile, proactive protection strategies, such as regular update of third-party libraries, are
Crystal defects, whether intrinsic or engineered, drive many fundamental phenomena and novel functionalities of quantum materials. Here, we report symmetry-breaking phenomena induced by Sn-vacancy defects on the surfa...
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Searching in large-scale unstructured peer-to-peer networks is challenging due to the lack of effective hint information to guide queries. In this paper, we propose POP, a parallel, cOllaborative and Probabilistic sea...
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