Sustainability has become a critical concern in today's world. The need for sustainable practices is increasing across all sectors of the world. The United Nations' Sustainable Development Goals (SDGs) provide...
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Organizational use of Artificial Intelligence (AI) to assist with performance evaluation has increased in recent years. However, limited understanding exists about employees' perceptions regarding AI usage for thi...
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Due to the aging of the world's population, the incidence of retinal diseases is on the rise. Machine learning is expected to have a crucial role in identifying retinal disease. Multiple medical institutions coope...
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作者：
Zhong, WenjieSun, TaoZhou, Jian-TaoWang, ZhuoweiSong, XiaoyuInner Mongolia University
College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology
School of Computer Science and Technology Guangzhou510006 China Portland State University
Department of Electrical and Computer Engineering PortlandOR97207 United States
Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design...
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In 3D human pose estimation, binocular vision typically relies on stereo matching to obtain depth information and calculates 3D keypoints using the disparity principle. However, the high computational cost of stereo m...
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Future wireless systems with massive antennas must balance data rates and RF chain costs. Antenna selection activating only a subset of antennas addresses this challenge. Recently, neural network-based approaches have...
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Future wireless systems with massive antennas must balance data rates and RF chain costs. Antenna selection activating only a subset of antennas addresses this challenge. Recently, neural network-based approaches have shown promise over traditional symbolic methods, offering fixed complexity in inference and suitability for hardware implementation. However, their closed-box nature raises concerns for safety-critical 6G applications like autonomous driving and drones, where reliable communication is vital. Specifically, it is often unclear how the neural network determines which antennas to select, making it difficult to interpret or trust the decision-making process. This paper investigates the robustness of neural networks for antenna selection in such contexts. While empirical robustness against finite random inputs sampled from a uniform distribution may suffice for general applications, certified robustness ensuring consistent inference under all possible perturbations is essential for safety-critical systems. Although certified robustness is well studied in vision and language tasks, we are the first, to our knowledge, to explore its application in telecommunications. We mathematically define robustness for antenna-selection networks and apply state-of-the-art linear relaxation-based perturbation analysis. Our findings show that pruned networks, beyond being more efficient, also exhibit superior certified robustness compared to their unpruned counterparts. We further compare certified and empirical robustness, identifying a significant gap that suggests the need for improved certification methods. Additionally, in our antenna selection setting, we observe that removing monotonic activations in the final layer improves certified robustness.
Space systems enable essential communications, navigation, imaging and sensing for a variety of domains, including agriculture, commerce, transportation, and emergency operations by first responders. Protecting the cy...
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Image clustering is a challenging task in computer vision, with performance heavily dependent on the quality of feature representations due to the inherent complexity of images. However, current image clustering metho...
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As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains ...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.
Existing self-knowledge distillation (Self-KD) solutions usually focus on transferring historical predictions of individual instances to the current network. However, this approach tends to create overconfidence for e...
Existing self-knowledge distillation (Self-KD) solutions usually focus on transferring historical predictions of individual instances to the current network. However, this approach tends to create overconfidence for easy instances and underconfidence for hard instances. The widely used temperature-based strategies to smooth or sharpen the predicted distributions can lead to inconsistencies across instances, causing sensitivity issues. To address this, our approach views a queue of instances as an ensemble rather than treating each instance independently. We propose a novel method that distills historical knowledge from a dimensional perspective, utilizing intra class characteristics and interclass relationships within each ensemble. First, we align each dimension distribution from the current network to the historical output. Second, we ensure each dimension is closer to similar dimensions than dissimilar ones, maintaining consistent attitudes from present and historical perspectives. Our insights reveal that distilling historical knowledge from a dimensional perspective is more effective than the traditional instance-based approach, with potential applications in related tasks. Empirical results on three famous datasets and various network architectures demonstrate the superiority of our proposed method. Our code is available at https://***/WenkeHuang/DimSelfKD.
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