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6D Movable Antenna Enhanced Wireless Network Via Discrete Position and Rotation Optimization

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arXiv 2024年

作者： Shao, Xiaodan Zhang, Rui Jiang, Qijun Schober, Robert Erlangen91054 Germany School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China

Six-dimensional movable antenna (6DMA) is an effective approach to improve wireless network capacity by adjusting the 3D positions and 3D rotations of distributed antenna surfaces based on the users' spatial distribution and statistical channel information. Although continuously positioning/rotating 6DMA surfaces can achieve the greatest flexibility and thus the highest capacity improvement, it is difficult to implement due to the discrete movement constraints of practical stepper motors. Thus, in this paper, we consider a 6DMA-aided base station (BS) with only a finite number of possible discrete positions and rotations for the 6DMA surfaces. We aim to maximize the average network capacity for random numbers of users at random locations by jointly optimizing the 3D positions and 3D rotations of multiple 6DMA surfaces at the BS subject to discrete movement constraints. In particular, we consider the practical cases with and without statistical channel knowledge of the users, and propose corresponding offline and online optimization algorithms, by leveraging the Monte Carlo and conditional sample mean (CSM) methods, respectively. Simulation results verify the effectiveness of our proposed offline and online algorithms for discrete position/rotation optimization of 6DMA surfaces as compared to various benchmark schemes with fixed-position antennas (FPAs) and 6DMAs with limited movability. It is shown that 6DMA-BS can significantly enhance wireless network capacity, even under discrete position/rotation constraints, by exploiting the spatial distribution characteristics of the users. Copyright © 2024, The Authors. All rights reserved.

关键词： Rotation

On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

ANTI: An Adaptive Network Traffic Indexing Algorithm for High-Speed Networks

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ANTI: An Adaptive Network Traffic Indexing Algorithm for Hig...

IEEE Conference on Global Communications (GLOBECOM)

作者： Jiale Chen Xingshu Chen Liangguo Chen Xiao Lan Yonggang Luo School of Cyber Science and Engineering Sichuan University China Key Laboratory of Data Protection and Intelligent Management Ministry of Education (Sichuan University) China Cyber Science Research Institute Sichuan University China

Network packets record communication behaviors and details, which is important for security audits, attack detection, and forensic analysis. For the effectiveness and timeliness of security analysis, it is necessary to fully store network packets and build an efficient packet index. However, the existing packet indexing algorithms based on the radix tree ignore the distribution characteristics of network traffic and use internal nodes with the same capacity for index construction, resulting in wasted disk space and poor retrieval performance. As a solution, $w$ e propose ANTI, an adaptive network traffic indexing algorithm similar to Adaptive Radix Tree, which can adaptively switch internal nodes with different capacity according to the density of network traffic and compress the common prefix and distinct suffix of traffic attributes to balance the index construction performance and space utilization. We also implement a packet-aware network traffic archiving and indexing system to achieve full packet archival, efficient indexing, and fast retrieval. Finally, we empirically evaluate ANTI in IPv4 (IPv6) traffic scenarios, and the results confirm the effectiveness of ANTI as well as the benefit of adopting ANTI for enhancing indexing and retrieval performance compared with other state-of-art algorithms.

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Dynamic Beam Coverage for Satellite Communications Aided by Movable-Antenna Array

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arXiv 2024年

作者： Zhu, Lipeng Pi, Xiangyu Ma, Wenyan Xiao, Zhenyu Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The School of Electronic and Information Engineering Beihang University Beijing100191 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

The low-earth orbit (LEO) satellite network has been recognized as a promising technology to enable the ubiquitous coverage and massive connectivity for future sixth-generation (6G) mobile communications. Due to the ultra-dense constellation, efficient beam coverage and interference mitigation are crucial to LEO satellite communication systems, while the conventional directional antennas and fixed-position antenna (FPA) arrays both have limited degrees of freedom (DoFs) in beamforming to adapt to the time-varying coverage requirement of terrestrial users. To address this challenge, we propose in this paper utilizing movable antenna (MA) arrays to enhance the satellite beam coverage and interference mitigation. Specifically, given the satellite orbit and the coverage requirement within a specific time interval, the antenna position vector (APV) and antenna weight vector (AWV) of the satellite-mounted MA array are jointly optimized over time to minimize the average signal leakage power to the interference area of the satellite, subject to the constraints of the minimum beamforming gain over the coverage area, the continuous movement of MAs, and the constant modulus of AWV. The corresponding continuous-time decision process for the APV and AWV is first transformed into a more tractable discrete-time optimization problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AWV, where the successive convex approximation (SCA) technique is utilized to obtain locally optimal solutions during the iterations. Moreover, to further reduce the antenna movement overhead, a low-complexity MA scheme is proposed by using an optimized common APV over all time slots. Simulation results validate that the proposed MA array-aided beam coverage schemes can significantly decrease the interference leakage of the satellite compared to conventional FPA-based schemes, while the low-complexity MA scheme can achieve a performance comparable

关键词： Beamforming

LDConv: Linear deformable convolution for improving convolutional neural networks

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arXiv 2023年

作者： Zhang, Xin Song, Yingze Song, Tingting Yang, Degang Ye, Yichen Zhou, Jie Zhang, Liming College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation is confined to a local window, so it cannot capture information from other locations, and its sampled shapes is fixed. On the other hand, the size of the convolutional kernel are fixed to k × k, which is a fixed square shape, and the number of parameters tends to grow squarely with size. Although Deformable Convolution (Deformable Conv) address the problem of fixed sampling of standard convolutions, the number of parameters also tends to grow in a squared manner, and Deformable Conv do not explore the effect of different initial sample shapes on network performance. In response to the above questions, the Linear Deformable Convolution (LDConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In LDConv, a novel coordinate generation algorithm is defined to generate different initial sampled positions for convolutional kernels of arbitrary size. To adapt to changing targets, offsets are introduced to adjust the shape of the samples at each position. LDConv corrects the growth trend of the number of parameters for standard convolution and Deformable Conv to a linear growth. Compared to Deformable Conv, LDConv provides richer choices and can be equivalent to deformable convolution when the number of parameters of LDConv is set to the square of K. Differently, this paper also explores the effect of neural networks by using LDConv with the same size and different initial sampling shapes. LDConv completes the process of efficient feature extraction by irregular convolutional operations and brings more exploration options for convolutional sampled shapes. Object detection experiments on repre

关键词： Convolutional neural networks

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognos...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlei Yu Ahmed Elazab Ruiquan Ge Hui Jin Xinchen Jiang Gangyong Jia Qing Wu Qinglei Shi Changmiao Wang School of Computer Science Hangzhou Dianzi University Hangzhou China School of Biomedical Engineering Shenzhen University Shenzhen China School of Management and Economics The Chinese University of Hong Kong Shenzhen Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics.

关键词： Neuroimaging Measurement Image segmentation Correlation Text recognition Refining Multitasking Prognostics and health management Hemorrhaging Biomedical imaging

SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization

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arXiv 2023年

作者： Zhong, Xiaojing Huang, Xinyi Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China SenseTime Research China

Makeup transfer is a process of transferring the makeup style from a reference image to the source image, while preserving the source image’s identity. In addition to achieving fine-grained control over the makeup transfer, incorporating semantic alignment into the transformation is crucial, as the poses of the reference and source images are often inconsistent. We propose a novel Semantic-guided Alignment and Region-Adaptive normalization framework (SARA) to effectively transfer makeup styles under misaligned poses, offering flexible control to meet the demands of real-world applications, such as partial transfer, intensity adjustment, and makeup removal. Specifically, SARA comprises three modules: Firstly, we propose a semantic-guided alignment module to explicitly construct dense correspondence between the reference image and the target semantic map, employing unbalanced optimal transport matching to handle semantic region mismatches. Secondly, a region-adaptive normalization module is responsible for dynamically combining the warped style features with shape-independent style codes obtained by region-wise average pooling, mitigating feature loss during the alignment. Lastly, a makeup fusion module progressively fuses the identity features with the makeup styles to render the final output image. Furthermore, we combine optimal transport with histogram matching to generate the pseudo ground truth, which is used to facilitate the transfer in terms of both spatial alignment and color distribution. Experimental results show that our proposed SARA outperforms existing methods on two public datasets. © 2023, CC BY.

关键词： Semantics

SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Model

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arXiv 2024年

作者： Qian, Xinyuan Gao, Jiaran Zhang, Yaodan Zhang, Qiquan Liu, Hexin Garcia, Leibny Paola Li, Haizhou The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Electrical Engineering and Telecommunications The University of New South Wales Sydney2052 Australia The College of Computing and Data Science Nanyang Technological University Singapore The Center for Language and Speech Processing Johns Hopkins University United States The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen51872 China

Speech enhancement plays an essential role in various applications, and the integration of visual information has been demonstrated to bring substantial advantages. However, the majority of current research concentrates on the examination of facial and lip movements, which can be compromised or entirely inaccessible in scenarios where occlusions occur or when the camera view is distant. Whereas contextual visual cues from the surrounding environment have been overlooked: for example, when we see a dog bark, our brain has the innate ability to discern and filter out the barking noise. To this end, in this paper, we introduce a novel task, i.e. Scene-aware AudioVisual Speech Enhancement (SAV-SE). To our best knowledge, this is the first proposal to use rich contextual information from synchronized video as auxiliary cues to indicate the type of noise, which eventually improves the speech enhancement performance. Specifically, we propose the VC-S2E method, which incorporates the Conformer and Mamba modules for their complementary strengths. Extensive experiments are conducted on public MUSIC, AVSpeech and AudioSet datasets, where the results demonstrate the superiority of VC-S2E over other competitive methods. We will make the source code publicly available. Project demo page: https://***/ © 2024, CC BY.

关键词： Speech enhancement

Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning

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arXiv 2024年

作者： Wang, Shuai Chen, Zhengyang Lee, Kong Aik Qian, Yanmin Li, Haizhou Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China Auditory Cognition and Computational Acoustics Lab The Department of Computer Science and Engineering The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning