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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wang, Junfeng Cui, Yue Zhou, Mingzhang Sun, Haixin Liu, Lanjun Zhang, Ailing Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin300384 China Mnr Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application China Tianjin Normal University College of Computer and Information Engineering Tianjin300387 China Xiamen University School of Information Science and Engineering Xiamen361005 China Ocean University of China College of Engineering Qingdao266100 China

ISBN: (纸本)9798350387384

Detecting the status of autonomous underwater vehicle, referred to hereinafter as an underwater target, is fundamental for successfully fulfilling cognition tasks or improving communication quality. In this article, we present a thorough study over the passive detection issue for multiple underwater targets emitting the linear frequency modulation signals. Specifically, hypothesis testing model, which comprises the signals received at the underwater detection node including periodic emission signals from the targets and additive noise, is firstly proposed for the practical scenarios. Then, the detection scheme has been investigated. Among this scheme, the behavior of the signals emitted by the underwater targets has been analyzed;and the primary aiming of the detection procedure, i.e., decision rule and its performance, has also been discussed and derived for the aforementioned scenarios. Towards the end, to confirm the advantages of our proposed studies, numerical simulation examples are carried out to examine the performance under different conditions. © 2024 IEEE.

关键词： Signal modulation

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Unsharp Mask Sharpening Detection via Global Analysis

Unsharp Mask Sharpening Detection via Global Analysis

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Irish Signals and Systems Conference (ISSC)

作者： Rong-Hui Lu Tzong-Jer Chen Junhong Pan Information Technology and Laboratory Management Center Wuyi University Wuyishan Fujian China School of Mathematics & Computer Science Wuyi University Wuyishan Fujian China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University

ISBN: (数字)9798350352986

ISBN: (纸本)9798350352993

Image sharpening prediction plays a significant role in image restoration, forensics, and computer vision. This work proposes a new method for detecting sharpening and indexing in digital images. Images initially undergo manipulation using Gaussian filters and Unsharp Masking (UM) with various degrees of intensity. This is followed by transformation into the frequency domain using Fourier transform. The resulting amplitude values are calculated, and their absolute values are summed along the frequency axis to produce a profile curve. These data curves are subsequently smoothed. The UM curves exhibit concavity and symmetry relative to the curve of the original image. The values of these curves at the concave positions, corresponding to various UM intensities, can be utilized as a sharpness index by comparing them to the curve values of the original image at the same positions. These sharpness indices are dependent on the UM amplification factors. This methodology finds utility in medical image processing and computer vision systems for sharpness detection and indexing. The features identified in this study, based on Fourier power spectrum analysis, can be input into a machine learning classifier for classification.

关键词： computer vision Filters Fourier transforms Frequency-domain analysis Forensics Machine learning Image restoration

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion

arXiv

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arXiv 2023年

作者： Ye, Zhe Mao, Terui Dong, Li Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China Ningbo City College of Vocational Technology Ningbo China

Deep speech classification has achieved tremendous success and greatly promoted the emergence of many real-world applications. However, backdoor attacks present a new security threat to it, particularly with untrustworthy third-party platforms, as pre-defined triggers set by the attacker can activate the backdoor. Most of the triggers in existing speech backdoor attacks are sample-agnostic, and even if the triggers are designed to be unnoticeable, they can still be audible. This work explores a backdoor attack that utilizes sample-specific triggers based on voice conversion. Specifically, we adopt a pre-trained voice conversion model to generate the trigger, ensuring that the poisoned samples does not introduce any additional audible noise. Extensive experiments on two speech classification tasks demonstrate the effectiveness of our attack. Furthermore, we analyzed the specific scenarios that activated the proposed backdoor and verified its resistance against fine-tuning. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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Design of Three Components for the Mobile application Supporting Software

Design of Three Components for the Mobile Application Suppor...

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International Conference on Network and Information Systems for computers (ICNISC)

作者： Tingting Shi Wenjie Du Xiyu Xu Beijing Institute of Computer Technology and Application Beijing China The Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China

The technology of mobile application supporting software design has been an increasingly field in the past decade. It is benefic to establish a generalized methodology to better direct the software engineering practice. After a brief description of architecture of the mobile application supporting software, this paper focuses on the design of three core components: integration network, development support environment, geographic information support. In each of the components several innovative techniques are introduced to commit the requirement specifications. The methodology of this paper can make solid contribute to the community.

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Deep Hypergraph Neural Networks with Tight Framelets 39

Deep Hypergraph Neural Networks with Tight Framelets

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Ming Fang, Yujie Wang, Yi Feng, Han Gu, Yongchun Bai, Lu Lio, Pietro Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China Zhejiang Institute of Optoelectronics China School of Computer Science and Technology Zhejiang Normal University China Department of Mathematics City University of Hong Kong Hong Kong School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University China Department of Computer Science and Technology Cambridge University United Kingdom

ISBN: (纸本)157735897X

Hypergraphs provide a flexible framework for modeling high-order (complex) interactions among multiple entities, extending beyond traditional pairwise correlations in graph structures. However, deep hypergraph neural networks (HGNNs) often face the challenge of oversmoothing with increasing depth, similar to issues in graph neural networks (GNNs). While oversmoothing in GNNs has been extensively studied, its implications in relation to hypergraphs are less explored. This paper addresses this gap by first theoretically exploring the reasons behind oversmoothing in deep HGNNs. Our novel insights suggest that a spectral-based hypergraph convolution, equipped with both low-pass and high-pass filters, can potentially mitigate these effects. Motivated by these findings, we introduce FrameHGNN, a framework that utilizes framelet-based hypergraph convolutions integrating tight framelet transforms with both low-pass and high-pass components, as well as the commonly used strategies in designing deep GNN architecture: initial residual and identity mappings. The experiment results on diverse benchmark datasets demonstrate that FrameHGNN outperforms several state-of-the-art models, effectively reducing oversmoothing while improving predictive accuracy. Our contributions not only advance the theoretical understanding of deep hypergraph learning but also provide a practical spectral-based approach for HGNNs, emphasizing the design of multifrequency channels. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning

arXiv

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arXiv 2023年

作者： Liu, Jiayu Huang, Zhenya Zhai, Chengxiang Liu, Qi Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China University of Illinois at Urbana-Champaign United States

Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge "learning" and "applying" organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Human-AI Collaboration: A Study on Anti-ChatGPT Strategies Employed in Innovative Practical Homework Towards "One-Click-Answer" Issue in AIGC 18th

Human-AI Collaboration: A Study on Anti-ChatGPT Strategies E...

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18th International Conference on computer Science and Education, ICCSE 2023

作者： Yiwen, Liu Yahui, Yang Jinrong, Fu Tao, Feng Ting, Yin Yingxi, Xiang Yanxia, Gao Taiguo, Qu Xian, Zhang School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China School of Foreign Languages Huaihua University Huaihua418000 China Furong School of Xinhong Dong Autonomous County Huaihua418000 China School of Fine Arts and Design Huaihua University Huaihua418000 China

ISBN: (纸本)9789819707362

ChatGPT as a representative of Artificial Intelligence in Generative Computing (AIGC) has had an impact on higher education. It promotes "human-computer collaboration" in coursework, showing both positive and negative effects. ChatGPT has the potential to provide students with personalized learning support. However, students may become too reliant on these tools and lack independent thinking. Aiming at the above problems, this paper introduces the idea of practical homework resource construction, and provides a series of anti-ChatGPT strategies, which can prevent students ChatGPT’s "one-click answer" from the source. By comparing activities such as teaching effectiveness, questionnaires, and talkback interviews conducted in the pilot course, the data illustrate the effectiveness of the methodology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Heng Qian Chuan Li Chengli Li Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Opo-electronic Information Technology School of Physics and Electronic Information Yunnan Normal University Kunming China

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad 2 /Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method.

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CGNet: Classification-Guided Multi-Task Interactive Network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion Information exchange Hyperspectral imaging

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