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International Conference on Data engineering

作者： Tsz Nam Chan Zhe Li Leong Hou U Reynold Cheng Department of Computer Science and Software Engineering Shenzhen University Alibaba Cloud Department of Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Department of Computer Science and Musketeers Foundation Institute of Data Science The University of Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods.

关键词： Training Support vector machines Additives Pedestrians Accuracy Data engineering Kernel

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Reversible Feature Learning for Brain Tumor Segmentation with Incomplete Modalities

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IEEE Signal Processing Letters 2025年 32卷 2419-2423页

作者： Fan, Yanbing Liu, Linghui Luan, Xiao Li, Weisheng Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing Key Laboratory of Image Cognition Chongqing400065 China

Accurate brain tumor segmentation is vital for clinical diagnosis and treatment. Due to motion artifacts and image damage, it is challenging to obtain accurate segmentation results of brain tumors in the presence of incomplete MRI modalities. We propose a multimodal reversible feature learning method to tackle this problem. This method can fully explore the potential feature similarities and complementarities between MRI modalities. To compensate the information of missing modalities, we propose a reversible feature interaction module. It explores information similarity among existing modalities as priors, which are used to reconstruct features of missing modalities at the feature level. With enhanced discriminative information, the model suppresses the noise in the missing modalities. Furthermore, we propose a dual-scale attention module to enhance the detail restoration and reconstruction accuracy of images. Comparison results on the BRATS challenge datasets show the superiority of our method over current popular methods. © 1994-2012 IEEE.

关键词： Image segmentation

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A Revocable Fast and Lightweight Parallel Encryption Scheme for IIoT

引用

IEEE Internet of Things Journal 2025年

作者： Lu, Junze Hu, Chunqiang Chen, Jiajun Xia, Hui Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing400030 China Ocean University of China College of Computer Science and Technology China

As the Industrial Internet of Things (IIoT) expands, the number of stakeholders increases. Many entities require significant amounts of data from industrial devices. These data streams improve information sharing and optimize the industrial chain. However, IIoT’s enormous data volumes pose challenges to traditional encryption methods, which are unable to meet efficiency and energy consumption requirements. Thus, a new, efficient encryption algorithm is essential for managing data flows among IIoT subscribers. In this paper, we propose a fast, low-energy, high-security encryption scheme to manage multi-entity data streams. First, we introduce a novel encryption scheme based on the Subset Sum Problem (SSP), which improves energy efficiency and speed. Second, to meet subscription requirements, we employ attribute-based encryption (ABE) for key forwarding. Finally, we handle subscription revocations with device key updates. The device owner utilizes their secret value to generate an identity proof with a key update request. © 2014 IEEE.

关键词： Information dissemination

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Lombard-VLD: Voice Liveness Detection Based on Human Auditory Feedback

Lombard-VLD: Voice Liveness Detection Based on Human Auditor...

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IEEE Symposium on Security and Privacy

作者： Hongcheng Zhu Zongkun Sun Yanzhen Ren Kun He Yongpeng Yan Zixuan Wang Wuyang Liu Yuhong Yang Weiping Tu School of Cyber Science and Engineering Wuhan University Wuhan China State Grid Wuhan Electric Power Supply Company Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Voice Liveness Detection (VLD) aims to protect speaker authentication from speech spoofing by determining whether speeches come from live speakers or loudspeakers. Previous methods mainly focus on their differences at the signal level. In this paper, we propose the first VLD that uses the human auditory feedback mechanism (i.e., the Lombard effect), called Lombard-VLD. The key idea is that live speakers can physiologically and involuntarily adjust their speaking patterns in a noisy background but loudspeakers cannot. Moreover, we design a reference-based dual input mode and a differential SE-ResBlock to model the acoustic differences caused by the Lombard effect. Experimental results show that Lombard-VLD achieves 0% and 0.24% EER in two datasets, outperforming the state-of-the-art methods. It is robust to various environmental factors, including different distances, postures of the speaker, and environmental noise, with an average accuracy of over 98.51%. It also has a good generalization to unseen speakers, genders, and datasets, with EER lower than 2.68%, 3.44%, and 7.32%, respectively. This work shows the advantages of the Lombard effect in VLD, which has fewer user limitations and better detection performance.

关键词： Loudspeakers Privacy Accuracy Working environment noise Authentication Physiology Environmental factors Acoustics Security Noise measurement

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GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

arXiv

引用

arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation

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Beyond pixels:Learning from multimodal hyperspectral superpixels for land cover classification

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Science China(Technological Sciences) 2022年第4期65卷 802-808页

作者： HONG DanFeng WU Xin YAO Jing ZHU XiaoXiang Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Remote Sensing Technology Institute German Aerospace Center(DLR)Wessling 82234Germany Data Science in Earth Observation Technical University of MunichMunich 80333Germany

Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance *** this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC *** can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective *** better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for *** experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.

关键词： classification hyperspectral image land cover multimodal multispectral image remote sensing subspace learning superpixels

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A Survey of Human Action Recognition and Posture Prediction

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Tsinghua Science and Technology 2022年第6期27卷 973-1001页

作者： Nan Ma Zhixuan Wu Yiu-ming Cheung Yuchen Guo Yue Gao Jiahong Li Beiyan Jiang Beijing Key Laboratory of Information Service Engineering the College of RoboticsBeijing Union UniversityBeijing 100101China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Software Tsinghua UniversityBeijing 100084China College of Robotics Beijing Union UniversityBeijing 100101China

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in *** are both active research topics in computer vision community,which have attracted considerable attention from academia and *** are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external *** the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning ***,it is necessary to make a comprehensive review of recent *** this paper,firstly,we attempt to present the background,and then discuss research ***,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction ***,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.

关键词： human action recognition posture prediction computer vision human-computer cooperation interactive cognition

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Adaptive Neighborhoods Locality Preserving Projections

Adaptive Neighborhoods Locality Preserving Projections

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Jiayi Xu Wendong Huang Yang Zhao Jihong Pei Xuan Yang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In contemporary society, the analysis and utilisation of big data is of paramount importance. However, traditional feature extraction methods frequently encounter the curse of dimensionality when dealing with high-dimensional data, which is no longer able to adapt to the current needs of practical applications. The manifold learning method, which is capable of effectively extracting features of high-dimensional data, ne-cessitates the resolution of the problem of how to choose an appropriate local neighbourhood. Consequently, this paper proposes an Adaptive neighborhood Locality Preserving Projection (LAPP) based on the Locality Preserving Projection (LPP). TThis method combines two neighborhood selection method. To achieve an adaptive radius selection for local neighborhoods of sample points based on the distance characteristics between sample points. The LAPP proposed in this paper markedly reduces the dimensionality reduction and enhances the computational efficiency while maintaining a certain degree of accuracy.

关键词： Manifolds Accuracy High dimensional data Signal processing Big Data Feature extraction Computational efficiency Manifold learning Data mining

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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Centerness-based Instance-aware Knowledge Distillation with Task-wise Mutual Lifting for Object Detection on Drone Imagery

arXiv

引用

arXiv 2024年

作者： Du, Bowei Liao, Zhixuan Zhang, Yanan Cai, Zhi Chen, Jiaxin Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

Developing accurate and efficient detectors for drone imagery is challenging due to the inherent complexity of aerial scenes. While some existing methods aim to achieve high accuracy by utilizing larger models, their computational cost is prohibitive for drones. Recently, Knowledge Distillation (KD) has shown promising potential for maintaining satisfactory accuracy while significantly compressing models in general object detection. Considering the advantages of KD, this paper presents the first attempt to adapt it to object detection on drone imagery and addresses two intrinsic issues: (1) low foreground-background ratio and (2) small instances and complex backgrounds, which lead to inadequate training, resulting insufficient distillation. Therefore, we propose a task-wise Lightweight Mutual Lifting (Light-ML) module with a Centerness-based Instance-aware Distillation (CID) strategy. The Light-ML module mutually harmonizes the classification and localization branches by channel shuffling and convolution, integrating teacher supervision across different tasks during back-propagation, thus facilitating training the student model. The CID strategy extracts valuable regions surrounding instances through the centerness of proposals, enhancing distillation efficacy. Experiments on the VisDrone, UAVDT, and COCO benchmarks demonstrate that the proposed approach promotes the accuracies of existing state-ofthe- art KD methods with comparable computational requirements. Codes will be available upon acceptance. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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