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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques

Novel Approach for Enhancing Mental Well-Being Through Machi...

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Advancements in Computing (ICAC), International Conference on

作者： O.V.D.E Roshani H.M.K.J.J Ayuwardhana P.H.M.S Rodrigo R.U Hewageegana Harinda Fernando Dilshan I. de Silva Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress.

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Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

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Chinese Journal of Electronics 2023年第2期21卷 254-259页

作者： Hongbing Cheng Chunming Rong Zhenghua Tan Qingkai Zeng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Information Engineering Jiangsu City Vocation College Nanjing China Department of Electronic Engineering and Computer Science University of Stavanger Stavanger Norway Department of Electronic Systems Aalborg University Aalborg Denmark

Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.

关键词： Access control Cloud computing Identity-based encryption Biometrics (access control) Authentication Process control Memory

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Symbiotic Control of Uncertain Dynamical systems: Harnessing Synergy Between Fixed-Gain Control and Adaptive Learning Architectures

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IEEE Transactions on Automatic Control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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A Novel Wavelet Based Generative Model for Time Series Prediction

A Novel Wavelet Based Generative Model for Time Series Predi...

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International Conference on Big Data and information Analytics (BigDIA)

作者： Chaofan Dai Xiaoguang Yuan Zongkai Tian Xinyue Hu Zhen Luan Youchen Wang National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Generative models have become an exciting area of research in recent years. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) have been utilized in various data augmentation applications. However, generative models can learn high-dimensional features of data through adversarial learning, making them suitable for nonlinear and nonstationary time series analysis, such as stock market prediction, high-frequency trading, and ocean current forecasting. In this paper, the researchers focus on using a wavelet-based GAN to predict stock market prices by generating synthetic stock market price trends. Historical stock price data from 2014 to 2024 is used for our experiments, and the results show that the wavelet-based GAN outperforms deep learning baseline models..

关键词： Wavelet transforms Analytical models Oceans Time series analysis Noise reduction Predictive models Generative adversarial networks Wavelet analysis Data models Stock markets

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Realization and Performance Tests of a Dual-Polarization Fiber Optic Gyroscope 8

Realization and Performance Tests of a Dual-Polarization Fib...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Zhou, Tong Zhu, Lanxin Cao, Yuwen Chen, Yanjun Huang, Huimin Li, Zhengbin School of Software and Microelectronics Peking University Beijing102600 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Interferometric fiber optical gyroscopes (IFOGs) have become one of the widely used sensors of inertial technology and rotational seismology, owing to their high precision and stability. In recent years, the dual-polarization IFOG, which allows two orthogonal polarization to work simultaneously, has been proved to achieve better measurement performance than the conventional minimum configuration through mutual compensation. In this work, a prototype of dual-polarization fiber-optic gyroscope is designed and realized. laboratory tests show the self-noise has reached 9 nrad/s/sqrt(Hz) over the frequency from 0.01 to 125 Hz and the angular random walk has achieved 2.6×10-5 deg/sqrt(hr). For an Open-loop IFOG, which does not need a confiscated feedback control system, it avoids the dead-zone of closed-loop IFOG. However, limited by the measurement range, as a result of the periodic and nonlinear response of open-loop demodulation, the scale factor of open-loop IFOG varies around 100~1000 parts per million (ppm) conventionally, which hinders its detection accuracy. In this work, with new measurement extension and linearity compensation methods, the prototype achieves an outstanding precision with scale factor nonlinearity up to 5 ppm within ±10 deg/s, and shows the potential of dual-polarization IFOGs for rotational seismology. © 2022 SPIE

关键词： Seismology

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on software engineering and Service Science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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Exploring Multi-Perspective Document Structures for News Discourse Profiling

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2349-2359页

作者： Hang Yang Dianbo Sui Yubo Chen Kang Liu Jun Zhao School of Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer Science and Software Engineering Harbin Institute of Technology Weihai China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Learning the discourse structures of news articles is vital for obtaining valuable structural information around main events, which has been shown useful for many natural language processing applications. This paper proposes the extraction of graphs explicitly representing the discourse structure, coreference structure, and semantic similarities within a document. Concept embeddings are then updated using a weighted combination of feature embeddings, modeled after capsule networks, and resulting in improved context-dependent representation of the content of each news article. Experimental tests on the NewsDiscourse dataset demonstrate the effectiveness of our proposed method.

关键词： Speech processing Routing Computational modeling Weapons Encoding Transformers Representation learning Natural language processing Context modeling Artificial intelligence

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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

作者： Yihuan Huang Yuanzhe Li Yanzhen Ren Weiping Tu Yuhong Yang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds.

关键词： Time-frequency analysis Accuracy Noise Speech recognition Feature extraction Acoustics Speaker recognition Low latency communication Speech processing

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