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Novel non-convex online algorithms for robust ranking

Journal of Information and Computational Science 2013年第17期10卷 5743-5759页

作者： Cheng, Fan Wang, Xufa Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Computer Anhui University Hefei 230039 China School of Computer University of Science and Technology of China Hefei 230027 China

Recently increasing attention has been focused on learning to rank, which aims to learn a ranking function from a set of training data with relevance labels. Many of the ranking algorithms are based on the pairwise preference framework. However, one disadvantage of pairwise ranking is that it is sensitive to the outliers. To tackle the problem, in this paper, we propose two novel online algorithms based on non-convex Ramp Loss which can suppress the influence of outliers. The first one directly defines a new objective function based on Ramp Loss. Considering that the objective function is non-convex, we use the Concave-convex Procedure (CCCP) for convex approximation, then design an online algorithm based on Dual Coordinate Descend Method to learn the final ranking model. The second algorithm applies selective sampling technology in the training phase. It ignores the instances that lie in the flat region of the Ramp Loss in advance, before they are processed by the learner. Experimental results show that the algorithms we proposed have significant robustness to outliers and yield better generalization performance than the baseline rankers, and the results also prove that our algorithms have less computational running time for their less support vectors. © 2013 Binary Information Press.

关键词： Approximation algorithms

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Congestion control strategy based on probability selection in delay tolerant mobile sensor network

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Journal of Information and Computational Science 2014年第14期11卷 5121-5130页

作者： Wang, Bingting Hu, Yanjun Liang, Hui Lin, Qibin Shi, Yonghua Wang, Xianbing School of Electronic and Electrical Engineering Chuzhou University Chuzhou China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China School of Information Science & Technology Southwest Jiaotong University Chengdu China

We analyzed the relationship between packet validity and encounter probability as well as the relationship between packet validity and remaining lifetime. Then we proposed the congestion control strategy based on probability selection, which implements the operations of congestion detection and congestion avoidance. When node buffer is fully occupied and needs to store a new packet, it will look for the packets whose remaining lifetime is lower than the threshold T, and then drop the packet with minimum encounter probability. If there is no packet whose remaining lifetime is lower than T, the packet with the shortest remaining lifetime will be dropped. Simulation results show that this congestion control strategy improves the performances of delivery probability, average delay and overhead ratio compared with four kinds of congestion control strategies. 1548-7741/Copyright © 2014 Binary Information Press

关键词： Probability

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An efficient implementation of double precision 1-D FFT for GPUs Using CUDA

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Journal of Information and Computational Science 2012年第2期9卷 387-394页

作者： Liu, Yanjun Guo, Licai Luo, Bin Zhang, Xingyi School of Computer Science and Technology Anhui University Hefei 230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China

Fast Fourier Transform (FFT) is a well known and widely used tool in many scientific and engineering fields. CUFFT, which is the NVIDIA's FFT library included in the CUDA toolkit, supports double precision FFTs. However, the implementation of CUFFT is not very efficient. In this paper, we implement an efficient double-precision Cooley-tukey algorithm for GPUs using CUDA. Some programming techniques are employed to exploit the hardware characteristics. These techniques include on-chip shared memory utilization, removing redundant computation, and coalescing the global memory access. Experiments show that the performance of our 1-D FFT is as fast as CUFFT. Furthermore, the performance of our FFT implementation is more than twice faster than CUFFT for small input sizes. Copyright © 2012 Binary Information Press.

关键词： Graphics processing unit

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Circularly polarized annular slit antenna based on metasurface

Circularly polarized annular slit antenna based on metasurfa...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Jingyang Wang Bo Wu Liang Zhang Xianliang Wu Yue Ma Ruikang Wang Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal University He-fei China

ISBN: (纸本)9781665482271

In this paper, a circularly polarized annular slit antenna with a center frequency of 2.15 GHz realized through a metasurface was designed. The metasurface is made up of 5×5 periodic units. The impedance bandwidth of annular slit antenna is 2.08-2.5 GHz and after loading the metasurface the frequency range in which the axis ratio of the antenna is less than 3dB is 2.12-2.18 GHz and the gain reaches 3.5 dBi. The linear polarization antenna can be directly converted into circular polarization antenna by loading the metasurface. Reserch show that antenna loaded with metasurface can be widely used in electronic communication field.

关键词： Microwave antennas Polarization Simulation Loading Millimeter wave technology Bandwidth Metasurfaces

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SSFam: Scribble Supervised Salient Object Detection Family

arXiv

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arXiv 2024年

作者： Liu, Zhengyi Deng, Sheng Wang, Xinrui Wang, Linbo Fang, Xianyong Tang, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China

Scribble supervised salient object detection (SSSOD) constructs segmentation ability of attractive objects from surroundings under the supervision of sparse scribble labels. For the better segmentation, depth and thermal infrared modalities serve as the supplement to RGB images in the complex scenes. Existing methods specifically design various feature extraction and multi-modal fusion strategies for RGB, RGB-Depth, RGB-Thermal, and Visual-Depth-Thermal image input respectively, leading to similar model flood. As the recently proposed Segment Anything Model (SAM) possesses extraordinary segmentation and prompt interactive capability, we propose an SSSOD family based on SAM, named SSFam, for the combination input with different modalities. Firstly, different modal-aware modulators are designed to attain modal-specific knowledge which cooperates with modal-agnostic information extracted from the frozen SAM encoder for the better feature ensemble. Secondly, a siamese decoder is tailored to bridge the gap between the training with scribble prompt and the testing with no prompt for the stronger decoding ability. Our model demonstrates the remarkable performance among combinations of different modalities and refreshes the highest level of scribble supervised methods and comes close to the ones of fully supervised methods. https://***/liuzywen/SSFam Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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An efficient co-evolutionary particle swarm optimizer for solving multi-objective optimization problems

An efficient co-evolutionary particle swarm optimizer for so...

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第27届中国控制与决策会议

作者： Daqing Wu Li Liu Xiang Jian Gong Li Deng Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Computer Science and Technology Institute University of South China Glorious Sun School of Business and Management Dong Hua University

An efficient co-evolutionary multi-objective particle swarm optimizer named ECMPSO was *** uses dynamic multiple swarms to deal with multiple objectives,taking one objective is optimized by each swarm into account,and maintains diversity of new found non-dominated solutions via adopts a three-level particle swarm optimization(PSO) updating rule wherein the particles learn their experiences based on personal,neighborhood,and external *** prove the validity of the ECMPSO algorithm for solving multi-objective problems,some benchmark problems and one real-life problem are selected to validate the performance of the ECMPSO *** experiment results show that the ECMPSO algorithm is better in terms of search precision and convergence performance than other three algorithms from the literature.

关键词： Multi-objective Optimization,Particle Swarm Optimizer,Neighborhood Best Particle,Dynamic Swarms,Economic Environmental Dispatch

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3D imaging algorithm and implement for through-wall synthetic aperture radar

3D imaging algorithm and implement for through-wall syntheti...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Jiabing Zhu Yi Hong Liang Tao East China Research Institute of Electronic East China Research Institute of Electronic Engineering Hefei China Education Ministry Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

ISBN: (纸本)9781424444793;9781424444809

Typical synthetic aperture radar (SAR) images are two-dimensional, providing range and azimuth information, but furnish few details with respect to elevation. First of all, one approach to extend SAR to three-dimensional imaging is considered. The simplest implementation of this would replace the single antenna element by a linear array oriented vertically. Secondly, the outlining data and image processing for a three-dimensional application is introduced in detail in the paper. Finally, simulation results show that the proposed algorithm is effective, while maintaining good image quality in terms of the reconstructed target response.

关键词： Azimuth Linear antenna arrays signal processing algorithms Synthetic aperture radar Spaceborne radar Image reconstruction Radar imaging Radar signal processing Pulse compression methods Image processing

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Maximizing Spectral Radius of Trees with Given Maximal Degree

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Journal of Mathematical Research and Exposition 2009年第5期29卷 806-812页

作者： FAN Yi Zheng ZHU Min Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education of the People's Republic of China Anhui University Anhui 230039 China School of Mathematical Sciences Anhui University Anhui 230039 China

In this paper, we characterize the trees with the largest Laplacian and adjacency spectral radii among all trees with fixed number of vertices and fixed maximal degree, respectively.

关键词： trees maximal degree Laplacian eigenvalue adjacency eigenvalue spectral radius.

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3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

3R: Word and Phoneme Edition based Data Augmentation for Lex...

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International Conference on Computational Intelligence and Security

作者： Aihua Zheng Naipeng Ye Xiao Wang Xiao Song Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Peking University Shenzhen Institute Shenzhen China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.

关键词： Training Vocabulary Semantics Training data Predictive models Noise measurement Security

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Self-Training with Contrastive Learning for Adversarial Domain Adaptation

Self-Training with Contrastive Learning for Adversarial Doma...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shanshan Wang Minbin Hu Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Traditional methods often focus on minimizing the distribution gap between feature spaces of the two domains to achieve domain-invariant representations. However, this approach may not fully leverage the inherent class-specific information and could adversely affect decision boundaries in the target domain. In this paper, we introduce an adversarial contrastive self-training framework. The model integrates both intra-class and inter-class domain differences for better alignment between the source and target domains. Initially, we augment target domain samples using Fourier transformations, obtaining reliable samples and pseudo-labels through self-training. We then employ intra-domain contrastive learning on the reliable samples and pseudo-labels, alongside labeled source domain samples, to achieve intra-domain compactness. Finally, we utilize adversarial learning to achieve global alignment. This comprehensive approach ensures improved adaptation performance while also addressing the limitations of previous methods.

关键词： Adaptation models Neural networks Contrastive learning Adversarial machine learning Reliability

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