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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yi Qiao Yang Liu Qing He Xiang Ao Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciencs Beijing China Institute of Intelligent Computing Technology Suzhou CAS

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have demonstrated impressive success across diverse fields when data satisfies in-distribution (ID) assumption. Nevertheless, GNN performance significantly declines in cases of distribution shifts between training and testing graph data. This degradation primarily stems from spurious correlations between irrelevant domain information and target labels in out-of-distribution (OOD) scenarios. Thus, maximizing the utilization of domain information becomes imperative. In light of this, we propose a novel approach named Domain-aware Node Representation Learning (DNRL), comprehensively incorporates domain information to bolster generalization capability. Specifically, DNRL selectively interpolates nodes with the same label but different domains, extending training data into unseen domains and alleviating the effects caused by domain-related spurious correlations. Futhermore, by introducing a domain-aware contrastive learning strategy, our method implicitly decouples domain information from node information to learn domain-independent node representations. Extensive experiments on graph out-of-distribution benchmarks demonstrate that DNRL can achieve effective OOD generalization performance across diverse domains.

关键词： Representation learning Training Degradation Interpolation Correlation Training data signal processing Graph neural networks Data models Speech processing

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High Energy Efficient In-Situ Storage computing Macro for Multiply-Accumulate Operations

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SSRN 2024年

作者： Peng, Chunyu Yan, Shengyuan Ding, Huayi Wang, Yana Lu, Wenjuan Dai, Chenghu Li, Xin Hu, Wei Wu, Xiulong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China School of Integrated Circuits Anhui University Hefei230601 China

This study presents a fully digital CIM macro featuring a novel self-write-back 12T cell. This bitcell is capable of performing Boolean logic operations and autonomously writing back results into the in-situ cell, circumventing the need for peripheral circuits. It enables in-situ logic operations, such as AND, and facilitates a 4-bit×4-bit MAC operation through an adder tree and shift-and-add process, thereby enhancing energy efficiency and throughput while minimizing area overhead. Further, we propose an 18T full adder structure with lower delay and power consumption than current state-of-the-art full adders. The proposed CIM macro, designed and simulated using a 28 nm CMOS process with a 16 Kb static random access memory (SRAM), demonstrates promising results. At VDD = 0.9 V, the logic operation energy consumption is 31.71 fJ/bit with an energy efficiency of 31.53 TOPS/W, and at VDD = 0.6 V, it decreases to 8.85 fJ/bit with an efficiency of 112.99 TOPS/W. © 2024, The Authors. All rights reserved.

关键词： Static random access storage

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A Dual Bandstop FSS with Low Profile and High Polarization Stability for EMC

A Dual Bandstop FSS with Low Profile and High Polarization S...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Yunshuai Gao Yingsong Li Luyu Zhao Zhixiang Huang Eugene Sinkevich Vladimir Mordachev Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R and D laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A novel miniaturized and low-profile FSS is presented for electromagnetic shielding, which consists of a layer of metal patch and a layer of dielectric substrate, resulting in a small unit and low profile with a thickness of only $0.0054\lambda$ , where $\lambda$ corresponds to the resonant frequency of the first operating band. Additionally, the folded structure not only reduces the unit size but also creates two stop bands, corresponding to around 7.7GHz and 16.7GHz. There is a low-insertion passband between the stopbands, which has a bandwdith of 2G Hz wide. The created FSS allows for signal transmission within the passband while it suppresses unrelated signals incident from both sides.

关键词： Wireless communication Conferences Dielectric substrates Resonant frequency Metals Interference Electromagnetic compatibility

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

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Enhanced Semantic Head for Cascade Instance Segmentation

Enhanced Semantic Head for Cascade Instance Segmentation

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Huang, Xuerong Su, Li Li, Guorong Zhang, Xinfeng Qing, Laiyun Huang, Qingming University of Chinese Academy of Sciences Beijing China Cas Key Lab of Big Data Mining and Knowledge Management Beijing China Institute of Computing Technology Cas Key Lab of Intelligent Information Processing Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, cascade instance segmentation inspired by cascade object detection has achieved notable performance. Due to the lack of global information, many methods suffer from incomplete segmentation such as missing edge regions and discontinuities within instances. To solve this problem, we proposed an effective and flexible semantic head to extract enhanced spatial context information. A vision transformer is utilized to generate global context features, and a convolution network is adopted to generate spatial context features. After combining the two modules, we obtain enhanced semantic segmentation features for segmentation. Extensive experiments show that the enhanced semantic head achieves 40.6% and 42.3% mask AP for cascade predictor HTC and DSC, which surpass about 0.9 and 1.4 percentage points respectively. The enhanced semantic head is universal and effective to improve the performance of different cascade predictors. © 2022 IEEE.

关键词： Object detection

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Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation

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Journal of Beijing Institute of Technology 2020年第1期29卷 110-119页

作者： Wenxia Bao Yaping Yang Dong Liang Ming Zhu Key Laboratory Intelligent Computing and Signal Processing of the Ministry of Education Anhui UniversityHefei 230039China

A multi-residual module stacked hourglass network(MRSH)was proposed to improve the accuracy and robustness of human body pose *** network uses multiple hourglass sub-networks and three new residual *** the hourglass sub-network,the large receptive field residual module(LRFRM)and the multi-scale residual module(MSRM)are first used to learn the spatial relationship between features and body parts at various *** the improved residual module(IRM)is used when the resolution is *** final network uses four stacked hourglass sub-networks,with intermediate supervision at the end of each hourglass,repeating high-low(from high resolution to low resolution)and low-high(from low resolution to high resolution)*** network was tested on the public datasets of Leeds sports poses(LSP)and MPII human *** experimental results show that the proposed network has better performance in human pose estimation.

关键词： human pose estimation residual learning image feature hourglass network

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A Microwave Sensor for Detection of Water Adulteration and Spoilage in Milk Based on Split-Ring Resonator

A Microwave Sensor for Detection of Water Adulteration and S...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Shuying Ding Yingsong Li Zhixiang Huang Eugene Sinkevich Vladimir Mordachev Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R and D Laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The paper introduces a microwave sensor based on split-ring resonators (SRR) for detecting the moisture content and spoilage of milk. This sensor is characterized by its simple structure and high sensitivity, utilizing SRR to enhance the local electric field intensity of the electromagnetic field. By measuring the transmission characteristics of microwave signals, it can accurately detect the moisture content and spoilage of milk samples. The sensor operates at a frequency of 6 GHz and has dimensions of 30×25×1.6 mm³. The study establishes the relationship between resonance frequency and water content in milk through simulated data, which provides an effective method for monitoring milk quality and holds potential applications in the food industry, contributing significantly to ensuring food safety.

关键词： Microwave measurement Microwave integrated circuits Dairy products Sensitivity Resonant frequency Moisture Microwave sensors

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AUCSeg: AUC-oriented pixel-level long-tail semantic segmentation 24

AUCSeg: AUC-oriented pixel-level long-tail semantic segmenta...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Boyu Han Qianqian Xu Zhiyong Yang Shilong Bao Peisong Wen Yangbangyan Jiang Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg.

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Object-to-Manipulation Graph for Affordance Navigation

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CAAI Artificial Intelligence Research 2024年第1期3卷 178-186页

作者： Xinhang Song Bohan Wang Liye Dong Gongwei Chen Xinyun Hu Shuqiang Jiang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Object navigation,whose goal is to let the agent to reach some places(or objects),has been a popular topic in embodied Artificial Intelligence(AI)***,in our real-world applications,it is more practical to find the targets with particular goals,raising the new requirements of finding the places to achieve the particular *** this paper,we define a new task of affordance navigation,whose goal is to find possible places to accomplish the required functions,achieving some particular *** first introduce a new dataset for affordance navigation,collected by the proposed affordance *** order to avoid the high cost of labor,the groundtruth of each episode which is annotated with the interaction data provided by the AI2-THOR *** addition,we also propose an affordance navigation framework,where an Object-to-Manipulation Graph(OMG)is constructed and optimized to emphasize the corresponding nodes(including object nodes and manipulation nodes).Finally,a navigation policy is implemented(trained by reinforcement learning)to guide the navigation to the target *** results on AI2-THOR simulator illustrate the effectiveness of the proposed approach,which achieves significant gains of 14.0%and 11.7%(on success rate and Success weighted by Path Length(SPL),respectively)over the baseline model.

关键词： navigation affordance manipulation graph neural network

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A Negative Survey based Method for Preserving Topology Privacy in Social Networks 4

A Negative Survey based Method for Preserving Topology Priva...

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4th International Conference on Data Intelligence and Security, ICDIS 2022

作者： Jiang, Hao Liao, Yuerong Yu, Qianwei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China The Second Affiliated Hospital of Anhui Medical University Department of Gastroenterology Hefei230601 China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Currently, the rapid popularity of social network platforms makes the real social relations in social networks face the potential risk of disclosure. Therefore, most users may refuse to provide their social relations for the purpose of privacy preservation, which is not conducive to the development of social networks. The negative survey is an emerging sensitive information collection method, which can collect sensitive information while preserving personal privacy. In this paper, based on negative survey, we propose an algorithm called NetNS to preserve the privacy of social relations, namely the topology privacy of social networks. The proposed NetNS is simple and efficient, which only randomly divides the nodes in social networks into different groups, and disturbs the topology of each group by a dedicated negative survey model. Experiments are carried out on three real networks of different sizes. The experimental results show that compared to three algorithms tailored for topology privacy of social networks, the proposed NetNS can against graph structure attack while realizing better utility of social networks. © 2022 IEEE.

关键词： Topology

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