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

作者： Junwei Qi Yingzhen Wang Qingchun Wang Yingsong Li College of Computer Science and Technology Harbin Engineering University Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China Shanghai Yanding Information Technology Co. Ltd. Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. Therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results.

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Applying Model Fusion to Augment Data for Entity Recognition in Legal Documents 1

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9th CCF International Conference on Natural Language processing and Chinese computing, NLPCC 2020

作者： Zhang, Hu Gao, Haihui Zhou, Jingjing Li, Ru School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computing Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan China

ISBN: (数字)9783030604509

ISBN: (纸本)9783030604493

Named entity recognition for legal documents is a basic and crucial task, which can provide important knowledge for the related tasks in the field of wisdom justice. However, it is still difficult to augment the labeled data of named entities for legal documents automatically. To address this issue, we propose a novel data augmentation method for named entity recognition by fusing multiple models. Firstly, we train a total of ten models by conducting 5-fold cross-training on the small-scale labeled datasets based on Bilstm-CRF and Bert-Bilstm-CRF models separately. Next, we try to apply single-model fusion and multi-model fusion modes, in which, single-model fusion is to vote on the prediction results of five models of the same baseline, while multi-model fusion is to vote on the prediction results of ten models with two different baselines. Further, we take the identified entities with high correctness in the multiple experimental results as effective entities, and add them to the training set for the next training. Finally, we conduct the different experiments on two public datasets and our built judicial dataset separately, which shows the experimental results using data augmentation are close to those based on 5 times of labeled dataset, and obviously better than those on the initial small-scale labeled datasets. © 2020, Springer Nature Switzerland AG.

关键词： Authentication

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Efficient meta neural heuristic for multi-objective combinatorial optimization 23

Efficient meta neural heuristic for multi-objective combinat...

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Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.

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A Novel Multi-Agent Deep Reinforcement Learning Approach

A Novel Multi-Agent Deep Reinforcement Learning Approach

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2020 International Conference on Computer Big Data and Artificial intelligence, ICCBDAI 2020

作者： Yin, Dong Zhao, Zhe Dai, Yinglong Long, Han College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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Investigation on ULA Fitting Promoting Low Coupling Sparse Arrays

Investigation on ULA Fitting Promoting Low Coupling Sparse A...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yichang Yao Wanlu Shi Yingsong Li Zhixiang Huang Guohui Yang Kuang Zhang College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

A low coupling sparse array (LCSA) is presented, analyzed and discussed on the basis of the recent proposed uniform linear array (ULA) fitting scheme with close-expressions, and its performance with different uniform subarrays in the basic layer is investigated via parameter estimation like direction-of-arrival(DOA). The results gives a superior performance for the presented LCSA.

关键词： Couplings Mutual coupling Direction-of-arrival estimation Fitting Estimation Computational electromagnetics

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Contamination-resilient anomaly detection via adversarial learning on partially-observed normal and anomalous data 24

Contamination-resilient anomaly detection via adversarial le...

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Proceedings of the 41st International Conference on Machine Learning

作者： Wenxi Lv Qinliang Su Hai Wan Hongteng Xu Wenchao Xu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Gaoling School of Artifical Intelligence Renmin University of China Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

Many existing anomaly detection methods assume the availability of a large-scale normal dataset. But for many applications, limited by resources, removing all anomalous samples from a large unlabeled dataset is unrealistic, resulting in contaminated datasets. To detect anomalies accurately under such scenarios, from the probabilistic perspective, the key question becomes how to learn the normal-data distribution from a contaminated dataset. To this end, we propose to collect two additional small datasets that are comprised of partially-observed normal and anomaly samples, and then use them to help learn the distribution under an adversarial learning scheme. We prove that under some mild conditions, the proposed method is able to learn the correct normal-data distribution. Then, we consider the overfitting issue caused by the small size of the two additional datasets, and a correctness-guaranteed flipping mechanism is further developed to alleviate it. Theoretical results under incomplete observed anomaly types are also presented. Extensive experimental results demonstrate that our method outperforms representative baselines when detecting anomalies under contaminated datasets.

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Design of MEMS Step Impedance Microstrip Filter in Ku Band

Design of MEMS Step Impedance Microstrip Filter in Ku Band

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tianyu Bai Guohui Yang Kuang Zhang Yingsong Li Tao Jiang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin China

According to the miniaturization characteristics of step impedance resonators, a step impedance bandpass filter with a center frequency of 16.55 GHz and a bandwidth of 500MHz is designed. The effects of parameter changes such as substrate resistivity and the shape of metal via etching on the performance of the filter are studied. Finally, the filter is fabricated and its performance is tested under MEMS technology.

关键词： Band-pass filters Micromechanical devices Microstrip filters Shape Resonator filters Metals Conductivity

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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ULA Fitting for MIMO Radar

TechRxiv

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TechRxiv 2022年

作者： Shi, Wanlu Liu, Xiaoguang Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China School of Microelectronics Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Anhui Hefei China

Uniform linear array (ULA) fitting (UF) principle is a newly proposed sparse array (SA) design scheme that aims to design SAs using cascaded ULAs, based on which a number of SAs with desired capacity are devised. In this letter, we aim to introduce the UF idea to devise sparse MIMO arrays with reduced mutual coupling and increased uniform degrees of freedom. However, existing sparse MIMO array design approach needs the transmit and receive arrays in MIMO radar have hole-free difference coarrays (DCAs), which is no longer the best choice for UF principle due to the fact that SAs designed via UF cannot guarantee hole-free DCAs. In this regard, considering the difference coarray of the sum coarray (DCSC) concept, a novel sparse MIMO array design strategy which is suitable for UF principle is proposed. Based on the novel strategy and UF principle, a new sparse MIMO array is designed. Numerical simulations verify the good performance of the proposed sparse MIMO array in high coupling scenarios. © 2022, CC BY.

关键词： MIMO radar

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A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image

A2J-Transformer: Anchor-to-Joint Transformer Network for 3D ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changlong Jiang Yang Xiao Cunlin Wu Mingyang Zhang Jinghong Zheng Zhiguo Cao Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Alibaba Group Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J- Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://***/ChanglongJiangGit/A2J-Transformer.

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