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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Shao, Chenze Ma, Zhengrui Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, which harms the global translation accuracy. In this paper, we present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint. Experimental results demonstrate that our approach consistently improves the performance of DA-Transformer while maintaining a similar decoding speedup. © 2022 Association for Computational Linguistics.

关键词： Decoding

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Lightweight 6-bit S-boxes with DPA Resistance

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IEEE Transactions on Network Science and Engineering 2025年

作者： Yan, Liuyan Li, Lang Song, Qingling Hengyang Normal University College of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Lightweight S-boxes have a significant impact on cryptographic algorithms for resource-constrained devices in the Internet of Things (IoT). Previous research on such S-oxes has focused on low area and good cryptographic properties, which neglects the resistance to side-channel analysis, especially differential power analysis (DPA). The reVisited transparency order (VTO) is one of the best indicators for evaluating S-boxes against DPA attacks so far. Therefore, this paper presents a scheme for designing 6-bit S-boxes. The designed S-boxes are suitable for lightweight block ciphers and have a certain ability to resist DPA attacks. Specifically, we first identify 23 3-bit S-boxes with the optimal cryptographic properties and the smallest hardware area through systematic screening. Then, the paper investigates the constructions of lightweight 6-bit S-boxes based on traditional algorithm structures. Finally, a novel structure named F-LM-F is proposed for designing lightweight 6-bit S-boxes by combining Feistel structure and Lai-Massey structure. It has been proven through comparison that the S-boxes under F-LM-F structure achieve fewer fixed points and lower VTO than the 6-bit S-box of BipBip cipher, with a 17.61% reduction in hardware area and a 13.58% decrease in CPU cycles © 2013 IEEE.

关键词： Side channel attack

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A Sensitive Nanothermometer Based on DNA Triplex Structure 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Chen, Zhekun Hu, Yingxin Xie, Chun Chen, Kuiting Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA has been used to construct a variety of nanoscale thermometers due to its unique thermodynamic properties. It has proven to be one of the most promising, yet challenging aspects of DNA-based thermometers, to obtain significant signal changes when the temperature changes slightly. Here, we propose a strategy to construct nanoscale thermometers with sensitive temperature responses based on DNA triplex. The thermometers consist of a CT-rich DNA strand and stabilizing strands, whose conformations vary with temperature. By adjusting the sequence design or introducing mismatch bases in stabilizing strands, the temperature response interval of the thermometer can be reduced to 7 ∘C, which lead to higher sensitivity compared to the interval of 12–15 ∘C for conventional designs. The temperature responses of the thermometer are characterized by fluorescence experiments. The fluorescence kinetics experiments demonstrate the good repeatability. The design of the nanothermometer will be helpful for constructing advanced nanosystem with sensitive temperature responses as well as long service times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermometers

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MDDMamba: A Model for Multi-Modal Depression Detection with a Memory-Saving Cross-Modal Attention Mechanism Based on Mamba

MDDMamba: A Model for Multi-Modal Depression Detection with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Mingzhou Wang, Zheyu Liu, Feng Nanjing University of Posts and Telecommunications School of Education Science and Technology Nanjing210023 China Nanjing University of Science and Technology ZiJin College School of Computer and Artificial Intelligence Nanjing210023 China Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China

ISBN: (纸本)9798350386226

Depression is a severe mental health disorder that constitutes a significant global health challenge, necessitating efficient and accurate automated diagnostic tools. Traditional approaches to automated depression assessment using multi-modal data often face three significant difficulties: small sample sizes in available datasets, high computational cost, and difficulties in effectively integrating different modalities. In this paper, we first mitigate the problem of dataset quality by utilizing an estimated imputation strategy for missing values based on a known data augmentation method. Additionally, we introduce a multi-modal depression detection model named MDDMamba, designed to address the latter two challenges by integrating features from pre-trained emotion models with the memory-efficient selective state space model named Mamba. The MDDMamba model combines features from pre-trained emotion models for both text and audio, enhancing the extraction of emotional information. The core of the MDDMamba model is the CrossMamba module, which replaces the traditional Transformer's quadratic computational complexity with a linear complexity alternative, facilitating efficient multi-modal fusion. Experimental results on CMDC and EATD-Corpus datasets show that the MDDMamba model achieves better classification and regression performance and stronger generalization capabilities in depression detection. Notably, the MDDMamba model achieves an F1 score of 0.94 and a Pearson correlation coefficient of 0.86 on the CMDC dataset while maintaining lower memory cost, outperforming most advanced multi-modal depression detection methods across various experimental scenarios. © 2024 IEEE.

关键词： Data fusion

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Research and Implementation of Chinese Resume Named Entity Recognition Method Based on BLDC 4

Research and Implementation of Chinese Resume Named Entity R...

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4th International Conference on Image processing and intelligent Control, IPIC 2024

作者： Chen, Min Wei, Bin Yu, Hongzhi Wan, Fucheng Feng, Yulin Lu, Baoqing Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Northwest Minzu University Gansu Province Gansu Lanzhou730000 China Lanzhou Technology and Business College Lanzhou China

ISBN: (纸本)9781510682313

Aiming at the semantic complexity and the difficulty of capturing long-distance dependencies in the task of named entity recognition in Chinese resume text, a BLDC (BERT-BiLSTM-Dense-CRF)-based named entity recognition method for Chinese resumes is investigated. The model first uses the pre-trained BERT-Base-Chinese model to obtain the semantic information of the text, then learns the contextual information of the sequence through the BiLSTM layer, followed by mapping the output of BiLSTM to the label space through the Dense layer, and finally uses the CRF layer to consider the dependencies between the labels for sequence annotation. Experimental results on public datasets show that the method outperforms other models in terms of precision, recall, and F1 value, and effectively improves the performance of named entity recognition in Chinese resume text. © 2024 SPIE.

关键词： Semantics

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Event-Triggered Adaptive Finite-Time Control for Switched Cyberphysical Systems With Uncertain Deception Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 136-146页

作者： Wang, Xin Zhou, Yuhao Luo, Biao Jiang, Yuming Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Automation Central South University Changsha410083 China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7491 Norway T&M University at Qatar Doha23874 Qatar

This article is concerned with the event-triggered adaptive finite-time control problem for a class of switched cyber-physical systems (CPSs) subject to uncertain deception attacks. We deploy the finite time command filter to alleviate the possible explosion of complexity rendered by the virtual control signal’s duplicative differentiation. To stabilize the target system with a guaranteed disturbance rejection performance index, a disturbance observer is constructed by using fuzzy logic systems (FLS) with the hope of estimating the unknown external disturbance. Furthermore, the Nussbaum gain is utilized to resist the unknown control efficiency generated by uncertain deception attacks. Based on the common Lyapunov function (CLF) method, sufficient conditions are derived to ensure that all signals in the closed-loop system are uniformly bounded at a finite time. Finally, the simulation example shows the validity and efficacy of the developed control method. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Closed loop systems

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HPPNET: MODELING THE HARMONIC STRUCTURE AND PITCH INVARIANCE IN PIANO TRANSCRIPTION 23

HPPNET: MODELING THE HARMONIC STRUCTURE AND PITCH INVARIANCE...

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23rd International Society for Music information Retrieval Conference, ISMIR 2022

作者： Wei, Weixing Li, Peilin Yu, Yi Li, Wei School of Computer Science and Technology Fudan University China Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9781732729926

While neural network models are making significant progress in piano transcription, they are becoming more resource-consuming due to requiring larger model size and more computing power. In this paper, we attempt to apply more prior about piano to reduce model size and improve the transcription performance. The sound of a piano note contains various overtones, and the pitch of a key does not change over time. To make full use of such latent information, we propose HPPNet that using the Harmonic Dilated Convolution to capture the harmonic structures and the Frequency Grouped Recurrent Neural Network to model the pitch-invariance over time. Experimental results on the MAESTRO dataset show that our piano transcription system achieves state-of-the-art performance both in frame and note scores (frame F1 93.15%, note F1 97.18%). Moreover, the model size is much smaller than the previous state-of-the-art deep learning models. © Weixing Wei, Peilin Li, Yi Yu, and Wei Li.

关键词： Harmonic analysis

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Advanced Scale-aware Attention for Enhanced Tiny Object Detection in Remote Sensing

Advanced Scale-aware Attention for Enhanced Tiny Object Dete...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Tingting Qiao He Chen Bingqian Chai Wenchao Liu Guijie Qi Yizhuang Xie National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches.

关键词： Image resolution Convolution Object detection Feature extraction Data processing Sensors Object recognition Kernel Remote sensing Signal resolution

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Optimized Multi-kernel Dictionary Learning and Its Application in Complex Industrial Processes Monitoring 41

Optimized Multi-kernel Dictionary Learning and Its Applicati...

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第41届中国控制会议

作者： Kai Zhong Zhengping Ding Donghui Pan Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information TechnologyAnhui University School of Mathematical Science Anhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The data in the chemical process is often complex in structure,and the coupling and correlation between variables are ***,it is difficult for conventional monitoring models to learn the essential characteristics of process data,which greatly affects the performance of the *** at the above problems,this paper proposes an optimized multi-kernel dictionary learning method,which is suitable for the industrial processes with strong nonlinearity and complex data ***,the synthetic kernel fused by multiple kernel functions is used to map the process data to a new high-dimensional feature space to mine the multivariate distribution characteristics of the ***,the grid search method is used to optimize the model parameters in the kernel function for better model ***,the effectiveness and practicability of the proposed method in fault detection are verified on TE benchmark platform.

关键词： Dictionary learning Multi-kernel function Chemical process monitoring

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SC-DVNet: A Dual-Branch Recognition Network for Synthetic Aperture Radar Images Based on Scattering Features

SC-DVNet: A Dual-Branch Recognition Network for Synthetic Ap...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Fan Chen Hao Shi Jingfei He Yifei Yin Fanyu Meng Liang Chen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Ship classification using Synthetic Aperture Radar (SAR) images is a crucial component in marine monitoring. With the rise of deep neural networks (DNN), the abstract feature maps obtained by CNN-based methods have significantly improved classification performance. However, most current DNN-based methods are predominantly inherited from optical imagery, designed only for SAR intensity images, which may present challenges for further improving recognition performance. Therefore, this paper proposes using the point Scattering Center (SC) model for scattering feature extraction combined with graph convolutional networks for ship classification tasks. Comprehensive ablation experiments were carried out to demonstrate the efficacy of the proposed approach. Finally, experimental results on OpenSARShip1.0 dataset verify that the performance of the suggested approach notably outperforms DNN-driven techniques.

关键词： Integrated optics Image recognition Scattering Artificial neural networks Feature extraction Optical imaging Adaptive optics Optical scattering Marine vehicles Synthetic aperture radar

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