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

作者： Hanqi Chen Lixing Yu Shaojie Zhan Penghui Yao Jiankun Shao Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN.

关键词： Neuromorphics Computational modeling Neurons Membrane potentials Estimation Spiking neural networks Parallel processing Signal processing Computational efficiency Speech processing

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(Information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of Electrical and Information Engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical Engineering and Automation Henan Institute of Technology School of Electronics and Information Zhongyuan University of Technology KINDI Center for Computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

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Research on IoT malware based on the ATT&CK model 11

Research on IoT malware based on the ATT&CK model

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2021 11th International Workshop on Computer Science and engineering, WCSE 2021

作者： Hao, Bo Kang, Fei Yang, Ju Xiong, Xiaobing State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China

ISBN: (纸本)9789811817915

The security of the IoT has become a hot research area in cyberspace security, among which the malware is a major threat. Based on the ATT&CK model, this paper studies the composition and behavior of IoT malware, constructs a malicious behavior model of IoT malware, and analyzes the technical implementation of each tactic in the malicious behavior model of IoT malware from three aspects: operating system related, target environment related and specific tools related. Based on this, we finally propose the evolution direction of IoT malware, which will be conducive to a more comprehensive grasp of the characteristics of IoT malware, and be supportive for maintaining the security of IoT. © 2021 11th International Workshop on Computer Science and engineering, WCSE 2021. All Rights Reserved.

关键词： Malware

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The Distributionally Robust Optimization Model of Sparse Principal Component Analysis

arXiv

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arXiv 2025年

作者： Wang, Lei Liu, Xin Chen, Xiaojun Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

We consider sparse principal component analysis (PCA) under a stochastic setting where the underlying probability distribution of the random parameter is uncertain. This problem is formulated as a distributionally robust optimization (DRO) model based on a constructive approach to capturing uncertainty in the covariance matrix, which constitutes a nonsmooth constrained min-max optimization problem. We further prove that the inner maximization problem admits a closed-form solution, reformulating the original DRO model into an equivalent minimization problem on the Stiefel manifold. This transformation leads to a Riemannian optimization problem with intricate nonsmooth terms, a challenging formulation beyond the reach of existing algorithms. To address this issue, we devise an efficient smoothing manifold proximal gradient algorithm. We prove the Riemannian gradient consistency and global convergence of our algorithm to a stationary point of the nonsmooth minimization problem. Moreover, we establish the iteration complexity of our algorithm. Finally, numerical experiments are conducted to validate the effectiveness and scalability of our algorithm, as well as to highlight the necessity and rationality of adopting the DRO model for sparse PCA. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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FirmVulSeeker—BERT and Siamese Network-Based Vulnerability Search for Embedded Device Firmware Images

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Journal on Internet of Things 2022年第1期4卷 1-20页

作者： Yingchao Yu Shuitao Gan Xiaojun Qin State Key Laboratory of Mathematical Engineering and Advanced Computing WuxiJiangsu214083China

In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity,achieved great ***,we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of *** this paper,we propose firmVulSeeker—a vulnerability search tool for embedded firmware images,based on BERT and Siamese *** first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary ***,a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first ***,it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it’s a real vulnerability *** evaluate the accuracy,robustness,scalability and vulnerability search capability of *** show that it can greatly improve the accuracy of matching semantically similar functions,and can successfully find more real vulnerabilities in real-world firmware than other tools.

关键词： Embedded device firmware vulnerability search BERT siamese network

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High-Performance Sensing: Application of FPGA-based Winograd CNN Accelerator in Electronic Nose Systems 5

High-Performance Sensing: Application of FPGA-based Winograd...

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5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Tan, Aolong Duan, Shukai Chen, Mingzhe Ding, Ke Li, Changqing Wang, Lidan State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China College of Artificial Intelligence Southwest University Chongqing400715 China National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology 400715 China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing400715 China Ministry of Education Southwest University Chongqing400715 China

ISBN: (纸本)9798331528911

Electronic nose technology has been widely used in various scenarios such as industrial, military, food and environmental, and its recognition accelerator is responsible for completing the detection and identification of gases. Therefore, the application of advanced artificial intelligence algorithms to recognition accelerators is a common practice among researchers nowadays. However, neural networks with high recognition rates tend to have complex structures and a large number of parameters, which exacerbates the difficulty of deployment. To address this challenge, this paper first analyses and processes the detected gas data and proposes a hardware-friendly lightweight gas classification network, and then, based on this network, designs an FPGA-based Winograd CNN accelerator through the study of hardware implementation of Convolutional Neural Networks (CNN). The Winograd algorithm is used to reduce the number of multiplications used in the convolutional operation so as to reduce the consumption of hardware computing resources by the accelerator. Finally, the designed gas classification network is deployed on FPGA to complete the implementation of the recognition acceleration engine in the electronic nose system. The detection and recognition experiments on the laboratory gas dataset demonstrated good performance in terms of 99.5% recognition rate and only 2.94W power consumption of the accelerator, respectively. © 2024 IEEE.

关键词： Electronic nose

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Fw-fuzz: A code coverage-guided fuzzing framework for network protocols on firmware

Fw-fuzz: A code coverage-guided fuzzing framework for networ...

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作者： Gao, Zicong Dong, Weiyu Chang, Rui Wang, Yisen Cyberspace Security Department State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Fuzzing is an effective approach to detect software vulnerabilities utilizing changeable generated inputs. However, fuzzing the network protocol on the firmware of IoT devices is limited by inefficiency of test case generation, cross-architecture instrumentation, and fault detection. In this article, we propose the Fw-fuzz, a coverage-guided and crossplatform framework for fuzzing network services running in the context of firmware on embedded architectures, which can generate more valuable test cases by introspecting program runtime information and using a genetic algorithm model. Specifically, we propose novel dynamic instrumentation in Fw-fuzz to collect the running state of the firmware program. Then Fw-fuzz adopts a genetic algorithm model to guide the generation of inputs with high code coverage. We fully implement the prototype system of Fw-fuzz and conduct evaluations on network service programs of various architectures in MIPS, ARM, and PPC. By comparing with the protocol fuzzers Boofuzz and Peach in metrics of edge coverage, our prototype system achieves an average growth of 33.7% and 38.4%, respectively. We further verify six known vulnerabilities and discover 5 0-day vulnerabilities with the Fw-fuzz, which prove the validity and utility of our framework. The overhead of our system expressed as an additional 5% of memory growth. © 2020 John Wiley & Sons, Ltd.

关键词： Firmware

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Adsorption of trichlorphon on phyllosilicate minerals:Effect of low-molecular-weight organic acids

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Pedosphere 2024年第1期34卷 78-87页

作者： Hongfeng CHEN Zhouyang HE Mingxia HOU Cilai TANG Yonghong WU College of Hydraulic&Environmental Engineering China Three Gorges UniversityYichang 443002China Hubei Field Observation and Scientific Research Stations for Water Ecosystem in Three Gorges Reservoir China Three Gorges UniversityYichang 443002China Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region China Three Gorges UniversityYichang 443002China State Key Laboratory of Soi and Sustainable Agriculture Institute of Soil SciencesChinese Academy of SciencesNanjing 210008China

The use of trichlorphon in large quantities causes a large number of organic pollutants to enter water, sediments, and soils. Phyllosilicate minerals are considered effective adsorbents for organic pollutants. However, the adsorption behavior of organic pollutants on soil minerals affected by low-molecular-weight organic acids(LMWOAs) is not fully understood. In this study, the effect of LMWOAs on the adsorption behavior of trichlorphon on phyllosilicate minerals was investigated using a combination of adsorption measurements and molecular spectroscopic techniques(attenuated total reflection-Fourier transform infrared spectroscopy(ATR-FTIR) and X-ray photoelectron spectroscopy(XPS)). The adsorption of trichlorphon onto kaolinite(KAO) and montmorillonite(MON) was suppressed by increasing pH, indicating that electrostatic interaction played a key role in adsorption, especially at low pH. In the presence of citric acid(CA), there was an obvious promotion of trichlorphon adsorption on KAO and MON. In the presence of oxalic acid(OA), the adsorption of trichlorphon on KAO was promoted, whereas the adsorption on MON was inhibited, especially at pH 4.0. The presence of CA and OA increased the adsorption by increasing the exposure of hydrophobic sites of KAO and MON. The results from ATR-FTIR and XPS also indicated that the hydrophobic Si–O sites of phyllosilicate minerals were the preferred adsorption sites for trichlorphon in the presence of CA and OA, probably driven by the hydrophobic effect. However, the weak effect of OA on MON caused an increase in the electrostatic repulsion between MON and trichlorphon molecules, thus inhibiting adsorption. This study is significant for a deeper understanding of self-purification of soil and sediment systems in the presence of organic pollutants.

关键词： adsorption behavior citric acid hydrophobic effect organic pesticide organic pollutant organochlorine oxalic acid

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Assessment of prediction model of the CPUE of neon flying squid with different sources of remote sensing data

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Acta Oceanologica Sinica 2023年第6期42卷 33-38页

作者： Liang Chang Yanli Yang Xinjun Chen Wei Yu Yangdong Li Guiping Feng Yang Zhang College of Marine Sciences Shanghai Ocean UniversityShanghai 201306China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China National Engineering Research Centre for Oceanic Fisheries Shanghai Ocean UniversityShanghai 201306China Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources of Ministry of Education Shanghai Ocean UniversityShanghai 201306China Key Laboratory of Oceanic Fisheries Exploration Ministry of Agriculture and Rural AffairsShanghai 201306China Scientific Observing and Experimental Station of Oceanic Fishery Resources Ministry of Agriculture and Rural AffairsShanghai 201306China

Accurately building the relationship between the oceanographic environment and the distribution of neon flying squid(Ommastrephes bartramii)is very important to understand the potential habitat pattern of ***,when building the prediction model of *** with traditional oceanographic variables(e.g.,chlorophyll a concentration(Chl a)and sea surface temperature(SST))from space-borne observations,part of the important spectrum characteristics of the oceanic surface could be masked by using the satellite data products *** this study,the neglected remote sensing information(i.e.,spectral remote sensing reflectance(Rrs)and brightness temperature(BT))is firstly incorporated to build the prediction model of catch per unit effort(CPUE)of *** from July to December during 2014–2018 in the Northwest Pacific *** show that both the conventional oceanographic variables and the neglected remote sensing data are suitable for building the prediction model,whereas the overall root mean square error(RMSE)of the predicted CPUE of *** with the former is typically less accurate than that with the ***,the Rrs and BT could be a more suitable data source than the Chl a and SST to predict the distribution of ***,highlighting that the potential value of the neglected variables in understanding the habitat suitability of ***.

关键词： Ommastrephes bartramii CPUE prediction remote sensing Rrs BT.

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