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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Xiaorui Luo, Gen Zhu, Chaoyang Tong, Bo Zhou, Yiyi Sun, Xiaoshuai Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Fujian Xiamen China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400706868

Recently, Segment Anything Model (SAM) has become a research hotspot in the fields of multimedia and computer vision, which exhibits powerful yet versatile capabilities on various (un) conditional image segmentation tasks. Although SAM can support different types of segmentation prompts, we note that, compared to point- and box-guided segmentations, it performs much worse on text-instructed tasks, e.g., referring image segmentation (RIS). In this paper, we argue that deep text instruction tuning is key to mitigate such shortcoming caused by the shallow fusion scheme in its default light-weight mask decoder. To address this issue, we propose two simple yet effective deep instruction tuning (DIT) methods for SAM, one is end-to-end and the other is layer-wise. With minimal modifications, DITs can directly transform the image encoder of SAM as a stand-alone vision-language learner in contrast to building another deep fusion branch, maximizing the benefit of its superior segmentation capability. Extensive experiments on three highly competitive benchmark datasets of RIS show that a simple end-to-end DIT can improve SAM by a large margin, while the layer-wise DIT can further boost the performance to state-of-the-art with much less data and training expenditures. Our code is released at: https://***/wysnzzzz/DIT. © 2024 ACM.

关键词： Deep learning

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Characteristics of catalytic destruction of dichloromethane and ethyl acetate mixture over H_(x)PO_(4)-RuO_(x)/CeO_(2) catalyst

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Journal of Environmental Sciences 2025年第2期148卷 336-349页

作者： Jiawen Chen Qingjie Meng Feng Bi Jingkun Chen Xiaole Weng Zhongbiao Wu Key Laboratory of Environment Remediation and Ecological Health Ministry of EducationCollege of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China School of Civil&Environmental Engineering and Geography Science Ningbo UniversityNingbo 315211China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311200China Zhejiang Provincial Engineering Research Centre of Industrial Boiler&Furnace Flue Gas Pollution Control Hangzhou 310058China

Catalytic destruction is an ascendant technology for the abatement of volatile organic compounds(VOCs)originating fromsolvent-based industrial *** varied composition tends to influence each VOC’s catalytic behavior in the reaction *** investigated the catalytic destruction of multi-component VOCs including dichloromethane(DCM)and ethyl acetate(EA),as representatives from pharmaceutical waste gases,over co-supported HxPO_(4)-RuOx/CeO_(2) catalyst.A mutual inhibitory effect relating to concentrations because of competitive adsorption was verified in the binary VOCs oxidation and EA posed a more negative effect on DCM oxidation owing to EA’s superior adsorption *** adsorption of EA on acidic sites(HxPO_(4)/CeO_(2))promoted DCM activation on basic sites(O^(2−))and the dominating EA oxidation blocked DCM’s access to oxidation centers(RuOx/CeO_(2)),resulting in boosted monochloromethane yield and increased chlorine deposition for DCM *** impaired redox ability of Ru species owing to chlorine deposition in turn jeopardized deep oxidation of EA and its by-products,leading to increased gaseous by-products such as acetic acid originating fromEA ***,DCM at low concentration slightly promoted EA conversion at low temperatures with or without water,consistent with the enhanced EA adsorption in co-adsorption *** was mainly due to that DCM impeded the shielding effect of hydrolysate deposition from rapid EA hydrolysis depending on the decreased ***,water benefited EA hydrolysis but decreased CO_(2) selectivity while the generated water derived from EA was likely to affect DCM *** work may provide theoretical guidance for the promotion of applied catalysts toward industrial applications.

关键词： Catalytic destruction Multi-component VOCs Mixture effect Interaction mechanism

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Sensor-Based Authentication on Smartphones via Integrating Auxiliary Information

Sensor-Based Authentication on Smartphones via Integrating A...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Yingjie Hu, Ruimin Wuhan University National Engineering Research Center for Multimedia Software Whuhan430072 China School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Whuhan430072 China

ISBN: (纸本)9781665410205

With the widespread use of smartphones, more and more private information is stored on the phone, and the loss or theft of the phone can lead to data leakage, theft of property, and other problems. Traditional active authentication methods based on passwords, faces, fingerprints, etc. authenticate only once at login, which still has some hidden dangers. Authentication methods based on behavioral biometrics such as walking gait, touch screen, keystroke, etc. can provide implicit and continuous authentication services, and thus have been widely studied recently. Considering the limited computational resources of smart devices, we first introduce the state-space model with linear model complexity, i.e., Mamba, to extract deep feature representations from raw signal sequences in the sensor-based authentication task. Then a series of auxiliary information from different domains are proposed and fused to the deep features to enhance the consistent semantic information and ultimately improve the discriminative ability of the model. Our model A-Mamba, i.e., Auxiliary-Mamba, is experimented on two public datasets. The experimental results show that our model outperforms existing approaches. © 2024 IEEE.

关键词： Information leakage

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 348-364页

作者： Naigong Yu Hongzheng Li Qiao Xu Ouattara Sie Essaf Firdaous Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing University of TechnologyBeijingChina Engineering Research Center of Digital Community Ministry of EducationBeijingChina College of Robotic UniversitéFélix Houphouët‐BoignyAbidjanCôte d’Ivoire

Non-destructive detection of wire bonding defects in integrated circuits(IC)is critical for ensuring product quality after ***-processing-based methods do not provide a detailed evaluation of the three-dimensional defects of the bonding ***,a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision,which can achieve non-destructive detection of bonding wire defects is *** contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding *** to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface,a point cloud segmentation method based on spatial surface feature detection(SFD)was *** can extract more distinct features from the bonding wire surface during the point cloud segmentation ***,in the defect detection process,a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding *** descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of *** experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires,and the average accuracy of defect recognition is 96.47%,which meets the production requirements of bonding wire defect detection.

关键词： bonding wire defect detection point cloud point cloud segmentation

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A Broadband Circularly Polarized Filtering Patch Antenna with Stacked Structure 12

A Broadband Circularly Polarized Filtering Patch Antenna wit...

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12th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2024

作者： Zhang, Jinping Kholodnyak, Dmitry Huang, Zhixiang Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Saint Petersburg Electrotechnical University Department of Microwave Electronics Russia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350350791

A double-layer stacked patch filtering antenna with broadband circular polarization performance is proposed in this paper. The main patch consists of a corner-Truncated square patch with two shorting pins. The designed two shorting pins are located in the main patch and the upper stacked patch structure are introduced and designed, which can gain two radiation nulls for realizing the filtering effect of the proposed antenna. The circularly polarized (CP) operation is realized by corner-Truncated square patch. In addition, to increase the axial ratio (AR) bandwidth, a rectangular slot is carved on the ground plane. The simulated results indicate that the researched antenna can provide the impedance bandwidth of 20.58% (2.68-3.29 GHz) and AR bandwidth of 9.5% (2.80-3.08 GHz). Moreover, The average realized gain of the simulation is 6.48 dBic over bandwidth. © 2024 IEEE.

关键词： Slot antennas

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Multiple Vandermonde Decomposition Based Channel Estimation for Mm Wave MIMO High-Mobility Communication in 5G and Beyond

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China Communications 2023年第4期20卷 12-25页

作者： Zhao Yi Xiaojun Jing School of Electronics and Information Hunan First Normal UniversityChangsha 410205China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Millimeter wave(mmWave)massive massive multiple input multiple output(MIMO)technique has been regarded as the viable solution for vehicular communications in 5G and *** achieve the substantial increase in date rates,it is important to take an effective channel state information(CSI).However,existing channel estimation strategies are unavailable since the users *** solve above issues,in this paper,inspired by a specific antenna structure,we propose a novel approach for fast time-varying channel ***,by considering the vehicle scenario with high-mobility,a corresponding mathematical model is firstly ***,based on the special structural of the sparse array,the switch network is used to replace the convention phase shifter of mmWave hybrid system,which can effectively reduce the number of radio-frequency(RF)chains and ***,by solving the semidefinite programming(SDP)duality problem,the Doppler frequency and path parameters are effectively *** results are shown that the computational complexity and estimation accuracy of the proposed algorithm is superior than that of the traditional schemes.

关键词： MIMO mmWave channel estimation high-mobility time-variant

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Catalytic destruction of chlorobenzene over K-OMS-2: Inhibition of high toxic byproducts via phosphate modification

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Journal of Environmental Sciences 2023年第5期127卷 844-854页

作者： Yunpeng Long Jiajia Liu Meiling Chen Renna Li Xiaole Weng Zhongbiao Wu Key Laboratory of Environment Remediation and Ecological Health Ministry of EducationCollege of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China Zhejiang Provincial Engineering Research Center of Industrial Boiler and Furnace Flue Gas Pollution Control Zhejiang University-Hangzhou Global Scientific and Technological Innovation CenterHangzhou 311200China

In the process of catalytic destruction of chlorinated volatile organic compounds(CVOCs),the catalyst is prone to chlorine poisoning and produce polychlorinated byproducts with high toxicity and persistence,bringing great risk to atmospheric environment and human *** solve these problems,this work applied phosphate to modify K-OMS-2 *** physicochemical properties of catalysts were determined by using X-ray powder diffraction(XRD),scanning electron microscope(SEM),X-ray photoelectron spectroscopy(XPS),hydrogen temperature programmed reduction(H_(2)-TPR),pyridine adsorption Fouriertransform infrared(Py-IR)and water temperature programmed desorption(H_(2)O-TPD),and chlorobenzene was selected as a model pollutant to explore the catalytic performance and byproduct inhibition function of *** results revealed that 1 wt.%phosphate modification yielded the best catalytic activity for chlorobenzene destruction,with the 90%conversion(T90)at approximately 247℃.The phosphating significantly decreased the types and yields of polychlorinated byproducts in *** phosphating,we observed significant hydroxyl groups on catalyst surface,and the active centerwas transformed into Mn(IV)-O…H,which promoted the formation of HCl,and enhanced the dechlorination ***,the enriched Lewis acid sites by phosphating profoundly enhanced the deep oxidation ability of the catalyst,which promoted a rapid oxidation of reaction intermediates,so as to reduce byproducts *** study provided an effective strategy for inhibiting the toxic byproducts for the catalytic destruction of chlorinated organics.

关键词： Catalytic destruction Chlorinated organics Phosphoric modification K-OMS-2 Secondary pollution

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology 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 Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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