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International Symposium on Autonomous Systems (ISAS)

作者： Jiandong Shi Yuting Chen Siwei Zhou Ming Li Department of Computer Science Zhejiang Normal University Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Zhejiang Institute of Optoelectronics Jinhua China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Multimodal sentiment analysis has emerged as a critical research area, aiming to analyze complex emotional states using data from multiple sources. While conventional approaches focus on sophisticated fusion techniques to integrate multimodal information, they often struggle with distributional variances among modalities. Bridging this modal distribution gap and optimizing the utilization of modality-sensitive information are crucial challenges. In response, our study presents GM2RC, an innovative framework designed to enhance modality representations for improved multimodal fusion efficacy. GM2RC adopts a two-step approach: intra-modal refinement and intermodal complementation. In the first step, it enhances distinct features within each modality, while the second step fosters the assimilation of shared information across modalities through pairwise learning, thus mitigating inter-modal disparity. This comprehensive approach leads to more accurate sentiment analysis by creating more robust multimodal representations. Further rigorous experiments demonstrate significant boosts of 1.59% in accuracy and 2.03% in w-F1 on the IEMOCAP dataset, and 1.65% in accuracy and 2.73% in w-F1 on the MELD dataset, showcasing the superiority of our proposed GM2RC model.

关键词： Representation learning Sentiment analysis Adaptation models Accuracy Semantics Focusing Multitasking

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Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images

arXiv

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arXiv 2024年

作者： Zou, Xuechao Zhang, Shun Li, Kai Wang, Shiying Xing, Junliang Jin, Lei Lang, Congyan Tao, Pin School of Computer Science & Technology Beijing Jiaotong University Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The Key Laboratory of Pervasive Computing Ministry of Education China The School of Computer Technology and Application Qinghai University Xining China The School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM) have demonstrated powerful generalization capabilities across various visual tasks. In this paper, we present a parameter-efficient adaptive approach, termed Cloud-Adapter, designed to enhance the accuracy and robustness of cloud segmentation. Our method leverages a VFM pretrained on general domain data, which remains frozen, eliminating the need for additional training. Cloud-Adapter incorporates a lightweight spatial perception module that initially utilizes a convolutional neural network (ConvNet) to extract dense spatial representations. These multi-scale features are then aggregated and serve as contextual inputs to an adapting module, which modulates the frozen transformer layers within the VFM. Experimental results demonstrate that the Cloud-Adapter approach, utilizing only 0.6% of the trainable parameters of the frozen backbone, achieves substantial performance gains. Cloud-Adapter consistently achieves state-of-the-art performance across various cloud segmentation datasets from multiple satellite sources, sensor series, data processing levels, land cover scenarios, and annotation granularities. We have released the code and model checkpoints at https://***/Cloud-Adapter/ to support further research. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Network configuration approach based on the reliability service 12

Network configuration approach based on the reliability serv...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Fengfei, Kuang Yaojm, Lin Xiehua, Yu Meilong, Chen University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Fujian Quanzhou362332 China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Wireless communication network plays an important role in various industries. One of the key concern in wireless communication is network security. This paper proposes an optimal transmission lines assignment with maximal reliabilities in the wireless communication. Firstly, a reliability evaluation metric with a transmission line assignment is proposed for calculating the reliability service under some constraints. Secondly, based on the reliability, non-dominated sorting Genetic algorithm is used for working out the optimal solution via considering the network security. Some simulated experiments are conducted that the proposed approach is able to obtain the reasonable solution under the time constraints. © 2022 IEEE.

关键词： Reliability

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A Revised Model for Sea-Surface Wind Inversion Data with Joint Interpolation Optimization and Residual Network 18

A Revised Model for Sea-Surface Wind Inversion Data with Joi...

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Lin, Weihong You, Lijun Huang, Tianqiang Huang, Liqing Luo, Haifeng Xu, Chao College of Computer and Cyber Security Fujian Norm. Univ. Fujian Provincal Eng. Res. Center of Big Data Analysis and Application Digital Fujian Institute of Big Data Security Technology Fuzhou China Fujian Meteorological Information Center Fujian Key Laboratory of Severe Weather Fuzhou China

ISBN: (纸本)9798350318401

The sea-surface wind fields are pivotal for studies in both oceanography and meteorology. However, the acquisition of ocean wind data is often challenged by practical constraints, especially near coasts, leading to significant data gaps. This paper introduces a two-stage approach that combines interpolation optimization with a residual network to improve satellite-derived sea-surface wind fields data. Initially, the Kriging interpolation method is employed to fill missing regions. Subsequently, a network structure similar to ResNet-18 is tailored to revise the interpolated data using buoy anemometer observations. By establishing a correlation model between the interpolated results and ground truth through supervised training, the approach ensures the interpolated results gravitate towards the actual measurements. The efficacy of the proposed model is corroborated through experimental visual analysis and error cure assessments. © 2023 IEEE.

关键词： Interpolation

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A Novel Approach for Auto-Formulation of Optimization Problems

arXiv

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arXiv 2023年

作者： Ning, Yuting Liu, Jiayu Qin, Longhu Xiao, Tong Xue, Shangzi Huang, Zhenya Liu, Qi Chen, Enhong Wu, Jinze Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China iFLYTEK AI Research iFLYTEK Co. Ltd China

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition1, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem;subtask 2: generating formulations for the optimization problem. In this paper, we present the solution of our team. First, we treat subtask 1 as a named entity recognition (NER) problem with the solution pipeline including pre-processing methods, adversarial training, post-processing methods and ensemble learning. Besides, we treat subtask 2 as a generation problem with the solution pipeline including specially designed prompts, adversarial training, post-processing methods and ensemble learning. Our proposed methods have achieved the F1-score of 0.931 in subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and third places respectively in this competition. Our code is available at https://***/bigdata-ustc/nl4opt. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation

arXiv

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arXiv 2024年

作者： Wang, Hao Han, Yongqiang Wang, Kefan Cheng, Kai Wang, Zhen Guo, Wei Liu, Yong Lian, Defu Chen, Enhong The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230026 China SUN YAT-SEN University China Huawei's Noah's Ark Laboratory Hong Kong

In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit semantic relationships between these behaviors to enhance recommendation performance. However, these methods often entail high computational complexity. To address concerns regarding efficiency, pre-training presents a viable solution. Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks. Nevertheless, previous pre-training methods have primarily focused on single-behavior data, while multi-behavior data contains significant noise. Additionally, the fully fine-tuning strategy adopted by these methods still imposes a considerable computational burden. In response to this challenge, we propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. Specifically, in the pre-training stage, we commence by proposing a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales, thereby facilitating the comprehension of the contextual semantics of multi-behavior sequences. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module, which generates personalized, progressive, and diverse prompts to fully exploit the potential of the pre-trained model effectively. Extensive experiments on three real-world datasets have unequivocally demonstrated that DPCPL not only exhibits high efficiency and effectiveness, requiring minimal parameter adjustments but also surpasses the state-of-the-art performance across a diverse range of downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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An Image Semantic Communication System Based on GAN and Relative Position-Encoding

引用

IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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Hierarchical Multi-Granular Decision Networks for Hierarchical Classification

SSRN

引用

SSRN 2024年

作者： Liu, Haoyang Hu, Xue gang Bai, Shengxing Lin, Yaojin Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Anhui Hefei230009 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Zhangzhou363000 China

Hierarchical classification learning is an effective means of solving large-scale classification problems, which models classification problems at different levels of granularity according to a hierarchical structure. In hierarchical classification tasks, classification results at different levels of granularity are mutually beneficial, where coarse-grained results can reduce the number of fine-grained candidate categories, and fine-grained results can provide a basis for coarse-grained classification. However, for the final decision, the contribution of the granularity levels in the different hierarchies is different and unknown. Feedback from different granularity levels should be flexibly integrated. In addition, errors in hierarchical classification are not equivalent, and some errors can have serious results for practical applications. In order to address the above challenges, we propose a new hierarchical network to integrate multi-granularity knowledge for decision making. On the one hand, the network computes the decision scores of instances at each granularity level and learns a set of weights to combine all scores. Based on the learnable weights, the model can effectively utilize the multi-granularity classification results to obtain the final decision. On the other hand, we design local and global classification losses to guide model optimization, which allows the model to gain a hierarchical perspective and the ability to understand the degree of hierarchical misclassification. As a result, the extent to which the method results in misclassification consequences is limited. Experimental results on five benchmark datasets show that our method arises as a state-of-the-art hierarchical classification method. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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To Build Digital Twin of Nuclear Reactor Decommissioning: Cutting and Packaging Method Based on Simulated Annealing Algorithm 24

To Build Digital Twin of Nuclear Reactor Decommissioning: Cu...

引用

Proceedings of the 2024 2nd International Conference on computer, Internet of Things and Smart City

作者： Tong Wu Deying Wang Nan Zhang Fengjuan Chen Fan Yang Ran Zhang Jiaqi Wang Computer Application Institute of Nuclear Industry China National Nuclear Corporation (CNNC) Beijing Beijing China Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education Beijing University of Technology Beijing Beijing China

ISBN: (纸本)9798400714726

The decommissioning of nuclear reactors is a worldwide problem because of process complex, high radioactive, high cost, leak of experience etc. Due to varying degrees of activation and radioactive nuclide contamination of the components within the nuclear reactor, appropriate treatment measures must be taken based on the radioactive levels of each component to ensure safe storage. The goal of this study is to design the cutting method for reactor components based on the dimensions of the solid waste packaging steel boxes to achieve optimal utilization of the cut blocks. For this specific purpose, a simulated annealing algorithm is adopted to optimize the packing of cut blocks, simplifying the complex three-dimensional irregular parts packing problem into a two-dimensional plane study to obtain the optimal number of cut blocks in each area of the reactor components. By determining the optimal number of cut blocks, a cutting plan is formulated, and a global packing plan for all cut blocks is derived. The research results show that this method can not only obtain the optimal cutting plan for different cutting areas of reactor components, but also determine the best placement of the cut blocks in the steel boxes while satisfying all cutting constraints. This method provides an effective solution for handling the cutting and packing issues during the decommissioning process of nuclear reactors and has potential for practical applications. This simulation and method will be applied to build of digital twin of nuclear reactor decommissioning.

关键词： Nuclear Power Plant

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CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning

CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lianggangxu Chen Xuejiao Wang Jiale Lu Shaohui Lin Changbo Wang Gaoqi He School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However, current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues, our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG)feature extractor. Specifically, we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly, to align the text with 3DSG, the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment, adjectives are exchanged for different objects as negative samples. Then, to align the image with 3DSG, the camera view is treated as a positive sample and other views as negatives. Lastly, the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.

关键词： Point cloud compression Training computer vision Three-dimensional displays Annotations Contrastive learning Feature extraction

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