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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Yang Yujun Yang Yimei Zhou Wang Li Liyun Peng Ling Xiang Jin Shu Huixia College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua China School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

The Wuling Mountains Area(WMA) is one of the important ecological protection areas in central China, known for its rich biodiversity and unique ecological environment. To effectively protect the species resources in this area and strengthen ecological monitoring and research, this paper proposes and designs the “Wuling Mountains Region Characteristic Species Expert Database System.” By integrating expert resources, species data, ecological environment information, and modern information technologies, an intelligent system has been developed that combines data management, expert support, species identification, and research analysis. This paper discusses in detail the system's requirements analysis, design concept, implementation technologies, and potential applications, aiming to provide technological support for biodiversity conservation and sustainable ecological development in the region.

关键词： Information processing Media Database systems Biodiversity Protection Intelligent systems Information technology Environmental management Monitoring

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Deep User and Item Inter-matching Network for CTR Prediction 28th

Deep User and Item Inter-matching Network for CTR Predicti...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yuan, Zhiyang Xiao, Yingyuan Yang, Peilin Hao, Qingbo Wang, Hongya Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China College of Computer Science and Engineering Donghua University Shanghai China

ISBN: (纸本)9783031306716

CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Zero-Shot Scene Graph Generation via Triplet Calibration and Reduction

arXiv

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arXiv 2023年

作者： Li, Jiankai Wang, Yunhong Li, Weixin State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Shanghai Artificial Intelligence Laboratory 100191 China

Scene Graph Generation (SGG) plays a pivotal role in downstream vision-language tasks. Existing SGG methods typically suffer from poor compositional generalizations on unseen triplets. They are generally trained on incompletely annotated scene graphs that contain dominant triplets and tend to bias toward these seen triplets during inference. To address this issue, we propose a Triplet Calibration and Reduction (T-CAR) framework in this paper. In our framework, a triplet calibration loss is first presented to regularize the representations of diverse triplets and to simultaneously excavate the unseen triplets in incompletely annotated training scene graphs. Moreover, the unseen space of scene graphs is usually several times larger than the seen space since it contains a huge number of unrealistic compositions. Thus, we propose an unseen space reduction loss to shift the attention of excavation to reasonable unseen compositions to facilitate the model training. Finally, we propose a contextual encoder to improve the compositional generalizations of unseen triplets by explicitly modeling the relative spatial relations between subjects and objects. Extensive experiments show that our approach achieves consistent improvements for zero-shot SGG over state-of-the-art methods. The code is available at https://***/jkli1998/T-CAR. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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Towards Time-Series key Points Detection Through Self-supervised Learning and Probability Compensation 28th

Towards Time-Series Key Points Detection Through Self-superv...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yuan, Mingxu Bi, Xin Huang, Xuechun Zhang, Wei Hu, Lei Yuan, George Y. Zhao, Xiangguo Sun, Yongjiao Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang110819 China Key Laboratory of Liaoning Province on Deep Engineering and Intelligent Technology Northeastern University Shenyang110819 China School of Computer Science and Engineering Northeastern University Shenyang China Thinvent Digital Technology Co. Ltd. Nanchang India College of Software Northeastern University Shenyang China

ISBN: (纸本)9783031306365

key points detection is crucial for signal analysis by marking the identification points of specific events. Deep learning methods have been introduced into key points detection tasks due to their significant representation learning ability. However, in contrast to common time series classification and prediction tasks, the target key points correspond to significantly different time-series patterns and account for an extremely small proportion in a whole sample. Consequently, existing end-to-end methods for key points detection encounter two major problems: specificity and sparsity. Thus, in this work, we address these issues by proposing a probability compensated self-supervised learning framework named ProCSS. Our ProCSS consists of two major components: 1) a pretext task module pretraining an encoder based on self-supervised learning to capture effective time-series representations with a higher generalization ability;2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival methods for time-series key points detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supervised learning

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Cryptcoder: An Automatic Code Generator for Cryptographic Tasks in Ethereum Smart Contracts

Cryptcoder: An Automatic Code Generator for Cryptographic Ta...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Libin Xia Jiashuo Zhang Che Wang Zezhong Tan Jianbo Gao Zhi Guan Zhong Chen Key Laboratory of High Confidence Software Technologies Peking University MoE Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Cryptographic APIs provided by Ethereum are widely adopted in decentralized applications (DApps) for cryptographic operations. However, developers who lack expertise in cryptography frequently encounter difficulties when working with low-level cryptographic APIs, thereby producing insecure code. To address this issue, we introduce Cryptcoder, an automatic code generator designed to bridge the gap between low-level cryptographic APIs and high-level cryptographic tasks in Ethereum. The fundamental component of Cryptcoder is Cryptlang, a Solidity-compatible domain-specific language (DSL) designed for cryptographic tasks. Developers can utilize Cryptlang for the straightforward and secure implementation of cryptographic tasks, such as signatures and commitments, and employ Cryptcoder for the automatic conversion into Solidity code. The evaluation of Cryptcoder demonstrates both its functionality in generating Solidity code and an acceptable overhead, evidenced by a mere 4% average increase in gas costs compared to the reference code. A demonstration video of Cryptcoder is available at https://***/AxhCdGiu7dw.

关键词： Codes Smart contracts Decentralized applications Generators Cryptocurrency Blockchains DSL

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CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

arXiv

引用

arXiv 2025年

作者： Xu, Junhao Zhang, Yanan Cai, Zhi Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as CoSDH. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that CoSDH achieves state-of-the-art detection accuracy and optimal bandwidth tradeoffs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://***/Xu2729/CoSDH. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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A method of VAE-based Video Colorization 5

A method of VAE-based Video Colorization

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5th International Conference on computer Information Science and Application Technology, CISAT 2022

作者： Chen, Bo Zheng, Wenguang Shi, Kai School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Country Tianjin Key Laboratory of Intelligent and Novel Software Technology Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781510660076

In this paper, we propose an automated method to colorize old black and white videos. The model consists of several trainable components, containing a coloring module for video frame coloring and a refinement module for spatio-temporal color refinement. Video coloring is a multimodal problem. We satisfy the need for diversity in the coloring results of video frames by using a video frame coloring network to generate diverse coloring results. Then we use the color refinement module to enhance the coloring effect of the initial coloring results. We also introduce the optical flow method to constrain the consistency of coloring between video frames. By predicting the creation of multiple different coloring results, the user has control over these coloring results. This is essential in practical engineering applications. Experimental results show that this method has better result performance than existing video coloring methods. © 2022 SPIE.

关键词： Optical flows

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