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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhiqiang Yao Hao Xuan Jiawei Xu Yue Sun Lei Wei Ruituo Jiang Qingjiang Shi Zheng Peng Hunan Province Key Laboratory of Credible Intelligent Navigation and Positioning Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan China Huawei Technologies Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Deriving the spatial features of user equipment (UE) and traffic distribution is crucial for optimizing real-world wireless network performance, such as load balancing and improving cell-edge user experience. In this paper, we creatively construct the beam-space grid with high-dimensional beam reference signal receiving power (RSRP), serving as spatial reference system for 5G mobile networks. However, this is a challenging task due to i) access to 3D UE geographic information is limited by privacy protection and device settings, ii) beam-RSRP with up to 75% loss in measurement reports (MRs), iii) large computation and storage requirements of grid information caused by the exploding grid number in high-dimensional beam-space. A novel approach is designed to tackle these challenges, that coherently integrate low-rank matrix completion and dimension reduction techniques. Using telco big data, specifically MRs from live networks, we recover the beam-RSRP with high-precision and construct the beam-space grid even without geographic information. This research ensures stable derivation of spatial distribution features in the beam-space grid while efficiently processing grid information.

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Research on patent recommendation method based on graph neural network

Research on patent recommendation method based on graph neur...

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International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

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Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

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SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.

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Model Predictive Control for Air Source Heat Pump Systems

SSRN

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SSRN 2024年

作者： Zhao, Ruochen Wei, Dong Jia, Yuchen Xu, Rongji Gao, Qiang Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing China Beijing University of Civil Engineering and Architecture School of Environment and Energy Engineering Beijing China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

Air source heat pump (ASHP) systems exhibit non-linearity with significant inertia, posing challenges to ensure its operational robustness and enhance energy efficiency. This study proposed a non-linear model predictive control (MPC) strategy for an ASHP water heating system. The non-linear modeling methodology, based on thermodynamics and fluid dynamics, was developed. The sequential quadratic programming was employed to handle the non-convex optimization problem for receding horizon optimization. Furthermore, a sensor-less speed control approach was designed to address the issue of the absence of Hall sensors within the DC compressor. Experimental results demonstrated that the proposed strategy reduced heating time by 14.9% and 15.7% compared to PID and fuzzy PID control approaches, diminishing energy consumption by 14.6% and 9.1%, respectively. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

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arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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An autofocus network for multi-channel phase errors with application to tomoSAR imaging

An autofocus network for multi-channel phase errors with app...

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IET International Radar Conference (IRC 2023)

作者： Muhan Wang Silin Gao Zhe Zhang Xiaolan Qiu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123 People's Republic of China

ISBN: (数字)9781837240982

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method.

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Technical Function-effect Based Patent Multi-to-One Negation Game Model 25

Technical Function-effect Based Patent Multi-to-One Negation...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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