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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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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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Hierarchical Multi-Granular Decision Networks for Hierarchical Classification

SSRN

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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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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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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...

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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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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

arXiv

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arXiv 2024年

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024, CC BY.

关键词：

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Continuous-variable measurement-device-independent quantum key distribution with passive state in oceanic turbulence 21

Continuous-variable measurement-device-independent quantum k...

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21st International Symposium on Distributed Computing and applications for Business Engineering and Science, DCABES 2022

作者： Chen, Guojun Yi, Jianmin Guo, Ying Wuxi Taihu University Jiangsu Key Laboratory of IoT Application Technology Wuxi214064 China College of Computer Internet of Tings Engineering Wuxi Taihu University Wuxi214064 China School of Automation Central South University Changsha410083 China

ISBN: (纸本)9781665454629

Quantum communication, as an absolutely secure communication method, has been widely studied in recent years. While CVQKD is easier to realize and has efficient information transmission, has attracted more attention than DVQKD. And there is another kind of detection strategy which places the detector on a third party called continuous-variable measurement device independent quantum key distribution(CV -MDI), can lower the cost of implementation because it don't need such a high precision Gaussian modulator and thus can be commercialized easily. We apply a CV -MDI with passive-state preparation protocol to a oceanic turbulence model, and then we analyze the secret key rate and other performance of the protocol in the particular scenario. © 2022 IEEE.

关键词： Turbulence models

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Orthogonal matrix of polarization combinations:concept and application to multichannel holographic recording

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Opto-Electronic Advances 2024年第10期7卷 4-13页

作者： Shujun Zheng Jiaren Tan Hongjie Liu Xiao Lin Yusuke Saita Takanori Nomura Xiaodi Tan Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhou 350117China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China Faculty of Systems Engineering Wakayama University930 SakaedaniWakayama640-8510Japan.

Orthogonal matrices have become a vital means for coding and signal processing owing to their unique distributional *** orthogonal matrices based on amplitude or phase combinations have been extensively explored,the orthogonal matrix of polarization combinations(OMPC)is a novel,relatively unexplored ***,we propose a method for constructing OMPCs of any dimension encompassing 4n(where n is 1,2,4,8,…)mutually orthogonal 2ncomponent polarization *** the field of holography,the integration of polarization multiplexing techniques with polarization-sensitive materials is expected to emerge as a groundbreaking approach for multichannel hologram multiplexing,offering considerable enhancements in data storage capacity and security.A multidimensional OMPC enables the realization of multichannel multiplexing and dynamical modulation of information in polarization holographic *** consolidating all information into a single position within the material,we effectively avoided extraneous crosstalk during the reconstruction *** results show that achieving four distinct holographic images individually and simultaneously depends on the polarization combination represented by the incident *** discovery opens up a new avenue for achieving highly holographic information storage and dynamically displayed information,harnessing the potential of OMPC to expand the heretofore limited dimensionality of orthogonal polarization.

关键词： orthogonal matrix of polarization combinations polarization-modulated multiplexing multichannel recording polarization holography dynamical information modulation

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A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution

arXiv

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arXiv 2023年

作者： Mei, Xiaoyong Yang, Yi Li, Ming Huang, Changqin Zhang, Kai Lió, Pietro The Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua China The Computer Vision Lab. ETH Zurich Switzerland The Department of Computer Science and Technology University of Cambridge United Kingdom

Reference-based super-resolution (RefSR) has gained considerable success in the field of super-resolution with the addition of high-resolution reference images to reconstruct low-resolution (LR) inputs with more high-frequency details, thereby overcoming some limitations of single image super-resolution (SISR). Previous research in the field of RefSR has mostly focused on two crucial aspects. The first is accurate correspondence matching between the LR and the reference (Ref) image. The second is the effective transfer and aggregation of similar texture information from the Ref images. Nonetheless, an important detail of perceptual loss and adversarial loss has been underestimated, which has a certain adverse effect on texture transfer and reconstruction. In this study, we propose a feature reuse framework that guides the step-by-step texture reconstruction process through different stages, reducing the negative impacts of perceptual and adversarial loss. The feature reuse framework can be used for any RefSR model, and several RefSR approaches have improved their performance after being retrained using our framework. Additionally, we introduce a single image feature embedding module and a texture-adaptive aggregation module. The single image feature embedding module assists in reconstructing the features of the LR inputs itself and effectively lowers the possibility of including irrelevant textures. The texture-adaptive aggregation module dynamically perceives and aggregates texture information between the LR inputs and the Ref images using dynamic filters. This enhances the utilization of the reference texture while reducing reference misuse. The source code is available at https://***/Yi-Yang355/FRFSR. © 2023, CC BY.

关键词： Textures

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Passive Detection for Multiple Underwater Targets with Linear Frequency Modulation Signals 17

Passive Detection for Multiple Underwater Targets with Linea...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wang, Junfeng Cui, Yue Zhou, Mingzhang Sun, Haixin Liu, Lanjun Zhang, Ailing Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin300384 China Mnr Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application China Tianjin Normal University College of Computer and Information Engineering Tianjin300387 China Xiamen University School of Information Science and Engineering Xiamen361005 China Ocean University of China College of Engineering Qingdao266100 China

ISBN: (纸本)9798350387384

Detecting the status of autonomous underwater vehicle, referred to hereinafter as an underwater target, is fundamental for successfully fulfilling cognition tasks or improving communication quality. In this article, we present a thorough study over the passive detection issue for multiple underwater targets emitting the linear frequency modulation signals. Specifically, hypothesis testing model, which comprises the signals received at the underwater detection node including periodic emission signals from the targets and additive noise, is firstly proposed for the practical scenarios. Then, the detection scheme has been investigated. Among this scheme, the behavior of the signals emitted by the underwater targets has been analyzed;and the primary aiming of the detection procedure, i.e., decision rule and its performance, has also been discussed and derived for the aforementioned scenarios. Towards the end, to confirm the advantages of our proposed studies, numerical simulation examples are carried out to examine the performance under different conditions. © 2024 IEEE.

关键词： Signal modulation

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