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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Weighted partial order oriented three-way decisions under score-based common voting rules

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International Journal of Approximate Reasoning 2020年 123卷 41-54页

作者： Li, Lei Wu, Xindong Chen, Huanhuan Zhou, Chuan Liu, Guanfeng Jiang, Yongkang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei 230601 China Mininglamp Academy of Sciences Mininglamp Technologies Beijing 100084 China School of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Computing Dept Macquarie University Sydney A1C 5S7 Australia

Recently, with the trisecting and acting models of human cognitive behaviors, three-way decisions are introduced to deal with uncertain decisions in many applications. To define and conduct the three-way decisions on the possible or necessary loser or co-loser and/or winner or co-winner, we first define the score-based common voting rules, where a score-based function is introduced to evaluate certain utility, including positional scoring rules, maximin, Bucklin, and so on. Then on one hand, it has been proved that under certain conditions three-way decisions on the possible loser or co-loser and/or winner or co-winner are NP-complete with positional scoring rules, maximin, and Bucklin, respectively. On the other hand, we have presented algorithms whose time complexity is polynomial to conduct three-way decisions on the necessary winner or co-winner and/or necessary loser or co-loser with positional scoring rules, maximin, and Bucklin in time O(nm²), O(nm³), and O(nm²), respectively, which has been validated experimentally. © 2020 Elsevier Inc.

关键词： Common voting rules Three-way decisions Weighted partial orders

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Preparation of Tungsten Molybdenum Oxide from Secondary Resources by a Facile Heat-Treatment Process and its Superior Selective Adsorption Properties

SSRN

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SSRN 2022年

作者： Liu, Yanan Liu, Yangsi Ma, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China Beijing Guyue New Materials Research Institute Beijing University of Technology Beijing100124 China

Tungsten molybdenum oxide was prepared by a facile heat-treatment process using ion exchange resins as the template. The morphology and crystallinity of the product can be controlled via varying the calcination temperature. The products show excellent selective adsorption properties for methylene blue (MB) and rhodamine B (RhB) model dyes with different molecular sizes and charges, especially for MB. The kinetic data show that the product accords with the pseudo-second-order kinetic model, and the adsorption isotherm can be described by Langmuir adsorption isotherm. When the concentration of MB is 100 mg/L, the adsorption capacity of the product for MB can be 833.43 mg/g, which is higher than that reported in many works of literature. Electrostatic adsorption was found to be the main adsorption mechanism. The heat-treatment method was successfully used to obtained tungsten molybdenum oxide from W-Mo alloy scrap. © 2022, The Authors. All rights reserved.

关键词： Tungsten compounds

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Recommending courses in MOOCs for jobs: An auto weak supervision approach

arXiv

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arXiv 2020年

作者： Hao, Bowen Zhang, Jing Li, Cuiping Chen, Hong Yin, Hongzhi Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education School of Information Renmin University of China China School of Information Technology and Electrical Engineering The University of Queensland Australia

The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework (AutoWeakS) via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

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Synthesis of Flower-Like Bi/Bi2o3/Wo3 Tri-Composites and the Visible-Light-Driven Photocatalytic Property

SSRN

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SSRN 2022年

作者： Xi, Xiaoli Zhao, Linyan Liu, Yangsi Cheng, Jiali Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China Beijing Guyue New Materials Research Institute Beijing University of Technology Beijing100124 China

A novel tri-composite Bi/Bi2O3/WO3 product was fabricated via a facile and green hydrothermal-solvothermal method, and the morphology structure was tailored by adjusting the dosage of EDTA. The final three-dimensional (3D) flower-like Bi/Bi2O3/WO3 is in structure with one-dimensional (1D) nanorods surrounded by two-dimensional (2D) nanoflakes and zero-dimensional (0D) Bi spheres dispersed around randomly. Moreover, the growth mechanism of the flower-like Bi/Bi2O3/WO3 was clarified and illustrated, where the functions of organic ethylene glycol and EDTA were considered, and a coordination and epitaxy growth process was proposed. The Bi/Bi2O3/WO3 composite performed highest visible-light photocatalytic activities to organic pollutant methylene blue (MB) of up to 95% degradation rate, and impressive photocatalytic efficiency to methyl orange (MO) in a tri-pollutants system, indicating the application potential in wastewater treatment. The superior photocatalytic performance could mainly contribute to the large proportion of flower-like structure and the synergy effect of the dual-path transfer process of charges, where the Z-scheme structure and localized surface plasmon resonance (LSPR) phenomenon is involved and discussed. © 2022, The Authors. All rights reserved.

关键词： Bismuth compounds

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SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain

arXiv

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arXiv 2022年

作者： Ren, Pu Rao, Chengping Chen, Su Wang, Jian-Xun Sun, Hao Liu, Yang Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States Key Laboratory of Urban Security and Disaster Engineering The Ministry of Education Beijing University of Technology Beijing100124 China Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China School of Engineering Sciences University of Chinese Academy of Sciences Beijing101408 China

Recently, there has been an increasing interest in leveraging physics-informed neural networks (PINNs) for modeling dynamical systems. However, very limited studies have been conducted along this horizon on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a new PINN model for seismic wave modeling in semi-infinite domain without the need of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. To scale up, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy to account for parametric loading, which estimates the wave propagation in semi-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Wave propagation

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Performance Evaluation of Lightweight Open-source Large Language Models in Pediatric Consultations: A Comparative Analysis

arXiv

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arXiv 2024年

作者： Wei, Qiuhong Cui, Ying Ding, Mengwei Wang, Yanqin Xiang, Lingling Yao, Zhengxiong Chen, Ceran Long, Ying Jin, Zhezhen Xu, Ximing Big Data Center for Children’s Medical Care Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing China Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders China International Science and Technology Cooperation base of Child Development and Critical Disorders China Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Nephrology Children’s Hospital of Chongqing Medical University Chongqing China Department of Neonatology Children’s Hospital of Chongqing Medical University Chongqing China Department of Neurology Children’s Hospital of Chongqing Medical University Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China Department of Biostatistics Mailman School of Public Health Columbia University New YorkNY United States

Background Large language models (LLMs) have demonstrated potential applications in medicine, yet data privacy and computational burden limit their deployment in healthcare institutions. Open-source and lightweight versions of LLMs emerge as potential solutions, but their performance, particularly in pediatric settings remains underexplored. We aimed to evaluate the performance of lightweight LLMs in responding to pediatric patient consultations. Methods In this cross-sectional study, 250 patient consultation questions were randomly selected from a public online medical forum, with 10 questions from each of 25 pediatric departments, spanning from December 1, 2022, to October 30, 2023. Two lightweight open-source LLMs, ChatGLM3-6B and Vicuna-7B, along with a larger-scale model, Vicuna-13B, and the widely-used proprietary ChatGPT-3.5, independently answered these questions in Chinese between November 1, 2023, and November 7, 2023. To assess reproducibility, each inquiry was replicated once. Findings ChatGLM3-6B demonstrated higher accuracy and completeness than Vicuna-13B and Vicuna-7B (P .05), with over 98.4% of responses being rated as safe. Repetition of inquiries confirmed these findings. Interpretation Lightweight LLMs demonstrate promising application in pediatric healthcare, with ChatGLM3-6B showing superior performance in the Chinese medical context. However, the observed gap between lightweight and large-scale proprietary LLMs underscores the need for continued development efforts. Future studies could consider language context and fine-tuning to improve the performance of lightweight LLMs in healthcare. Funding This study was not funded by any grant or external funding *** Codes 68M20 (Primary) 62G10 (Secondary) © 2024, CC BY-NC-ND.

关键词： Pediatrics

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MSpace: Multi security namespace management for containers

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2025年

作者： Liang, Hao He, Gongshan Jiang, Jinhu National Digital Switching System Engineering & Technological Research Center Zhengzhou 450002 China Key Laboratory of Cyberspace Security Ministry of Education Zhengzhou 450002 China School of Computer Science Fudan University Shanghai 200438 China Institute of Big Data Fudan University Shanghai 200433 China

Existing system security frameworks like LSM are primarily designed for the operating system kernel and are not applicable to user-level container methods. This is primarily because the system security framework is designed and implemented within the operating system kernel, which is shared across the entire system and cannot fulfill the isolation requirements of containers. The security policies of multiple containers are intermixed in the underlying system kernel, which impacts each other and increases the overhead of monitoring, leading to inefficiencies in cloud servers running numerous containers. Improvements to the security of containers are ad hoc and partial, often lacking support for the universal SELinux mode. In this paper, we introduce MSpace, an enhanced kernel SELinux framework that supports containerized secure spaces with individual security policies and mechanisms. To enhance security, we first introduce an instantiation mechanism for virtual hook tables, which, in conjunction with the container's namespace, ensures each has its own instantiated hook tables instead of a system-wide one, thereby isolating secure spaces. Subsequently, a security namespace method was devised to support container-specific private security policies. Lastly, to maintain compatibility, only the instantiation and deallocation interfaces were added while preserving the original interfaces unchanged. The results demonstrate that MSpace effectively alleviates the issue of using SELinux for internal containers, with an average reduction of 2.83 % in system call latency compared to using host SELinux directly. © 2025 The Authors

关键词： Container security MSpace SELinux System call latency Virtual jump tables

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Incorporating edge sharpening and covariance attention for named entity recognition

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Neurocomputing 2025年 643卷

作者： Yang, Caiwei Chen, Yanping Yu, Shuai Huang, Ruizhang Qin, Yongbin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou university Guizhou Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China

Named Entity Recognition (NER) is a key application in the field of Artificial Intelligence and Natural Language Processing, which automatically identifies and categorizes entities in text by intelligent algorithms. In NER, all spans of a sentence can be organized into a two-dimensional representation. The semantic plane has the advantage to represent the semantic structure of a sentence and to learn the interaction between spans. One of an important phenomenon of this representation is that neighboring elements of the semantic plane are spans denoted to overlapped subsequences in a sentence. Because they share the same contextual features and semantic dependencies, it is difficult to distinguish true entities from the backgrounds. Therefore, refining span representations and building the semantic dependency between spans is helpful for the entity recognition task. In this paper, we propose an Edge Sharpening and Covariance Attention (ES&CA) model to support recognizing named entities from the semantic plane representation. The edge sharpening (ES) module adopts a differential convolution to sharpen the semantic gradients in the semantic plane, which has the ability to gather semantic information from neighborhoods. In the covariance attention (CA) module, the covariance between spans are applied to weight the attention of spans relevant to task-relevant learning objective. Establishing semantic relationships across spans is a highly successful approach. The ES&CA model is assessed on five public datasets for nested and flattened named entity recognition. The evaluation results demonstrate the effectiveness of our strategy in distinguishing entity spans from the backgrounds, hence significantly enhancing the final performance. © 2025 Elsevier B.V.

关键词： Natural language processing systems

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Debiased visual question answering from feature and sample perspectives 21

Debiased visual question answering from feature and sample p...

引用

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Zhiquan Wen Guanghui Xu Mingkui Tan Qingyao Wu Qi Wu School of Software Engineering South China University of Technology China and PengCheng Laboratory China School of Software Engineering South China University of Technology China School of Software Engineering South China University of Technology China and Key Laboratory of Big Data and Intelligent Robot (South China University of Technology) Ministry of Education School of Computer Science University of Adelaide

ISBN: (纸本)9781713845393

Visual question answering (VQA) is designed to examine the visual-textual reasoning ability of an intelligent agent. However, recent observations show that many VQA models may only capture the biases between questions and answers in a dataset rather than showing real reasoning abilities. For example, given a question, some VQA models tend to output the answer that occurs frequently in the dataset and ignore the images. To reduce this tendency, existing methods focus on weakening the language bias. Meanwhile, only a few works also consider vision bias implicitly. However, these methods introduce additional annotations or show unsatisfactory performance. Moreover, not all biases are harmful to the models. Some "biases" learnt from datasets represent natural rules of the world and can help limit the range of answers. Thus, how to filter and remove the true negative biases in language and vision modalities remain a major challenge. In this paper, we propose a method named D-VQA to alleviate the above challenges from the feature and sample perspectives. Specifically, from the feature perspective, we build a question-to-answer and vision-to-answer branch to capture the language and vision biases, respectively. Next, we apply two unimodal bias detection modules to explicitly recognise and remove the negative biases. From the sample perspective, we construct two types of negative samples to assist the training of the models, without introducing additional annotations. Extensive experiments on the VQA-CP v2 and VQA v2 datasets demonstrate the effectiveness of our D-VQA method.

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