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arXiv 2024年

作者： Wu, Lanhu Zhang, Miao Piao, Yongri Yao, Zhenyan Sun, Weibing Tian, Feng Lu, Huchuan The School of Information and Communication Engineering Dalian University of Technology Dalian116024 China The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province the DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian116024 China The Affiliated Zhongshan Hospital Dalian University Dalian116024 China

Automatic and precise medical image segmentation (MIS) is of vital importance for clinical diagnosis and analysis. Current MIS methods mainly rely on the convolutional neural network (CNN) or self-attention mechanism (Transformer) for feature modeling. However, CNN-based methods suffer from the inaccurate localization owing to the limited global dependency while Transformer-based methods always present the coarse boundary for the lack of local emphasis. Although some CNN-Transformer hybrid methods are designed to synthesize the complementary local and global information for better performance, the combination of CNN and Transformer introduces numerous parameters and increases the computation cost. To this end, this paper proposes a CNN-Transformer rectified collaborative learning (CTRCL) framework to learn stronger CNN-based and Transformer-based models for MIS tasks via the bi-directional knowledge transfer between them. Specifically, we propose a rectified logit-wise collaborative learning (RLCL) strategy which introduces the ground truth to adaptively select and rectify the wrong regions in student soft labels for accurate knowledge transfer in the logit space. We also propose a class-aware feature-wise collaborative learning (CFCL) strategy to achieve effective knowledge transfer between CNN-based and Transformer-based models in the feature space by granting their intermediate features the similar capability of category perception. Extensive experiments on three popular MIS benchmarks demonstrate that our CTRCL outperforms most state-of-the-art collaborative learning methods under different evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Li, Lingling Li, Caihong Wu, Yuan Yang, Yi Zhang, Yixuan Zhang, Hairui Wu, Bin Liu, Li Xie, Rong School of Information Science and Engineering Lanzhou University Lanzhou730000 China Silk Road Economic Belt Research Center Lanzhou University Lanzhou730000 China Division of Biological Science University of California at San Diego La Jolla United States Communication and Network Center Lanzhou University Lanzhou730000 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China School of Software Engineering Chongqing University Chongqing400044 China School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9783030012977

In this paper, the combination of Near-Infrared (NIR) spectroscopy and a novel forecasting algorithm called XGBoost was proposed for food internal quality evaluation. First, the original NIR spectral data was preprocessed by Savitzky-Golay smoothing method to reduce the influence of noises. Secondly, the preprocessed spectra was submitted to PCA to extract essential information. Finally, the model was established by using the XGBoost algorithm. The performance of the proposed model was examined by comparing with different models including back propagation neural network (BPNN) and support vector regression (SVR). The results showed that the new proposed model outperformed other two models and this XGBoost-based tool was suitable for food internal quality control. © 2018, Springer Nature Switzerland AG.

关键词： Near infrared spectroscopy

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Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning

arXiv

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arXiv 2024年

作者： Xia, Tianle Ding, Liang Wan, Guojia Zhan, Yibing Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia JD Explore Academy China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Answering complex queries over incomplete knowledge graphs (KGs) is a challenging task. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex reasoning schema over KG upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements (brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： Binary trees

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Revisiting Token Dropping Strategy in Efficient BERT Pretraining

arXiv

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arXiv 2023年

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Liu, Xuebo Zhang, Min Du, Bo Tao, Dacheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China JD Explore Academy China Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University China Institute of Computing and Intelligence Harbin Institute of Technology China University of Sydney Australia

Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers. It can effectively reduce the training time without degrading much performance on downstream tasks. However, we empirically find that token dropping is prone to a semantic loss problem and falls short in handling semantic-intense tasks (§2). Motivated by this, we propose a simple yet effective semantic-consistent learning method (SCTD) to improve the token dropping. SCTD aims to encourage the model to learn how to preserve the semantic information in the representation space. Extensive experiments on 12 tasks show that, with the help of our SCTD, token dropping can achieve consistent and significant performance gains across all task types and model sizes. More encouragingly, SCTD saves up to 57% of pretraining time and brings up to +1.56% average improvement over the vanilla token dropping. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Joint Partial Offloading and Resource Allocation for Distributed Multi-access Edge computing Enabled Vehicular Network

TechRxiv

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TechRxiv 2022年

作者： Ma, Guifu Hu, Manjiang Wang, Xiaowei Wu, Di Li, Haoran Bian, Yougang Zhu, Konglin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Hunan University Wuxi214072 China The College of Design Hunan University Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China The College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio. Different from existing two-level centralized solution methods, we give a new perspective to deal with multi-variable non-convex optimization problem in a distributed manner. That is, by analyzing the feasibility conditions of the problem, we separate the coupling between variables and derive the bounds of partial offloading ratio and resource allocation ratio based on the constraints. We further separate the coupling variables among ICVs and transform the original problem into an equivalent form and decompose it into a two-level convex problem, where each sub-problem satisfies the task offloading requirements. We then develop two low-complexity distributed algorithms to obtain the optimal solution. Finally, extensive simulations demonstrate that our proposed algorithms can converge faster and significantly outperform the baseline schemes in terms of system cost. © 2022, CC BY.

关键词： Resource allocation

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SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using Snapshots

arXiv

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arXiv 2022年

作者： Li, Junqiang Li, Senyi Sun, Gang Chen, Ting Yu, Hongfang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Chengdu611731 China Agile and Intelligent Computing Key Laboratory of Sichuan Province Chengdu611731 China Center for Cybersecurity University of Electronic Science and Technology of China Chengdu611731 China Peng Cheng Laboratory Shenzhen518055 China

Greybox fuzzing has been widely used in stateless programs and has achieved great success. However, most state-of-the-art greybox fuzzers generally have the problems of slow speed and shallow state depth coverage in the process of fuzzing stateful network protocol programs which are able to remember and store details of the interactions. The existing greybox fuzzers for network protocol programs send a series of well-defined prefix sequences of input messages first and then send mutated messages to test the target state of a stateful network protocol. The process mentioned above causes a high time cost. In this paper, we propose SNPSFuzzer, a fast greybox fuzzer for stateful network protocol using snapshots. SNPSFuzzer dumps the context information when the network protocol program is under a specific state and restores it when the state needs to be fuzzed. Furthermore, we design a message chain analysis algorithm to explore more and deeper network protocol states. Our evaluation shows that, compared with the state-of-the-art network protocol greybox fuzzer AFLNET, SNPSFuzzer increases the speed of network protocol fuzzing by 112.0%-168.9% and improves path coverage by 21.4%-27.5% within 24 hours. Moreover, SNPSFuzzer exposes a previously unreported vulnerability in program Tinydtls. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet protocols

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Data interoperability technology based on chain-network convergence

Data interoperability technology based on chain-network conv...

引用

IEEE International Conference on Blockchain (Blockchain)

作者： Haoyu Wang Jiping Xu Xin Zhang Cheng Chi Zihang Yin Yanjie Shao Beijing Technology and Business University Beijing School of Computing and Artificial Intelligence Beijing China Key Laboratory of Industrial Internet and Big Data Chinese National Light Industry Beijing China China Academy of information and communication Institute Of Industrial Internet And Internet Of Things Beijing China Institute of Software Chinese Academy of Sciences Beijing China

With the rapid development of industrial Internet services, the era of the Internet of everything has arrived, which puts forward higher requirements for the network security of the industrial Internet. In order to guarantee the safe and trustworthy flow of industrial Internet data, this paper proposes a chain network connector architecture that integrates blockchain technology and industrial Internet technology, which utilizes the characteristics of blockchain traceability, tampering, collective maintenance, and openness and transparency to guarantee that the identification data in the industrial Internet is more secure and trustworthy. In addition, the architecture adopts quantum identity authentication technology in the identity authentication part to reduce the problem of illegal user access due to identity forgery. Meanwhile, for the problem of low efficiency of large-capacity and high-concurrency data processing generated by actual enterprises in the production process, this paper proposes to use analytical hierarchy process and Hungarian algorithm to co-optimize the chain network connector in order to make the resource allocation more reasonable and the transmission efficiency higher. The experimental results show that different algorithms are used for task allocation in different scenarios to maximize the data transmission efficiency, and also greatly improve the security of industrial Internet identification data.

关键词：

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Crowd flow-based information for crowd simulation

International Journal of Digital Content Technology and its ...

引用

International Journal of Digital Content Technology and its Applications 2012年第23期6卷 145-152页

作者： Wu, He Qi, Li Rao, Yunbo Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu Sichuan 610068 China Digital Media Department Sichuan Normal University Chengdu Sichuan 610068 China School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan 610031 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan 6100541 China

In this paper, we propose a novel algorithm for crowd simulation in real-time virtual environments. The proposed algorithm uses a navigation mesh for global planning in a polygonal multilayered threedimensional (3D) environment and crowd flow-based information to help individuals collision-free. At the same time, the proposed algorithm also focuses on addressing two problems: 1) in order to improve precision of collision-free, we propose based on hierarchy priority for collision-free of the individuals. 2) To avoid "shaking" behaviors of the individuals in the original place, we propose the stopping rule for solving it. Experimental results show that the proposed algorithm could be used for crowd simulation in complicated virtual environments.

关键词： Virtual reality

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Spatiotemporal Action Detection Based on Fine-Grained

SSRN

引用

SSRN 2024年

作者： Cheng, Yan Mike, Jack Fang, Chengxing Huang, Jiawen Wen, Yu School of Software Jiangxi Normal University Nanchang330022 China School of Computer Information Engineering Jiangxi Normal University Nanchang330022 China Business College of Jiangxi Normal University Nanchang330022 China Key Laboratory of Intelligent Information Processing and Emotional Computing in Jiangxi Province Nanchang330022 China Shanxi Datong University China

In the field of computer vision, spatiotemporal action detection is a challenging task. Traditional methods such as YOWO have achieved certain results in real-time and accuracy, but still have shortcomings in dealing with missing detail information, capturing long-distance dependencies, and target overlap problems. In response to these issues, this article proposes a spatiotemporal action detection method based on fine-grained enhancement. Firstly, a dual path fine-grained enhancement module is designed to enhance the ability to extract fine-grained features;Secondly, the self-attention&convolution module was introduced, which can better capture long-distance dependencies and cross level feature relationships;Finally, in order to address the issue of target overlap, the SIoU loss function is introduced to more comprehensively evaluate the similarity between predicted boxes and real boxes. The experimental results show that the proposed method achieves 86.4% F-mAP and 52.6% V-mAP on the UCF101-24 dataset, 20.5% F-mAP on the AVA dataset, and only 44.0 GFlops with an FPS of 34, ensuring the real-time performance of the proposed method with higher accuracy than SOTA model. In addition, the ablation experiment further demonstrated the effectiveness of each module in this paper. Overall, this method significantly improves the accuracy of spatiotemporal action detection while maintaining high efficiency, providing an effective solution for real-time action detection. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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