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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiangyang Li Zihan Wang Jiahao Yang Yaowei Wang Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The re-trieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method. The source code is available at https://***/XiangyangLi20/KERM.

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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Scheduling Jobs with Release and Delivery Times Subject to Nested Eligibility Constraints

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Journal of the Operations Research Society of China 2021年第1期9卷 63-77页

作者： Yu-Zhong Zhang Shu-Guang Li Institute of Operations Research Qufu Normal UniversityRizhao 276826ShandongChina Key Laboratory of Intelligent Information Processing in Universities of Shandong(Shandong Institute of Business and Technology) Yantai 264005ShandongChina College of Computer Science and Technology Shandong Institute of Business and TechnologyYantai 264005ShandongChina

The problem of scheduling jobs with release and delivery time subject to machine eligibility constraints is *** eligible sets of the jobs are nested,and pre-emptions are not *** goal is to minimize the maximum delivery completion time,i.e.,the time by which all jobs are *** the special case of equal release time,a 2-approximation algorithm is presented whose running time depends linearly on the number of *** the general case of unequal release time,a polynomial time approximation scheme is derived.

关键词： Scheduling Nested eligibilityconstraints Release time Deliverytime Polynomial timeapproximationscheme

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Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks

Video Anomaly Detection via Sequentially Learning Multiple P...

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International Conference on Computer Vision (ICCV)

作者： Chenrui Shi Che Sun Yuwei Wu Yunde Jia Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Learning multiple pretext tasks is a popular approach to tackle the nonalignment problem in unsupervised video anomaly detection. However, the conventional learning method of simultaneously learning multiple pretext tasks, is prone to sub-optimal solutions, incurring sharp performance drops. In this paper, we propose to sequentially learn multiple pretext tasks according to their difficulties in an ascending manner to improve the performance of anomaly detection. The core idea is to relax the learning objective by starting with easy pretext tasks in the early stage and gradually refine it by involving more challenging pretext tasks later on. In this way, our method is able to reduce the difficulties of learning and avoid converging to sub-optimal solutions. Specifically, we design a tailored sequential learning order for three widely-used pretext tasks. It starts with frame prediction task, then moves on to frame reconstruction task and last ends with frame-order classification task. We further introduce a new contrastive loss which makes the learned representations of normality more discriminative by pushing normal and pseudo-abnormal samples apart. Extensive experiments on three datasets demonstrate the effectiveness of our method.

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective 29

Decorrelate Irrelevant, Purify Relevant: Overcome Textual Sp...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Dou, Shihan Zheng, Rui Wu, Ting Gao, Songyang Shan, Junjie Zhang, Qi Wu, Yueming Huang, Xuanjing School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China KTH Royal Institute of Technology Stockholm Sweden Nanyang Technological University Singapore

Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance on out-of-distribution ones. Most of the existing debiasing methods often identify and weaken these samples with biased features (i.e., superficial surface features that cause such spurious correlations). However, down-weighting these samples obstructs the model in learning from the non-biased parts of these samples. To tackle this challenge, in this paper, we propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective. Specifically, we introduce Random Fourier Features and weighted re-sampling to decorrelate the dependencies between features to mitigate spurious correlations. After obtaining decorrelated features, we further design a mutual-information-based method to purify them, which forces the model to learn features that are more relevant to tasks. Extensive experiments on two well-studied NLU tasks demonstrate that our method is superior to other comparative approaches. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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A novel dual-stage algorithm for capacitated arc routing problems with time-dependent service costs

arXiv

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arXiv 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most significant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Specifically, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modified benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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A Semantic Overlay on Blockchain for Supporting Traceability in Cross-border Logistics

A Semantic Overlay on Blockchain for Supporting Traceability...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Xiaoping Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

Traceability is important for maintaining quality of logistics transactions and improving logistics efficiency. Implementing traceability for cross-border logistics is facing challenges when it lacks global centralized logistics information management services. Blockchain techniques enable a decentralized cross-border logistics management by publishing logistics transaction on blockchain transactions to support privacy protection and traceability. Logistics transactions involves complex interactions among participants. How to publish those interactions on a blockchain platform is a challenge. We propose a semantic overlay model that publishes a logistics transaction as a semantic link on a blockchain platform for supporting traceability. A semantic link schema model is designed and published on the blockchain platform to validate semantics links for ensuring the correctness of logistics transaction execution. The proposed semantic overlay on blockchain supports further extension of semantic computing in decentralized applications.

关键词： Privacy Overlay networks Publishing Law Computational modeling Semantics Smart contracts

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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International Conference on Big Data computing and Communications (BIGCOM)

作者： Xiaodong Wu Ronghai Wang Guoxin Lin School of Mathmatics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms.

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A Novel Dual-Stage Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs

SSRN

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SSRN 2024年

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most signiﬁcant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Speciﬁcally, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modiﬁed benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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