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Online Variant of Parcel Allocation in Last-Mile Delivery

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Online Variant of Parcel Allocation in Last-Mile Delivery

International Conference on Measuring technology and Mechatronics Automation, ICMTMA

作者： Yuan Liang Changmei Mao Xiuguo Bao State Key Laboratory of Software Development Environment School of Computer Science Beihang University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

We investigate the problem of last-mile delivery, where a large amount of crowd-workers have performed a great quantity of location-specific urban logistics parcels. Current existing approaches mainly focus on offline scenarios, where all the spatial-temporal information of parcels and workers are given. However, the offline scenarios can be impractical since parcels and workers appear dynamically in reality, and the information of workers is unknown in advance. In this paper, we study the problem of last-mile delivery on online scenarios to resolve the shortcomings of the offline setting. We first formalize the online parcel allocation in last-mile delivery problem, where all parcels were put in pop-stations in advance, and workers arrive dynamically. Then we propose a baseline algorithm with no competitive ratio, and an algorithm providing theoretical guarantee for the parcel allocation in last-mile delivery. Finally, we verify the effectiveness and efficiency of proposed algorithms through extensive experiments on real and synthetic datasets.

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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

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arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation

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arXiv 2021年

作者： Tang, Song Yang, Yan Ma, Zhiyuan Hendrich, Norman Zeng, Fanyu Ge, Shuzhi Sam Zhang, Changshui Zhang, Jianwei The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Group Department of Informatics Universität Hamburg Hamburg Germany The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Lab. for Novel Software Technology Nanjing University Nanjing China The Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore The Department of Automation Tsinghua University Beijing China

In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is available. This paper proposes a novel deep clustering method for this challenging task. Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process. Concretely, we propose a geometry-based constraint, named semantic consistency on the nearest neighborhood (SCNNH), and use it to encourage robust clustering. To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry. Also, we develop a more SCNNH-compliant structure with an additional semantic credibility constraint, named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our method to this new geometry. Extensive experiments on three challenging UDA datasets indicate that our method achieves state-of-the-art results. The proposed method has significant improvement on all datasets (as we adopt SHNNH, the average accuracy increases by over 3.0% on the large-scaled dataset). Code is available at https://***/tntek/N2DCX. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

The paired domination number of cubic graphs

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arXiv 2020年

作者： Sheng, Bin Lu, Changhong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing Jiangsu211106 China Department of Mathematics Shanghai Key Laboratory of PMMP East China Normal University Shanghai China

Let G be a simple undirected graph with no isolated vertex. A paired dominating set of G is a dominating set which induces a subgraph that has a perfect matching. The paired domination number of G, denoted by γpr(G), is the size of its smallest paired dominating set. Goddard and Henning conjectured that γpr(G) ≤ 4n/7 holds for every graph G with δ(G) ≥ 3, except the Petersen Graph. In this paper, we prove this conjecture for cubic graphs. © 2020, CC-BY.

关键词： Undirected graphs

Model-based safety analysis for an aviation software specification

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International Journal of Performability Engineering 2020年第2期16卷 238-254页

作者： Hu, Jun Chen, Shuo Chen, Defeng Kang, Jiexiang Wang, Hui College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Software China National Aeronautic Radio Electronics Research Institute Shanghai200233 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210007 China School of Mechanical Engineering Hefei University of Technology Hefei230601 China

Model-based safety analysis (MBSA) is a kind of safety analysis technology that combines system fault models with formal analysis methods. In this paper, a real flight guidance subsystem (FGS) in aviation domain is studied, and an example of safety modeling and formal analysis of high-level software requirement specification is given. A framework of model transformation is established, which can transform a high-level FGS software requirement model described by Requirement State Machine Language (RSML-e) into a formal NuSMV model. Then, according to the real system requirements and engineering experience, the relevant failure modes and the safety properties that need to be verified are designed. Finally, formal safety analysis and verification based on NuSMV are implemented in a platform xSAP. This case study shows that the MBSA method can be used effectively for the safety analysis of the real aviation system. © 2020 Totem Publisher, Inc.

关键词： Requirements engineering

Research and Design of Fast Special Human Face Recognition System 2

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Research and Design of Fast Special Human Face Recognition S...

2nd International Conference on Broadband Communications, Wireless Sensors and Powering, BCWSP 2020

作者： Muwardi, Rachmat Qin, Huangyao Gao, Hongmin Ghifarsyam, Harun Usman Hajar, Muhammad Hafizd Ibnu Yunita, Mirna Universitas Mercu Buana Department of Electrical Engineering Jakarta Indonesia Research and Development Center Jee Power Technology Co. Ltd Shanghai China Beijing Jiaotong University School of Computer and Information Technology Beijing China Universitas Mercu Buana Faculty of Engineering Department of Electrical Engineering Jakarta Indonesia Universitas Mercu Buana Faculty of Engineering Department of Electrical Engineering Jakarta Indonesia

ISBN: (数字)9781728174501

ISBN: (纸本)9781728174495

During the use of face recognition systems appears the problems such as the system can not find a face and has not enough information of the image, changing illumination, occlusion, face shape changes and so on. In order to solve the current problems and improve the ability of face recognition and detection, new methods for data acquisition and some tools are adopted, and the application development of improved face recognition system is proposed based on ARM architecture design, software development of face recognition system is taken in ARM embedded platform. The solve project for the acquisition of three-dimensional face data, biometric recognition, computer vision, computer graphics is researched and designed. The image edge contour detection method is used for face image features positioning. The adaptive feature extraction method of image entropy is used for facial feature extraction, the face recognition algorithm is optimized, and the face image output module is constructed. The simulation results show that the face recognition system designed in this paper has good robustness performance, and it can improve the accurate probability of face recognition. © 2020 IEEE.

关键词： Face recognition

Cross-modality brain tumor segmentation via bidirectional global-to-local unsupervised domain adaptation

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arXiv 2021年

作者： He, Kelei Ji, Wen Zhou, Tao Li, Zhuoyuan Huo, Jing Zhang, Xin Gao, Yang Shen, Dinggang Zhang, Bing Zhang, Junfeng Medical School of Nanjing University Nanjing China The National Institute of Healthcare Data Science Nanjing University Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Department of Radiology Nanjing Drum Tower Hospital Nanjing University Medical School China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Artificial Intelligence Korea University Seoul Korea Republic of

Accurate segmentation of brain tumors from multi-modal Magnetic Resonance (MR) images is essential in brain tumor diagnosis and treatment. However, due to the existence of domain shifts among different modalities, the performance of networks decreases dramatically when training on one modality and performing on another, e.g., train on T1 image while perform on T2 image, which is often required in clinical applications. This also prohibits a network from being trained on labeled data and then transferred to unlabeled data from a different domain. To overcome this, unsupervised domain adaptation (UDA) methods provide effective solutions to alleviate the domain shift between labeled source data and unlabeled target data. In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under an UDA scheme. Specifically, A bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network. Further, a global-to-local consistency learning module is proposed to build robust representation alignments in an integrated way. Extensive experiments on a multi-modal brain MR benchmark dataset demonstrate that the proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin, while a comprehensive ablation study validates the effectiveness of each key component. The implementation code of our method will be released at https://***/KeleiHe/BiGL. © 2021, CC BY-NC-SA.

关键词： Generative adversarial networks

HAWK: Rapid Android malware detection through heterogeneous graph attention networks

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arXiv 2021年

作者： Hei, Yiming Yang, Renyu Peng, Hao Wang, Lihong Xu, Xiaolin Liu, Jianwei Liu, Hong Xu, Jie Sun, Lichao The School of Cyber Science and Technology Beihang University Beijing100083 China The School of Computing University of Leeds LeedsLS2 9JT United Kingdom The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computer Science and Software Engineering East China Normal University Shanghai Trusted Industrial Control Platform Co. Ltd. Shanghai200062 China The Department of Computer Science and Engineering Lehigh University Bethlehem United States

Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this issue, we present HAWK, a new malware detection framework for evolutionary Android applications. We model Android entities and behavioural relationships as a heterogeneous information network (HIN), exploiting its rich semantic meta-structures for specifying implicit higher-order relationships. An incremental learning model is created to handle the applications that manifest dynamically, without the need for re-constructing the whole HIN and the subsequent embedding model. The model can pinpoint rapidly the proximity between a new application and existing in-sample applications and aggregate their numerical embeddings under various semantics. Our experiments examine more than 80,860 malicious and 100,375 benign applications developed over a period of seven years, showing that HAWK achieves the highest detection accuracy against baselines and takes only 3.5ms on average to detect an out-of-sample application, with the accelerated training time of 50x faster than the existing approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Android (operating system)

EDOA: an efficient delay optimization approach for mixed-polarity Reed-Muller logic circuits under the unit delay model

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Frontiers of computer Science 2019年第5期13卷 1102-1115页

作者： Zhenxue HE Limin XIAO Fei GU Li RUAN Zhisheng HUO Mingzhe LI Mingfa ZHU Longbing ZHANG Rui LIU Xiang WANG College of Information Science and Technology Hebei Agricultural University Baoding 071001 China State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China School of Computer Science and Engineering Beihang University Beijing 100083 China School of Electronic and Information Engineering Beihang University Beijing 100083 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100083 China

Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay op-timization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is ob-tained using a novel Reed-Muller expression simplification approach (RMESA) considering don't-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark cir-cuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most cir-cuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.

关键词： delay optimization mixed-polarity Reed-Muller logic circuits unit delay model don't-care terms,