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29th British Machine Vision Conference, BMVC 2018

作者： Li, Annan Chen, Zhiyuan Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

In computer vision tasks, the position and size of a target object can be represented by a rectangle bounding box. Because its efficiency and simplicity, bounding box is widely used. However, using this simple method cannot deal with the diversity of shape. An obvious drawback is non-target elements are inevitably included in the box, which is a crucial issue in object tracking. Tracking-by-segmentation is an effective way of overcoming the limitation of bounding box. However, related benchmark dataset is still rare. To address this problem, a dataset of 150 video sequences with fine foreground mask is presented in this paper. It provides a preciser benchmark for object tracking and can be also used for video segmentation. © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Image segmentation

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CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

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Matter 2025年第6期8卷

作者： Zhao, Guobin Brabson, Logan M. Chheda, Saumil Huang, Ju Kim, Haewon Liu, Kunhuan Mochida, Kenji Pham, Thang D. Prerna Terrones, Gianmarco G. Yoon, Sunghyun Zoubritzky, Lionel Coudert, François-Xavier Haranczyk, Maciej Kulik, Heather J. Moosavi, Seyed Mohamad Sholl, David S. Siepmann, J. Ilja Snurr, Randall.Q. Chung, Yongchul G. School of Chemical and Biomolecular Engineering Pusan National University Busan46241 Korea Republic of School of Chemical and Biomolecular Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Chemistry and Chemical Theory Center University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemical Engineering and Materials Science University of Minnesota 421 Washington Avenue SE MinneapolisMN55455 United States Department of Chemistry Kavli Energy Nanoscience Institute and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States Chemical Engineering and Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Department of Chemical and Biological Engineering Northwestern University EvanstonIL60208 United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Chimie ParisTech PSL University CNRS Institut de Recherche de Chimie Paris Paris75005 France Air Liquide Campus Innovation Paris Les Loges-en-Josas78350 France IMDEA Materials Institute Getafe Madrid28906 Spain Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States of Data Science Pusan National University Busan46241 Korea Republic of

We present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications. © 2025 Elsevier Inc.

关键词： Crystal structure

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Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

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Wireless Communications and Mobile computing 2021年第1期2021卷

作者： Liu, Suhui Yu, Jiguo Hu, Chunqiang Li, Mengmeng School of Computer Science Qufu Normal University Rizhao Shandong276826 China Jinan Shandong250353 China Jinan Shandong250014 China Shandong Laboratory of Computer Networks Jinan250014 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University China

Cloud-assisted Internet of Things (IoT) significantly facilitate IoT devices to outsource their data for high efficient management. Unfortunately, some unsettled security issues dramatically impact the popularity of IoT, such as illegal access and key escrow problem. Traditional public-key encryption can be used to guarantees data confidentiality, while it cannot achieve efficient data sharing. The attribute-based encryption (ABE) is the most promising way to ensure data security and to realize one-to-many fine-grained data sharing simultaneously. However, it cannot be well applied in the cloud-assisted IoT due to the complexity of its decryption and the decryption key leakage problem. To prevent the abuse of decryption rights, we propose a multiauthority ABE scheme with white-box traceability in this paper. Moreover, our scheme greatly lightens the overhead on devices by outsourcing the most decryption work to the cloud server. Besides, fully hidden policy is implemented to protect the privacy of the access policy. Our scheme is proved to be selectively secure against replayable chosen ciphertext attack (RCCA) under the random oracle model. Some theory analysis and simulation are described in the end. © 2021 Suhui Liu et al.

关键词： Internet of things

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A robust collision source method for rank adaptive dynamical low-rank approximation in radiation therapy

arXiv

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arXiv 2021年

作者： Kusch, Jonas Stammer, Pia Karlsruhe Institute of Technology Mathematical Science and Computational Methods Karlsruhe Germany Karlsruhe Institute of Technology Steinbuch Centre for Computing Karlsruhe Germany German Cancer Research Center - DKFZ Department of Medical Physics in Radiation Oncology Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany

Deterministic models for radiation transport describe the density of radiation particles moving through a background material. In radiation therapy applications, the phase space of this density is composed of energy, spatial position and direction of flight. The resulting six-dimensional phase space prohibits fine numerical discretizations, which are essential for the construction of accurate and reliable treatment plans. In this work, we tackle the high dimensional phase space through a dynamical low-rank approximation of the particle density. Dynamical low-rank approximation (DLRA) evolves the solution on a low-rank manifold in time. Interpreting the energy variable as a pseudo-time lets us employ the DLRA framework to represent the solution of the radiation transport equation on a low-rank manifold for every energy. Stiff scattering terms are treated through an efficient implicit energy discretization and a rank adaptive integrator is chosen to dynamically adapt the rank in energy. To facilitate the use of boundary conditions and reduce the overall rank, the radiation transport equation is split into collided and uncollided particles through a collision source method. Uncollided particles are described by a directed quadrature set guaranteeing low computational costs, whereas collided particles are represented by a low-rank solution. It can be shown that the presented method is L2-stable under a time step restriction which does not depend on stiff scattering terms. Moreover, the implicit treatment of scattering does not require numerical inversions of matrices. Numerical results for radiation therapy configurations as well as the line source benchmark underline the efficiency of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Phase space methods

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A discovery of Two Slow Pulsars with FAST: "Ronin" from the Globular Cluster M15

arXiv

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arXiv 2023年

作者： Zhou, Dengke Wang, Pei Li, Di Fang, Jianhua Miao, Chenchen Freire, Paulo C.C. Zhang, Lei Zhang, Dandan Chen, Huaxi Feng, Yi Xiao, Yifan Xie, Jintao Zhang, Xu Jin, Chenwu Wang, Han Ke, Yinan Guo, Xuerong Zhao, Rushuang Niu, Chenhui Zhu, Weiwei Xue, Mengyao Wang, Yabiao Wu, Jiafu Gan, Zhenye Sun, Zhongyi Wang, Chengjie Zhang, Junshuo Cao, Jinhuang Lu, Wanjin Zhang, Jie Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China University of Chinese Academy of Sciences Beijing100049 China New Cornerstone Science Laboratory Shenzhen518054 China Max Planck Institut für Radioastronomie Bonn53121 Germany Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn3122 Australia School of Mathematical Sciences School of Physics and Electronic Sciences Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang550001 China School of Physics and Electronic Engineering Qilu Normal University Jinan250200 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China College of Physical Science and Technology Central China Normal University Wuhan430079 China Tencent Youtu Lab Shanghai201103 China

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars (P 100 ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period pulsars. We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density (Smin) of pulsars. Subsequently, we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 seconds and 3.960716 seconds, respectively. On the P − P diagram, both pulsars are positioned below the spin-up line, suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on. According to timing results, these two pulsars exhibit remarkably strong magnetic fields. If the magnetic fields were weakened during the accretion process, then a short duration of accretion might explain the strong magnetic fields of these pulsars. © 2023, CC BY.

关键词： Magnetic fields

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Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

Multi-label Contrastive Focal Loss for Pedestrian Attribute ...

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International Conference on Pattern Recognition

作者： Xiaoqiang Zheng Zhenxia Yu Lin Chen Fan Zhu Shilong Wang Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of computer science Chengdu University Of Information Technology Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep convolutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of network parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in term of mean accuracy.

关键词： Training Visualization Force Complex networks Feature extraction Pattern recognition Convolutional neural networks

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A predictive maintenance framework based on real-time credibility evaluation of remaining useful life prediction results

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Reliability Engineering & System Safety 2025年 264卷

作者： Guannan Shi Xiaohong Zhang Jianchao Zeng Haitao Liao Jie Gan Jinhe Wang Zhijian Wang School of Economics and Management Taiyuan University of Science and Technology Taiyuan 030024 China Division of Industrial and System Engineering Taiyuan University of Science and Technology Taiyuan 030024 China Institute for Big Data and Visual Computing North University of China Taiyuan 030051 China Industrial Engineering Department University of Arkansas Fayetteville AR 72701 USA School of Mechanical Engineering North University of China Taiyuan Shanxi 030051 China

The increasing availability of remaining useful life (RUL) prediction methods has incentivized the development of predictive maintenance (PdM) for engineering systems. The performance of RUL prediction results is often expected to improve as more condition monitoring data are collected. However, achieving a credible RUL prediction result remains a critical challenge that is often overlooked in current PdM literature. This article proposes a PdM framework to optimize maintenance plans by a PdM utility model correlates the expected maintenance net revenues and losses with the credibility of RUL prediction result to determine the optimal PdM timing. In addition, considering the dynamic characteristics of PdM decision-making driven by condition monitoring data and on the corresponding RUL prediction results, an updating strategy that control the updating frequency is proposed to minimize computational resource waste and avoid decision redundancy. Finally, the proposed PdM framework is validated using the C-MAPSS dataset of turbofan engines.

关键词： Credibility evaluation Dynamic update Predictive maintenance Rul prediction Utility theory

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Weakly supervised learning for textbook question answering

TechRxiv

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TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Unsuccessful Story about Few Shot Malware Family Classification and Siamese Network to the Rescue

Unsuccessful Story about Few Shot Malware Family Classificat...

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International Conference on Software Engineering (ICSE)

作者： Yude Bai Zhenchang Xing Xiaohong Li Zhiyong Feng Duoyuan Ma Tianjin Key Laboratory of Advanced Networking (TANK) School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin China Research School of Computer Science Australian National University Data61 CSIRO Australia School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9781450371216

ISBN: (纸本)9781728165196

To battle the ever-increasing Android malware, malware family classification, which classifies malware with common features into a malware family, has been proposed as an effective malware analysis method. Several machine-learning based approaches have been proposed for the task of malware family classification. Our study shows that malware families suffer from several data imbalance, with many families with only a small number of malware applications (referred to as few shot malware families in this work). Unfortunately, this issue has been overlooked in existing approaches. Although existing approaches achieve high classification performance at the overall level and for large malware families, our experiments show that they suffer from poor performance and generalizability for few shot malware families, and traditionally downsampling method cannot solve the problem. To address the challenge in few shot malware family classification, we propose a novel siamese-network based learning method, which allows us to train an effective MultiLayer Perceptron (MLP) network for embedding malware applications into a real-valued, continuous vector space by contrasting the malware applications from the same or different families. In the embedding space, the performance of malware family classification can be significantly improved for all scales of malware families, especially for few shot malware families, which also leads to the significant performance improvement at the overall level.

关键词： Learning systems Machine learning Multilayer perceptrons Malware Task analysis Software engineering

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Blockchain-Based Participant Selection for Federated Learning 2nd

Blockchain-Based Participant Selection for Federated Learnin...

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2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Zhang, Keshan Huang, Huawei Guo, Song Zhou, Xiaocong School of Data and Computer Science Sun Yat-Sen University Guangzhou China National Engineering Research Center of Digital Life Sun Yat-Sen University Guangzhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong Shenzhen Research Institute The Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (纸本)9789811592126

Federated Learning (FL) advocates training a global model using a large-scale of distributed devices such that the collaborative model training can be benefited from the rich local datasets, while preserving the privacy of local training dataset in each participant. This is because only the training results, i.e., the updated model parameters and model weights are needed to report to the FL server for aggregation in each round of FL-training. However, during the model transmission of the original FL protocol, there is no security guarantee towards the training results. Thus, every step during model uploading phase can be attacked by malicious attackers. To address such threat, we propose a new federated learning architecture by taking the advantages of blockchain into account. The proposed architecture includes two-phase design. The first phase is a numerical evaluation, which can prevent the malicious devices from being selected. For the second phase, we devise a participant-selection algorithm that enables the FL server to select the appropriate group of devices for each round of FL-training. We believe that our study can shed new light on the joint research of blockchain and federated learning. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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