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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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Spintronic Terahertz Polarization Programmable System for Information Encoding

SSRN

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China Beijing Advanced Innovation Center for Big Data and Brain Computing China

Terahertz (THz) wave as an important electromagnetic wave has been increasingly applied in wireless communication and imaging sensing technology. It has been an increasingly practical application. Ultra-high data rates in communication technology push toward a broadband, controllable polarization, and multiple transmission channels signal sources. Here, we propose a programmable polarization spintronics terahertz source (PP-STS), in which contains a controllable polarization spintronic THz emitter and a microcontroller unit. The PP-STS not only can generate THz waves with wide frequency bandwidth, but also has the function of switching the orthogonal linear polarization state of the generated THz waves flexibly. By coding the microcontroller unit, the designed PP-SPS can directly program the orthogonal linear polarization states as a series of information to transmit. To achieve the interconversions between digital codes and the THz polarization information, an optimum matching THz polarization beam splitter (THz-PBS) is also designed via simulations and experiments. The THz-PBS, as a decoder, can accurately split the transverse electric (TE) and transverse magnetic (TM) modes into two THz detectors. At last, a coding and decoding prototype system of spintronic THz polarization is built by integrating the PP-STS, the THz-PBS and two THz detectors. The send and receive information with high speed and accuracy are verified in this prototype system as well. We believe that our work takes THz applications for multiplexing communications one step further and sheds light on commercializing the PP-STS with exciting manufacturing lines for efficient and economical THz polarization technology. © 2023, The Authors. All rights reserved.

关键词： Microcontrollers

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An overview of uni- and multi-biometric identification of identical twins

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IEIE Transactions on Smart Processing and computing 2019年第1期8卷 71-84页

作者： Mohammed, Bayan Omar Shamsuddin, Siti Mariyam Hasan, Shafaatunnur UTM Big Data Centre IbnuSina Institute for Scientific and Industrial Research UniversitiTeknologi Malaysia Johor Malaysia Faculty of Computing UniversitiTeknologi Malaysia Johor Malaysia College of Science and Technology University of Human Development Sulaymaniyah Iraq

Biometric techniques can differentiate identical twins, and this makes the techniques a subject of interest. Studies on biometric techniques have been conducted in many disciplines. Unimodal biometric systems with twins, however, have issues including intra-class variations, noisy data, limited freedom levels, non-universality, vulnerability to spoof attacks, and intolerable rates of error. Several of these limitations are manageable through the use of multimodal biometric systems that combine the evidence brought by many sources of information. This survey highlights the relevant literature, the accessible datasets for research, and the subjects of uncertainty, and proposes future research. Copyrights © 2019 The institute of Electronics and Information Engineers.

关键词： Biometrics

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Discriminative features matter: Multi-layer bilinear pooling for camera localization 30

Discriminative features matter: Multi-layer bilinear pooling...

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30th British Machine Vision Conference, BMVC 2019

作者： Wang, Xin Wang, Xiang Wang, Chen Bai, Xiao Wu, Jing Hancock, Edwin Robert School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science University of York York United Kingdom

Deep learning based camera localization from a single image has been explored recently since these methods are computationally efficient. However, existing methods only provide general global representations, from which an accurate pose estimation can not be reliably derived. We claim that effective feature representations for accurate pose estimation shall be both "informative" (focusing on geometrically meaningful regions) and "discriminative" (accounting for different poses of similar images). Therefore, we propose a novel multi-layer factorized bilinear pooling module for feature aggregation. Specifically, informative features are selected via bilinear pooling, and discriminative features are highlighted via multi-layer fusion. We develop a new network for camera localization using the proposed feature pooling module. The effectiveness of our approach is demonstrated by experiments on an outdoor Cambridge Landmarks dataset and an indoor 7 Scenes dataset. The results show that focusing on discriminative features significantly improves the network performance of camera localization in most cases. Codes will be available soon. © 2019. The copyright of this document resides with its authors.

关键词： Deep learning

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HEPScore: A new CPU benchmark for the WLCG

arXiv

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arXiv 2023年

作者： Giordano, Domenico Barbet, Jean-Michel Boccali, Tommaso Borge, Gonzalo Menéndez Hollowell, Christopher Innocente, Vincenzo Lampl, Walter Michelotto, Michele Meinhard, Helge Ondris, Ladislav Sciabà, Andrea Schnepf, Matthias J. Sobie, Randall J. Southwick, David Sullivan, Tristan S. Valassi, Andrea Wenzel, Sandro Willis, John L. Yan, Xiaofei CERN Geneva Switzerland Subatech UMR 6457 CNRS-IN2P3 IMT Atlantique Université de Nantes Nantes France INFN Sezione di Pisa L.go Pontecorvo 3 Pisa56127 Italy ICSC-National Center for HPC Big Data and Quantum Computing Italy Brookhaven National Laboratory United States University of Arizona United States INFN Istituto Nazionale di Fisica Nucleare Padua Italy Karlsruhe Institute of Technology Karlsruhe Germany Institute of Particle Physics University of Victoria VictoriaBC Canada California Institute of Technology PasadenaCA United States Institute of High Energy Physics Beijing China

HEPScore is a new CPU benchmark created to replace the HEPSPEC06 benchmark that is currently used by the WLCG for procurement, computing resource pledges, usage accounting and performance studies. The development of the new benchmark, based on HEP applications or workloads, has involved many contributions from software developers, data analysts, experts of the experiments, representatives of several WLCG computing centres and WLCG site managers. In this contribution, we review the selection of workloads and the validation of the new HEPScore benchmark. © 2023, CC BY.

关键词： Benchmarking

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Information retrieval:a view from the Chinese IR community

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Frontiers of Computer Science 2021年第1期15卷 197-211页

作者： Zhumin CHEN Xueqi CHENG Shoubin DONG Zhicheng DOU Jiafeng GUO Xuanjing HUANG Yanyan LAN Chenliang LI Ru LI Tie-Yan LIU Yiqun LIU Jun MA Bing QIN Mingwen WANG Jirong WEN Jun XU Min ZHANG Peng ZHANG Qi ZHANG School of Computer Science and Technology Shandong UniversityJinan 250100China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China School of Information Renmin University of ChinaBeijing 100872China School of Computer Science Fudan UniversityShanghai 200433China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Big Data Shanxi UniversityTaiyuan 200433China Microsoft Research Asia Beijing 100080China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China School of Computer Information and Engineering Jiangxi Normal UniversityNanchang 330022China School of Computer Science and Technology Tianjin UniversityTianjin 300072China

During a two day strategic workshop in February 2018,22 information retrieval researchers met to discuss the future challenges and opportunities within the *** outcome is a list of potential research directions,project ideas,and *** report describes the major conclusions we have obtained during the workshop.A key result is that we need to open our mind to embrace a broader IR field by rethink the definition of information,retrieval,user,system,and evaluation of *** providing detailed discussions on these topics,this report is expected to inspire our IR researchers in both academia and industry,and help the future growth of the IR research community.

关键词： information retrieval redefinition information scope of retrieval retrieval models users system architecture evaluation

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Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method 15th

Random Forest-Based Ensemble Estimator for Concrete Compress...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Lv, Yuanxin Shi, Xiaoyu Ran, Longyu Shang, Mingsheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030325909

As one of the most important building materials, the quality of concrete directly affects the safety of buildings. Hence, it is an important and hot issue to predict the compressive strength of concrete with highly accuracy. Most of existing methods heavily depend on building a single model to predict the compressive strength of concrete. However, the proposed single models are not one-size-fits-all, different model have different limitations, making them a better or worse fit for different situation. To address this issue, this paper proposes a novel predict model for concrete compressive strength based on ensemble learning method. In detail, we build our ensemble framework via using the AdaBoost method, while the random forests methods as weak classifier are integrated to the AdaBoost framework. For dealing with the noisy and missing value problems, a set of statistical methods are employed. Furthermore, we utilize the Pearson correlation coefficient to analysis the relationship between different input materials, which can effectively drop out the irrelevant and redundant features. Experimental results on two industrial data sets show that proposed ensemble estimator can significantly improve the prediction accuracy in comparing with other five state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Reconstructing the dark energy density in light of DESI BAO observations

arXiv

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arXiv 2025年

作者： Berti, Maria Bellini, Emilio Bonvin, Camille Kunz, Martin Viel, Matteo Zumalacarregui, Miguel Département de Physique Théorique Center for Astroparticle Physics Université de Genève Quai E. Ansermet 24 Genève 4CH-1211 Switzerland Center for Astrophysics and Cosmology University of Nova Gorica Nova Gorica Slovenia INAF Istituto Nazionale di Astrofisica Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 TriesteI-34151 Italy SISSA - International School for Advanced Studies Via Bonomea 265 TriesteI-34136 Italy INFN – National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Am Mühlenberg 1 Potsdam-GolmD-14476 Germany

In light of the evidence for dynamical dark energy (DE) found from the most recent Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) measurements, we perform a non-parametric, model-independent reconstruction of the DE density evolution. To do so, we develop and validate a new framework that reconstructs the DE density through a third-degree piece-wise polynomial interpolation, allowing for direct constraints on its redshift evolution without assuming any specific functional form. The strength of our approach resides in the choice of directly reconstructing the DE density, which provides a more straightforward relation to the distances measured by BAO than the equation of state parameter. We investigate the constraining power of cosmic microwave background (CMB) observations combined with supernovae (SNe) and BAO measurements. In agreement with results from other works, we find a preference for deviations from ΛCDM, with a significance of 2.4σ when using the Dark Energy Survey Year 5 (DESY5) SNe data, and 1.3σ with PantheonPlus. In all the cases we consider, the derived DE equation of state parameter presents evidence for phantom crossing. By investigating potential systematic effects in the low-redshift samples of DESY5 observations, we confirm that correcting for the offset in apparent magnitude with respect to PantheonPlus data, as suggested in previous studies, completely removes the tension. Furthermore, we assess the risk of potentially overfitting the data by changing the number of interpolation nodes. As expected, we find that with lesser nodes we get a smoother reconstructed behavior of the DE density, although with similar overall features. The pipeline developed in this work is ready to be used with future high-precision data to further investigate the evidence for a non-standard background evolution. Copyright © 2025, The Authors. All rights reserved.

关键词： Supernovae

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Driving Behavior Safety Assessment in Edge computing Using Multitask Discriminative TS Fuzzy Model

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IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

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Multi-Time Scale Attention Network for WEEE Reverse Logistics Return Prediction

SSRN

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SSRN 2022年

作者： Zhang, Jia Gao, Min Zhao, Liang Hu, Jiaqi Gao, Jinyong Deng, Meiling Wan, Chao Yang, Linda School of Big Data and Software Engineering Chongqing University Chongqing401331 China China National Electric Apparatus Research Institute Co. Ltd. Guangzhou510300 China Aibo Green Reverse Supply Chain Co. Ltd. Shenzhen518000 China School of Computing University of Portsmouth Hampshire PortsmouthPO1 3HE United Kingdom

In recent years, the reverse logistics (RL) management of waste electrical and electronic equipment (WEEE) has aroused widespread interest due to its vital role in improving ecological and economic benefits. As accurate quantification and estimate of future WEEE are fundamental to adequate planning and efficient treatment, we propose the Multi-time Scale Attention Network (MULAN) model in this paper for WEEE RL return prediction. In MULAN, we take multiple windows of historical data to capture distinct characteristics presented at different time scales and use attention-based alignment to fuse information. We then propose smooth time-series embedding method based on neighborhood data aggregation and temporal feature embedding method from four kinds of time positions to tackle the challenges of dealing with high noise data and high sensitivity to temporal dependencies. We conduct extensive experiments on real-world datasets to demonstrate that MULAN has significantly improved prediction accuracy over other baseline prediction methods in most cases. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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