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3rd International Conference on Information science and System, ICISS 2020

作者： Duan, Xuliang Guo, Bing Shen, Yan Shen, Yuncheng Dong, Xiangqian Zhang, Hong College of Computer Science Sichuan University Chengdu610065 China School of Control Engineering Chengdu University of Information Technology Chengdu610065 China

ISBN: (纸本)9781450377256

Data currency is a temporal reference of data, which is related to the value of data and affects the results of data analysis and mining. The currency rules that reflect the time series features of data can be used not only for data repairing, but also for data quality evaluation. However, with the rapid growth and dynamic update of data volume, both the forms and algorithms of basic currency rule are facing severe challenges in application. Therefore, based on the research on data currency repairing, we extended the basic currency rule form, and proposed rule extraction and incremental updating algorithms that can run in parallel on dynamic data set. The experimental results show that, compared with non-parallel methods, the efficiency of parallel algorithms is significantly improved. © 2020 ACM.

关键词： Parallel algorithms

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Traffic Congestion Prediction: A Spatial-Temporal Context Embedding and Metric Learning Approach

Traffic Congestion Prediction: A Spatial-Temporal Context Em...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Hongsheng Hao Liang Wang Zenggang Xia Zhiwen Yu Jianhua Gu Ning Fu School of Computer Science Northwestern Polytechnical University Xi'an China College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781665408790

In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.

关键词： Measurement Correlation Roads Volume measurement Predictive models Trajectory Planning

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Friendship Understanding by Smartphone-based Interactions: A Cross-space Perspective

Friendship Understanding by Smartphone-based Interactions: A...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Liang Wang Haixing Xu Zhiwen Yu Rujun Guan Bin Guo Zhuo Sun School of Computer Science Northwestern Polytechnical University Xi’an China College of Electrical and Control Engineering Xi’an University of Science And Technology Xi’an China

ISBN: (纸本)9781665406697

Thanks to the growing popularity and functionality, smartphone has become rapidly valuable potential tool for human behavior research, e.g., friendship relationship recognition, friend ship prediction, etc. Until recently, there have been many research efforts to study this issue using the sensed data collected from smartphones. However, almost previous works in finding friendship strength are based on several physical features or a few dimensions, such as using Bluetooth scanning and demographic data to explain friendship. Actually, friendship is complicated and coupled with many factors, such as physical propinquity, social, physical and psychological homophily. So, it is necessary and beneficial to examine it comprehensively, by taking into account all the involved factors. Aiming at closing part of this research gap, in this paper, from cross-space perspectives, we launch a friendship relationship study with smartphone-based sensing paradigm from cyber space, physical mobility, and personality trait homophily. By integrating the involved heterogeneous interactions, we propose a Deep AutoEncoder-based unified framework to predict the strength of friendship connections between users, where the friendship strength is categorized and asymmetrical. We conduct extensive experiments on a practically collected sensing data set, and show the efficiency and effectiveness of our proposed approaches.

关键词： Bluetooth Psychology Sensors Marine vehicles

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Proactive Application Deployment for MEC: An Adaptive Optimization Based on Imperfect Multi-Dimensional Prediction

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IEEE Transactions on Vehicular Technology 2025年

作者： Shi, You Wang, Jian Yi, Changyan Wang, Ran Chen, Bing Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

This paper explores proactive application deployment in Multi-Access Edge Computing (MEC) systems through dual-dimensional prediction of user mobility and computing resource demands. We reveal a fundamental trade-off between prediction accuracy and temporal scope: Shorter-term mobility predictions enhance accuracy but increase deployment costs, while longer-term predictions reduce costs at the expense of higher service delays. Dynamic computing demands and constrained edge resources further complicate application instance allocation. To address these challenges, we propose an adaptive deployment framework leveraging multi-user multi-period predictions. This scheme simultaneously determines optimal application placement across edge nodes, deployment timing, and instance quantity. We employ a residual Long Short-Term Memory (LSTM) framework for joint mobility and computing resource requirement prediction. Based on this, we develop an advanced branch-and-bound deployment algorithm to obtain near-optimal solutions and a low-complexity greedy algorithm for rapid decision-making. Simulations demonstrate the feasibility and superiority of our proposed scheme over counterparts. © 1967-2012 IEEE.

关键词： Risk perception

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Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation

Attention Residual Network for White Blood Cell Classificati...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meng Zhao Lingmin Jin Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781728194202

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods.

关键词： Training White blood cells Costs Convolution Training data Feature extraction Generators

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An Adaptive Framework of Multimodal Emotion Recognition Based on Collaborative Discriminative Learning

An Adaptive Framework of Multimodal Emotion Recognition Base...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yadi Wang Xiaoding Guo Yibo Zhang Yiyuan Ren Wendi Huang Zunyan Liu Yuming Feng Xiangguang Dai Wei Zhang Hangjun Che School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

Multimodal emotion recognition has great application scenarios in the field of human-computer interaction, and the main task lies in how to capture and handle the consistency and complementarity of multimodal signals. However, in numerous scenarios, multimodal fusion is significantly challenged by variances in the strengths and weaknesses of different modality signals in carrying emotional information. This paper aims to learn the essential information of strong modalities and inter-modality correlations thus achieving better emotion recognition. We propose an adaptive multimodal emotion recognition framework based on collaborative discriminative learning (AMCDL), a framework for emotion recognition that engages in collaborative learning and adjusts to the information-processing capabilities of various emotion recognition models. AMCDL calculates the inter-modality correlation using Canonical Correlation Analysis (CCA) and uses it as an influencing factor to boost the multiplicative combination method to create a class loss function. When trained on several modalities, the class loss function can integrate complementary information to enable strong modal expression, enhancing emotion perception. On the benchmark dataset CMU-MOSI, AMCDL significantly improves classification accuracy when compared to the state-of-the-art models. The experiments showed the flexibility and generality of AMCDL by applying several emotion recognition models to it and producing better classification results than the original model.

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Structural and functional analysis of a potent human neutralizing antibody against enterovirus A71

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science China(Life sciences) 2022年第12期65卷 2517-2526页

作者： Zhe Chen Linlin Bao Bin Zhu Hua Fu Shuangli Zhu Tianjiao Ji Ying Xue Chuan Liu Xurong Wang Fengdi Li Qi Lv Feifei Qi Pin Yu Wei Deng Wenbo Xu Chuan Qin Hongrong Liu Qi Jin MOH Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases NHC Key Laboratory of Human Disease Comparative MedicineInstitute of Laboratory Animal ScienceChinese Academy of Medical Sciences and Comparative Medicine CenterPeking Union Medical CollegeBeijing 100021China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China National Institute for Viral Disease Control and Prevention China CDCBeijing 102206China Ministry of Education Key Laboratory of Protein Sciences Beijing Advanced Innovation Center for Structural BiologyTsinghua UniversityBeijing 100084China Key Laboratory for Matter Microstructure and Function of Hunan Province Key Laboratory of Low-dimensional Quantum Structures and Quantum ControlSchool of Physics and ElectronicsHunan Normal UniversityChangsha 410081China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310058China

Enterovirus A71(EV-A71) causes major outbreaks of hand,foot,and mouth disease(HFMD) in many countries,most frequently affecting children,and a small proportion of cases may lead to ***,no vaccine is available in most endemic regions,and no licenced treatments for EV-A71 infection are ***,we characterize a human monoclonal antibody(Hu MAb),E1,by screening a Fab antibody phage library derived from patients who recovered from EV-A71 infection.E1 exhibits strong neutralizing activity against EV-A71 virus in *** cryo-electron microscopy(cryo-EM) structures of the EV-A71 virion in complex with E1 Fab fragments demonstrated that E1 recognized an epitope formed by residues in the BC and HI loops of *** a mouse model,E1 effectively protected against lethal EV-A71 challenge in both prophylactic and therapeutic *** particular,E1 significantly reduces virus titers and muscle damage.E1 might represent a potential adjunct to EV-A71 treatment.

关键词： enterovirus A71 antibody mouse models cryo-electron microscopy

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Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming

arXiv

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arXiv 2024年

作者： Fawakherji, Mulham Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico Daniele Department of Built Environment College of Science and Technology North Carolina A&T University 1601 E Market St Greensboro27411 United States Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Faculty of Political Science and Sociopsychological Dynamics UNINT University Via Cristoforo Colombo 200 Roma00147 Italy

The use of deep learning methods for precision farming is gaining increasing interest. However, collecting training data in this application field is particularly challenging and costly due to the need of acquiring information during the different growing stages of the cultivation of interest. In this paper, we present a method for data augmentation that uses two GANs to create artificial images to augment the training data. To obtain a higher image quality, instead of re-creating the entire scene, we take original images and replace only the patches containing objects of interest with artificial ones containing new objects with different shapes and styles. In doing this, we take into account both the foreground (i.e., crop samples) and the background (i.e., the soil) of the patches. Quantitative experiments, conducted on publicly available datasets, demonstrate the effectiveness of the proposed approach. The source code and data discussed in this work are available as open source. © 2024, CC BY-NC-ND.

关键词： Crops

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More than a contaminant: How zinc promotes carbonate-mineralizing bacteria metabolism and adaptation by reshaping precipitation conditions

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science of the Total Environment 2024年 956卷 177333页

作者： Li, Shiyao Li, Yongyun Yang, Yanbing Wang, Can Xu, Fei Peng, Dinghua Huang, Huayan Guo, Yong Xu, Heng Liu, Huakang Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education College of Life Sciences Sichuan University Sichuan Chengdu610065 China College of Computer Science Sichuan University Sichuan Chengdu610065 China School of Life Science and Engineering Southwest Jiaotong University Sichuan Chengdu610031 China Agriculture and Rural Affairs Bureau of Jingyang District Sichuan Deyang618000 China Key Laboratory of Environment Protection Soil Ecological Protection and Pollution Control Sichuan University & Department of Ecology and Environment of Sichuan Sichuan Chengdu610065 China

Although microbial-induced carbonate precipitation (MICP) technology is both environmentally friendly and cost-effective, its efficiency is constrained by challenges such as low bacterial activity and heavy metal stress. This study explored the enhancement of mineralization efficiency by incorporating zinc (Zn) into the cultivation system of carbonate-mineralized bacteria. All Zn salts at a concentration of 30 μmol/L significantly enhanced the density and heavy metal resistance of bacterial cells, while also promoting CO2 hydration efficiency. The activities of urease and carbonic anhydrase (CA) were significantly elevated after treatment with 30 μmol/L ZnCl2 and Zn(C3H5O3)2 (ZnL) compared to the control. The results from qRT-PCR and ELISA confirmed that ZnL exhibited a stable biological effect on CA gene expression. Through the analysis of surface chemistry of cells and the subcellular distribution pattern of cadmium (Cd), it was observed that Zn supplementation maintained the cell surface stability and strengthened the cellular barrier against Cd uptake. SEM, FTIR and XRD results further confirmed that Zn supplementation significantly increased the complexity of the mineral morphology, resulting in a more stable crystal structure of CdCO3. This study offers additional theoretical and technical backing, opening a new avenue for the practical application of MICP technology in heavy metal remediation. © 2024 Elsevier B.V.

关键词： Zinc chloride

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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