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检索条件"任意字段=11th International Conference on Computer-Aided Design and Computer Graphics"

共 8488 条记录，以下是1411-1420 订阅

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A unified generative framework for various NER subtasks 59

A unified generative framework for various NER subtasks

引用

Joint conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th international Joint conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Yan, Hang Gui, Tao Dai, Junqi Guo, Qipeng Zhang, Zheng Qiu, Xipeng Shanghai Key Laboratory of Intelligent Information Processing Fudan University China School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China New York University United States Pazhou Lab Guangzhou China

ISBN: (纸本)9781954085527

Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. these subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets. © 2021 Association for Computational Linguistics

关键词： Classification (of information)

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MOEA/D with Gradient-Enhanced Kriging for Expensive Multiobjective Optimization 11th

MOEA/D with Gradient-Enhanced Kriging for Expensive Multiobj...

引用

11th international conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Liu, Fei Zhang, Qingfu Han, Zhonghua Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China Institute of Aerodynamic and Multidisciplinary Design Optimization National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of Aeronautics Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783030720612

Expensive multiobjective optimization problem poses a big challenge. In many real-world engineering design problems, the time-consumed function evaluation is done by solving partial differential equations. the partial derivatives of a candidate solution can be calculated as a byproduct. Naturally, these problems can be solved more efficiently if gradient information is used. this paper proposes such a method, called MOEA/D-GEK, which combines MOEA/D and gradient-enhanced Kriging to solve expensive multiobjective problem. the gradient information is used for the construction of the Kriging model. Experimental studies on a set of test instances and a real-world aerodynamic design problem show high efficiency and effectiveness of our proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Kriging

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Multi-source Multi-label Causal Feature Selection 11

Multi-source Multi-label Causal Feature Selection

引用

11th international conference on Information Technology in Medicine and Education, ITME 2021

作者： Wang, Yun-An Lin, Yaojin Wang, Jie Wei, Zhisen Chen, Jinkun Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Xiamen University Department of Artificial Intelligence Xiamen China

ISBN: (纸本)9781665406796

Multi-label causal feature selection plays a vital role in dealing with high-dimensional multi-label data, since the selected causal features signify the causal mechanism with respect to the class attribute. At present, the descriptive information of data is collected from different data sources in many practical applications. therefore, there is a need of developing multi-source multi-label causal feature selection algorithms. In addition, existing multi-label causal feature selection algorithms cannot directly address multi-source heterogeneous distribution feature spaces. To address these problems, in this paper, we utilize the concept of causal invariance in causal inference. Firstly, we formulate the multi-label causal feature selection problem for multiple data sources as an invariant set search problem across the data sources. Secondly, we present the upper and lower bounds of the causal invariant set. Finally, we design a novel Multi-source Multi-label Causal Feature Selection (MMCFS) algorithm. Extensive experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods on synthetic datasets. © 2021 IEEE.

关键词： Feature Selection

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On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D 11th

On the Parameter Setting of the Penalty-Based Boundary Inter...

引用

11th international conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Wang, Zhenkun Deng, Jingda Zhang, Qingfu Yang, Qite School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China

ISBN: (纸本)9783030720612

Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. the subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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An Efficient Privacy Preservation Scheme with Blockchain-Based Reputation Management for IoV-Based Mobile Crowdsensing

An Efficient Privacy Preservation Scheme with Blockchain-Bas...

引用

international conference on Behavior, Economic and Social Computing (BESC)

作者： Genyuan Yang Wenjuan Li Ben Wang Chengjie Pan Jian Cao School of Information Science and Technology Hangzhou Normal University Hangzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Mobile crowdsensing (MCS) based on the Internet of Vehicles (IoV) has emerged as a viable solution for data collection. MCS utilizes a large number of mobile users to perform sensing tasks, thus reducing sensor deployment costs and improving data quality. However, IoV-based MCS faces two main challenges: privacy concerns and the presence of malicious vehicles. To address these challenges, this paper proposes an efficient privacy preservation scheme with blockchain-based reputation management (PPBRM) for Iov-based MCS. Specifically, we design an efficient sensing task allocation scheme that not only preserves location privacy, identity privacy, and sensing data privacy but also reduces computational and communication overhead. To identify malicious vehicles, ensure the security and reliability of sensing vehicle reputation values, and achieve efficient updates, we introduce a blockchain-based reputation management scheme suitable for IoV-based MCS, which fully considers the quality of sensing data, thereby ensuring the accuracy and fairness of reputation evaluation. Additionally, we introduce the concept of blockchain sharding and propose a multi-level graph partitioning-based blockchain sharding (MGPBS) method suitable for Iov-based MCS. MGPBS enhances the efficiency of blockchain reputation management by balancing the workload of each shard. the final simulation experiment results demonstrate the effectiveness and efficiency of the PPBRM scheme and MGPBS method.

关键词： Privacy Data privacy Sharding Social computing Crowdsensing Sensors Blockchains Computational efficiency Resource management Mobile computing

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Observing the Patient Experience: Inside the Doctor's Office in a Pandemic 11

Observing the Patient Experience: Inside the Doctor's Office...

引用

11th IEEE international conference on Consumer Electronics, ICCE-Berlin 2021

作者： Campbell, Mark Redfern, Sam McEvoy, John William Concannon, Fiona National University of Ireland Galway Computer Science Galway Ireland National University of Ireland Galway College of Medicine Nursing and Health Sciences Galway Ireland Galway Ireland

ISBN: (纸本)9781665428316

Focusing on the clinical doctor-patient consultation setting, this paper outlines the methodology and presents the results from a series of observational studies between doctor and patients undertaken in September and October 2020 within a public hospital setting during the COVID-19 pandemic. Using a design thinking methodology, the authors gained empathy and insights into the challenges experienced from both doctor and patient perspectives, during this period. this paper also discusses emergent themes from this qualitative investigation and examines the role of empathy in helping define the extent of the challenges that arose. As the first phase in a planned set of research phases, this work is informing and helping to shape subsequent ideation and design of multimedia related interventions to see if these human-centred design interventions can assist in improving the complex doctor-patient communication process. © 2021 IEEE.

关键词： COVID-19

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SMP-Track: SAM in Multi-Pedestrian Tracking

SMP-Track: SAM in Multi-Pedestrian Tracking

引用

international conference on Data Science and Advanced Analytics (DSAA)

作者： Shiyin Wang Huafeng Liu Qiong Wang Yichao Zhou Yazhou Yao School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Multiple Object Tracking (MOT) plays a crucial role in security data analysis as a fundamental problem for video surveillance. Our goal is to design a robust tracker for data damaged by attacks, while also emphasizing privacy protection. the mainstream paradigm for MOT is tracking-by-detection (TBD), which involves object detection followed by target association. In the association stage, most models rely on Intersection over Union (IoU) similarity of bounding boxes for short-range matching and cosine similarity of appearance features for long-range matching. However, both of these similarities contain a lot of redundant background regions except the target. To this end, we propose a new tracker named SMP-Track that integrates Segment Anything Model (SAM) into Multi-Pedestrian tracking method. Firstly we extract the pedestrian masks based on box prompt, focusing solely on the foreground information. then we introduce a new similarity metric that combines the advantages of motion and foreground information (i.e., box-mask similarity). Extensive experiments demonstrate that SMP-Track increases main metrics on the MOT17 validation set, and achieves comparable performance to other state-of-the-art methods on the MOT17 and MOT20 test sets. Furthermore, by incorporating pedestrian masks, we reduce reliance on raw pedestrian images or features, making the model robust to corrupted data and mitigating the risk of privacy leakage.

关键词： Measurement Data privacy Analytical models Target tracking Pedestrians Video surveillance Data models Object tracking Security Protection

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An Online Calibration Method for Robust Multi-Modality 3D Object Detection

An Online Calibration Method for Robust Multi-Modality 3D Ob...

引用

international conference on Data Science and Advanced Analytics (DSAA)

作者： Xingyu Li Yige Yao Jianming Hu Zhidong Deng Department of Automation Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Multi-modality sensor fusion for 3D perception is a significant part for autonomous driving perception, which enables a comprehensive integration of different sensors and obtains a holistic understanding of the surrounding environment to improve accuracy, stability and reliability. Nevertheless, vibrations, collisions, and acceleration/deceleration in motion may result in a minor disturbance to the position of sensors, leading to offsets in sensor calibration. To address the limitations of the original method demanding considerable manual effort and time for hand-annotating checkerboards, we introduce an online Camera-LiDAR calibration method, which can perform calculations during the operation of autonomous vehicles to eliminate sensor biases. Different from the previous CNN-based online calibration algorithm, our approach differs in utilizing multi-scale features to accomplish alignment with a foundational ResNet and FPN Backbone alongside an attention-based multi-scale feature fusion module. Moreover, in the design of the loss function, we incorporate depth loss and point cloud loss in addition to the original smooth L1 norm loss to facilitate network backpropagation. Ultimately, our proposed method achieves an average translation error of 0.88 cm and a rotation error of 0.073° on the KITTI odometry dataset, which can significantly limit the discrepancies between sensors and thus enhance detection accuracy.

关键词： Point cloud compression Vibrations three-dimensional displays Accuracy Object detection Sensor fusion Feature extraction Stability analysis Calibration Autonomous vehicles

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Forecasting Dissolved Oxygen Based on Two-Stage Decomposition with BiLSTM-Attention and weighted Huber Loss Function

Forecasting Dissolved Oxygen Based on Two-Stage Decompositio...

引用

international conference on Data Science and Advanced Analytics (DSAA)

作者： Neha Pant Durga Toshniwal Bhola Ram Gurjar Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Civil Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Accurate forecasting of water quality is vital for safeguarding public health, aquatic ecosystems and ensuring economic stability. this study introduces a novel hybrid approach using two-stage decomposition combined with Bidirectional Long Short-Term Memory and Attention mechanism (BiLSTM-Attention) for short-term forecasting of Dissolved Oxygen (DO). First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the original data into a set of Intrinsic Mode Functions (IMFs) and residual component. Variational Mode Decomposition(VMD) further decomposes the IMF with the highest frequency into a set of modes. then, BiLSTM-Attention is employed to generate the final forecast. the existing studies predominantly use MSE or MAE as the loss function for water quality forecasting. We design a customized dynamically weighted Huber loss function (DWHL) to optimize the training of our proposed forecasting model. the model is tested on data from ten locations along the river Ganga, consid-ering the forecasting horizons of one, two, and three hours, and compare it with ten models, both with and without decomposition. the results of the Diebold Mariano test suggests that there is a statistically significant difference in forecast accuracy of the proposed and compared models. the outcomes demonstrate that the proposed CEEMDAN-VMD-DWHL-BiLSTM-Attention approach effectively identifies complex and non-linear patterns in the data, resulting in significantly improved forecasts.

关键词： Training Analytical models Adaptation models Accuracy Oxygen Empirical mode decomposition Water quality Predictive models Data models Forecasting

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Combining User Knowledge and Online Innovization for Faster Solution to Multi-objective design Optimization Problems 11th

Combining User Knowledge and Online Innovization for Faster ...

引用

11th international conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Ghosh, Abhiroop Deb, Kalyanmoy Averill, Ronald Goodman, Erik Michigan State University East LansingMI48824 United States

ISBN: (纸本)9783030720612

Real-world optimization problems often come with additional user knowledge that, when accommodated within an algorithm, may produce a faster convergence to acceptable solutions. Modern population-based optimization algorithms, aided by recent advances in AI and machine learning, can also learn and utilize patterns of variables from past iterations to improve convergence in subsequent iterations – an approach termed innovization. In this paper, we discuss ways to combine user-supplied heuristics and machine-learnable patterns and rule sets in developing efficient multi-objective optimization algorithms. Two practical large-scale design problems are chosen to demonstrate the usefulness of integrating human-machine information within a multi-objective optimization in finding similar quality solutions as that obtained by the original algorithm with less computational time. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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