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SSRN 2024年

作者： Fan, Yu Jing, Siyu Zhan, Min Zhang, Jian School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Laboratory of Big Data Decision Making for Green Development Beijing100192 China School of Digital Media Engineering and Humanities Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory School of Business Central South University Changsha410083 China Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application Beijing100192 China

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results show the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOX (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOX and CO2 emissions were stronger driven by final effects structure and types, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Human-in-The-Loop Optimization for Vehicle Body Lightweight Design

SSRN

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SSRN 2024年

作者： Hao, Jia Deng, Ruofan Jia, Liangyue Li, Zuoxuan Alizadeh, Reza Soltanisehat, Leili Liu, Binyi Sun, Zhibin Shao, Yiping Industrial and Systems Engineering Laboratory Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China School of Industrial and Systems Engineering University of Oklahoma NormanOK United States Charlton College of Business University of Massachusetts- Dartmouth MA United States College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China

Automatic optimization algorithms are crucial for vehicle body lightweight design;however, existing methods remain inefficient leading to excessive iterations that increase both time and costs. Current interactive optimization strategies partially mitigate this issue but lack a broad range of manipulation points and auxiliary information models. As such, we introduce a novel approach, "Human-in-the-Loop based method for Vehicle Body Lightweight Design" (HIL-VBLD). This method integrates human decision-making with optimization algorithms to reduce unproductive iterations. HIL-VBLD comprises two key components: (1) an innovative interaction mode that provides multiple manipulation points including constraint modification, algorithm switching, and selection of solutions of interest (SOI);(2) A comprehensive auxiliary information model that supports decision-making for designers. Our analysis demonstrates HIL-VBLD's efficacy, showing a 54.5% reduction in iteration cycles for genetic algorithm using SOI selection. Algorithm switching led to a 4.5% mass reduction, avoiding local optimum pitfalls associated with gradient algorithms. Additionally, the auxiliary information model achieved a further 1.25% mass reduction, enhancing optimization robustness. Compared to conventional automatic algorithm switching strategies, HIL-VBLD maintains equivalent accuracy with 23.9% fewer iterations. © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Emotion-Cause Pair Extraction Based on knowledge-Transfer 21

Emotion-Cause Pair Extraction Based on Knowledge-Transfer

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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Fengyuan, Zhao Dexi, Liu Qizhi, Wan Changxuan, Wan Xiping, Liu Guoqiong, Liao School of Information Management Jiangxi University of Finance and Economics Jiangxi Nanchang China Jiangxi University of Finance and Economics Key Laboratory of Data and Knowledge Engineering Jiangxi Nanchang China

The existing emotion cause pair extraction models do not improve the performance of emotion cause pair extraction by incorporating external knowledge. In this work, we propose an emotion-cause pair extraction model based on knowledge transfer (ECPE-KT), in which knowledge is utilized to obtain explicit knowledge encoding of text. Subsequently, the implicit knowledge encoding of clauses is obtained by the transfer of external sentiment classification corpus. Moreover, conduct filtering via adding the relative position and prediction probability to the representation of clause semantic features. Transformer with window mechanism is used to optimize the calculation pressure. Experimental results show that the proposed method outperforms the state-of-the-art method, i.e., ECPE-2D. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Extraction

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Hypernode: Entity Fusion for data Traceability and Link Prediction

Hypernode: Entity Fusion for Data Traceability and Link Pred...

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IEEE International Conference on data Mining (ICDM)

作者： Bingbing Dong Zan Zhang Jiao Li Yi Zhu Chenyang Bu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China School of Information Engineering Yangzhou University Yangzhou China

In the era of big data, fragmented knowledge, multisource heterogeneity, and different representation forms of the same entities in various data sources have posed considerable challenges to entity fusion. How to effectively integrate multisource knowledge for the same entities has provoked vast amounts of attention and research from multiple disciplines. Most existing methods for entity fusion can be categorized into two classes: one is to establish an association between the same entities, and the other is to delete duplicate entities after knowledge fusion and create a new fusion entity. However, in these two classes of methods, the former does not achieve true knowledge fusion and semantic interoperability, while the latter may cause irreversible loss of original information. In this paper, we propose a novel entity fusion scheme: Hypernode. Hypernode fuses the same entity in different data sources into a new entity while retaining the original data. We verify the effectiveness of Hypernode on multiple models of link prediction experiments. Several practical application cases illustrate the applicability of Hypernode in data traceability, open domain knowledge fusion, and multi-modal knowledge graph fusion.

关键词： Fuses Soft sensors Semantics Predictive models Big data Cognition data mining

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knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai He Bing Mao Xiangyu Zhou Yufei Li Tieliang Gong Chen Li Jialun Wu School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge.

关键词： Biological system modeling knowledge based systems Logic gates Performance gain Task analysis Bioinformatics Commonsense reasoning

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SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation

arXiv

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arXiv 2022年

作者： Fan, Zhaoxin Yang, Kaixing Zhang, Min Song, Zhenbo Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China

Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. Though there are some works research height limit estimation, existing methods are either too computational expensive or not accurate enough. In this paper, we propose a novel stereo-based pipeline named SHLE for height limit estimation. Our SHLE pipeline consists of two stages. In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image. Then, in stage 2, the depth is temporally measured, extracted and filtered to calculate the height limit device. To benchmark the height limit estimation task, we build a large-scale dataset named "Disparity Height", where stereo images, pre-computed disparities and ground-truth height limit annotations are provided. We conducted extensive experiments on "Disparity Height" and the results show that SHLE achieves an average error below than 10cm though the car is 70m away from the devices. Our method also outperforms all compared baselines and achieves state-of-the-art performance. Code is available at https://***/Yang-Kaixing/SHLE. Copyright © 2022, The Authors. All rights reserved.

关键词： Pipelines

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Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction

Uncertainty-guided Mutual Consistency Training for Semi-supe...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bing Mao Chang Jia Yucheng Huang Kai He Jialun Wu Tieliang Gong Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Biomedical relation extraction seeks to automatically extract biomedical relations from biomedical text, which plays an important role in biomedical studies. However, constructing high-quality biomedical annotation data is not only time-consuming but also requires a high level of knowledge in the biomedical field. To alleviate this problem, Semi-supervised Biomedical Relation Extraction aims to extract relation facts from the limited labeled data and the more readily available unlabeled samples. Existing works can be roughly categorized as self-training methods and self-ensembling methods. The former aims to generate pseudo labels, which may lead to the gradual drift problem. The latter aims to encourage the output of one model to be consistent with the other model, where the acquisition of the model is tedious. To alleviate these issues, we propose a novel Uncertainty-Guided Mutual Consistency Training framework(UG-MCT) for semi-supervised Biomedical relation extraction. Specifically, our framework consists of two models with the same structure, which differ only when updating their weights, and then an intersecting pseudo-label mechanism is designed to convert the prediction discrepancies of the two models into mutual consistency training loss, thus promoting the consistency of model predictions. In addition, we utilize uncertainty as guided information to assist the model in focusing on the confident pseudo labels and mitigate the noise of inaccurate pseudo labeling during training. Thus, our model is very simple and efficient while mitigating the noise introduced by pseudo-labels. UG-MCT is evaluated on multiple datasets in different settings and the experimental results demonstrate that our method is highly effective in semi-supervised biomedical relation extraction compared to the state-of-the-art.

关键词： Training Adaptation models Uncertainty Annotations Biological system modeling Focusing Predictive models

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Once read is enough: domain-specific pretraining-free language models with cluster-guided sparse experts for long-tail domain knowledge 24

Once read is enough: domain-specific pretraining-free langua...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Fang Dong Mengyi Chen Jixian Zhou Yubin Shi Yixuan Chen Mingzhi Dong Yujiang Wang Dongsheng Li Xiaochen Yang Rui Zhu Robert Dick Qin Lv Fan Yang Tun Lu Ning Gu Li Shang China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow Bayes Business School City St George's University of London Department of Electrical Engineering and Computer Science University of Michigan Department of Computer Science University of Colorado Boulder School of Microelectronics Fudan University

ISBN: (纸本)9798331314385

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models.

关键词：

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Access control of blockchain based on dual-policy attribute-based encryption 22

Access control of blockchain based on dual-policy attribute-...

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22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Han, Daojun Chen, Jinyu Zhang, Lei Shen, Yatian Wang, Xueheng Gao, Yihua Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781728176499

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through 'pseudo-Anonymity'. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center;instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally,

关键词： Blockchain

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Delving Globally into Texture and Structure for Image Inpainting

arXiv

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arXiv 2022年

作者： Liu, Haipeng Wang, Yang Wang, Meng Rui, Yong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Lenovo Research Beijing China

Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch is reconstructed from all other patches across the whole image, to match the coarsely filled information, especially the structure information over the masked regions. Unlike the current decoder-only transformer within the pixel level for image inpainting, our model adopts the transformer pipeline paired with both encoder and decoder. On one hand, the encoder captures the texture semantic correlations of all patches across image via self-attention module. On the other hand, an adaptive patch vocabulary is dynamically established in the decoder for the filled patches over the masked regions. Building on this, a structure-texture matching attention module anchored on the known regions comes up to marry the best of these two worlds for progressive inpainting via a probabilistic diffusion process. Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting. The extensive experiments over the benchmarks validate its superiority. Our code is available here. Copyright © 2022, The Authors. All rights reserved.

关键词： Textures

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