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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Are Your Apps Accessible? A GCN-based Accessibility Checker for Low Vision Users

arXiv

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arXiv 2025年

作者： Zhang, Mengxi Liu, Huaxiao Song, Shenning Chen, Chunyang Huang, Pei Zhao, Jian College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China Department of Computer Science Technical University of Munich Heilbronn85354 Germany Department of Computer Science Stanford University CA94305 United States College of Computer Science Changchun University Jilin Changchun130012 China

Context: Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend. Objective: In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components. Method: ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues. Results: Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models. Conclusion: To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

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Science China(Information Sciences) 2020年第12期63卷 266-268页

作者： Jing-Dong DIAO Jin GUO Changyin SUN School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education

Dear editor,With the rapid development of network and communication technology, group systems interrelated in terms of both time and space are commonly encountered, such as sensor networks, multi-agent systems, and sm... 详细信息

关键词： system identification event-triggered binary-valued observations packet loss convergence

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Organometallic-Inorganic Hybrid MXenes with Tunable Superconductivity

arXiv

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arXiv 2025年

作者： Fan, Qi Bo, Tao Guo, Wei Chen, Minghua Tang, Qing Yang, Yicong Li, Mian Chen, Ke Ge, Fangfang Li, Jialu Qiao, Sicong Wang, Changda Song, Li Yu, Lijing Guo, Jinghua Naguib, Michael Chai, Zhifang Huang, Qing Dun, Chaochao Kang, Ning Gogotsi, Yury Liang, Kun Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing100049 China Qianwan Institute of CNITECH Ningbo315336 China Key Laboratory for the Physics and Chemistry of Nanodevices Department of Electronics Peking University Beijing100871 China Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Synchrotron Radiation Laboratory Key Laboratory of Precision and Intelligent Chemistry School of Nuclear Science and Technology University of Science and Technology of China Hefei230029 China Key Laboratory of Materials and Surface Technology Ministry of Education Sichuan Energy Equipment Intelligent Engineering Research Center School of Materials Science and Engineering Xihua University Chengdu610039 China Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States Department of Materials Science and Engineering A. J. Drexel Nanomaterials Institute Drexel University PhiladelphiaPA19104 United States

Ti-based two-dimensional transition-metal carbides (MXenes) have attracted attention due to their superior properties and are being explored across various applications1,2. Despite their versatile properties, superconductivity has never been demonstrated, not even predicted, for this important group of 2D materials. In this work, we have introduced an electrochemical intercalation protocol to construct versatile organometallic-inorganic hybrid MXenes and achieved tunable superconductivity in the metallocene-modified layered crystals. Through structural editing of MXene matrix at atomic scale and meticulously modulated intercalation route, Ti3C2Tx intercalated with metallocene species exhibits a superconductive transition temperature (Tc) of 10.2 K. Guest intercalation induced electron filling and strain engineering are responsible for the emerging superconductivity in this intrinsically non-superconducting material. Theoretically, simulated electron-phonon interaction effects further elucidate the nature of the changes in Tc. Furthermore, the Tc of crafted artificial superlattices beyond Ti-based MXenes have been predicted, offering a general strategy for engineering superconductivity and magnetism in layered hybrid materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Electron-phonon interactions

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Task-oriented Multi-domain Adversarial Network for fake news detection

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Applied Soft Computing 2025年 177卷

作者： Zeqi, Guo Jihong, Ouyang Ximing, Li Changchun, Li College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

The proliferation of fake news on online social media has severely misled public perception of event authenticity. To combat this, various Fake News Detection (FND) methods have been developed for specific domains, typically requiring large amounts of labeled data for effective training. However, in some domains, labeled data remains scarce. Multi-domain Fake News Detection (MFND) provides a solution by training a model with data from multiple domains to identify news veracity across domains. Despite its potential, discrepancies in feature distributions across domains, known as domain shift, can negatively impact the effectiveness of joint training. To mitigate this, we propose the Task-oriented Multi-domain Adversarial Network (TMDA-NET), a novel MFND model designed to align feature distributions across domains within a unified feature space using domain adversarial learning. This approach enhances generalization in data-scarce domains. Given that FND is domain-dependent, we also propose a fusion mechanism with adjustable weights to incorporate domain-specific characteristics. Additionally, we introduce two task-oriented auxiliary classification tasks to efficiently align and integrate features, ultimately improving FND accuracy. Extensive experiments on publicly available Chinese and English datasets validate the effectiveness of our proposed model. © 2025

关键词： Labeled data

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Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1293-1304页

作者： Zhu, Yi Wu, Xindong Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou University School of Information Engineering Yangzhou225012 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei230002 China

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods. © 2020 IEEE.

关键词： Noise abatement

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A framework for personalized management of domain ontologies

A framework for personalized management of domain ontologies

引用

2012 IEEE/ACIS 11th International Conference on Computer and Information Science, ICIS 2012

作者： Liu, Runnan Wang, Puwei Zheng, Shixian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China

ISBN: (纸本)9780769546940

As the sharable and reusable domain knowledge, domain ontology increasingly serves as a foundation for semantic Web. Personalized management of domain ontologies is to provide personalized views of domain ontologies to users during run time according to user preferences. It helps that users can focus on their interested parts, instead of the whole. It increases the efficiency of ontology-based application. This paper proposes a framework for personalized management of domain ontologies. In this framework, a user model firstly is introduced to describe user preferences. Secondly, domain ontologies are decomposed into moderate-scale modules with high cohesion and low coupling. During run time, modules that a user is interested in are selected out based on the user's preferences. Finally, the selected modules are combined to construct personalized views of domain ontologies for the user. © 2012 IEEE.

关键词： Ontology

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An integrated classification model for massive short texts with few words 19

An integrated classification model for massive short texts w...

引用

2019 International Conference on Robotics Systems and Vehicle Technology, RSVT 2019

作者： Tang, Xuetao Zhu, Yi Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

ISBN: (纸本)9781450362429

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models. © 2019 Association for Computing Machinery.

关键词： Embeddings

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Qualitative Spatial Reasoning with Oriented Point Relation in 3D Space

引用

Chinese Journal of Electronics 2019年第2期28卷 325-330页

作者： WANG Shengsheng DONG Ruyi SONG Wenzhuo WANG Chuangfeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin Vocational College of Industry and Technology

Among qualitative direction relation models, Oriented point relation algebra（OPRAm） is a remarkable model for robot navigation with uncertain direction information. It has great advantages in providing powerful expressions with very limited information compared with other point-based spatial relation *** original OPRAm is defined in 2D space, and its model and reasoning algorithm are found not applicable in 3D space. We proposed a novel direction relation model named OPRA3Dmto extend the original OPRAm to 3D space,and presented a new reasoning algorithm on Oriented point relation algebra in three dimension（OPRA3Dm）. A further study was carried out for composition reasoning on OPRA3Dm. The proposed reasoning algorithm will deduce new information which cannot be directly detected by hardware. The experiment showed the algorithm had some practical significance, it can be applied to the Unmanned aerial vehicle（UAV） navigation and similar scenarios.

关键词： Qualitative spatial reasoning Oriented point relation algebra in three dimension(OPRA3Dm) Composition reasoning Unmanned aerial vehicle navigation

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ETD-cache: An expiration-time driven cache scheme to make SSD-based read cache endurable and cost-efficient 15

ETD-cache: An expiration-time driven cache scheme to make SS...

引用

12th ACM International Conference on Computing Frontiers, CF 2015

作者： Dai, Ningwei Chai, Yunpeng Liang, Yushi Wang, Chunling Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China

ISBN: (纸本)9781450333580

Recently flash-based solid-state drives (SSDs) have been widely deployed as cache devices to boost system performance. However, classical SSD cache algorithms (e.g. LRU) replace the cached data frequently to maintain high hit rates. Such aggressive data updating strategies result in too many writing operations on SSDs and make them wear out quickly, which finally leads to high costs of SSDs for enterprise applications. In this paper, we propose a novel Expiration-Time Driven Cache (ETD-Cache) method to solve this problem. In ETD-Cache, an active data eviction mechanism is adopted. An already cached block leaves the SSD cache if and only if there is no access to it for a time longer than a specified expiration time. This mechanism gives more time for the cached contents to wait for their following accesses and limits the admission of newly arrived blocks to generate less SSD writes. In addition, a low-overhead candidate management module is designed to maintain the most popular data in the system for the potential cache replacement. The simulations driven by a series of typical real-world traces indicate that due to the great reduction on data updating frequency, ETD-Cache lowers the total SSD costs by 98.45% compared with LRU under the same cache hit rate. Copyright 2015 ACM.

关键词： Costs

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