检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Yang, Guang Cao, Juan Sheng, Qiang Qi, Peng Li, Xirong Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors;2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and models their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Further, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures. The code and more details are in https://***/guang-yanng/DRAG. © 2022, CC BY.

关键词： Object detection

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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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Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019

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Journal of data and information Science 2020年第4期5卷 86-115页

作者： Haotian Hu Dongbo Wang Sanhong Deng School of Information Management Nanjing UniversityNanjing 210023China College of Information and Technology Nanjing Agricultural UniversityNanjing 210095China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing 210023China

Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI ***/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network ***:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative *** with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration *** social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international *** at the institution level reflects obvious regional and economic *** are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the ***,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration *** limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration ***,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of *** implications:The findings fill the current shortage of research on international collaboration in A

关键词： Artificial intelligence International collaboration Collaboration pattern Bibliometric analysis Social network analysis

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Image Fusion Method Based on NSCT and Adaptive Sparse Representation 4

Image Fusion Method Based on NSCT and Adaptive Sparse Repres...

引用

4th International Conference on Video and Image Processing, ICVIP 2020

作者： Dong, Zhangyu Wei, Xin Wang, Mengyang School of Computer and Information Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230009 China

ISBN: (纸本)9781450389075

ABSTRACT: Aiming at the imaging characteristics of multispectral and panchromatic image and the problem that the traditional image fusion methods cannot take into account the spectral quality and spatial resolution of the image at the same time, a novel fusion method based on the Non-subsampled Contourlet Transform (NSCT) domain and adaptive sparse representation (ASR) algorithm is presented in this paper. It combines the advantages of principal component substitution and multi-scale analysis, and uses the ASR model to encode the low-frequency components, which improves the shortcomings of the traditional SR-based approach applications. Firstly, NSCT transform is performed on thefirst principal component of multi-spectral image obtained by PCA transform and panchromatic image,and the low-pass and high-pass coefficients of the source image are obtained. Secondly, the Max-absolute' rule and the ASR model are used to fuse the separated high-pass and low-pass bands respectively. Finally, we obtained the final fused image by the corresponding inverse transformation reconstruction. The results show that the fusion algorithm proposed in this paper performs well in both spatial resolution and spectral performance;it not only improves the subjective visual effect, but also improves the objective evaluation index. © 2020 ACM.

关键词： Image fusion

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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

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IEEE International Conference on data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

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Hybrid Local Causal Discovery

arXiv

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arXiv 2024年

作者： Ling, Zhaolong Peng, Honghui Zhang, Yiwen Zhou, Peng Wu, Xingyu Yu, Kui Wu, Xindong School of Computer Science and Technology Anhui University Anhui Hefei230601 China School of Hong Kong Polytechnic University Department of Computing 999077 Hong Kong Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Local causal discovery aims to learn and distinguish the direct causes and effects of a target variable from observed data. Existing constraint-based local causal discovery methods use AND or OR rules in constructing the local causal skeleton, but using either rule alone is prone to produce cascading errors in the learned local causal skeleton, and thus impacting the inference of local causal relationships. On the other hand, directly applying score-based global causal discovery methods to local causal discovery may randomly return incorrect results due to the existence of local equivalence classes. To address the above issues, we propose a Hybrid Local Causal Discovery algorithm, called HLCD. Specifically, HLCD initially utilizes a constraint-based approach combined with the OR rule to obtain a candidate skeleton and then employs a score-based method to eliminate redundant portions in the candidate skeleton. Furthermore, during the local causal orientation phase, HLCD distinguishes between V-structures and equivalence classes by comparing the local structure scores between the two, thereby avoiding orientation interference caused by local equivalence classes. We conducted extensive experiments with seven state-of-the-art competitors on 14 benchmark Bayesian network datasets, and the experimental results demonstrate that HLCD significantly outperforms existing local causal discovery algorithms. Copyright © 2024, The Authors. All rights reserved.

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Sentiment Analysis of Semester Learning Essays in Design Education

Sentiment Analysis of Semester Learning Essays in Design Edu...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Z. H. Wang Z. J. Ming G. X. Wang F. Mistree J. K. Allen School of Mechanical Engineering Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing China The Systems Realization Laboratory The University of Oklahoma Norman United States

AME4163: Principles of engineering Design is a design, build and test course offered at the University of Oklahoma, Norman, USA. Throughout the semester students are expected to reflect on authentic and immersive experiences and write Take-aways and Learning Statements. At the end of the semester each student submits a summative Semester Learning Essay. Between 2019–2021 we collected about 10,000 Take-aways and Learning Statements from the Semester Learning Essays of nearly 400 students. In this paper, we attempt to help instructors gain insight of what students have learned through these materials and thence improve the delivery of the course in the future. We analyze their Take-aways from summative Semester Learning Essays by using dictionary-based sentiment analysis to assess students' subjective feelings toward what they have learned. The results prove that we gain insight into their learning thereby providing instructors with evidence-based guidance on modifying the course from different perspectives. The method proposed is generalizable to courses that involve authentic immersive experiences.

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A Character-Word Graph Attention Networks for Chinese Text Classification

A Character-Word Graph Attention Networks for Chinese Text C...

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IEEE International Conference on Big knowledge (ICBK)

作者： Shigang Yang Yongguo Liu Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665438599

Text classification is an important task in natural language processing. Different from English, Chinese text owns two representations, character-level and word-level. The former has abundant connotations and the latter owns specific meanings. Current researches often simply concatenated two-level features with little processing and failed to explore the affiliation relation-ship between Chinese characters and words. In this paper, we proposed a character-word graph attention network (CW-GAT) to explore the interactive information between characters and words for Chinese text classification. A graph attention network is adopted to capture the context of sentences and the interaction between characters and words. Extensive experiments on six real Chinese text datasets show that the proposed model outperforms the latest baseline methods.

关键词： Conferences Text categorization Prediction algorithms Natural language processing Classification algorithms Task analysis Faces

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A Coffee-Ring Effect-Based Paper Sensor Chip for the Determination of Β-Lactoglobulin in Foods Via a Smartphone

SSRN

引用

SSRN 2022年

作者： Lu, Yang Yang, Jingyi Zhang, Yong Wu, Lin State Key Laboratory for Food Nutrition and Safety College of Food Science and Engineering Tianjin University of Science and Technology Tianjin300457 China State Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

To determine allergen β-lactoglobulin (β-LG) in foods, a method by a coffee-ring effect (CRE)-based paper sensor chip combined with a smartphone has been developed. The strategy was based on the principle of fluorescence quenching by the quantum dot-aptamer-graphene oxide (QD-Apt-GO) and where the degree of quenching was inversely proportional to the concentration of β-LG. First, the prepared β-LG solution was added to the QD-aptamer fluorescent probe solution followed by an appropriate amount of GO. After the reaction, a certain amount of the mixture was dispensed onto the detection zone of the sensor chip, resulting in the spread of the analyte solution by evaporation-driven flow. The QD-aptamer and the QD-Apt-β-LG in the mixed solution formed a visible fluorescent coffee-ring. For signal quantitation, an image of the coffee-ring was taken by a smartphone and imported into self-programmed software for analysis. The quantitative results for β-LG were acquired rapidly based on the magnitude of the generated fluorescent coffee-ring. The limit of detection (LOD) for β-LG was 0.048 mg L -1 and the linear range for the optimized assay was 0.39–1000 mg L -1 . The method was validated for analysis of real food samples and the results were consistent with those of a commercial ELISA assay kit. The smartphone-based assay provides a simple, economic, compact, and visual detection platform for the determination of β-LG in foods. © 2022, The Authors. All rights reserved.

关键词： Graphene

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A method for measuring journal discriminative capacity and its application in WOS 15th

A method for measuring journal discriminative capacity and i...

引用

15th International Conference on Sustainable Digital Communities, iConference 2020

作者： Wang, Hao Zhang, Baolong Deng, Sanhong Su, Xinning School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030436865

Journal discriminative capacity refers to the degree of difference between the journals in research subjects, and is of great significance for detecting the level of journal differentiation. Current research on journal discrepancies is predominantly focused on the quantitative analysis of journal content, and rarely measures the degree of journal difference. To address this lapse in research, a method from the perspective of difference is proposed in this paper to quantitatively measure and analyze the discriminative capacity of journals. Using the bibliographic of Science Citation Index (SCI), Social Science Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI) journals from 2015–2017 as the data source, the contents differences of 23 Library and information Science (LIS) journals are analyzed through hierarchical clustering and journal discriminative capacity (JDC) measurement. The discriminative capacity of disciplines is then calculated and combined with multidimensional scaling analysis to detect the difference characteristics of journals in ten disciplines, and the mean JDC is used to explore the overall differences of disciplines. Results show that the discriminative capacity of LIS journals is obviously stratified, the journals of library science are the most discriminating, and the journals of evaluation and review are the weakest. In addition, the journals of different disciplines have distinct disciplinary characteristics, with the discriminative capacity of SCI journals as the most notable, followed by SSCI journals, and with A&HCI journals displaying the weakest discriminative capacity. © Springer Nature Switzerland AG 2020.

关键词： Digital libraries

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