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Global Green Digital Economy: Challenges and Development: Preface for ITQM 2022

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Procedia Computer Science 2022年第C期214卷 1-3页

作者： Shi, Yong Li, Jianping Kou, Gang Berg, Daniel Tien, James College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Engineering University of Miami Coral GablesFL33124 United States

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

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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

Article Reranking by Memory-Enhanced key Sentence Matching for Detecting Previously Fact-Checked Claims

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arXiv 2021年

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei 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

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021, CC BY-SA.

关键词： Semantics

A Secure Collusion-Aware and Probability-Aware Range Query Processing in Tiered Sensor Networks

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A Secure Collusion-Aware and Probability-Aware Range Query P...

Reliable Distributed Systems

作者： Lei Dong Xuan Chen Jianxiang Zhu Hong Chen Ke Wang Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China School of Computing Science Simon Fraser University Burnaby BC Canada Key Lab. of Data Eng. & Knowledge Eng. of Minist. of Educ. Beijing China

With high expansibility and efficient power usage, tiered wireless sensor networks are widely deployed in many fields as an important part of Internet of Things (IoTs). It is challenging to process range query while protecting sensitive data from adversaries. Moreover, most existing work focuses on privacy-preserving range query neglecting collusion attacks and probability attacks, which are more threatening to network security. In this paper, we first propose a secure range query protocol called secRQ, which not only protects data privacy, but also resists collusion attacks and probability attacks in tiered wireless sensor networks. Generalized inverse matrices and distance-based range query mechanism are used to guarantee security as well as high efficiency and accuracy. Besides, we propose the mutual verification scheme to verify the integrity of query results. Finally, both theoretical analysis and experimental results confirm the security, efficiency and accuracy of secRQ.

关键词： Wireless sensor networks Protocols data privacy Biomedical monitoring Query processing Monitoring Heart rate

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

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arXiv 2022年

作者： Fan, Zhaoxin Song, Zhenbo Xu, Jian Wang, Zhicheng Wu, Kejian 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 Nreal China

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

Feature re-learning with data augmentation for video relevance prediction

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arXiv 2020年

作者： Dong, Jianfeng Wang, Xun Zhang, Leimin Xu, Chaoxi Yang, Gang Li, Xirong College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China Beijing100872 China

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image and video convolutional neural network models, deep visual features are widely used for video content representation. However, as how two videos are relevant is task-dependent, such off-the-shelf features are not always optimal for all tasks. Moreover, due to varied concerns including copyright, privacy and security, one might have access to only pre-computed video features rather than original videos. We propose in this paper feature re-learning for improving video relevance prediction, with no need of revisiting the original video content. In particular, re-learning is realized by projecting a given deep feature into a new space by an affine transformation. We optimize the re-learning process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, we propose a new data augmentation strategy which works directly on frame-level and video-level features. Extensive experiments in the context of the Hulu Content-based Video Relevance Prediction Challenge 2018 justify the effectiveness of the proposed method and its state-of-the-art performance for content-based video relevance prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

Τ-FPL: Tolerance-constrained learning in linear time 32

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Τ-FPL: Tolerance-constrained learning in linear time

32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan Shanghai Key Laboratory of Trustworthy Computing MOE International Joint Lab of Trustworthy Software School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research China Georgia Institute of Technology Atlante United States

ISBN: (纸本)9781577358008

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ-False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mathematical transformations

IKGN: Intention-aware knowledge Graph Network for POI Recommendation

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IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Dual Encoding for Zero-Example Video Retrieval

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Dual Encoding for Zero-Example Video Retrieval

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Jianfeng Dong Xirong Li Chaoxi Xu Shouling Ji Yuan He Gang Yang Xun Wang College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba Group

ISBN: (纸本)9781728132945

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval.

关键词： Videotapes video retrieval End users end-to-end Dual coding