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A Chinese Topic Crawler Focused on Customer Development

Procedia CIRP 2016年 56卷 476-480页

作者： Tong Wu Yanchun Liang Chunguo Wu Shifeng Piao Deyin Ma Guozhong Zhao Xiaosong Han College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun 130012 China Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China School of Business Jilin University Changchun 130012 China Daqing Oilfield Personnel Development Institute CNPC Daqing 163000 China

This paper presents a Chinese topic crawler focused on customer development, in order to meet the needs of users for more accurate and particular Internet information. The concept of meta-search engine is introduced, and the keywords are expanded by the ontology of HowNet. Through the web crawler, preprocessing and classification, the information on customer relations can be divided into three categories: company, platform and meaningless. Numerical experiments show that satisfactory results can be obtained in some particular information-seeking areas. The average accuracy for classification is more than 80%, which can meet customer needs in most cases.

关键词： Topic crawler Customer development Meta-search engine Semantic similarity

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Why Misinformation is Created? Detecting them by Integrating Intent Features

arXiv

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arXiv 2024年

作者： Wang, Bing Fu, Bo Li, Ximing Pei, Songwen Li, Changchun Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun Jilin China Liaoning Normal University Liaoning Dalian China University of Shanghai for Science and Technology Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Jilin University China

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (Dm-inter). To evaluate the performance of Dm-inter, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that Dm-inter can outperform the existing baseline MD methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Li Yirui Wu Hexuan Hu Hu Lu Yong Lai Shaohua Wan Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University College of Computer and Information Hohai University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Computer Science and Communication Engineering Jiangsu University Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like ”black box” without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods.

关键词： Deep learning Training Visualization Image analysis Semantics Feature extraction Risk management

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Curvature regularization to prevent distortion in graph embedding

arXiv

引用

arXiv 2020年

作者： Pei, Hongbin Wei, Bingzhe Zhang, Chunxu Chang, Kevin Chen-Chuan Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign United States Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving proximity, may distort in the ambient embedding Euclidean space, and hence to detect them becomes difficult for machine learning models. To address the problem, we propose curvature regularization, to enforce flatness for embedding manifolds, thereby preventing the distortion. We present a novel angle-based sectional curvature, termed ABS curvature, and accordingly three kinds of curvature regularization to induce flat embedding manifolds during graph embedding. We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Dual Personalization on Federated Recommendation

arXiv

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arXiv 2023年

作者： Zhang, Chunxu Long, Guodong Zhou, Tianyi Yan, Peng Zhang, Zijian Zhang, Chengqi Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Computer Science and Technology Jilin University China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Australia Computer Science and UMIACS University of Maryland United States

Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Translating Query for Deep Web Using Ontology

Translating Query for Deep Web Using Ontology

引用

International Conference on Computer Science and Software engineering

作者： Hao Liang Wanli Zuo Fei Ren Junhua Wang College of Computer Science and Technology Department of Technology Jilin University Changchun Taxation China College of Computer Science and Technology Key Laboratory of Symbol Computation andKnowledge Engineering of the Ministry of Education Jilin University China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9781424439027

The e-commerce information on the surface Web is supported by the Deep Web, which can not be accessed directly by the search engines or the Web crawlers. The only way to access the backend database is through query interface. Extracting valid attributes from the query forms and automatic translating the source query into a target query is a solvable way for addressing the current limitations in accessing Deep Web data sources. To generate a valid query, we have to reconcile the key attributes and their semantic relation. We present our framework to solve the problem. To enrich the set of attributes contained in the semantic form, we use the WordNet as kinds of ontology technique and we try to find the semantic relation of the attributes in the same query from and different forms. Extensive experiments over real-word domains show the utility of our query translation framework.

关键词： Ontologies Databases Computer science Educational institutions Search engines Data mining Laboratories knowledge engineering Computer science education Crawlers

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Efficient and Privacy-Preserving Eclipse Query Over Encrypted Data

Efficient and Privacy-Preserving Eclipse Query Over Encrypte...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Weiyu Song Yonggang Zhang Lili Sun Yandong Zheng Rongxing Lu 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 State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Faculty of Computer Science University of New Brunswick Fredericton Canada

As the mobile Internet grows rapidly, location-based services (LBSs) are widely applied in the tourism and transportation fields. To fully mine the data collected, location service providers (LSPs) intend to offer various query services to users, which include eclipse query. The eclipse query can generalize nearest neighbor queries and skyline queries and allow users to set more rough and customizable preference ranges. In addition, with the boom of cloud computing, more and more LSPs hope to leverage the cloud to offer better query services. Given that the data could potentially contain confidential information, the data are required to be encrypted prior to outsourcing them. Therefore, eclipse queries need to be executed on the ciphertext. Although several schemes for eclipse queries have been proposed in existing works, they have little focus on privacy issues. To address this issue, we propose an efficient and privacy-preserving scheme for eclipse queries (EPEQ) in this paper. First, we develop a MinValue tree to construct an index for the dataset. Then, by utilizing the MinValue tree and a symmetric homomorphic encryption technique, we design a secure minimum value comparison protocol to obtain a skyline data and a secure undominated data acquisition protocol to obtain the data not skyline dominated by the skyline data. After that, we present our scheme. We analyze the security of the EPEQ scheme and perform experimental evaluations, demonstrating the security and efficiency of our EPEQ scheme.

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Multi-Task Self-Supervised Learning for Medical Image Segmentation

Multi-Task Self-Supervised Learning for Medical Image Segmen...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Wang Hang Zhao Xiongfei Li Mingjie Tian Bo Huang Feiyang Yang Intelligent Information Processing Laboratory Yanbian University Yanji China 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 School of Artificial Intelligence Jilin University Changchun China

Although medical image segmentation has achieved remarkable results with supervised learning, obtaining labeled data remains challenging and costly. To counteract this, we present the MTSPSeg, a multi-task self-supervised learning framework. We establish the dynamic gradient learning rate (DGLR) strategy and the dynamic multi-task loss weight adjustment method (DWL) to relieve potential early convergence and diverse learning difficulties of self-supervised learning. Our method deeply integrates the prior knowledge inherent in medical images, facilitates collaborative learning among tasks, and delves further into the latent information within the images. Experimental results demonstrate that regardless of the proportion of labeled data, our method outperforms baseline models on the KiTS19 and LiTS datasets, achieving state-of-the-art performance on the KiTS19 dataset.

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Generating the semantic containers for the query interfaces of deep web

Generating the semantic containers for the query interfaces ...

引用

International Conference on Management and Service Science, MASS 2009

作者： Hao, Liang Wanli, Zuo Jian, Chen Yunge, Mao College of Computer Science and Technology Jilin University Changchun 130012 China Department of Information Changchun Taxation College Changchun 130117 China Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education Changchun 130012 China Network Center of Changchun Taxation College Changchun 130117 China

ISBN: (纸本)9781424446391

With the myriad emergence of the online Web Database, the Web is divided into a two layers information platform which is composed of Surface Web and Deep Web. The huge information hidden in the Deep Web is higher in the quality than the Surface Web. This interests the researchers of the traditional Database community. In order to adapt the Web community, the idea of information fusion is further transformed. However, the nature of the Web is great different from the Database in structure, principle and function. Query reformulation is one of the technical challenges. The only accessible method to get the information in the Deep Web is through the query interfaces provided by the Website of Deep Web. There are many semantic restrictions between the attributes in the query interface. Some attributes are co-occurred and the others are exclusive sometimes. To generate a valid query, we have to reconcile the key attributes and their semantic relations. To address the problem, a method based on the HTML codes is presented in this paper. Different kinds of semantic containers can be got through analyzing the codes of the query interface. The experiment is carried out on the famous Dataset of Deep Web to show the feasibility of the method. ©2009 IEEE.

关键词： Semantics

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Intelligent Sub-Object Image Retrieval System

Intelligent Sub-Object Image Retrieval System

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Wenhui Li Dongfeng Han Yi Wang Xiaosuo Lu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science&Technology Jilin University Changchun Jilin China

This paper describes a sub-object retrieval system based on a segmentation method. We also use dynamic partial function (DPF) and indexing by locality sensitive hashing (LSH) for improving system performance. Such a system is useful for finding a sub-object from a large image database. In order to obtain the sub-object from a sample image, we use a segmentation method to cut out the object. The system utilizes the segmentation results to capture the higher-level concept of images and gets a stable and accurate result. Experimental and comparison results, which are performed using a general purpose database containing 20,000 images, are encouraging

关键词： Image retrieval Image segmentation Image databases Vehicle dynamics Indexing Information retrieval Content based retrieval Spatial databases Humans Laboratories

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