检索结果-内蒙古大学图书馆

26th International Conference on Pattern Recognition, ICPR 2022

作者： Chen, Tao Zheng, Tong Shi, Jinlong Guo, Yanrong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783031376597

Major Depressive Disorder (MDD) has been a major mental disease in recent years, imposing huge negative impacts on both our society and individuals. The current clinical MDD detection methods, such as self-report scales and interviews, often suffer from their subjectiveness. In contrast, electroencephalography (EEG) signals reflect brain activity and can be used as an effective information source for MDD detection. However, the learning-based MDD detection methods with EEG data usually neglect the valuable information contained in the unlabeled data, therefore possibly suffering from the label scarcity issue. In this context, we propose a simple EEG-based Joint Semi-supervised Learning (JSL) model for the MDD detection task, which fully leverages the information from unlabeled data. Specifically, by equipping the unlabeled samples with the intermediately predicted pseudo labels, the accurate position of the class-specific centroids in the feature space can be gradually refined. Based on that, the auxiliary labels based on a heuristic centroid-based classifier can be obtained, which plays an effective complementary role in predicting the sample labels. The experiments on a public dataset demonstrate the effectivenes s of our method, showing an obvious performance gain via using the proposed semi-supervised strategy. © 2023, Springer Nature Switzerland AG.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model 12

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

引用

12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhi, Wenwu Zhang, Yuhong Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education !!!School of Computer Science and Information Engineering @@@Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs. © 2021 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Detecting stealthy malware with inter-structure and imported signatures 11

Detecting stealthy malware with inter-structure and imported...

引用

6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011

作者： Liang, Bin You, Wei Shi, Wenchang Liang, Zhaohui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781450305648

Recent years have witnessed an increasing threat from kernel rootkits. A common feature of such attack is hiding malicious objects to conceal their presence, including processes, sockets, and kernel modules. Scanning memory with object signatures to detect the stealthy rootkit has been proven to be a powerful approach only when it is hard for adversaries to evade. However, it is difficult, if not impossible, to select fields from a single data structure as robust signatures with traditional techniques. In this paper, we propose the concepts of inter-structure signature and imported signature, and present techniques to detect stealthy malware based on these concepts. The key idea is to use cross-reference relationships of multiple data structures as signatures to detect stealthy malware, and to import some extra information into regions attached to target data structures as signatures. We have inferred four invariants as signatures to detect hidden processes, sockets, and kernel modules in Linux respectively and implemented a prototype detection system called DeepScanner. Meanwhile, we have also developed a hypervisor-based monitor to protect imported signatures. Our experimental result shows that our DeepScanner can effectively and efficiently detect stealthy objects hidden by seven real-world rootkits without any false positives and false negatives, and an adversary can hardly evade DeepScanner if he/she does not break the normal functions of target objects and the system. Copyright 2011 ACM.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Effcient Distributed Skyline Queries for Mobile Applications

引用

Journal of Computer Science & Technology 2010年第3期25卷 523-536页

作者：肖迎元陈跃国 Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System Tianjin University of Technology School of Computing National University of Singapore Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites （database servers） which are interconnected through a high-speed wired network, and queries are issued by mobile units （laptop, cell phone, etc.） which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing （EDS-MC）. In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.

关键词： skyline distributed query processing mobile database system

来源：评论

学校读者我要写书评

暂无评论

COCA: More accurate multi-dimensional histograms out of more accurate correlations detection

COCA: More accurate multi-dimensional histograms out of more...

引用

9th International Conference on Web-Age Information Management, WAIM 2008

作者： Wei, Cao Xiongpai, Qin Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531854

Detecting and exploiting correlations among columns in relational databases are of great value for query optimizers to generate better query execution plans (QEPs). We propose a more robust and informative metric, namely, entropy correlation coefficients, other than chi-square test to detect correlations among columns in large datasets. We introduce a novel yet simple kind of multi-dimensional synopses named COCA-Hist to cope with different correlations in databases. With the aid of the precise metric of entropy correlation coefficients, correlations of various degrees can be detected effectively;when correlation coefficients testify to mutual independence among columns, the AVI (attribute value independence) assumption can be adopted undoubtedly. COCA can also serve as a data-mining tool with superior qualities as CORDS does. We demonstrate the effectiveness and accuracy of our approach by several experiments. © 2008 IEEE.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

BERT-INT: A BERT-based interaction model for knowledge graph alignment 29

BERT-INT: A BERT-based interaction model for knowledge graph...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Tang, Xiaobin Zhang, Jing Chen, Bo Yang, Yang Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Information School Renmin University of China China Zhejiang University China

ISBN: (纸本)9780999241165

knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of HitRatio@1 on the dataset DBP15K. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Spatial data mining under Smart Earth

Spatial data mining under Smart Earth

引用

2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Wang, Shuliang International School of Software of Wuhan University Wuhan University Wuhan Hubei 430072 China Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

ISBN: (纸本)9781457703713

Complex spatial data is the fundamental content of Smart Earth. Spatial data mining plays an important role in Smart Earth. In this paper, it is proposed that "Smart earth equals to Digital Earth plus the Internet of Things", which brings a new challenge for spatial data mining. First, the background of spatial data is summarized. Second, Smart Earth is put forward to be an integration of Digital Earth and the Internet of Things. Third, by analyzing the spatial data sources, types and structures, it uncovers that spatial data is the resource of Smart Earth. Fourth, spatial knowledge to discover in Smart Earth are analyzed on its connotations, extensions and categories. It indicates that spatial knowledge is the intelligent sources of Smart Earth. Fifth, based on spatial data characteristics and possible methodologies, the framework of spatial data mining is given under Smart Earth. Finally, the perspective of applicable fields is prospected for spatial data mining under Smart Earth. It is concluded that spatial data mining is one of the fundamental tools to achieve Smart Earth. © 2011 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Graph-Based Exercise- and knowledge-Aware Learning Network for Student Performance Prediction 1st

Graph-Based Exercise- and Knowledge-Aware Learning Network ...

引用

1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Liu, Mengfan Shao, Pengyang Zhang, Kun Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783030930455

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students’ knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students’ scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and knowledge-Aware Learning Network for accurate student score prediction. Specifically, we learn students’ mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN. © 2021, Springer Nature Switzerland AG.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

引用

Wuhan University Journal of Natural Sciences 2011年第5期16卷 376-382页

作者： ZHOU Hongwei1,2,3,SHI Wenchang1,2,LIANG Zhaohui1,2,LIANG Bin1,2 1.key laboratory of data engineering and knowledge engineering,Ministry of Education,Beijing 100872,China 2.School of Information,Renmin University of China,Beijing 100872,China 3.Institute of Electronic Technology,Information engineering University of PLA,Zhengzhou 450004,Henan,China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Information Engineering University of PLA Zhengzhou Henan China

Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.

关键词： trust subjective logic opinion fusion weizht

来源：评论

学校读者我要写书评

暂无评论

Multi-Label Truth Inference for Crowdsourcing Using Mixture Models

引用

IEEE Transactions on knowledge and data engineering 2021年第5期33卷 2083-2095页

作者： Zhang, Jing Wu, Xindong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei 230009 China

When acquiring labels from crowdsourcing platforms, a task may be designed to include multiple labels and the values of each label may belong to a set of various distinct options, which is the so-called multi-class multi-label annotation. To improve the quality of labels, requesters usually let one task be independently completed by a group of heterogeneous crowdsourced workers. Then, the true values of the multiple labels of each task are inferred from these repeated noisy labels. In this paper, we propose two novel probabilistic models MCMLI and MCMLD to address the multi-class multi-label inference problem in crowdsourcing. MCMLI assumes that the labels of each task are mutually independent and MCMLD utilizes a mixture of multiple independently multinoulli distributions to capture the correlation among the labels. Both models can jointly infer multiple true labels of each instance as well as estimate the reliability of crowdsourced workers modeled by a set of confusion matrices with an expectation-maximization algorithm. Experiments with three typical crowdsourcing scenarios and a real-world dataset show that our proposed models significantly outperform existing competitive alternatives. When the labels are strongly correlated, MCMLD substantially outperforms MCMLI. Furthermore, our models can be easily simplified to the one-coin models, which show more advantageous when errors are uniformly distributed, or labels are sparse. © 1989-2012 IEEE.

关键词： Crowdsourcing maximum likelihood estimation mixture models probabilistic graphical models statistical inference

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：