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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Liao, Wenrui Zhao, Zhong-Qiu Shen, Hao Tian, Weidong School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China

ISBN: (纸本)9781665438643

Recently, lightweight neural networks with different manual designs have presented a promising performance in single image super-resolution (SR). However, these designs rely on too much expert experience. To address this issue, we focus on searching a lightweight block for efficient and accurate image SR. Due to the frequent use of various residual blocks and attention mechanisms in SR methods, we propose the residual attention search block (RASB) which combines an operation search block (OSB) with an attention search block (ASB). The former is used to explore the suitable operation at the proper position, and the latter is applied to discover the optimal connection of various attention mechanisms. Moreover, we build the modified residual attention network (MRAN) with stacked found blocks and a refinement module. Extensive experiments demonstrate that our MRAN achieves a better trade-off against the state-of-the-art methods in terms of accuracy and model complexity. © 2021 IEEE

关键词： Economic and social effects

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks

Publish me and protect me: Personalized and flexible locatio...

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International Workshop on Quality of Service

作者： Yao Wu Hui Peng Xiaoying Zhang Hong Chen Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data.

关键词： Legged locomotion Weight measurement

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A novel hybrid butterfly optimization algorithm for feature selection with sine cosine velocity in the high-dimensional classification data

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 369-391页

作者： Zhang, Li Chen, Xiaobo Key Laboratory of Data Science and Intelligence Education Hainan Normal University Ministry of Education Haikou Hainan China School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Changzhou City Center Branch People's Bank of China Changzhou Jiangsu China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm's global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, individual butterflies' autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm's effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. © 2024 - IOS Press. All rights reserved.

关键词： Optimization algorithms

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RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network 17th

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Plac...

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17th European Conference on Computer Vision, ECCV 2022

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031250552

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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EFFICIENT COMPUTATIONS OF LINK-BASED SIMILARITY MEASURES ON THE GPU

EFFICIENT COMPUTATIONS OF LINK-BASED SIMILARITY MEASURES ON ...

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2012 3rd IEEE International Conference on Network Infrastructure and Digital Content

作者： Yong-Yeon Jo Duck-Ho Bae Sang-Wook Kim Data and Knowledge Engineering Laboratory Hanyang University SeoulKorea

In this paper,we first analyze how the characteristics of the GPU affect the performance of link-based similarity *** on the analysis,we describe our strategies to improve the performance of link-based similarity meas... 详细信息

关键词： GPU Link-based similarity

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Opportunities and challenges faced by digital libraries in the big data era

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Journal of Library Science in China 2016年 5-16页

作者： SU Xinning School of Information Management Jiangsu Key Laboratory of Data Engineering and Knowledge Services Nanjing University

Construction of network-based academic resources is undergoing rapid development in this big data era, displaying a trend to replace digital libraries. This challenge calls for the construction of digital libraries to have big data related considerations, i.e., from resource construction aspect, we should expand the resource scope, aggrandize the breadth of resource integration, and increase the depth of the resource organization and processing;from technology application aspect, we should attach great importance to the semantic technology, emphasize the application of clustering technology, widely adopt data analysis technology, and elevate the retrieval technology level;from service aspect, we need to enrich services and product categories of digital libraries, and convert the passive, and general service mode to the proactive and personalized mode. The construction of next generation digital libraries must differ from the traditional way. Comprehensive service ideas should be established;comprehensive resource services should be provided;the conventional service mode and transmission form also need to be expanded;even the librarians of digital libraries are required to become data analysts.

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Capability description and discovery of Internetware entity

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Science China(Information Sciences) 2010年第4期53卷 685-703页

作者： WANG PuWei1*, JIN Zhi2,3* & LIU HongYan4 1Key laboratory of data engineering and knowledge engineering of the Ministry of Education, Renmin University of China, Beijing 100872, China 2Key laboratory of High Confidence Software Technologies of the Ministry of Education, Peking University, Beijing 100871, China 3Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China 4Department of Management Science and engineering, Tsinghua University, Beijing 100084, China 1. Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Renmin University of China Beijing 100872 China2. Key Laboratory of High Confidence Software Technologies of the Ministry of Education Peking University Beijing 100871 China3. Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing 100190 China4. Department of Management Science and Engineering Tsinghua University Beijing 100084 China

Internetware can be formed through aggregation of Internetware entities bottom-up to satisfy the user's requirements. In the process, capability description of Internetware entity plays an important role. This paper proposes a capability description of Internetware entity based on environment ontology. The environment ontology includes environment entities and possible interactions with the environment entities in a particular domain. Tree-like hierarchical state machines are also included in the ontology to describe domain life-cycle of the environment entities. On the basis of the ideas, the capability of Internetware entity is embodied by interacting with environment entities and the changes of the environment entities during the interactions. Finally, this paper describes the matchmaking between capability descriptions of Internetware entities to support the discovery of Internetware entity. An example of online education course is given to illustrate the ideas.

关键词： Internetware capability description domain environment ontology tree-like hierarchical state machine

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A new steganography without embedding based on adversarial training 20

A new steganography without embedding based on adversarial t...

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2020 ACM Turing Celebration Conference - China, ACM TURC 2020

作者： Jiang, Wenjie Hu, Donghui Yu, Cong Li, Meng Zhao, Zhong-Qiu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education and College of Computer Science and Information Engineering !!!Hefei University of Technology Hefei China College of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781450375344

Steganography is an art to hide information in the carriers to prevent from being detected, while steganalysis is the opposite art to detect the presence of the hidden information. With the development of deep learning, several state-of-the-art steganography and steganalysis based on deep learning techniques have been proposed to improve hiding or detection capabilities. Generative Adversarial Networks (GANs) based steganography directly uses the minimax game between the generator and discriminator, to automatically generate steganography algorithms resisting being detected by powerful steganalysis. The steganography without embedding (SWE) based on GANs, where the generated cover images themselves are stego ones carrying secret information has shown its state-of-the-art steganography performance. However, SWE based on GANs has serious weaknesses, such as low information recovery accuracy, low steganography capacity and poor natural showing. To solve these problems, this paper proposes a new SWE based on adversarial training, with carefully designed generator, discriminator and extractor, as well as their loss functions and optimized training mode. The proposed method can achieve a very high information recovery accuracy (100% in some cases), and at the same time improve the steganography capacity and image quality. © 2020 ACM.

关键词： Steganography

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Randomly Sampling based Fuzzy Rough Reduction

Randomly Sampling based Fuzzy Rough Reduction

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Yu Chen Suyun Zhao Hong Chen Key Laboratory of Data Engineering and Knowledge (Renmin University of China)

ISBN: (纸本)9781479938414

Now most methods of the existing fuzzy rough based attribute reduction can't be applied to large scale data sets because of high time and space consuming. To overcome this problem, we proposed a randomly sampling based method to decrease the computing consumption. First, we propose some pseudo concepts of fuzzy rough sets, such as the pseudo lower approximation and pseudo attribute reduction, which is not exactly, but approximately equal to classical concepts, and then it drastically accelerate computation and significantly reduce space consumption. The numerical experiments show that the random sampling algorithm is significantly efficient both in time and space with little loss of information.

关键词： Randomly sampling Fuzzy rough sets Attribute reduction Pesudo approximation operators

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The Research on the Algorithms of Keyword Search in Relational database

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2008年 4977卷 134-143页

作者： Li, Peng Zhu, Qing Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)354089375X

With the development of relational database, people require better database not only in the aspect of database performance, but also in the aspect of the database’s interactive ability. So that the database is much more friendly than just before and it is possible for a common user who do not have any special knowledge on database can access the database, without knowing the schema of database and writing intricate SQL. For the reason that the information retrieval on the web has developed well to some extent, when we develop the technology of keyword search in relational database, we can draw some ideas from information retrieve. But the great differences between the text database on the web and the relational database also bring some new challenges: 1) The answer needed by user is not only one tuple in database, but the tuple sets consisting of the tuple connect from different table using the "primary key-foreign key" relationship. 2)The results of the evaluation criteria is more important, because it is directly related to the effectiveness of Search System. 3)The structure of relational database is much more intricate than text database, and the algorithms of information retrieval are not fit the relational database. So in this paper, we introduce a novel keyword search algorithm and a modified criteria of evaluating answers in order to enhance efficiency of the keyword search and return much more effective information to users, finally, the search algorithm’s performance is tested and evaluated. © Springer-Verlag Berlin Heidelberg 2008

关键词： Information retrieval

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