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International Conference on Services Systems and Services Management, ICSSSM

作者： Gang Yang Junyan Yi Jieping Xu Xirong Li Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781479983292

In this paper, an algorithm, named CCHN, is proposed to solve the partitional clustering problem. An outer chaotic mechanism with annealing strategy is introduced into the competitive Hopfield neural network to construct CCHN for expecting better opportunities of converging to the optimal solution. In addition to retain the competitive characteristics of the conventional competitive Hopfield neural network, CCHN displays a rich range of complex and flexible chaotic dynamics. The chaotic dynamics and the annealing strategy guarantee the powerful searching ability and the effective convergence of CCHN. Results simulated on clustering benchmark problems show that CCHN algorithm is more likely to find an optimal or near-optimal solution with a higher successful ratio than previous algorithms.

关键词： Clustering algorithms Partitioning algorithms Neurons Heuristic algorithms Convergence Neural networks Benchmark testing

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Initializing convolutional filters with semantic features for text classification

Initializing convolutional filters with semantic features fo...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Li, Shen Zhao, Zhe Liu, Tao Hu, Renfen Du, Xiaoyong Institute of Chinese Information Processing Beijing Normal University China UltraPower-BNU Joint Laboratory for Artificial Intelligence Beijing Normal University China College of Chinese Language and Culture Beijing Normal University China School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China

ISBN: (纸本)9781945626838

Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification. © 2017 Association for Computational Linguistics.

关键词： Sentiment analysis

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Image manipulation detection by multi-view multi-scale supervision

arXiv

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arXiv 2021年

作者： Chen, Xinru Dong, Chengbo Ji, Jiaqi Cao, Juan Li, Xirong MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. By exploiting noise distribution and boundary artifact surrounding tampered regions, the former aims to learn semantic-agnostic and thus more generalizable features. The latter allows us to learn from authentic images which are nontrivial to be taken into account by current semantic segmentation network based methods. Our thoughts are realized by a new network which we term MVSS-Net. Extensive experiments on five benchmark sets justify the viability of MVSS-Net for both pixel-level and image-level manipulation detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Generalized Unique Game Problem

Generalized Unique Game Problem

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2012年全国理论计算机科学学术年会(NCTCS2012)

作者： Peng Cui Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Resource Management Renmin University of China Beijing 100872 P.R.China

this paper,the author defines Generalized Unique Game Problem (GUGP),where weights of the edges are allowed to be *** special types of GUGP are illuminated,GUGP-NWA,where the weights of all edges are negative,and GUGP-PWT(ρ),where the total weight of all edges are positive and the negative-positive ratio is at most ρ.

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COCO-CN for cross-lingual image tagging, captioning and retrieval

arXiv

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arXiv 2018年

作者： Li, Xirong Xu, Chaoxi Wang, Xiaoxu Lan, Weiyu Jia, Zhengxiong Yang, Gang Xu, Jieping 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

This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods. We propose COCO-CN, a novel dataset enriching MS-COCO with manually written Chinese sentences and tags. For more effective annotation acquisition, we develop a recommendation-assisted collective annotation system, automatically providing an annotator with several tags and sentences deemed to be relevant with respect to the pictorial content. Having 20,342 images annotated with 27,218 Chinese sentences and 70,993 tags, COCO-CN is currently the largest Chinese-English dataset that provides a unified and challenging platform for cross-lingual image tagging, captioning and retrieval. We develop conceptually simple yet effective methods per task for learning from cross-lingual resources. Extensive experiments on the three tasks justify the viability of the proposed dataset and methods. data and code are publicly available at https://***/li-xirong/coco-cn. Copyright © 2018, The Authors. All rights reserved.

关键词： Image annotation

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Extracting Feature and Opinion Words Effectively from Chinese Product Reviews

Extracting Feature and Opinion Words Effectively from Chines...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Wei Wei Hongyan Liu Jun He Hui Yang Xiaoyong Du Information School Renmin University of China China School of Economics and Management Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering MOE China

Nowadays more and more people like to publish their comments on a product on the Web. Mining such unstructured data (product reviews) is exciting hot and challenging research and application topic. In this paper, we focus on mining product reviews written in Chinese. We aim at extract the structural information from Chinese product reviews. By structural information, we mean product features and corresponding opinion words expressed in each review text. There are already some works done for reviews written in English, but less in Chinese. In this paper, we propose an effective method to extract candidate features and some effective pruning rules to prune the features. Also, we introduce a pattern extraction and matching step to improve our results. The experiment results show our approach is very effective, and has a good recall and precision.

关键词： Feature extraction data mining Negative feedback Information retrieval Fuzzy systems Conference management knowledge management engineering management Helium laboratories

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Image Manipulation Detection by Multi-View Multi-Scale Supervision

Image Manipulation Detection by Multi-View Multi-Scale Super...

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International Conference on Computer Vision (ICCV)

作者： Xinru Chen Chengbo Dong Jiaqi Ji Juan Cao Xirong Li MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

ISBN: (纸本)9781665428132

关键词： Representation learning Image segmentation Computer vision Sensitivity Costs Image edge detection Semantics

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Detecting Spam Comments with Malicious Users’ Behavioral Characteristics

Detecting Spam Comments with Malicious Users’ Behavioral Ch...

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2010 IEEE International Conference on Information Theory and Information Security(2010 IEEE 国际信息论与信息安全会议)

作者： Wenchang Shi Bin Liang Zhaohui Liang Qianqian Wang Wei Sun Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) MOE Schoo Beijing Venustech Cybervision Co. Ltd Beijing 100094 China

In recent years, the spread of spam comments has become a main obstacle which limits the development of commercialized social networks. This paper analyzes the differences of behavioral characteristics between normal users and malicious users. Based on these characteristics, we propose several heuristic methods to detect spam comments. These methods evaluate comments from three perspectives, including time-frequency characteristic of comments, text similarity of comments and the number of target domains each user's comments refer to. In our collected dataset, our experimental results indicate the accuracy of our detection strategy I (strategy for high accuracy) and strategy II (strategy for wide coverage) are 100% and 92.6%, respectively. The preliminary evaluation of the proposed detection methods shows promising result.

关键词： Books Conferences Unsolicited electronic mail Accuracy Commercialization Time series analysis Filtering

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An Edit Distance Algorithm with Block Swap

An Edit Distance Algorithm with Block Swap

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The 9th International Conference for Young Computer Scientists(第九届国际青年计算机大会)

作者： Tian Xia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Resource Management Renmin University of China. Beijing 100872 China

The edit distance between two given strings X and Y is the minimum number of edit operations that transform X into Y. In ordinary course, string editing is based on character insert, delete, and substitute operations. It has been suggested that extending this model with block edits would be useful in applications such as DNA sequence comparison and sentence similarity computation. However, the existing algorithms have generally focused on the normalized edit distance, and seldom of them consider the block swap operations at a higher level. In this paper, we introduce an extended edit distance algorithm which permits insertions, deletions, and substitutions at character level, and also permits block swap operations. Experimental results on randomly generated strings verify the algorithm's rationality and efficiency. The main contribution of this paper is that we present an algorithm to compute the lowest edit cost for string transformation with block swap in polynomial time, and propose a breaking points selection algorithm to improve the computation speed.

关键词： Edit distance edit operation block swap string matching.

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BADet: Boundary-Aware 3D Object Detection from Point Clouds

arXiv

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arXiv 2021年

作者： Qian, Rui Lai, Xin Li, Xirong Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China School of Mathematics Renmin University of China Beijing100872 China

Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a handful of high-quality proposals in a bottom-up fashion. 2) Resize and pool the semantic features from the proposed regions to summarize RoI-wise representations for further refinement. Note that these RoI-wise representations in step 2) are considered individually as uncorrelated entries when fed to following detection headers. Nevertheless, we observe these proposals generated by step 1) offset from ground truth somehow, emerging in local neighborhood densely with an underlying probability. Challenges arise in the case where a proposal largely forsakes its boundary information due to coordinate offset while existing networks lack corresponding information compensation mechanism. In this paper, we propose BADet for 3D object detection from point clouds. Specifically, instead of refining each proposal independently as previous works do, we represent each proposal as a node for graph construction within a given cut-off threshold, associating proposals in the form of local neighborhood graph, with boundary correlations of an object being explicitly exploited. Besides, we devise a lightweight Region Feature Aggregation Module to fully exploit voxel-wise, pixel-wise, and point-wise features with expanding receptive fields for more informative RoI-wise representations. We validate BADet both on widely used KITTI dataset and highly challenging nuScenes dataset. As of Apr. 17th, 2021, our BADet achieves on par performance on KITTI 3D detection leaderboard and ranks 1st on Moderate difficulty of Car category on KITTI BEV detection leaderboard. The source code is available at https://***/rui-qian/BADet. © 2021, CC BY.

关键词： Object detection

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