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15th IEEE International Conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 IEEE.

关键词： Question answering

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FOSS: A Self-Learned Doctor for Query Optimizer

FOSS: A Self-Learned Doctor for Query Optimizer

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International Conference on data engineering

作者： Kai Zhong Luming Sun Tao Ji Cuiping Li Hong Chen Renmin University of China China MOE Key Laboratory of Data Engineering and Knowledge Engineering China Shanghai Yunxi Technology Co. Ltd. Engineering Research Center of Database and Business Intelligence MOE China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks.

关键词： Training Query processing Medical services Benchmark testing Deep reinforcement learning data engineering Distance measurement

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Competitive hopfield neural network with chaotic dynamics for partitional clustering problem

Competitive hopfield neural network with chaotic dynamics fo...

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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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Multi-lingual acquisition on multimodal pre-training for cross-modal retrieval 22

Multi-lingual acquisition on multimodal pre-training for cro...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Liang Zhang Anwen Hu Qin Jin School of Information Renmin University of China School of Information Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering (MOE) Renmin University of China

ISBN: (纸本)9781713871088

Vision and diverse languages are important information sources in our living world. A model that understands multi-modalities and multi-languages can be applied to a wider range of real-life scenarios. To build such a multimodal and multilingual model, existing works try to ensemble vision-language data from multiple languages in pre-training. However, due to the large number of languages, these works often require huge computing resources and cannot be flexibly extended to new languages. In this work, we propose a Multi-Lingual Acquisition (MLA) framework that can easily empower a monolingual Vision-Language Pre-training (VLP) model with multilingual capability. Specifically, we design a lightweight language acquisition encoder based on state-of-the-art monolingual VLP models. We further propose a two-stage training strategy to optimize the language acquisition encoder, namely the Native Language Transfer stage and the Language Exposure stage. With much less multilingual training data and computing resources, our model achieves state-of-the-art performance on multilingual image-text and video-text retrieval benchmarks.

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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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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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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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What matters for ad-hoc video search? A large-scale evaluation on TRECVID

arXiv

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arXiv 2021年

作者： Chen, Aozhu Hu, Fan Wang, Zihan Zhou, Fangming Li, Xirong AIMC Lab. School of Information Renmin University of China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China

For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in terms of their choices of cross-modal matching models, visual features and training data. As such, what one may conclude from the evaluation is at a high level that is insufficient to reveal the influence of the individual components. In order to bridge the gap between the current solution-level comparison and the desired component-wise comparison, we propose in this paper a large-scale and systematic evaluation on TRECVID. By selected combinations of state-of-the-art matching models, visual features and (pre-)training data, we construct a set of 25 different solutions and evaluate them on the TRECVID AVS tasks 2016–2020. The presented evaluation helps answer the key question of what matters for AVS. The resultant observations and learned lessons are also instructive for developing novel AVS solutions. Copyright © 2021, The Authors. All rights reserved.

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Interference and power constrained Broadcasting and Multicasting in wireless ad hoc networks with directional antennas

Interference and power constrained Broadcasting and Multicas...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zheng Li Deying Li Ming Liu Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781424451142

Broadcasting/Multicasting problems have been well studied in wireless ad hoc networks. However, only a few approaches take into account the low interference and energy efficiency as the optimization objective simultaneously. In this paper, we study the interference and power constrained broadcast/multicast and the delay-bounded interference and power constrained broadcast/multicast routing problems in wireless ad hoc networks using directional antennas. We propose an approximation and a heuristic algorithm for the two problems, respectively. Importantly, motivated by the study of above optimization problems, we propose approximation schemes for two multi-constrained directed Steiner tree problems, respectively. Broadcast/Multicast message by using the trees found by our algorithms tend to have less channel collisions and higher network throughput. The theoretical results are corroborated by simulation studies.

关键词： Interference constraints Broadcasting Mobile ad hoc networks Directional antennas Multicast algorithms Energy efficiency Routing Approximation algorithms Heuristic algorithms Throughput

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Assessing the influence probability between objects: A random walker approach

Assessing the influence probability between objects: A rando...

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IEEE Symposium on Computational Intelligence and data Mining (CIDM)

作者： Pei Li Zhixu Li Jun He Xiaoyong Du Hongyan Liu Key Laboratories of Data Engineering and Knowledge Engineering MOE China & School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

Influence between objects needs to be assessed in many applications. Lots of measures have been proposed, but a domain-independent method is still expected. In this paper, we give a probabilistic definition of influence based on the random walker model on graphs. Two approaches, linear systems method and Basic InfRank algorithm, are shown and return equal results, but Basic InfRank is more efficient by iterative computation. Two variants on bipartite graphs and star graphs are discussed. Experiments show InfRank algorithms have good accuracy, fast convergent rate and high performance.

关键词： data engineering knowledge engineering Iterative algorithms Attenuation measurement Helium knowledge management engineering management Linear systems Iterative methods Bipartite graph

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