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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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Stable RF transfer over a fiber-optic ring with DSBCS modulation and a DSB RF signal

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Chinese Optics Letters 2020年第2期18卷 10-14页

作者： Pingyang Shi Guiling Wu Liang Hu Qi Li Jianping Chen Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and NetworksDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai 200240China

We propose a radio frequency(RF)transfer technique with passive phase noise compensation over a fiber-optic *** adopting different frequencies and same wavelength transmission and double sideband(DSB)with carrier suppression(DSBCS)modulation,the impact of backscattering can be effectively suppressed.A stable RF signal can be obtained via frequency mixing at an arbitrary access site along the fiber-optic *** the two directional transmissions adopt the same fiber and same wavelength from the same laser,the bidirectional propagation symmetry can be maximally *** experimentally demonstrate 2 GHz RF signal transfer along a 100 km standard single-mode fiber-optic ring.

关键词： frequency transfer fiber loop link DSBCS modulation DFSW method

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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

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IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography data mining Mental disorders Multisensory integration

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Special issue on 2016 International Conference on Intelligence Science and Big data engineering

Special issue on 2016 International Conference on Intelligen...

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作者： Zheng, Wei-Shi Gao, Xinbo Li, Yuanqing Lai, Jianhuang School of Data and Computer Science Sun Yat-sen University China State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China School of Automation Science and Engineering South China University of Technology China Guangzhou Key Laboratory of Brain Computer Interface and Applications Guangzhou 510640 China School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China

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Enabling Efficient NVM-Based Text Analytics without Decompression

Enabling Efficient NVM-Based Text Analytics without Decompre...

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International Conference on data engineering

作者： Xiaokun Fang Feng Zhang Junxiang Nong Mingxing Zhang Puyun Hu Yunpeng Chai Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Department of Computer Science and Engineering Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many important scenarios where the capacity of DRAM is limited, such as memory-constrained systems. Non-volatile memory (NVM) is a novel storage technology that combines the advantage of reading per-formance and byte addressability of DRAM with the durability of traditional storage devices like SSD and HDD. Unfortunately, no research demonstrates how to use NVM to reduce DRAM utilization in compressed data analytics. In this paper, we propose N-TADOC, which substitutes DRAM with NVM while maintaining TADOC's analytics performance and space savings. Utilizing an NVM block device to reduce DRAM utilization presents two challenges, including poor data locality in traversing datasets and auxiliary data structure reconstruction on NVM. We develop novel designs to solve these challenges, including a pruning method with NVM pool management, bottom-up upper bound estimation, correspondent data structures, and persistence strategy at different levels of cost. Experimental results show that on four real-world datasets, N-TADOC achieves 2.04× performance speedup compared to the processing directly on the uncompressed data and 70.7% DRAM space saving compared to the original TADOC.

关键词： Performance evaluation Upper bound data analysis Costs Nonvolatile memory Random access memory Estimation

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Face micro-expression recognition algorithm based on ResNet depth model

Face micro-expression recognition algorithm based on ResNet ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Liquan Wang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education Hefei China

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted; SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASMEⅡ, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition.

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Rethinking data-Free Quantization as a Zero-Sum Game

arXiv

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arXiv 2023年

作者： Qian, Biao Wang, Yang Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independent of Q, specialized as failing to consider the adaptability of the generated samples, i.e., beneficial or adversarial, over the learning process of Q, resulting into non-ignorable performance loss. Building on this, several crucial questions — how to measure and exploit the sample adaptability to Q under varied bit-width scenarios? how to generate the samples with desirable adaptability to benefit the quantized network? — impel us to revisit DFQ. In this paper, we answer the above questions from a game-theory perspective to specialize DFQ as a zero-sum game between two players — a generator and a quantized network, and further propose an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG reformulates DFQ as a dynamic maximization-vs-minimization game process anchored on the sample adaptability. The maximization process aims to generate the sample with desirable adaptability, such sample adaptability is further reduced by the minimization process after calibrating Q for performance recovery. The Balance Gap is defined to guide the stationarity of the game process to maximally benefit Q. The theoretical analysis and empirical studies verify the superiority of AdaSG over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaSG. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Ontology-based Categorization of Web Search Results Using YAGO

Ontology-based Categorization of Web Search Results Using YA...

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The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

作者： Anjian Ren Xiaoyong Du Puwei Wang Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing 100782 China

One of the major limitations of current search engines is that users could not quickly locate what they want if the input query is too general. Some existing techniques try to cluster web search results into groups so as to user's quick browsing. In this paper, we present a new approach to categorize the web search results by using YAGO ontology. It utilizes the YAGO ontology to automatically generate categories for the user's specific query and classify the search results into appropriate categories. Our experimental results indicate that our method is feasible and effectiveness.

关键词： Ontologies Web search Feedback Image retrieval Information retrieval Image databases Humans Set theory Information systems Radio frequency

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ParaCube: A Scalable OLAP Model Based on Distributed Aggregate Computing with Sibling Cubes

ParaCube: A Scalable OLAP Model Based on Distributed Aggrega...

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International Asia-Pacific Web Conference (APWEB)

作者： Yansong Zhang Shan Wang Wei Huang Key Laboratory of Ministry of Education of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

The requirements of OLAP applications increase rapidly by dramatically increased data volume, users, query volume and query complexity. The requirement for shortening update period in data warehouse is another crucial factor for a scalable OLAP application. In this paper, we propose a scalable OLAP prototype to support the query processing with increasing data volume by distributing the whole fact tuples to multiple servers to construct a set of sibling cubes which can be merged together to obtain the whole cube. We employ a light weight distribution policy with fully duplicated dimension tables in each sibling server on the observation of very low proportion of space cost for dimension tables. OLAP query with distributed aggregate functions can be transformed into queries to be performed parallel in sibling servers. For non-distributed computing aggregate functions, such as median, the optimized median aggregate computing algorithm is proposed to reduce transmission volume between servers while computing the global median values. We also present a three-level framework in data warehouse to meet the requirement of shorter update period in "operational business intelligence". An asynchronous tunnel model is proposed to reduce update latency by pre-fetching updated tuples to OLAP processing server. Finally, we set up prototype system ParaCube to evaluate performance in SN (shared-nothing) system and multi-core platforms.

关键词： Aggregates Distributed computing Concurrent computing data warehouses Merging Application software Prototypes Query processing Acceleration Material storage

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Biggy: An implementation of unified framework for big data management system

arXiv

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arXiv 2018年

作者： Wu, Yao Guan, Henan Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

Various tools, softwares and systems are proposed and implemented to tackle the challenges in big data on different emphases, e.g., data analysis, data transaction, data query, data storage, data visualization, data privacy. In this paper, we propose datar, a new prospective and unified framework for Big data Management System (BDMS) from the point of system architecture by leveraging ideas from mainstream computer structure. We introduce five key components of datar by reviewing the current status of BDMS. datar features with configuration chain of pluggable engines, automatic dataflow on job pipelines, intelligent self-driving system management and interactive user interfaces. Moreover, we present biggy as an implementation of datar with manipulation details demonstrated by four running examples. Evaluations on efficiency and scalability are carried out to show the performance. Our work argues that the envisioned datar is a feasible solution to the unified framework of BDMS, which can manage big data pluggablly, automatically and intelligently with specific functionalities, where specific functionalities refer to input, storage, computation, control and output of big data. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

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