检索结果-内蒙古大学图书馆

Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

引用

arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Big data challenge： a data management perspective

引用

Frontiers of Computer Science 2013年第2期7卷 157-164页

作者： Jinchuan CHEN Yueguo CHEN Xiaoyong DU Cuiping LI Jiaheng LU Suyun ZHAO Xuan ZHOU Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China China Beijing 100872

There is a trend that, virtually everyone, rang- ing from big Web companies to traditional enterprisers to physical science researchers to social scientists, is either al- ready experiencing or anticipating unprecedented growth in the amount of data available in their world, as well as new op- portunities and great untapped value. This paper reviews big data challenges from a data management respective. In partic- ular, we discuss big data diversity, big data reduction, big data integration and cleaning, big data indexing and query, and fi- nally big data analysis and mining. Our survey gives a brief overview about big-data-oriented research and problems.

关键词： big data performance databases

来源：评论

学校读者我要写书评

暂无评论

Residual network with delayed max pooling for very large scale plant identification 18

Residual network with delayed max pooling for very large sca...

引用

18th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2017

作者： Hang, Siang Thye Aono, Masaki Knowledge Data Engineering and Information Retrieval Laboratory Department of Computer Science and Engineering Toyohashi University of Technology Japan

In our approach, we applied a few modifications to the 50-layered Residual Network. Our preliminary experiments with the Plant-CLEF 2016 dataset showed that the modifications improved classification performance. We have trained three models based on the modified Residual Network configuration with different combinations of trusted and noisy PlantCLEF 2017 datasets. Using confidence scores extracted from the three models, we have submitted four runs and our methods showed competitive classification performance.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models

引用

IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 841-858页

作者： Xiong, Luolin Wang, Haofen Chen, Xi Sheng, Lu Xiong, Yun Liu, Jingping Xiao, Yanghua Chen, Huajun Han, Qing-Long Tang, Yang Ministry of Education Engineering Research Center of Process System Engineering East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai 200237 China College of Design and Innovation Tongji University Shanghai 200092 China School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai 200433 China School of Software Beihang University Beijing 100191 China School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China College of Computer Science and Technology Zhejiang University AZFT Joint Laboratory for Knowledge Engine Hangzhou Innovation Center Hangzhou 310058 China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne 3122 VIC Australia

DeepSeek, a Chinese artificial intelligence (AI) startup, has released their V3 and R1 series models, which attracted global attention due to their low cost, high performance, and open-source advantages. This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts, the mainstream large language model (LLM) paradigm, and the DeepSeek paradigm. Subsequently, the paper highlights novel algorithms introduced by DeepSeek, including multi-head latent attention (MLA), mixture-of-experts (MoE), multi-token prediction (MTP), and group relative policy optimization (GRPO). The paper then explores DeepSeek's engineering breakthroughs in LLM scaling, training, inference, and system-level optimization architecture. Moreover, the impact of DeepSeek models on the competitive AI landscape is analyzed, comparing them to mainstream LLMs across various fields. Finally, the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models, particularly in data, training, and reasoning. © 2014 Chinese Association of Automation.

关键词： DeepSeek large AI models reasoning capability reinforcement learning test-time scaling

来源：评论

学校读者我要写书评

暂无评论

MRST-- An Efficient Monitoring Technology of Summarization on Stream data

引用

Journal of Computer Science & Technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

来源：评论

学校读者我要写书评

暂无评论

Indexing Future Trajectories of Moving Objects in a Constrained Network

引用

Journal of Computer Science & Technology 2007年第2期22卷 245-251页

作者：陈继东孟小峰 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.

关键词： database spatial database access methods moving objects

来源：评论

学校读者我要写书评

暂无评论

Search Result Diversification Based on Query Facets

引用

Journal of Computer Science & Technology 2015年第4期30卷 888-901页

作者：胡莎窦志成王晓捷文继荣 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.

关键词： query intent query facet search result diversification

来源：评论

学校读者我要写书评

暂无评论

Summarizing Large-Scale database Schema Using Community Detection

引用

Journal of Computer Science & Technology 2012年第3期27卷 515-526页

作者：王雪周烜王珊 School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China

Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus difficult to separate these tables into clusters that represent different topics. Moreover, as a schema can be very big, the schema summary needs to be structured into multiple levels, to further improve the usability. In this paper, we introduce a new schema summarization approach utilizing the techniques of community detection in social networks. Our approach contains three steps. First, we use a community detection algorithm to divide a database schema into subject groups, each representing a specific subject. Second, we cluster the subject groups into abstract domains to form a multi-level navigation structure. Third, we discover representative tables in each cluster to label the schema summary. We evaluate our approach on Freebase, a real world large-scale database. The results show that our approach can identify subject groups precisely. The generated abstract schema layers are very helpful for users to explore database.

关键词： schema summarization large scale community detection

来源：评论

学校读者我要写书评

暂无评论

Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

引用

Journal of Computer Science & Technology 2023年第6期38卷 1403-1422页

作者：赵丹赵素云陈红刘睿瑄李翠平张晓莹 Institute of Scientific and Technical Information of China Beijing 100038China Key Laboratory of Data Engineering and Knowledge Engineering(Ministry of Education) School of InformationRenmin University of ChinaBeijing 100872China

Local differential privacy(LDP)approaches to collecting sensitive information for frequent itemset mining(FIM)can reliably guarantee *** current approaches to FIM under LDP add"padding and sampling"steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of *** current state-of-the-art approach,namely set-value itemset mining(SVSM),must balance variance and bias to achieve accurate ***,an unbiased FIM approach with lower variance is highly *** narrow this gap,we propose an Item-Level LDP frequency oracle approach,named the Integrated-with-Hadamard-Transform-Based Frequency Oracle(IHFO).For the first time,Hadamard encoding is introduced to a set of values to encode all items into a fixed vector,and perturbation can be subsequently applied to the *** FIM approach,called optimized united itemset mining(O-UISM),is pro-posed to combine the padding-and-sampling-based frequency oracle(PSFO)and the IHFO into a framework for acquiring accurate frequent itemsets with their ***,we theoretically and experimentally demonstrate that O-UISM significantly outperforms the extant approaches in finding frequent itemsets and estimating their frequencies under the same privacy guarantee.

关键词： local differential privacy frequent itemset mining frequency oracle

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：