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Video emotional description with fact reinforcement and emotion awaking

Journal of Ambient Intelligence and Humanized Computing 2024年第6期15卷 2839-2852页

作者： Tang, Pengjie Rao, Hong Zhang, Ai Tan, Yunlan Electronics and Information Engineering College Jinggangshan University 28 Xueyuan Road Jiangxi Ji’an343009 China School of Software Nanchang University 235 Nanjing East Road Jiangxi Nanchang330096 China Key Laboratory of Jiangxi Electronic Data Control and Forensics Jinggangshan University Ji’an China

Video description aims to translate the visual content in a video with appropriate natural language. Most of current works only focus on the description of factual content, paying insufficient attention to the emotions in the video. And the sentences always lack flexibility and vividness. In this work, a fact enhancement and emotion awakening based model is proposed to describe the video, making the sentence more attractive and colorful. The strategy of deep incremental leaning is employed to build a multi-layer sequential network firstly, and multi-stage training method is used to sufficiently optimize the model. Secondly, the modules of fact inspiration, fact reinforcement and emotion awakening are constructed layer by layer to discovery more facts and embed emotions naturally. The three modules are cumulatively trained to sufficiently mine the factual and emotional information. Two public datasets including EmVidCap-S and EmVidCap are employed to evaluate the proposed model. The experimental results show that the performance of the proposed model is superior to not only the baseline models, but also the other popular methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Reinforcement

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TYG: A tag-as-you-go online annotation tool for web browsing and navigation

TYG: A tag-as-you-go online annotation tool for web browsing...

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6th International Conference on knowledge Science, engineering and Management, KSEM 2013

作者： Hu, He Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

ISBN: (纸本)9783642397868

We present TYG (Tag-as-You-Go) in this paper, a chrome browser extension for personal knowledge annotation on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can group similar html nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Websites

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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LOB: Bucket based index for range queries

LOB: Bucket based index for range queries

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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Wang, Jieping Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531854

database-as-a-Service is a promising data management paradigm in which data is encrypted before being sent to the untrusted server. Efficient querying on encrypted data is a performance critical problem which has various solutions, among which bucket based index is an effective and flexible one. In previous research some metrics are proposed to measure security and efficiency. In this paper, we illustrate by example the limitations of these metrics and introduce a new security metric based on probability distribution variance and efficiency metric based on overlapping ratio. Based on these metrics we propose a local overlapping bucket algorithm (LOB) with time complexity of O(nlogn), where n represents the cardinality of the table. Experiments on synthetic and real dataset show that our algorithm can achieve higher security by trading off efficiency. © 2008 IEEE.

关键词： Information management

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Meta-path Enhanced knowledge Graph Convolutional Network for Recommender Systems 12

Meta-path Enhanced Knowledge Graph Convolutional Network for...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Wang, Ru Wu, Meng Ji, Shengwei Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Ministry of Education School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665438582

knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG;2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively. © 2021 IEEE.

关键词： knowledge graph

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Fault-tolerant routing: κ-inconnected many-to-one routing in wireless networks

Fault-tolerant routing: κ-inconnected many-to-one routing i...

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3rd International Conference on Combinatorial Optimization and Applications, COCOA 2009

作者： Li, Deying Zhu, Qinghua Yang, Huiqiang School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China

ISBN: (纸本)3642020259

This paper addresses the problem of fault-tolerant many-to-one routing in static wireless networks with asymmetric links, which is important in both theoretical and practical aspects. The problem is to find a minimum energy subgraph for a given subset and a destination node such that there are κ node-disjoint paths from each node in the subset to the destination node in the subgraph. We prove that the problem is NP-hard, and propose two efficient heuristic approaches, namely, the minimum weight κ node-disjoint paths based (MWkNDPB) approach and the minimum energy κ node-disjoint paths based (MEkNDPB) approach. Extensive simulations have been conducted to show that proposed algorithms are efficient. © 2009 Springer Berlin Heidelberg.

关键词： Wireless networks

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FARP: Mining fuzzy association rules from a probabilistic quantitative database

FARP: Mining fuzzy association rules from a probabilistic qu...

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作者： Pei, Bin Zhao, Suyun Chen, Hong Zhou, Xuan Chen, Dingjie Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing a novel Shannon-like Entropy, we aggregate and measure the information contained in an item with the coexistence of fuzzy uncertainty hidden in quantitative values and random uncertainty. We then propose Support and Confidence metrics for a fuzzy-probabilistic database to quantify association rules. Finally, we design an algorithm, called FARP (mining Fuzzy Association Rules from a Probabilistic quantitative data), to discover frequent fuzzy-probabilistic itemsets and fuzzy association rules using the proposed interest measures. The experimental results show the effectiveness of our method and its practicality in real-world applications. © 2013 Elsevier Inc. All rights reserved.

关键词： Entropy

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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A Protecting-Privacy Path Query Supporting Semantic-Based Multikeyword Search Over Ciphertext Graph data in Cloud Computing

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Bin Wu Zhuolin Mei Jiaoli Shi Zongmin Cui Zhiqiang Zhao Jinzhou Huang School of Computer and Big Data Science Jiujiang University Jiujiang Jiangxi 332005 China *** Jiujiang Key Laboratory of Network and Information Security Jiujiang Jiangxi 332005 China School of Mathematics and Computer Science Ningxia Normal University Guyuan Ningxia 756099 China *** School of Computer Engineering Hubei University of Arts and Science Xiangyang Hubei 441053 China ***

With the rapid development of information technology and intelligence, there are more and more usage scenarios of graph data. Path queries have always been a hot topic of research for scholars. There are already many mature methods and ideas in the study of the path query on the plaintext graph. For the path query on graph data in the case of cloud outsourcing, it is necessary to consider both the construction of query algorithms and the protection of privacy information. The processing of graph privacy information through encryption, and then outsourcing to the cloud platform, is a common measure. The semantic-based path query supporting multikeyword search is an extended path query method, which can improve the query function. For users, it is very troublesome to access and process the encrypted graph data. In this article, we propose a protecting-privacy semantic-based multikeyword path query scheme on the ciphertext graph (PSMP). Firstly, based on the principle of searchable encryption and vector operation, a secure index is constructed, and then the cloud server uses the secure index to implement path queries. This article demonstrates its security through formal analysis and verifies its effectiveness through experimental comparison and analysis. The work of this article has a certain promoting effect on the query processing and analysis of ciphertext graph data.

关键词： ciphertext graph multikeyword search path query protecting-privacy semantic-based

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Scalable process modeling based on net synthesis under cloud computing environment

Scalable process modeling based on net synthesis under cloud...

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Zhou, Jiantao Liang, Rong Wang, Yan College of Computer Science Inner Mongolia University Hohhot China Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

ISBN: (纸本)9781457703713

Recently research on modeling methods of complicated processes under complex network environments has become a focus in workflow field. Now cloud computing environment provides a specific application background for the interaction and collaboration of workflows. In this paper, workflow processes were modeled as Semantics Constrained Net (SCN), which is one kind of extended Petri net. Then net synthesis mechanism constrained by semantics was researched, including synthesis rules and liveness proven. And a scalable solution of complex workflow process modeling in cloud was proposed. An instance of e-business was given to describe cooperative workflow under cloud computing environment. © 2011 IEEE.

关键词： Semantics

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