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CBA: multi source fusion model for fast and intelligent target intention identification

Journal of Systems Engineering and Electronics 2024年第2期35卷 406-416页

作者： WAN Shichang LI Qingshan WANG Xuhua LU Nanhua School of Computer Science and Technology Xidian UniversityXi’an 710071China School of Mathematics and Computer Application Shangluo UniversityShangluo 726000China

How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.

关键词： intention massive data deep network artificial intelligence

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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Science China(Information Sciences) 2024年第10期67卷 244-260页

作者： Xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong XIE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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Deep learning-based open API recommendation for Mashup development

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Science China(Information Sciences) 2023年第7期66卷 94-111页

作者： Ye WANG Junwu CHEN Qiao HUANG Xin XIA Bo JIANG School of Computer and Information Engineering Zhejiang Gongshang University Software Engineering Application Technology Lab

Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses or resources online as open APIs, finding the right high-quality open APIs is not an easy task from a library with several open APIs. To solve this problem, this paper proposes a deep learning-based open API recommendation(DLOAR) approach. First, the hierarchical density-based spatial clustering of applications with a noise topic model is constructed to build topic models for Mashup clusters. Second,developers' requirement keywords are extracted by the Text Rank algorithm, and the language model is built. Third, a neural network-based three-level similarity calculation is performed to find the most relevant open APIs. Finally, we complement the relevant information of open APIs in the recommended list to help developers make better choices. We evaluate the DLOAR approach on a real dataset and compare it with commonly used open API recommendation approaches: term frequency-inverse document frequency, latent dirichlet allocation, Word2Vec, and Sentence-BERT. The results show that the DLOAR approach has better performance than the other approaches in terms of precision, recall, F1-measure, mean average precision,and mean reciprocal rank.

关键词： Mashup development open API recommendation deep learning neural network service discovery

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UniTrans:Unified Parameter-Efficient Transfer Learning and Multimodal Alignment for Large Multimodal Foundation Model

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computers, Materials & Continua 2025年第4期83卷 219-238页

作者： Jiakang Sun Ke Chen Xinyang He Xu Liu Ke Li Cheng Peng Chengdu Institute of Computer Application Chinese Academy of SciencesChengdu610213China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing101499China

With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource ***,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task *** paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple *** leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter *** further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable *** evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable ***,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.

关键词： Parameter-efficient transfer learning multimodal alignment image captioning image-text retrieval visual question answering

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Tibetan Medicine Intelligent Consultation System Based on Graph Database 4

Tibetan Medicine Intelligent Consultation System Based on Gr...

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4th International Conference on computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2024

作者： Zhou, Zexian Liu, Xiaojing School of Computer Technology and Application Qinghai University Qinghai China

ISBN: (纸本)9798350378542

With the widespread application of artificial intelligence technology, the medical informatization has entered a new stage. Although intelligent question-answering systems have achieved a certain extent of promotion and landing in various consulting fields, the Tibetan medicine field is still blank. This article designs an intelligent consultation system for Tibetan medicine knowledge and uses a graph database to store data, displaying the content of the graph database in the form of a dynamic web page for users. This is beneficial for the further promotion and dissemination of Tibetan medicine culture. © 2024 IEEE.

关键词： graph database question-answering system responsive system tibetan medicine

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New Proper Reparameterization of Plane Rational Bézier Curves

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Journal of computer Science & technology 2024年第5期39卷 1193-1206页

作者： Zhen-Fei Wang Xiao-Diao Chen Jun-Hai Yong School of Computer Hangzhou Dianzi UniversityHangzhou 310018China Haihe Laboratory of Information Technology Application Innovation Tianjin 300480China School of Software Tsinghua UniversityBeijing 100084China

Coincidence detection of two curves or two surfaces has wide application in computer-aided design(CAD)and computer-aided geometric design(CAGD).Proper reparameterization is the most complicated part in the *** paper presents and proves the efficient and necessary coincidence condition for two rational Bézier curves in a new *** also proposes an effective and efficient proper reparameterization method,Algorithm 1,for detecting a rational Bézier curve which can be degenerated into a new one of a lower degree.A numerical proper reparameterization method,Algorithm 2,and examples are also *** 1 is up to ten times faster than other prevailing methods,and Algorithm 2 is twice as fast and half as close as other prevailing *** CAD systems using Algorithm 1 and Algorithm 2 will hold accuracy and little computation time.

关键词： rational Bézier curve coincidence condition control polygon degeneration proper reparameterization

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Unveiling factuality and injecting knowledge for LLMs via reinforcement learning and data proportion

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Science China(Information Sciences) 2024年第10期67卷 389-390页

作者： Wenjun KE Ziyu SHANG Zhizhao LUO Peng WANG Yikai GUO Qi LIU Yuxuan CHEN School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Beijing Institute of Technology Zhuhai Beijing Institute of Computer Technology and Application

Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that conflict with reality due to the unreliable distribution of facts within their training data, which is particularly critical for applications requiring high credibility and accuracy [3].

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A Hybrid Recommendation Model for Tourist Using Evolutionary Algorithm Combined with Local Search Algorithm for Trip Planning

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SN computer Science 2024年第6期5卷 743页

作者： Lakshmi, J.V.N. Pallavi, M.O. School of Computer Science and Application Reva University Department of MCA Acharya Institute of Technology

Recommender systems play a crucial role in assisting tourists with travel recommendations considering the customized demands is a necessary practice for improving the tourism, business, and aids significantly in decision-making process. The study proposes using a evolutionary genetic algorithm (GA) to efficiently determine the shortest tourist route within a constrained area, allowing for quick visits to multiple destinations. The goal is to find the most efficient route with minimal physical exertion. By exploring all possible routes, the GA can identify the quickest path from the starting point to the destination. The study first provides a brief overview of the enhanced GA, followed by a detailed analysis of its construction and solution. The paper introduces a novel approach to recommendation systems in the tourism industry, combining the Genetic algorithm with 2-Opt algorithm for local search adaptation. The hybrid model is used to optimize the system by considering multiple criteria. Data for this study were collected through online google map resources defining a positive ideal distance matrix for 11 locations with 121 edges. Finally, the GA is further optimized by pipeline the 2-opt algorithm, and a simulation is conducted to examine the average path convergence for each individual route by customizing based on the preferences. Finally proposed model is compared against the traditional algorithms such as Heuristic and Graph network algorithms. Subsequently, the hybrid algorithm searches among destinations to recommend the best tourist trip plan to users. Selecting the shortest trip route plan will reduce the travel cost and time. This model enhances the practical value for researchers and for practical application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2-opt algorithm Evolutionary algorithm Genetic algorithm Hybrid recommendation Optimization Travel planning

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Cryptocurrency Price Prediction with Deep Neural Networks: A Comparative Analysis of Machine Learning Approaches 11

Cryptocurrency Price Prediction with Deep Neural Networks: A...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Jain, Pallavi Kumar, Aryan Pathak, Nikhil Kumar Chaudhary, Manvi School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (纸本)9798350350357

In the current study, the immediate correlation coefficient and root mean square error (RMSE) are combined to create a fusion model that can accurately predict cryptocurrency prices. Multivariate linear regression, MARS, artificial neural networks (ANN), random forests, support vector machines (SVM), bootstrap aggregation, decision trees, and extreme gradient boosting with XG Boost are just a few of the deep learning and machine learning models that we use. Utilizing long short-term memory (LSTM) is a crucial element. LSTM emerges as the most accurate model for predicting cryptocurrency prices during January 1, 2023, to March 31, 2023. © 2024 IEEE.

关键词： Support vector machines

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An Interactive Framework for Personalized Navigation Based on Metacosmic Cultural Tourism and Large Model Fine-tuning

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IEEE Access 2025年 13卷 81450-81461页

作者： Guo, Hang Liu, Zhiqiang Tang, Chuanqian Zhang, Xiaodan Qinghai University School of Computer Technology and Application Xining China Qinghai University Qinghai Provincial Intelligent Computing and Application Laboratory Xining China

With the wide application of Large Language models (LLMs) and the rapid growth of metaverse tourism demand, the digital tour and personalized interaction of historical sites have become the key to improving users’ digital travel experience. Creating an environment where users can access rich cultural information and enjoy personalized, immersive experiences is a crucial issue in the field of digital cultural travel. To this end, we propose a tourism information multimodal generation personalized question-answering interactive framework TIGMI based on LLM fine-tuning, which aims to provide a richer and more in-depth experience for virtual Tours of historical monuments. Taking Qutan Temple as an example, the framework combines LLM, retrieval augmented generation (RAG), and auto-prompting engineering techniques to retrieve accurate information related to the historical monument from external knowledge bases and seamlessly integrates it into the generated content. This integration mechanism ensures the accuracy and relevance of the generated answers. Through TIGMI’s LLM-driven command interaction mechanism in the 3D digital scene of Qutan Temple, users are able to interact with the building and scene environment in a personalized and real-time manner, successfully integrating historical and cultural information with modern digital technology. This integration significantly enhances the naturalness of interaction and personalizes the user experience, thereby improving user immersion and information acquisition efficiency. Evaluation results show that TIGMI excels in question-answering and multimodal interactions, significantly enhancing the depth and breadth of services provided by the personalized virtual tour. We conclude by addressing the limitations of TIGMI and briefly discuss how future research will focus on further improving the accuracy and user satisfaction of the generated content to adapt to the dynamically changing tourism environment. © 2013 IEEE.

关键词： Leisure

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