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arXiv 2024年

作者： Yan, Haoqiu Zhu, Yongxin Zheng, Kai Liu, Bing Cao, Haoyu Jiang, Deqiang Xu, Linli School of Computer Science and Technology University of Science and Technology China School of Data Science University of Science and Technology China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information present in speech, which is crucial for understanding human communication nuances. This oversight can lead to misinterpretations of speakers' intentions, resulting in inconsistent or even contradictory responses within dialogues. To bridge this gap, in this paper, we propose PerceptiveAgent, an empathetic multi-modal dialogue system designed to discern deeper or more subtle meanings beyond the literal interpretations of words through the integration of speech modality perception. Employing LLMs as a cognitive core, PerceptiveAgent perceives acoustic information from input speech and generates empathetic responses based on speaking styles described in natural language. Experimental results indicate that PerceptiveAgent excels in contextual understanding by accurately discerning the speakers' true intentions in scenarios where the linguistic meaning is either contrary to or inconsistent with the speaker's true feelings, producing more nuanced and expressive spoken dialogues. Code is publicly available at: https://***/Haoqiu-Yan/PerceptiveAgent. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech communication

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2023 International Conference on Ubiquitous Communication, Ucom 2023

作者： Lin, Dongting Liu, Yuan Yang, Jiarong Zheng, Beixiong Chen, Fangjiong Chen, Wen Zhang, Rui School of Electronic and Information Engineering South China University of Technology Guangzhou China Shanghai Jiao Tong University Department of Electronics Engineering Shanghai China The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350340433

It is commonly believed that 6G will be established on ubiquitous artificial intelligence (AI). Federated learning is recognized as one of the vital solutions for 6G intelligent applications due to its distributed AI structure and privacy preservation nature. In this paper, we consider an integrated federated and centralized learning network that consists of multiple users and a server for acquiring perceived data from mobile devices. The data similarity rule is adopted at users, where the received data offloaded from devices is either retained at users for distributed training or relayed to server for centralized training, and thus the server also aggregates local models from users. By optimizing bandwidth resource allocation and the number of communication rounds, we formulate an optimization problem with the goal of energy consumption minimization under the constraints of model loss and communication delay. Simulation results show that the proposed scheme not only improves model performance but also reduces energy consumption. © 2023 IEEE.

关键词： Resource allocation

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems

arXiv

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arXiv 2025年

作者： Dereich, Steffen Jentzen, Arnulf Riekert, Adrian Institute for Mathematical Stochastics University of Münster Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Institute for Analysis and Numerics University of Münster Germany

Deep learning methods – usually consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method – are nowadays omnipresent in data-driven learning problems as well as in scientific computing tasks such as optimal control (OC) and partial differential equation (PDE) problems. In practically relevant learning tasks, often not the plain-vanilla standard SGD optimization method is employed to train the considered class of DNNs but instead more sophisticated adaptive and accelerated variants of the standard SGD method such as the popular Adam optimizer are used. Inspired by the classical Polyak–Ruppert averaging approach, in this work we apply averaged variants of the Adam optimizer to train DNNs to approximately solve exemplary scientific computing problems in the form of PDEs and OC problems. We test the averaged variants of Adam in a series of learning problems including physics-informed neural network (PINN), deep backward stochastic differential equation (deep BSDE), and deep Kolmogorov approximations for PDEs (such as heat, Black–Scholes, Burgers, and Allen–Cahn PDEs), including DNN approximations for OC problems, and including DNN approximations for image classification problems (ResNet for CIFAR-10). In each of the numerical examples the employed averaged variants of Adam outperform the standard Adam and the standard SGD optimizers, particularly, in the situation of the scientific machine learning problems. The Python source codes for the numerical experiments associated to this work can be found on GitHub at https://***/deeplearningmethods/averagedadam. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Stock index prediction using global market indices: A Granger causality-based graph representation learning method 10

Stock index prediction using global market indices: A Grange...

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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Wang, Yunong Wu, Jie Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Global stock markets are increasingly connected and may experience strongly correlated stock price fluctuations due to external shocks, cooperation, and competition. In this paper, we propose a Granger causality-based graph representation learning method to improve the accuracy of stock index prediction. In the first stage, a Granger causality test is performed using the historical data of the target stock market and other global stock markets to select the neighboring stock markets with Granger causality and get the relational weight of each neighboring stock market. Then, the temporal weight of trading days is obtained based on the constructed VAR model coefficients. Subsequently, the daily graph embedding of the target stock index is learned by graph convolution operation for the prediction of the next day's closing price. The method developed in this paper experimented on four well-known Chinese stock indices, and it outperformed the benchmark model used in this paper. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Financial markets

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Image Texture Analysis Based on Gray Level Co-Occurrence Matrix 13

Image Texture Analysis Based on Gray Level Co-Occurrence Mat...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Meilong, Chen Xiehua, Yu Shaojun, Su Daqing, Lin University of Fujian Province MinNan Science and Technology University Key Laboratory of Big Data and Artificial Intelligence Quanzhou China State Grid Fujian Electric Power Co. Ltd. Nan'an Power Supply Company Quanzhou China Nan 'An NO.3 Primary School of Fujian Province Quanzhou China

ISBN: (纸本)9798350319156

This paper studies the algorithm of Gray Level Co-occurrence Matrix (GLCM), explains the concrete meaning of 14 texture features based on GLCM, and points out the redundancy among texture features. Through the calculation and analysis of the gray level co-occurrence matrix of texture image and the experiments of texture feature extraction, it is shown that the gray level co-occurrence matrix can reflect the features of the image, and corresponds to the features of the image described by the texture features. At the same time, there is a certain degree of redundancy among the 14 texture features of the image. In practice, we can choose several prominent texture features to classify the image according to the difference of texture features. The analysis of texture features and experimental results are of great significance to the application of image texture features. © 2023 IEEE.

关键词： Redundancy

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Multi-Scale Attention Semi-Supervised Network Based on Confidence Iteration and Pseudo-Label Optimization for COVID-19 CT Segmentation 42

Multi-Scale Attention Semi-Supervised Network Based on Confi...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Kun Chen, Mei Xie, Junfei Mao, JingKun Liu, WeiPeng School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China Tianjin University of Technology Department of Automation Tianjin300384 China Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Tianjin300401 China

ISBN: (纸本)9789887581543

Computed tomography (CT) image segmentation for COVID-19 lung infection has made great progress. However, accurate segmentation remains a major challenge due to the varying shape and size of COVID-19 infected areas, the more similar tissue of infected lung images to normal lung images, and the additional problem of a limited number of medical images annotated at the pixel level. In this paper, we propose a semi-supervised segmentation network based on confidence iteration and pseudo-label optimization for COVID-19 CT segmentation. In addition, we develop a multi-scale attention module which can achieve attentional feature fusion at different scales. The experiment evaluated on the COVID-19 CT Segmentation dataset, demonstrate our method achieves higher segmentation accuracy than the state-of-art methods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： COVID-19

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Robust Control For Connected Automated Vehicle Platoon With Multiple-Predecessor Following Topology Considering Communication Loss

SSRN

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SSRN 2024年

作者： Yang, Lei Sun, Zhanbo Liu, Yafei Chen, Linbin School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

The paper presents a robust control method for effectively managing uncertainties and communication loss in a connected automated vehicle (CAV) platoon under the multiple-predecessor following (MPF) topology. The proposed approach incorporates uncertainties in vehicle dynamics, such as vehicle parameters and environmental resistances, into the closed-loop platoon system to enhance the robustness of the platoon controller. The impacts of communication loss are analyzed specifically for the MPF topology, considering potential disruptions in information flow among different numbers and locations of predecessors in a CAV platoon. A novel formulation of desired spacing, suitable for the CAV platoon with MPF topology under communication loss, is then developed based on the constant time headway (CTH) policy. Furthermore, the paper derives and proves the sufficient and necessary conditions for the local stability of the proposed robust platoon controller using Kharitonov's theorem. The sufficient conditions for string stability are also discussed through frequency-domain analysis. These conditions establish the stability region for the robust controller of a CAV platoon with varying locations and numbers of unconnected predecessors. Simulation experiments are conducted to demonstrate that the stability region of the controller diminishes as the number of unconnected predecessors increases, with the greatest impact observed when the communication with the nearest connected predecessor is lost. Additionally, the control performance is affected by uncertain dynamics and the range of time headway, resulting in a significant reduction in the stability region. The findings highlight the importance of fine-tuning control parameters within the stability region guided by the derived stability conditions to ensure both local and string stability of CAV platoons. © 2024, The Authors. All rights reserved.

关键词： Topology

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EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding

arXiv

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arXiv 2024年

作者： Huang, Muye Lai, Han Zhang, Xinyu Wu, Wenjun Ma, Jie Zhang, Lingling Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China MOE KLINNS Lab Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs’ capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models’ chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA. © 2024, CC BY.

关键词： Visual languages

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Research on the Physical Mechanism of Glass-Forming Ability Based on Ensemble Deep Learning and Shap Method

SSRN

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SSRN 2023年

作者： Lin, Yuancheng Liang, Yongchao Institute of Advanced Optoelectronic Materials and Technology School of Big Data and Information Engineering Guizhou University Guiyang550025 China

Glass forming ability (GFA) of Metallic Glasses (MGs) is the result of the interaction of multiple factors. Most previous studies have only focused on characteristic temperatures, and established machine learning models have significant limitations due to their inability to fully understand the kinetics and topological structure of MGs. Therefore, three deep learning models are build using thermodynamics parameters such as glass transition temperature Tg, liquidus temperature Tl and onset crystallization temperature Tx, as well as kinetics and topological parameters such as total electronegative TEN, average atomic volume VA, atomic size difference d, and mixing entropy Sm. To predict GFA and explore the mechanism of physical impacts of different characteristics on GFA, artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory network (LSTM) are utilized. Experimental results show that low Tl and high Tx effectively improve the GFA. Furthermore, high Sm promotes amorphous formation by ANN. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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