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3rd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Meng, Wujun Kong, Yongkang Wei, Dapeng Yang, Fuping Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing China Third Military Medical University State Key Laboratory of Trauma Burn and Combined Injury Chongqing4000XX China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781665490788

It is reported that more than 300 million people are currently suffering from or facing the risk of severe food insecurity, and the detection and diagnosis of diseases and insect pests is one of the most challenging research areas in the agricultural field and has been highly valued. Traditional disease detection methods rely on extracting manual features from acquired images to identify infection types. Furthermore, the performance of these works is entirely dependent on the nature of the selected manual features. This can be solved by automatically learning features through Convolutional Neural Networks (CNN). This paper presents a convolutional neural network called ToNet that uses two different dimensionality reduction structures for detecting infection types on tomato leaves. Experiments were carried out using 10 types of tomato leaves from the PlantVillage Dataset. The images were normalized and input into the neural network, and compared with several classic convolutional neural networks. Comparing several different activation functions, it is found that the accuracy of using Mish activation function is the highest. The experimental results show that, the proposed model can achieve a test accuracy of 95.96%. © 2023 IEEE.

关键词： Convolutional neural networks

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Bridging Trajectory-Aware Evolutionary Graph Learning and Large Language Models for Enhancing Navigability in Social Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Bi, Xin Han, Zhubin Yao, Xin Zhao, Xiangguo Wang, Yu-Ping Yuan, Ye State Key Laboratory of Intelligent Deep Metal Mining and Equipment China Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Software Shenyang110819 China Beijing Institute of Technology School of Computer Science and Technology Beijing100091 China

Social Internet of Things (SIoT) has emerged as a novel paradigm that enhances IoT service capabilities by leveraging device-level social relationships. However, the explosive growth of heterogeneous devices, the dynamic mobile device trajectories, and complex spatiotemporal interaction patterns severely hinder SIoT network navigability. Particularly, the device mobility and contextual diversity pose significant challenges to social relation classification, a critical task for efficient routing and service discovery. Existing approaches, primarily designed for static or homogeneous networks, fail to adequately capture the evolving contextual dependencies and the spatiotemporal heterogeneity in SIoT. To address these challenges, we propose Trajectory-Aware Graph LLM (TAGLLM), a novel framework that enhances SIoT navigability through context-aware relation classification. TAGLLM introduces a multi-feature fusion trajectory evolutionary graph encoder to jointly model complex device attributes, social relations, and dynamic trajectories. Furthermore, a structural graph-text token alignment strategy is designed to exploit the generalization ability and contextual understanding capabilities of Large Language Models (LLMs), enabling more effective modeling of heterogeneous and dynamic SIoT scenarios. Extensive experiments on real-world SIoT datasets demonstrate that TAGLLM outperforms state-of-the-art baselines across multiple evaluation metrics, highlighting its potential to push the frontier of graph learning and LLM integration in SIoT applications. © 2014 IEEE.

关键词： Network theory (graphs)

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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Hunger games search optimization with deep learning model for sustainable supply chain management

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Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning

arXiv

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arXiv 2023年

作者： Wang, Jianfeng Hu, Xiaolin Lukasiewicz, Thomas Computer Science Department University of Oxford United Kingdom Institute for Artificial Intelligence The State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Institute of Logic and Computation TU Wien Austria

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte-Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on five public datasets under three semi-supervised image classification settings, namely, the standard semi-supervised image classification, the imbalanced semi-supervised image classification, and the multi-label semi-supervised image classification, and NP-Match outperforms state-of-the-art (SOTA) approaches or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL. © 2023, CC BY.

关键词： Image classification

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Towards Calibrating Financial Market Simulators with High-frequency Data

arXiv

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arXiv 2025年

作者： Yang, Peng Ren, Junji Wang, Feng Tang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science Wuhan University Wuhan430072 China

The fidelity of financial market simulation is restricted by the so-called "non-identifiability" difficulty when calibrating high-frequency data. This paper first analyzes the inherent loss of data information in this difficulty, and proposes to use the Kolmogorov-Smirnov test (K-S) as the objective function for high-frequency calibration. Empirical studies verify that K-S has better identifiability of calibrating high-frequency data, while also leads to a much harder multi-modal landscape in the calibration space. To this end, we propose the adaptive stochastic ranking based negatively correlated search algorithm for improving the balance between exploration and exploitation. Experimental results on both simulated data and real market data demonstrate that the proposed method can obtain up to 36.0% improvement in high-frequency data calibration problems over the compared methods. Copyright © 2025, The Authors. All rights reserved.

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Heat source recognition sensor mimicking the thermosensation function of human skin

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The Innovation 2024年第5期5卷 84-90,83页

作者： Wenting Sun Pengxiang Zhang Xinyi Lin Yupeng Wang Shuaihua Wang Bin Yang Zijian Zheng Weishu Liu Department of Materials Science and Engineering Southern University and Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices Southern University of Science and TechnologyShenzhen 518055China School of Fashion and Textiles The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic UniversityHong Kong SAR 999077China Research Institute for Intelligent Wearable Systems(RI-IWEAR) The Hong Kong Polytechnic UniversityHong Kong SAR 999077China Research Institute for Smart Energy(RISE) The Hong Kong Polytechnic UniversityHong Kong SAR 999077China

The human skin maintains a comfortable and healthy somatosensory state by sensing different aspects of the thermal environment,including temperature value,heat source,energy level,and ***,state-of-the-art thermosensors only measure basic temperature values,not the full range of the thermosensation function of human ***,we propose a heat source recognition(hsr)sensor of poly(butyl acrylate)-lithium bis(n-fluoroalkylsulfonyl)imide(PBA-Li:nFSI;n=1,3,5),which enables response to temperature,pressure,and proximity stimulus signals based on the relaxation behavior of the ionic gel and distinguished between different types of heat sources(i.e.,radiation,convection,and conduction).The hsr sensor was integrated into a prosthetic limb covered by an e-skin with isothermal regulation,and experiments with a robot showed that it could achieve human-like thermosensation function,recognizing multidimensional information about thermal environments,such as temperature value,comfort level,and heat source *** work deeply mimics the human body’s thermosensation function and provides a reliable solution for the development of bionic e-skin for intelligent robots and prosthetics.

关键词： robot function source

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SIMLOB: LEARNING REPRESENTATIONS OF LIMITED ORDER BOOK FOR FINANCIAL MARKET SIMULATION

arXiv

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arXiv 2024年

作者： Li, Yuanzhe Wu, Yue Zhong, Muyao Liu, Shengcai Yang, Peng Department of Statistics and Data Science Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Statistics and Data Science Department of Computer Science and Engineering Southern University of Science and Technology China

Financial market simulation (FMS) serves as a promising tool for understanding market anomalies and underlying trading behaviors. To ensure high-fidelity simulations, it is crucial to calibrate the FMS model to generate data closely resembling the observed market data. Previous efforts primarily focused on calibrating merely the mid-price data, leading to essential information loss of the market activities and thus biasing the calibrated model. The Limit Order Book data (LOB) is the fundamental data that fully captures the market microstructure and is adopted by exchanges worldwide. However, LOB is not applicable to existing calibration objective functions due to its unique properties, including high dimensionality, differences in feature scales, and cross-feature constraints. This paper proposes an efficient Transformer-based autoencoder to explicitly learn the vectorized representations of LOB, addressing its unique challenges. The resulted latent vector, which captures the major information of LOB, can then be applied for calibration. Extensive experiments show that the learned latent representation preserves not only the non-linear auto-correlation in the temporal axis but also the precedence between successive price levels of LOB. Besides, we verify that the representation learning aligns with the downstream calibration tasks, making the first advancement in calibrating FMS on LOB. © 2024, CC BY.

关键词： Financial markets

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Harvesting random embedding for high-frequency change-point detection in temporal complex systems

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National science Review 2022年第4期9卷 92-104页

作者： Jia-Wen Hou Huan-Fei Ma Dake He Jie Sun Qing Nie Wei Lin Research Institute of Intelligent Complex Systems Fudan University Centre for Computational Systems Biology Institute of Science and Technology for Brain-Inspired IntelligenceFudan University School of Mathematical Sciences Soochow University Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine School of Mathematical Sciences and Shanghai Center for Mathematical Sciences Fudan University Department of Mathematics Department of Developmental and Cell Biologyand NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of California Shanghai Key Laboratory for Contemporary Applied Mathematics LNMS (Fudan University)and LCNBI (Fudan University) State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceFudan University

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection(TCD), and integrate both dynamical and statistical methods to address this important challenge in a novel way. The proposed TCD approach, basing on exploitation of spatial information of the observed time series of high dimensions, is able not only to detect the separate change points of the concerned systems without knowing, a priori, any information of the equations of the systems, but also to harvest all the change points emergent in a relatively high-frequency manner, which cannot be directly achieved by using the existing methods and *** effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.

关键词： temporal systems time series change-point detection complex dynamical systems

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