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Information technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Pengju Tang Yanxia Gao Yujun Yang Meixiu Tang College of Computer and Artificial Intelligence (Software College) Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China Huaihua University Library Huaihua University Huaihua China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

University computer rooms generally prefer batch purchasing strategy to simplify the management process such as system deployment and software update. However, with the growth of the life of the equipment, the failure of electronic products is inevitable, especially once the motherboard problem occurs, it is often difficult to find a replacement that exactly matches the original motherboard. Even if the replacement is found, its price is far beyond the market average level. After several years of operation, even the motherboard purchased in the same batch may also lead to the coexistence of a variety of models due to various reasons. In this situation, it becomes impractical to carry out unified network cloning. To solve this problem, we propose an innovative solution: deploying a local backup system combined with a server-controlled startup mode. The core of this solution is that through the local backup system, we can effectively deal with the challenge of different motherboard models, and ensure that each machine can have its own system snapshot. At the same time, the server-controlled boot mode further enhances the flexibility and efficiency of management, so that even in the hardware diversity of the environment, it can also achieve batch system recovery. The introduction of local backup system not only solves the problem of differentiated computer room management, but also significantly improves the convenience and stability of computer room operation and maintenance.

关键词： Biological system modeling Ecosystems Cloning System recovery Stability analysis Software Hardware Maintenance Information technology Consumer electronics

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Graph Structure Prefix Injection Transformer for Multi-Modal Entity Alignment

SSRN

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SSRN 2024年

作者： Li, Zhifei Luo, Xiangyu Zhang, Miao Zhang, Yan Xiao, Kui School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Cyber Science and Technology Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan430062 China

Multi-modal entity alignment aims to integrate equivalent entities in diverse multi-modal knowledge graphs. However, previous studies have primarily focused on multi-modal fusion methods without considering the impact of graph structure heterogeneity on entity alignment tasks due to the distinct structural features of different knowledge graphs. Moreover, the multi-modal information that enriches entity representations, including image, textual content, and numerical attributes, frequently incorporates noise due to modal discrepancies. To tackle these challenges, we introduce the Graph Structural Prefix Injection transformer (GSIEA) for multi-modal entity alignment. This novel approach minimizes structural heterogeneity’s impact and optimizes structural data utilization by integrating a structural prefix inject interaction module within the multi-head attention mechanism. Then, we adopt a convolutional enhancement module to extract local features of multi-modal information and complete feature fusion by cross-modal weights. Extensive experiments on two datasets demonstrate that GSIEA outperforms previous baseline models, showcasing an average performance improvement of 3.26% on MRR, with a maximum enhancement of 12.5%. Additionally, the average performance improvement on Hits@1 is 4.96%, with a maximum enhancement of 16.92%. Our code is available at https://***/HubuKG/GSIEA. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid model

Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid m...

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第十四届中国卫星导航年会

作者： Dengao Li Xinfang Zhang Jumin Zhao Fanming Wu Ran Feng Jinhua Zhao Danyang Shi College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content(TEC)in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network(LSTM)and the fast training speed of gated recurrent unit network(GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour(HD)are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error(RMSE)of the prediction results of this BLSLSTM-GRU model is decreased by 23.07%,17.48% and 9.46%compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy.

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Analysis of parallel helix tension measurement based on frequency domain reflection method 5

Analysis of parallel helix tension measurement based on freq...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Li, Chaopeng Li, Qing Chen, Huamin Xu, Xiajie Miao, Xinyu Zhang, Yangke College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China National and Local Joint Engineering Research Center for Disaster Monitoring Technology and Instruments China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability and Application Zhejiang Hangzhou310061 China Jinhua Geological Environment Monitoring Station Zhejiang Jinhua321000 China

ISBN: (纸本)9781510668621

Distributed measurement of underground rock and soil deformation is an important part of geological hazard monitoring. Using parallel helical transmission lines as sensors and combining frequency domain reflection (FDR) analysis enables continuous distributed measurement of larger deformation quantities. It is known that the characteristic impedance of the parallel helical transmission line increases with the increase of tensile force. Through FDR, the measurement and location of the tensile point can be determined. This paper explains the principle of FDR measurement of tensile deformation in parallel helical transmission lines. In response to practical application issues of FDR in parallel helical transmission lines, this paper analyzes the influence of sweep range, frequency, and number of sampling points on measurement results through experimental data. The paper also measures different incident signals under different circumstances, identifies and analyzes factors that may affect measurement results, and selects a better measurement scheme. The proposed method is used to simulate the actual use of parallel helical transmission lines and obtain actual measurement data, which is analyzed for better understanding. © 2023 SPIE.

关键词： Electric lines

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video datasets

Temporal Transformation Network Based On Scale Sequences for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

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arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

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Oriented Object Detection in Aerial Images with Double Midpoint Vectors

Oriented Object Detection in Aerial Images with Double Midpo...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Qi Hu Xiaopan Chen Caihong Yuan Minghao Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results.

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Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm

arXiv

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arXiv 2023年

作者： Li, Jinli Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

A high-dimensional and incomplete (HDI) matrix can describe the complex interactions among numerous nodes in various big data-related applications. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm learns a latent factor relying on the stochastic gradient of current instance error only without considering past update information. To address this critical issue, this paper innovatively proposes a Fuzzy PID-incorporated SGD (FPS) algorithm with two-fold ideas: 1) rebuilding the instance learning error by considering the past update information in an efficient way following the principle of PID, and 2) implementing hyper-parameters and gain parameters adaptation following the fuzzy rules. With it, an FPS-incorporated LFA model is further achieved for fast processing an HDI matrix. Empirical studies on six HDI datasets demonstrate that the proposed FPS-incorporated LFA model significantly outperforms the state-of-the-art LFA models in terms of computational efficiency for predicting the missing data of an HDI matrix with competitive accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model

arXiv

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arXiv 2024年

作者： Xing, Yinghui Qu, Litao Zhang, Shizhou Zhang, Kai Zhang, Yanning Ran, Lingyan Zhang, Xiuwei Yin, Hanlin The Shaanxi Provincial Key Laboratory of Speech and Image Information Processing The National Engineering Laboratory for Integrated Aerospace-GroundOcean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China The Research Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral information of MS. Due to the absence of high-resolution MS images, available deep-learning-based methods usually follow the paradigm of training at reduced resolution and testing at both reduced and full resolution. When taking original MS and PAN images as inputs, they always obtain sub-optimal results due to the scale variation. In this paper, we propose to explore the self-supervised representation of pansharpening by designing a cross-predictive diffusion model, named CrossDiff. It has two-stage training. In the first stage, we introduce a cross-predictive pretext task to pre-train the UNet structure based on conditional DDPM, while in the second stage, the encoders of the UNets are frozen to directly extract spatial and spectral features from PAN and MS, and only the fusion head is trained to adapt for pansharpening task. Extensive experiments show the effectiveness and superiority of the proposed model compared with state-of-the-art supervised and unsupervised methods. Besides, the cross-sensor experiments also verify the generalization ability of proposed self-supervised representation learners for other satellite’s datasets. We will release our code for reproducibility. © 2024, CC BY.

关键词： Deep learning

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