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Computing and Machine Intelligence (ICMI), International Conference on

作者： S M Masfequier Rahman Swapno Sm Nuruzzaman Nobel Md Babul Islam R. Haque V. P. Meena F. Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of EEE Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine Learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99 %. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Training Plant diseases Accuracy Plantations Machine learning Safety Reliability

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Analysis of Orbital Angular Momentum Channels in Bent Inverse Raised Cosine fiber 3

Analysis of Orbital Angular Momentum Channels in Bent Invers...

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3rd IEEE International Conference on Design and Test of Integrated Micro and Nano-Systems, DTS 2021

作者： Rjeb, Alaaeddine Fathallah, Habib MacHhout, Mohsen University of Monastir Laboratory of Electronics and Microelectronics Department of Physics Faculty of Sciences of Monastir Monastir5019 Tunisia University of Carthage Laboratory of Artificial Intelligence and Data Engineering Applications Computer Department Faculty of Sciences of Bizerte Tunis1054 Tunisia

ISBN: (纸本)9780738132631

Orbital angular momentum (OAM) has gained a widespread interest in diverse areas especially in telecommunication due to its capability to elevate the capacity transmission and substantially improving the spectral efficiency of optical communication in optical fibers. In this work, we numerically investigate the bending impacts on OAM channels transmitted through inverse raised cosine few mode fiber (IRC-FMF). The obtained results show that the investigated OAM-Fiber supports 8 OAM data carriers, already presented in straight case, even under tight bend (i.e. at bending radius Rb=4 mm) with maximum loss of 0.0001 dB/m. This confirms the great tolerance of IRC-FMF to bending condition and confirms the capability of such fiber to handle robust OAM channels even under realistic environment. © 2021 IEEE.

关键词： Space division multiple access

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PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Yue, Xianghu Chen, Yiming Zhang, Xueyi Gao, Xiaoxue Feng, Mengling Lao, Mingrui Zhuang, Huiping Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China Institute for Infocomm Research A*STAR 138632 Singapore Saw Swee Hock School of Public Health National University of Singapore 117583 Singapore Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou510006 China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forgetting. While existing studies primarily focus on the integration and utilization of multi-modal information for MMCIL, a critical challenge remains: the issue of missing modalities during incremental learning phases. This oversight can exacerbate severe forgetting and significantly impair model performance. To bridge this gap, we propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios. Concretely, we devise modality-specific prompts to compensate for missing information, facilitating the model to maintain a holistic representation of the data. On this foundation, we reformulate the MMCIL problem into a Recursive Least-Squares task, delivering an analytical linear solution. Building upon these, PAL not only alleviates the inherent under-fitting limitation in analytic learning but also preserves the holistic representation of missing-modality data, achieving superior performance with less forgetting across various multi-modal incremental scenarios. Extensive experiments demonstrate that PAL significantly outperforms competitive methods across various datasets, including UPMC-Food101 and N24News, showcasing its robustness towards modality absence and its anti-forgetting ability to maintain high incremental accuracy. © 2025, CC BY.

关键词： Contrastive Learning

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Twin Analysis of Drill Hole Quality by computer Vision and Monitoring Wear and Operational Activities

Twin Analysis of Drill Hole Quality by Computer Vision and M...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： M.K. Srinath N. Lakshmi Deepthi Ginni Nijhawan Nadimpalli Venkata Ganapathi Raju Taqi Mohammed Khattab Al-Rubaye Neeraj Chahuan Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

An online digital twin for controlling a clamping system used in composite component machining is introduced in this study. It is essential to support significant and freeform composite pieces so they do not flex while drilled. If the component is bent, it could tear the margins of the drilled holes and cause delamination. To prevent bending during drilling, the latest active clamping technology enables the realization of a stable fixture specific to the region. The quality of the holes that are drilled is greatly enhanced by this. An Internet of Things (IoT) edging device, capable of bidirectional connection with the cloud, is included inside the clamping device. The digital twin in the cloud uses computer vision to assess the drilled hole quality, track drill wear, and flag improper active clamping device functioning. To learn more about how the drill with the active clamping device works, all the data is saved in the cloud. The whole rollout takes place in the cloud on Microsoft Azure. This makes the regular machine compatible with Industry 4.0 standards.

关键词： Drilling Cloud computing computer vision Force computer architecture Digital twins Fourth Industrial Revolution Clamps Internet of Things Monitoring

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations 25

ZuantuSet: A Collection of Historical Chinese Visualizations...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru Peking University Beijing China University of Oxford Oxford United Kingdom National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Department of Computer Science University of Oxford United Kingdom Center for Research on Ancient Chinese History Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China PKU-WUHAN Institute for Artificial Intelligence China

ISBN: (纸本)9798400713941

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flow visualization

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Joint Optimization of Sensing and Communications in Vehicular Networks: A Graph Neural Network-Based Approach

Joint Optimization of Sensing and Communications in Vehicula...

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IEEE International Conference on Communications (ICC)

作者： Xuefei Li Mingzhe Chen Zhilong Zhang Danpu Liu Yuchen Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

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Application of 1-Dimensional Convolution Neural Network based Machine Learning Approach for Prediction of Air Quality Index 19

Application of 1-Dimensional Convolution Neural Network base...

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19th Orissa Information Technology Society International Conference on Information Technology, OCIT 2021

作者： Nanda, Santosh Kumar Tripathy, Debi Prasad Mohapatra, Rajanikanta Ray, Niranjan K. Techversant Infotech Pvt Ltd Techversant AI-Data Science Team Kerala Trivandrum India National Institute of Technology Department of Mining Engineering Odisha Rourkela India School of Computer Engineering KIIT Deemed To Be University Odisha Bhubaneswar24 India

ISBN: (纸本)9781665416641

Prediction of air quality is one of the major necessities of current time to quantify the effect of air pollution in our health and warning us to controlling air pollution. However, high volatility and dynamic nature of pollution make the task extremely complicated. The traditional approaches for monitoring air quality are not that effective because of the complexity of processing data and hence machine learning approaches are to be tried which best fits in solving the problem. In this research work several robust machine learning approaches such as Multiple Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Support Vector Machine (SVM) Regression (RBF and Linear), Multilayer Perceptron, Decision Tree Regression, Convolutional Neural Network(CNN) have been employed for analysing air quality of some of the major cities of Kerala. The coefficient of determination (R2) findings demonstrated astounding presentation of L-Dimensional CNN based on air quality models because of the high R2 values that produces efficacious data assessment. © 2021 IEEE.

关键词： Decision trees

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Divert more attention to vision-language tracking 22

Divert more attention to vision-language tracking

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Mingzhe Guo Zhipeng Zhang Heng Fan Liping Jing Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University DiDi Chuxing Beijing China Department of Computer Science and Engineering University of North Texas

ISBN: (纸本)9781713871088

Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-reliance is not necessary and the pure ConvNets are still competitive and even better yet more economical and friendly in achieving SOTA tracking. Our solution is to unleash the power of multimodal vision-language (VL) tracking, simply using ConvNets. The essence lies in learning novel unified-adaptive VL representations with our modality mixer (ModaMixer) and asymmetrical ConvNet search. We show that our unified-adaptive VL representation, learned purely with the ConvNets, is a simple yet strong alternative to Transformer visual features, by unbelievably improving a CNN-based Siamese tracker by 14.5% in SUC on challenging LaSOT (50.7%→65.2%), even outperforming several Transformer-based SOTA trackers. Besides empirical results, we theoretically analyze our approach to evidence its effectiveness. By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer.

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Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

arXiv

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arXiv 2023年

作者： Ji, Jiahao Zhang, Wentao Wang, Jingyuan Huang, Chao School of Computer Science and Engineering Beihang University Beijing China SCSE Beihang University Beijing China MIIT Key Laboratory of Data Intelligence and Management SEM Beihang University Beijing China Department of Computer Science Musketeers Foundation Institute of Data Science University of Hong Kong Hong Kong

Traffic prediction is essential for intelligent transportation systems and urban computing. It aims to establish a relationship between historical traffic data X and future traffic states Y by employing various statistical or deep learning methods. However, the relations of X → Y are often influenced by external confounders that simultaneously affect both X and Y, such as weather, accidents, and holidays. Existing deep-learning traffic prediction models adopt the classic front-door and back-door adjustments to address the confounder issue. However, these methods have limitations in addressing continuous or undefined confounders, as they depend on predefined discrete values that are often impractical in complex, real-world scenarios. To overcome this challenge, we propose the Spatial-Temporal sElf-superVised confoundEr learning (STEVE) model. This model introduces a basis vector approach, creating a base confounder bank to represent any confounder as a linear combination of a group of basis vectors. It also incorporates self-supervised auxiliary tasks to enhance the expressive power of the base confounder bank. Afterward, a confounder-irrelevant relation decoupling module is adopted to separate the confounder effects from direct X → Y relations. Extensive experiments across four large-scale datasets validate our model’s superior performance in handling spatial and temporal distribution shifts and underscore its adaptability to unseen confounders. Our model implementation is available at https://***/bigscity/STEVE_CODE. Copyright © 2023, The Authors. All rights reserved.

关键词： Urban transportation

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ENHANCING AND DISSECTING CROWD COUNTING BY SYNTHETIC data

arXiv

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arXiv 2022年

作者： Houyi, Yi Hou Li, Chengyang Lu, Yuheng Zhu, Liping Li, Yuan Jia, Huizhu Xie, Xiaodong National Engineering Laboratory for Video Technology Peking University China School of Electronics Engineering and Computer Science Peking University Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity. The experimental results of using this dataset as data enhancement show that the performance of the proposed streamlined and efficient benchmark network ESA-Net can be improved by 8.4%. The other two classic heterogeneous architectures MCNN and CSRNet pre-trained on CrowdX also show significant performance improvements. Considering many influencing factors determine performance, such as background, camera angle, human density, and resolution. Although these factors are important, there is still a lack of research on how they affect crowd counting. Thanks to the CrowdX dataset with rich annotation information, we conduct a large number of data-driven comparative experiments to analyze these factors. Our research provides a reference for a deeper understanding of the crowd counting problem and puts forward some useful suggestions in the actual deployment of the algorithm. © 2022, CC BY.

关键词： Large dataset

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