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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Srivastava, Saurabh Ahmed, Tasneem Integral University Advanced Computing Research Laboratory Department of Computer Application Uttar Pradesh Lucknow India

ISBN: (纸本)9789380544472

Urbanization, which refers to the growth of a city or a built-up region, is the process of transitioning from rural to urban regions. Urbanization is primarily brought on by natural population expansion, rural-to-urban migration, and annexation. Urban monitoring is the practice of observing and quantifying stability or change over a range of timescales inside cities. In addition to being a useful tool for studying the Earth's surface, satellite images may also be used to estimate different aspects behind rapid urbanization. Sentinel-2 images from 2017 to 2022 are utilised in this paper to assess the urban development in Lucknow, the Indian city of Uttar Pradesh's state. Hence, image indices like normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and built-up index (BUI) extracted from Senitnel-2 images are utilized to classify the various land cover classes by applying the supervised classification technique. The classified images are further used to measure the urban areas and a deep learning-based change detection approach has been developed to detect the changes in urban areas that have been transformed into urban areas from agriculture or other areas. After conducting a critical analysis of change areas, it is observed that 52% of positive changes have been observed in urban areas, which shows that a large area has been transformed from non-urban to urban areas during the assessment period. In the future, an efficient and accurate urban monitoring system can be developed by using the proposed approach to establish sustainable and planned urban areas. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Urban growth

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Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

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computers, Materials & Continua 2024年第7期80卷 661-678页

作者： Haotang Tan Song Sun Tian Cheng Xiyuan Shu Key Laboratory of GIS Application Research School of Geography and TourismChongqing Normal UniversityChongqing401331China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Department of Military Installations Army Logistics AcademyChongqing401331China

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and *** the limitations of conventional convolutional neural networks,we propose an innovative transformer-based *** method leverages transformers,which are adept at processing data sequences,to enhance cloud detection ***,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud *** extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud *** integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation ***,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

关键词： Cloud transformer image segmentation remotely sensed imagery pyramid vision transformer

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Long Short-Term Memory Auto-Encoder-Based Position Prediction Model for Fixed-Wing UAV during Communication Failure

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 173-181页

作者： Kant, Ravi Saini, Poonam Kumari, Julee Defence Electronics Application Laboratory Defence Research and Development Organisation Dehradun248001 India Department of Computer Science and Engineering Punjab Engineering College Chandigarh148023 India Centre for Development of Advanced Computing Mohali160071 India

Unmanned aerial vehicles (UAV's) safe flight is one of the most important tasks of ground control stations (GCS). However, sometimes due to communication failure between ground data terminal (GDT) and UAV, GCS is not able to ensure a safe flight, which could be hazardous. In such conditions, the accurate UAV position finding prediction becomes essential to serve as sustenance for UAVs. Hence, this article proposes a UAV's position finding prediction model using a deep neural network application for successfully pointing the GDT toward UAV in real time. The proposed work consists of three components, namely data preprocessing, long short-term memory autoencoder (LSTM-AE)-based deep learning model, and mathematical formulation to calculate the ground radar look angle. First, we use different preprocessing techniques on a historical raw dataset for better visualization and generate patterns. To achieve this, Tensor flow along with Keras packages have been used. Next, an LSTM autoencoder deep learning model is applied to the same dataset in order to predict an accurate 4-D position of the UAV. Finally, the output from the LSTM autoencoder model is used in a mathematical model to calculate the GDT look-angles called Azimuth and Elevation. Both the parameters have been used to point the GDT toward the predicted point on the Airspace. We evaluate our proposed model against famous evaluation matrices, namely mean absolute error, mean square error, and root-mean-square error to validate the experimental results. © 2022 IEEE.

关键词： Real time systems

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GBC: Gaussian-Based Colorization and Super-Resolution for 3D Reconstruction 19

GBC: Gaussian-Based Colorization and Super-Resolution for 3D...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its applications in Industry, VRCAI 2024

作者： Liao, Jiecheng He, Shi Yuan, Yichen Zhang, Hui Department of Computer Science and Technology BNU-HKBU United International College ZhuHai China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400713484

In this paper, we introduce GBC, an advanced framework for transforming low-resolution, monochrome video sequences into high-resolution, colorized, and geometrically accurate 3D models. GBC combines Bidirectional Optical Flow Super-Resolution (BOF-SR) for temporal super-resolution with a novel colorization approach, Temporal Optical Flow-based Colorization (TOF-CO), designed to enhance frame-to-frame temporal consistency. The integration of these modules with COLMAP-based 3D Gaussian splatting further extends GBC's capability to reconstruct high-fidelity 3D scenes. Additionally, we created a custom dataset tailored to the challenges of low-light, low-quality historical footage, enabling robust evaluation alongside public datasets. This solution offers a new approach to video restoration, advancing temporal coherence, color accuracy, and 3D scene fidelity. The source code is publicly available at https://***/ffftuanxxx/GBC. © 2024 ACM.

关键词： 3D reconstruction

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

Chain of Condition: Construct, Verify and Solve Conditions f...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

ISBN: (纸本)9798891761681

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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Science China Earth Sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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An Approach to Monitor Urban Growth through Deep Learning based Change Detection Technique using Sentinel-2 Satellite Images

An Approach to Monitor Urban Growth through Deep Learning ba...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Saurabh Srivastava Tasneem Ahmed Department of Computer Application Advanced Computing Research Laboratory Integral University Lucknow Uttar Pradesh India

关键词： Support vector machines Satellites Smart cities Urban areas Stability criteria Sociology Vegetation mapping

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Detection and Analysis of Crop Stresses in Precision Agriculture Using Sentinel-2 Images

Detection and Analysis of Crop Stresses in Precision Agricul...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Gausiya Yasmeen Tasneem Ahmed Ajay Kumar Maurya Department of Computer Application Advanced Computing Research Laboratory Integral University Lucknow Uttar Pradesh India Department of Electronics and Communication Engineering National Institute of Technology Patna

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The ability to identify and monitor plant reactions to stress remotely has become a very challenging task for precision agriculture. Amongst the key factors in improving productivity of agriculture, one is, the identification of crop stresses. Therefore, relying just on visual inspection may lead to an inaccurate diagnosis, which would ultimately prevent the damaged plants from receiving corrective measures. Crop Stress can be abiotic like drought, flood, and salinity, while biotic stress includes weeds, other wild plants, and pests. In this paper, a focus has been laid to identify various causes of crop stress in the area located near the district of Gonda, situated in Uttar Pradesh state, India. For this purpose, Sentinel-2 images have been utilized to obtain the various vegetation indices such as NDVI, NDWI, GNDVI, PSSR, and Aphid Index, which are the most common to extract abiotic and biotic stress in crops.

关键词： Precision agriculture Visualization Image processing Salinity (geophysical) Crops Vegetation mapping Satellite images Reliability Stress Monitoring

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Intelligent Virtual Breast Palpation Environment

Intelligent Virtual Breast Palpation Environment

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IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

作者： Ourahmoune Amel Department of Artificial Intelligence and Data Sciences Faculty of Computer Sciences Research in Intelligent Computing Mathematics and Application Laboratory University of Sciences and Technology Houari Boumediene Algiers Algeria

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Manual breast palpation is a primary examination technique for the detection of breast cancer. This skill requires rigorous training to be effective. In this study, we have developed an immersive virtual environment to assist learners in mastering breast medical palpation. This study used a Leap Motion controller to acquire the learner's three hand fingers: the middle, index, and ring finger movements. An immersive environment rendering is ensured with the Oculus Quest 2 head-mounted display. The proposed virtual environment included an intelligent agent who supervised the learner by utilizing gesture recognition by Mobile Net V3 a convolution neural network in transfer learning. This study considered gestures in three different patterns: circular, star, and sinusoidal shapes. Mobile net V3 classified and predicted images of gesture shapes with high confidence and an accuracy of 98%. It evaluated the correctness of palpation gestures in real-time, which makes it suitable for virtual reality use.

关键词： Training Shape Convolution Neural networks Transfer learning Virtual environments Stars Gesture recognition Rendering (computer graphics) Real-time systems

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