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检索条件"任意字段=2012 3rd International Conference on Computer and Communication Technology, ICCCT 2012"

共 1592 条记录，以下是11-20 订阅

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What Images Say/What Users See. Exploring Mobile Augmented Reality for Visual History-Telling of Architectural Heritage 3rd

What Images Say/What Users See. Exploring Mobile Augmented R...

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3rd international and Interdisciplinary conference on Image and Imagination, IMG 2021

作者： Maiezza, Pamela Franchi, Fabio Tata, Alessandra Graziosi, Fabio Brusaporci, Stefano University of L’Aquila L’Aquila Italy

ISBN: (纸本)9783031259050

Aim of the paper is the study of the potentialities offered by augmented reality visualizations of 3D models to communicate the history of architectural heritage, also for its valorization. The case study is the Basilica of Collemaggio in L’Aquila (IT). The church was the subject of a stylistic restoration that returned the building to an ideal former medieval appearance. Based on the digital survey of the current church, a 3D model of the baroque structure of the main nave, which no longer exists, is created by studying the graphic and photographic documentation of the period. An augmented reality application is developed at the University of L’Aquila, and used with the aim of visualising the 3D model to tell the story of the Basilica, so that superimposed information can be displayed dynamically and in real time. Young people aged around 20 years were asked to navigate the visualisations and then to answer a form with questions in order to evaluate the effect of computer based visualisations. The answers are analysed, and in particular it can be seen that while there is not a high level of explicit awareness of digital tools and techniques, there is clearly a habit of frequenting digital environments. Visualizations in augmented reality arouse particular interest. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visual communication

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Design of Oil Field Data Fusion Integration Platform 3

Design of Oil Field Data Fusion Integration Platform

引用

3rd international conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Song, Caili Liang, Bin Xu, Yuanzhi He, Zhizhou School of Computer Science Xi'an Shiyou University Xi'an710065 China Shaanxi Textile Science Institute Co. Ltd. Xi'an710038 China

ISBN: (纸本)9781643684444

With the deepening of oilfield digitization process, oilfield data sharing can improve data utilization and is conducive to mining oilfield production laws. Due to historical reasons, there are great differences in the formats of databases and data tables used in different oil fields. Under the condition of keeping the data storage environment and format of each oil field unchanged, the establishment of an integrated data fusion platform can improve the access speed of data and enhance the utilization effect. The platform integrates the data in Oracle, SQL Server 2008, SQL Server 2012 and other databases in the form of data mapping. The integrated platform adopts SQL Server 2012. In the process of data fusion, data integrity and legitimacy directly affect the accuracy of data mining and data analysis. The system belong to B / S architecture, uses HTML and C# language to jointly carry out front and back-end development in vs 2015 environment, and realizes data fusion of different databases. Taking the information of 10000 wells of Chuanqing drilling company as the test data, the system can extract and display the data as required, and remind the error of unreasonable data, so that users can check and calibrate the data, so as to achieve the goal of data availability. © 2023 The Authors.

关键词： Data fusion

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Sharing platform of digital specimen of wood canker based on WebGIS in Xinjiang province: Architecture, design and implementation 3

Sharing platform of digital specimen of wood canker based on...

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3rd international conference on computers, Information Processing and Advanced Education, CIPAE 2022

作者： Li, Quansheng Sun, Wei Ma, Rong College of Computer and Information Engineering Xinjiang Agricultural University Urumchi China Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China Collage of Forestry Xinjiang Agricultural University Urumchi China

ISBN: (纸本)9781665468121

The collection, integration, analysis and sharing of digital specimens of wood canker is an important basic work for conducting relevant scientific research, education and teaching. In this study, for all kinds of users, such as scientific research, education, experts and the public, the architecture of Sharing platform of digital specimen of wood canker in Xinjiang province was designed by microservice architecture, the microservice function tree was constructed by domain-driven design method, the microservice cluster was built by using Spring Cloud technology components, and 8 kinds of microservices were developed based on WebGIS technology. The application results show that the platform is easy to use, provides data sharing and services through visualization and interactivity, and can provide decision-making data resources support for scientific research, education, monitoring, quarantine and prevention of wood canker. © 2022 IEEE.

关键词： Wood

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Parallel Model to Detect Attacks Using Evolutionary Based Technique

Parallel Model to Detect Attacks Using Evolutionary Based Te...

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Advances in Computing, communication, Embedded and Secure Systems (ACCESS), international conference on

作者： Sunitha Guruprasad Rio D’Souza G. L. Department of Computer Science and Engineering St Joseph Engineering College Mangaluru India

Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimization-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation.

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Enhancing Weakly Supervised Semantic Segmentation through Patch-Based Refinement

Enhancing Weakly Supervised Semantic Segmentation through Pa...

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Iranian Machine Vision and Image Processing (MVIP)

作者： Narges Javid Tajrishi Sepehr Amini Afshar Shohreh Kasaei Department of Computer Engineering Sharif University of Technology Tehran Iran

Weakly-Supervised Semantic Segmentation (WSSS) with image-level labels, commonly uses Class Activation Maps (CAM) to generate pseudo-labels. However, Convolutional Neural Networks (CNNs), with their limited local receptive field, often struggle to identify entire object regions. Recently, the Vision Transformer (ViT) architecture has been employed instead of CNNs to capture long-range feature dependencies, by using the self-attention mechanism. Despite its advantages, ViT tends to overlook local feature details, leading to attention maps with low quality and unclear object details. This paper introduces a novel method to enhance the local details in attention maps by leveraging local patches. These local patches are selected from regions that are more likely to contain the desired objects. By effectively utilizing these local patches during the training and generation stages, the model yields more detailed attention maps. Extensive experiments were conducted on the PASCAL VOC 2012 benchmark dataset to demonstrate the efficacy of the proposed approach. The results show significant improvements (+2.6% mIoU) with minimal computational overhead, underscoring the potential of the proposed method in the field of Weakly-Supervised Semantic Segmentation.

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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches

Empirical Analysis of Digital Currency Herding and Contagion...

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Electrical Power Engineering, communication and Computing technology(ODICON), Odisha international conference on

作者： Nrusingha Tripathy Sarbeswara Hota Debahuti Mishra Biswajit Dash Subrat Kumar Nayak Jogeswar Tripathy Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Science and Engineering Raajdhani Engineering College Bhubaneswar Odisha India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets.

关键词： Deep learning Adaptation models Smoothing methods Recurrent neural networks Quantum computing Online banking Computational modeling Data models Forecasting Long short term memory

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Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation

Leveraging Swin Transformer for Local-to-Global Weakly Super...

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Iranian Machine Vision and Image Processing (MVIP)

作者： Rozhan Ahmadi Shohreh Kasaei Department of Computer Engineering Sharif University of Technology Tehran Iran

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudolabels from class activation maps (CAMs). Due to the localized pattern detection of Convolutional Neural Networks (CNNs), CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that Vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://***/RozhanAhmadi/SWTformer

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A Rapid Image Semantic Segment Method Based on Deeplab V3+ 3rd

A Rapid Image Semantic Segment Method Based on Deeplab V3+

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3rd international conference on Artificial Intelligence, 2022

作者： Zhang, Hailong Luo, Hanwu Li, Wenzhen Qin, Ruofeng Wuhan NARI Limited Liability Company State Grid Electric Power Research Institute Wuhan China East Inner Mongolia Electric Power Co. Ltd. Erdos China

ISBN: (纸本)9789811694226

As one of research directions in the fields of image processing and computer vision, image semantic segmentation classifies images by pixel for semantic comprehension. Due to the lack of a general theory of segmentation, conventional image semantic segmentation algorithms are mostly on the strength of specific image models, so a variety of methods develope with their respective range for using and relative merits. What’s more, it’s a pity that most of them can only use the underlying features to generate simple segmentation results of foreground and background. Since the breakthrough in deep learning at the ImageNet competition in 2012, convolutional neural networks have taken every field of computer vision by storm. Image semantic segmentation has broken through the limits of traditional methods rapidly after the integration of deep learning. After making use of convolutional neural networks, its accuracy for segmentation has been improved effectively so that image semantic segmentation at present can further meet the requirements of segmentation technology in different application scenarios. In recent years, it has developed rapidly and gradually become a research hotspot. This paper mainly studies the image semantic segmentation methods about Deeplab series based on deep learning and raises a lightweight model for semantic segmentation based on Deeplab v3+. Regarding MobileNet as backbone to extract features, it makes use of atrous spatial pyramid pooing to gain global multi-scale features. Then the decoder structure concatenates multi-scale features and low-level features to enhance the expression for spatial features and improve its performance. After sufficient experiments on PASCAL VOC using the fast semantic segmentation model, the result reveals that it obtains a good balance between accuracy and speed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Role of Educational Data Mining and Learning Analytics Techniques Used for Predictive Modeling

Role of Educational Data Mining and Learning Analytics Techn...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： Kanksha Kaur Omdev Dahiya School of Computer Science and Engineering Lovely Professional University Punjab India

The use of data mining techniques to answer important educational questions is done with educational data mining that may be related to predicting students' performance or assessment. The purpose of these remains to address the goal of meeting and fulfilling the learning objectives. Learning analytics is a new approach for teachers to think about education and involves the visualization of data about students to improve learning. It is related to management strategies that prioritize quantitative measurements, which can sometimes be at odds with a teaching-focused approach to education. The purpose of this study is to perform a systematic literature review of the various techniques used in both educational data mining and learning analytics. The area of educational data mining utilizes data mining techniques to examine educational data. Meanwhile, learning analytics centers on utilizing data to gain knowledge about learning processes and student performance. The review will consider various techniques, including statistical methods, machine learning algorithms, and data visualization techniques, which are commonly used. The goal is to identify the most effective techniques for analyzing educational data and improving student learning outcomes. By performing a systematic literature review, this study will provide an overview of the current state of research in educational data mining and learning analytics. It will also identify gaps in the literature and suggest areas for future research. Ultimately, the findings of this study will be of great value to researchers, educators, and policymakers who are interested in using data to enhance the learning experience. For performing this, a few research questions have been designed to select relevant studies. In this study, the research articles from a decade, 2012 to 2022, are taken into consideration. From the overall search results, 41 studies have been taken into consideration that has addressed the scope and significan

关键词： Epidemics Systematics Machine learning algorithms Statistical analysis Bibliographies Education Data visualization

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings 3

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Xu, Zhen Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings. © 2022 IEEE.

关键词： adaptive weights cross-swap generalization inter-domain intra-domain normalization RUL

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