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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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2023 IEEE 12th international conference on Cloud Networking, CloudNet 2023

2023 IEEE 12th International Conference on Cloud Networking,...

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12th IEEE international conference on Cloud Networking, CloudNet 2023

ISBN: (纸本)9798350313062

the proceedings contain 68 papers. the topics discussed include: efficient algorithms for obnoxious facility location on a line segment or circle;vehicular edge computing-driven optimized multihop clustering with data aggregation;cloud telescope: a distributed architecture for capturing internet background radiation;a multi-stakeholder cloud-continuum framework for 6G networks security & service management;unveiling equity: exploring feature dependency using complex-valued neural networks and attention mechanism for fair data analysis;RoMA: resilient multi-agent reinforcement learning with dynamic participating agents;attribute-based searchable proxy re-encryption blockchain data sharing scheme;and a sensor predictive model for power consumption using machine learning.

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Analytic outlier removal in line fitting 12

Analytic outlier removal in line fitting

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12th IAPR international conference on pattern recognition - conference B: pattern recognition and Neural Networks, ICPR 1994

作者： Netanyahu, Nathan S. Weiss, Isaac Space Data and Computing Division NASA Goddard Space and Flight Center GreenbeltMD20771 United States Center for Automation Research University of Maryland College ParkMD20742 United States

ISBN: (纸本)0818662700

the conventional ordinary least squares (OLS) method of fitting a. line to a set of data points is notoriously unreliable when the amount of random noise in the input (such as an image) is significant compared with the amount of data that is correlated with the line itself. Although points which lie far away from the line (i.e., outliers) are usually only to noise, they contribute the most to the squared distances, thereby skewing the line estimate from its correct position. In this paper we present an analytic method of separating the data of interest from the outliers. We assume that the overall data (i.e., the line data plus the noise) can be modeled as a mixture of two statistical distributions. Applying a variant of the method of moments (MoM) to the assumed model yields an analytic estimate of the desired line. Key words: Line fitting, outliers, noise removal, mixture models, method of moments. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Method of moments

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Texture of Activities: Exploiting Local Binary patterns for Accelerometer Data Analysis 12

Texture of Activities: Exploiting Local Binary Patterns for ...

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12th international conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Asuroglu, Tunc Acici, Koray Erdas, Cagatay Berke Ogul, Hasan Baskent Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781509056989

recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. the problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary pattern (LBP) approach, which is frequently used in identifying textures in images, in one-dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. the experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.

关键词： activity recognition accerelometer data wearable sensor classification local binary pattern

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12th international Congress in Telematics and computing, WITCOM 2023

12th International Congress in Telematics and Computing, WIT...

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12th international Congress in Telematics and computing, WITCOM 2023

ISBN: (纸本)9783031453151

the proceedings contain 35 papers. the special focus in this conference is on Telematics and computing. the topics include: A Novel Method Based on Gunnar Farneback Method, Mathematical Morphology, and Artificial Vision for Flow Analysis in Electrochemical Reactors;search Space Reduction in Road Networks for the Ambulance Location and Allocation Optimization Problems: A Real Case Study;development of a Web-Based Calculator to Simulate Link Budget for Mobile Communications Systems at Urban Settlements;scientific Information Management System for Multidisciplinary Teams;air Quality Prediction in Smart Cities Using Wireless Sensor Network and Associative Models;User Interface of Digital Platforms Used TDHA Patients: Case Study in Educational Environment;recognition of Pollen-Carrying Bees Using Convolutional Neural Networks and Digital Image Processing Techniques;enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System;multi-labeling of Malware Samples Using Behavior Reports and Fuzzy Hashing;APOS is Not Enough: Towards a More Appropriate Way to Estimate Computational Complexity in CIC Decimation Architectures;Computational Simulation Applied to 3.5 GHz Band Microstrip Yagi Array Antenna Design for 5G Technology Mobile Wireless Device;3D Point Cloud Outliers and Noise Reduction Using Neural Networks;Development and Coding of a Data Framing Protocol for IoT/LPWAN Networks Based on 8-Bit Processing Architectures;performance Analysis of Variable Packet Transmission Policies in Wireless Sensor Networks;about a Sentiment Analysis Technique on Twitter and Its Application to Regional Tourism in Quintana Roo;comparative Study of pattern recognition Techniques in the Classification of Vertebral Column Diseases;security Verification of Instant Messaging Cryptographic Protocols;Elaboration of an Information System to Improve Operational Discipline in PEMEX with LAGS Methodology;ICIS: A Model for Context-Based Classification of Sensitive Personal In

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Construction of Sensitive Image Datasets Based on Generative Methods 12

Construction of Sensitive Image Datasets Based on Generative...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Liu, Chang Zhang, Jie Yu, Bihui Wei, Jingxuan Chen, Xiaowei Yang, Hongxin Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang110168 China College of Information Liaoning University Shenyang110031 China

ISBN: (纸本)9798350332919

Due to the special nature of sensitive information, available sensitive data resources are relatively scarce to meet the needs of research in this field. the purpose of this paper is to construct a large sensitive image dataset by generative methods, with a view to supporting research exploration in related aspects. To achieve this goal, we adopt a new idea. First, we crawl sensitive and non-sensitive class datasets from web pages and filter them manually. Here, the sensitive classes mainly involve violence, terror and pornography;second, the selected data are used as the training set to fine-tune the pre-trained generative model;then, the fine-tuned generative model is used for sensitive/non-sensitive image generation, which expands the training dataset;finally, the generated training set is passed into the classification model to do the classification task for result verification. the experimental results show that the classification results of the expanded dataset using the generative method are higher than those of the original dataset, which proves the effectiveness of the generative method to construct the dataset. © 2023 IEEE.

关键词： Sensitive data

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A Picture of the Growing ICER Community 16

A Picture of the Growing ICER Community

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12th ACM Annual conference on international computing Education Research (ICER)

作者： Simon Univ Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9781450344494

this bibliometric study examines the authorship of the papers presented at ICER since the conference began in 2005. It finds that the pattern of authorship complies well with Lotka's law, an accepted model of author distribution within a discipline. ICER's most prolific authors are identified and their contributions quantified, along with measures of the collaborations between authors. New authors are found to be joining the community at a steady rate, some beginning as co-authors with established community members and some joining alone or with other new authors. the analysis extends to the contributions from different countries. the conclusion is that the community of ICER authors is truly international, has a solid core around which excellent growth is evident, displays strong collaboration, and has at least one of the characteristics of a full-fledged discipline.

关键词： computing education research author productivity bibliometrics

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Road Traffic Accident pattern Analysis with Data Mining 12

Road Traffic Accident Pattern Analysis with Data Mining

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12th international conference on Awareness Science and Technology, iCAST 2023

作者： Zhe Chai, Albe Bing Lau, Bee theng Tsun Tee, Mark Kit McCarthy, Chris Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350324693

the discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. this work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. this work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. the Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns. © 2023 IEEE.

关键词： Association rules

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Semantic Filtering Global and Local Embeddings Fusion for Few-Shot Image Classification 12

Semantic Filtering Global and Local Embeddings Fusion for Fe...

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12th international conference on computing and pattern recognition, ICCPR 2023

作者： Nong, Luyu Wu, Min Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9798400707988

Neural networks have demonstrated superior performance in image classification tasks, however, acquiring sufficient labeled data for training them in real-world scenarios remains challenging. Few-shot learning (FSL) was proposed to address this challenge in low-data scenarios and has attracted considerable attention in recent years. In few-shot image classification tasks, most of the existing FSL methods focus on designing discriminative network structures for exploring image features, either neglecting semantic information that is crucial for accurate classification, or tending to prioritize either global or local image features. To address this issue, we propose in this work a novel model Semantic Filtering Global and Local embeddings for fusion (SFGL) for few-shot image classification. SFGL effectively integrates both global and local features while leveraging semantic information. Extensive experiments conducted on benchmark datasets demonstrate that our proposed SFGL model outperforms state-of-the-art methods in FSL tasks. © 2023 ACM.

关键词： Image classification

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Progress in Artificial Intelligence and pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Yanio Hernández Heredia Vladimir Milián Núñez José Ruiz Shulcloper

ISBN: (数字)9783031495526

ISBN: (纸本)9783031495519

this book constitutes the refereed proceedings of the 8th international Workshop on Artificial Intelligence and pattern recognition, IWAIPR 2023, held in Varadero, Cuba, in October 2023.;the 68 papers presented in the proceedings set were carefully reviewed and selected from 38 submissions.;the IWAIPR conference aims to provide a leading international forum to promote and disseminate ongoing research into mathematical methods of computing techniques for Artifical Intelligence and pattern recognition.

关键词： Image Processing and Computer Vision Computer Imaging, Vision, pattern recognition and Graphics Signal, Image and Speech Processing Artificial Intelligence

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Generalized query by transduction for online active learning

Generalized query by transduction for online active learning

引用

2009 IEEE 12th international conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Balasubramanian, Vineeth Chakraborty, Shayok Panchanathan, Sethuraman School of Computing and Informatics Arizona State University Tempe AZ 85281 United States

ISBN: (纸本)9781424444427

Transductive inference has gained popularity in recent years as a means to develop pattern classification approaches that address the specific issue of predicting the class label of a given data point, instead of the more general problem of inferring the ideal classifier function. In this paper, we propose a Generalized Query by Transduction (GQBT) approach for active learning in the online setting. this approach is based on the theory of conformal predictions, which has recently been proposed based on principles of algorithmic randomness, transductive inference and hypothesis testing. the proposed GQBT approach can be used along with any existing pattern classification algorithm, and can also be used to combine multiple criteria in selecting an unlabeled example appropriately in the active learning process. the results of our experiments with different datasets from the UCI Machine Learning repository demonstrate high promise in the proposed approach, with significantly lower label complexities than other existing online active learning approaches. the GQBT approach was also evaluated on face recognition using videos from the VidTIMIT dataset, and the observed superior performance supports the potential of applicability of the proposed approach in real-world problems. ©2009 IEEE.

关键词： Face recognition

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