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

ISBN: (纸本)9798400707988

The proceedings contain 85 papers. The topics discussed include: entity attribute alignment method based on attribute value distribution;solving the lunar lander problem with multiple uncertainties using a deep Q-learning based short-term memory agent;sentiment analysis of Weibo text using a deep combination model;an ontology-enhanced knowledge graph embedding method;Chinese event extraction algorithm of multi-information semantic enhancements;comparative analysis of classification methods for diagnosing myasthenia gravis based on lumbar electromyography;rare association rule mining based on reinforcement learning;research on identification and prediction of financial fraud of listed companies based on machine learning;using process enhancement to predict organizational citizenship behavior via the role of sustainable training practices;self-supervised contrastive few-shot learning for motor imagery brain-computer interfaces;and a wearable brain-computer interface system for fatigue detection in driving.

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Hysteretic Oscillator-Based Reservoir computing for Audio Waveform pattern recognition

Hysteretic Oscillator-Based Reservoir Computing for Audio Wa...

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international Joint conference on Neural Networks (IJCNN)

作者： Ota, Reiji Kato, Yuzuru Katori, Yuichi Future Univ Hakodate Sch Syst Informat Sci Hakodate Hokkaido Japan

ISBN: (纸本)9798350359329;9798350359312

Reservoir computing (RC), a framework for recurrent neural networks, is adept at learning the dynamics of time series data. RC, requiring less computational cost for training than traditional recurrent neural networks, is versatile in applications such as time series generation, prediction, pattern recognition, and robot control. Recently, the integration of physical system dynamics, particularly oscillatory phenomena, into RC has been explored. This study presents an RC model incorporating oscillators with hysteresis as network elements, focusing on their ubiquitous nature. In speech pattern recognition, the audio waveform, a complex vibration pattern of air, is typically preprocessed into a frequency component time series. This study, however, attempts pattern recognition by using the raw speech waveform as the direct input to the oscillator-based reservoir. The application of this model in recognizing and classifying time series vocal data is investigated, including an assessment of the oscillator elements' bifurcation parameter on RC performance.

关键词： reservoir computing coupled oscillators timeseries recognition

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Proceedings of the 2022 11th international conference on computing and pattern recognition, ICCPR 2022

Proceedings of the 2022 11th International Conference on Com...

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11th international conference on computing and pattern recognition, ICCPR 2022

ISBN: (纸本)9781450397056

The proceedings contain 98 papers. The topics discussed include: multi-scale channel attention for Chinese scene text recognition;recognition of human walking motion using a wearable camera;an efficient lightweight spatio-temporal attention module for action recognition;rice disease recognition and feature visualization using a convolutional neural network;vehicle re-identification based on multi-scale attention feature fusion;improving pedestrian attribute recognition with dual adaptive fusion attention;coordinate attention-enabled ship object detection with electro-optical image;few-shot object detection via refining eigenspace;object detection algorithm based on coordinate attention and context feature enhancement;Swin transformer with multi-scale residual attention for semantic segmentation of remote sensing images;drivable area segmentation in unstructured roads for autonomous vehicles based on multi-sensor fusion;and a brief comparison of deep learning methods for semantic segmentation.

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pattern learning for scheduling microservice workflow to cloud containers

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international JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第9期15卷 3701-3714页

作者： Li, Wenzheng Li, Xiaoping Chen, Long Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China

patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms.

关键词： Service computing Scheduling optimization Microservice workflow Container cloud pattern recognition

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Accelerating Neural Field Training via soft Mining

Accelerating Neural Field Training via Soft Mining

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Kheradmand, Shakiba Rebain, Daniel Sharma, Gopal Isack, Hossam Kar, Abhishek Tagliasacchi, Andrea Yi, Kwang Moo Univ British Columbia Vancouver BC Canada Google Res Mountain View CA USA Google DeepMind London England Simon Fraser Univ Burnaby BC Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350353006

We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uniformly sampling the training domain, or through handcrafted heuristics. We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely, we weigh the corresponding loss by a scalar. To implement our idea we use Langevin Monte-Carlo sampling. We show that by doing so, regions with higher error are being selected more frequently, leading to more than 2x improvement in convergence speed. The code and related resources for this study are publicly available at project page.

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Demo of a Smart Musical Instrument-based Real Time pattern Detection System 24

Demo of a Smart Musical Instrument-based Real Time Pattern D...

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19th international Audio Mostly conference (AM)

作者： Silva, Nishal Turchet, Luca Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9798400709685

This manuscript focuses on a real-time pattern detection system using smart musical instruments, and its importance in Internet of Musical Things (IoMusT) applications, where smart musical instruments equipped with wireless connectivity and embedded computing devices can detect musical patterns and use them as controls for various peripheral devices. The demonstration showcases a pattern detection algorithm controlled by a digital musical instrument. The algorithm is capable of identifying pre-defined patterns during live performances and using them to trigger peripheral devices and other stage equipment. The demo features two smart musical instruments, a smart guitar, and a smart keyboard, each equipped with embedded computing devices running the real-time pattern detection algorithm.

关键词： Smart Musical Instruments Real-Time pattern recognition Internet of Musical Things

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Research on pattern recognition of English Online Discussion Text Mining Data Based on Corpus

Research on Pattern Recognition of English Online Discussion...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and soft computing, CITSC 2025

作者： Ge, Yanli Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (纸本)9798331504205

This paper studies a pattern recognition method for English online discussion text mining data based on corpus, aiming to reveal the main language patterns and potential information structures in online discussions. By constructing a special English online discussion corpus, the paper deeply processes a large amount of text data and use text mining technology and pattern recognition algorithms to extract core features. The system design includes three modules: data preprocessing, feature extraction, and pattern classification. Among them, the data preprocessing module mainly completes data cleaning and standardization;the feature extraction module uses word frequency-inverse document frequency (TF-IDF) and word embedding technology (such as Word2Vec) to obtain high-dimensional semantic features;the pattern classification module uses a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) to accurately classify text data. To verify the effectiveness of the algorithm, the paper conducted simulation experiments on the constructed corpus. The results show that the algorithm proposed in this study is superior to traditional methods in both accuracy and recall of pattern recognition. In multiple experiments, the average accuracy of pattern recognition reached 92.3% and the recall rate reached 89.7%, which significantly improved the accuracy of pattern recognition. © 2025 IEEE.

关键词： Semantics

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Causality-Aware pattern Mining Scheme for Group Activity recognition 48

Causality-Aware Pattern Mining Scheme for Group Activity Rec...

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48th Annual IEEE international Computers, software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Kim, Hyunju Son, Heesuk Lee, Dongman Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9798350376975;9798350376968

Human activity recognition is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for human activity recognition in smart spaces without privacy and accessibility issues, data streams generated by deployed ambient sensors are leveraged. In this paper, we focus on group activities by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme that extracts causality patterns from ambient sensor event sequences, to support as good recognition accuracy as the state-of-the-art models with missing or false data tolerance. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. Then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern-matching algorithm computes the likelihood of stored group activities to the given test event sequence using matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from real-world testbed and open datasets where users perform their tasks on a daily basis. Experiment results show that the proposed scheme performs higher recognition accuracy and is tolerant to missing or false data with a smaller amount of runtime overhead than the existing schemes.

关键词： Human Activity recognition Causality-Aware Learning Shallow Neural Network Ubiquitous computing Sequential pattern Mining Internet of Things

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Leveraging On-board UAV Motion Estimation for Lightweight Macroscopic Crowd Identification 22

Leveraging On-board UAV Motion Estimation for Lightweight Ma...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Anjum, Khizar Chowdhury, Tahmeed Mandava, Sreeram Piccoli, Benedetto Pompili, Dario Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08855 USA Rutgers State Univ Dept Math Sci Piscataway NJ USA

ISBN: (纸本)9798350326031;9798350326048

This paper introduces a novel, lightweight, on-board approach to crowd pattern identification, ingeniously using the processes of existing video compression standards, particularly H.264 or MPEG-4. Piggy-backing on the H.264 video-encoding algorithm, we propose real-time crowd pattern recognition and identification methodologies that can identify macroscopic patterns in as low as 2 milliseconds on NVIDIA TX2, resulting in around 45x execution time reduction compared to existing approaches. Furthermore, we introduce a temporally aware approach to pinpoint and adapt to crowd movement patterns, continuously recalibrating as a drone's Point Of View (POV) varies or observed motions diverge. Evaluating our method against publicly available datasets, we emphasize our system's performance and computational advantages, especially when faced with real-time observational shifts. In conclusion, our approach elegantly bridges the gap between crowd safety imperatives and the challenges of UAV monitoring, heralding a new era of real-time drone-centric crowd management intelligence.

关键词： Crowd pattern recognition Crowd Surveillance On-board Computation Video Encoding

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28th international conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th international conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on Image Processing, Computer Vision, and pattern recognition. The topics include: Image-Based Seal recognition: Approaches and Challenges in Current Automated Systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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