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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

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SSRN

SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification

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Heliyon 2024年第19期10卷 e38515页

作者： Agbesi, Victor Kwaku Chen, Wenyu Yussif, Sophyani Banaamwini Ukwuoma, Chiagoziem C. Gu, Yeong Hyeon Al-antari, Mugahed A. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology No. 610059 Sichuan Chengdu China Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea

Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both semantic and contextual information from text at various levels of granularity while avoiding overfitting. Prior methods have often demonstrated suboptimal performance, largely due to the limitations of the feature extraction techniques employed. To address these challenges, this study introduces Multi-TextCNN, an advanced feature extractor designed to capture essential textual information across multiple levels of granularity. Multi-TextCNN is integrated into a proposed classification model named MuTCELM, which aims to enhance text classification performance. The proposed MuTCELM leverages five distinct sub-classifiers, each designed to capture different linguistic features from the text data. These sub-classifiers are integrated into an ensemble framework, enhancing the overall model performance by combining their complementary strengths. Empirical results indicate that MuTCELM achieves average improvements across all datasets in accuracy, precision, recall, and F1-macro scores by 0.2584, 0.2546, 0.2668, and 0.2612, respectively, demonstrating significant performance gains over baseline models. These findings underscore the effectiveness of Multi-TextCNN in improving model performance relative to other ensemble methods. Further analysis reveals that the non-overlapping confidence intervals between MuTCELM and baseline models indicate statistically significant differences, suggesting that the observed performance improvements of MuTCELM are not attributable to random chance but are indeed statistically meaningful. This evidence indicates the robustness and superiority of MuTCELM across various languages and text classification tasks. © 2024 The Author(s)

关键词： Deep learning Ensemble learning Feature extraction Multi-TextCNN Text classification

Sport Field Reservation Based on mobile application 7

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Sport Field Reservation Based on Mobile Application

7th International Conference on ICT for Smart Society, ICISS 2020

作者： Claudinus, Tonto Prayoga Wicaksana, Made Kornelius Sitorus, Nicolas Ariqi Gustiandza, Marsa Oktavia, Tanty Lumban Gaol, Ford Hosoda, Takaaki Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665404228

This paper presents the sports apps based on a mobile application that has a function to help the provider or the owner of the sports field and sports vendor to promote their business in the apps. Due to the popularity and convenience that mobile applications had and high interest from many people in having a healthy life, it is a great opportunity to combine both of them. Therefore, this study focusses to build mobile sports application using object-oriented analysis and design method, that could meet the demand of lots of peoples that wants to do a sport especially a ball sport. Otherwise, the application can help and support the community and public, especially for ball sports fans, to make reservations for the sports field easier without having to come to the location and also meet their sporting needs. © 2020 IEEE.

关键词： Sports

Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment

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arXiv 2024年

作者： Khalid, Mutahira Rahman, Raihana Abbas, Asim Kumari, Sushama Wajahat, Iram Bukhari, Syed Ahmad Chan TIB - Leibniz Information Centre for Science and Technology Lower Saxony Hannover Germany Department of Statistics Harvard University CambridgeMA United States School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Sr Technical Program Manager Routing and Planning Amazon BellevueWA United States Institute for Biotechnology St. John's University New YorkNY United States Division of Computer Science Mathematics and Science St. John's University New YorkNY United States

Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automation and completeness. Despite efforts in automation and the utilization of expert-created ontologies, gaps in connectivity remain prevalent within KGs. In response to these challenges, we propose an innovative approach termed "Medical Knowledge Graph Automation (M-KGA)". M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, thereby enhancing the completeness of knowledge graphs through the integration of pre-trained embeddings. Our approach introduces two distinct methodologies for uncovering hidden connections within the knowledge graph: a cluster-based approach and a node-based approach. Through rigorous testing involving 100 frequently occurring medical concepts in Electronic Health Records (EHRs), our M-KGA framework demonstrates promising results, indicating its potential to address the limitations of existing knowledge graph automation techniques. © 2024, CC BY-NC-ND.

关键词： Knowledge graph

Classifying human activities with inertial sensors: A machine learning approach

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arXiv 2021年

作者： Imran, Hamza Ali Wazir, Saad Iftikhar, Usman Latif, Usama Department of Computing School of Electrical Engineering & Computer Science National University of Science and Technology Islamabad Pakistan Pakistan Mobile Communications Limited

Human Activity Recognition (HAR) is an ongoing research topic. It has applications in medical support, sports, fitness, social networking, human-computer interfaces, senior care, entertainment, surveillance, and the list goes on. Traditionally, computer vision methods were employed for HAR, which has numerous problems such as secrecy or privacy, the influence of environmental factors, less mobility, higher running costs, occlusion, and so on. A new trend in the use of sensors, especially inertial sensors, has lately emerged. There are several advantages of employing sensor data as an alternative to traditional computer vision algorithms. Many of the limitations of computer vision algorithms have been documented in the literature, including research on Deep Neural Network (DNN) and Machine Learning (ML) approaches for activity categorization utilizing sensor data. We examined and analyzed different Machine Learning and Deep Learning approaches for Human Activity Recognition using inertial sensor data of smartphones. In order to identify which approach is best suited for this application. © 2021, CC BY.

关键词： Digital signal processing

The Fisher Geometry and Geodesics of the Multivariate Normals, without Differential Geometry

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arXiv 2023年

作者： Lawson, Brodie A.J. Burrage, Kevin Mengersen, Kerrie Santos, Rodrigo Weber dos Centre for Data Science Queensland University of Technology Australia School of Mathematical Sciences Queensland University of Technology Australia ARC Centre of Excellence for Plant Success in Nature and Agriculture Australia Department of Computer Science University of Oxford United Kingdom Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil

Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling measure of statistical distance, and paths of shorter distance improve sampling techniques that leverage a sequence of distributions in their operation. Unfortunately, even for a distribution as generally tractable as the multivariate normal distribution, this information geometry proves unwieldy enough that closed-form solutions for shortest-distance paths or their lengths remain unavailable outside of limited special cases. In this review we present for general statisticians the most practical aspects of the Fisher geometry for this fundamental distribution family. Rather than a differential geometric treatment, we use an intuitive understanding of the covariance-induced curvature of this manifold to unify the special cases with known closed-form solution and review approximate solutions for the general case. We also use the multivariate normal information geometry to better understand the paths or distances commonly used in statistics (annealing, Wasserstein). Given the unavailability of a general solution, we also discuss the methods used for numerically obtaining geodesics in the space of multivariate normals, identifying remaining challenges and suggesting methodological *** Codes 62B11 (primary), 62-02, 62-08 (secondary) © 2023, CC BY.

关键词： Geometry

Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

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arXiv 2022年

作者： He, Hui Zhang, Qi Yi, Kun Shi, Kaize Niu, Zhendong Cao, Longbing The School of Medical Technology Beijing Institute of Technology Beijing100081 China The School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computer Science Tongji University Shanghai201804 China The Data Science and Machine Intelligence Lab University of Technology SydneyNSW2007 Australia The Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China The DataX Research Centre School of Computing Macquarie University SydneyNSW2109 Australia

Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE’s superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world *** Codes 68Txx Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

A Software Defect Prediction Method Based on Learnable Three-Line Hybrid Feature Fusion

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SSRN

SSRN 2023年

作者： Tang, Yu Dai, Qi Du, Ye Chen, Lifang Niu, Xuanwen School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Automation College of Information Science and Engineering China University of Petroleum-Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China

Software defect prediction plays a crucial role in ensuring the security and quality of software systems. However, it faces challenges posed by high-dimensional features present in software defect datasets and the limited effectiveness of traditional nonlinear dimensionality reduction methods in extracting essential feature information. To address these issues, we propose a novel approach called learnable three-line hybrid feature fusion (LTHFFA), which incorporates the principle of three-line hybrid breeding into feature fusion for the first time. In this method, three distinct dimensionality reduction techniques are initially employed to obtain three separate sets of features. Subsequently, a learnable weight factor feature fusion method is proposed to facilitate automatic learning and dynamic adjustment of feature weights. By integrating the three feature sets based on the principle of three-line hybrid breeding, we derive learnable three-line hybrid fusion features. These features are then utilized in the context of software defect prediction. Experimental results demonstrate the superior performance of LTHFFA compared to nine other dimensionality reduction methods across seventeen publicly available software defect datasets. LTHFFA exhibits the ability to effectively integrate multiple feature sets, reduce feature redundancy, and enhance predictive accuracy. Moreover, statistical analysis using Friedman ranking and Holm's post-hoc test confirms the significant advantage of LTHFFA over alternative dimensionality reduction methods. © 2023, The Authors. All rights reserved.

关键词： Defects

An Analysis of Encapsulating Security Payload in Wireless Network Security

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An Analysis of Encapsulating Security Payload in Wireless Ne...

Emerging Research in Computational science (ICERCS), International Conference on

作者： Devika A. Verma Vaishali Singh Beemkumar. N Ritu Shree Deepak Minhas S. Yuvaraj Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The Encapsulating Safety Payload (ESP) is a necessary part of the IPsec suite of protocols, imparting a critical layer of security within a Wi-Fi community. This paper aims to evaluate ESP and its software in wireless community protection comprehensively. ESP capabilities as a secured payload encapsulating data via imparting integrity, authentication, and confidentiality. It commonly accomplishes this by using a mixture of cryptography and tunneling to shield the contents of the packets from alteration, eavesdropping, and unauthorized get entry. Moreover, this paper outlines the distinctive ESP modes and cipher suites, presenting how they may be used to defend data being despatched over a Wi-Fi community. ESP additionally affords numerous safety enhancements, such as protection from replay assaults, packet length control, and critical negotiation. Furthermore, this paper explores the challenges associated with implementing and deploying ESP in wireless networks. Subsequently, the paper will pay attention to the numerous overall performance issues for ESP utilization by analyzing the effect of latency, throughput, and power intake on ESP-enabled Wi-Fi networks.

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