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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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Free-space Continuous-variable Quantum Secret Sharing

arXiv

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arXiv 2025年

作者： Yang, Fangli Chang, Liang Qiu, Daowen Pan, Minghua Xiong, Wanjun Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

Free-space quantum cryptography has the potential to enable global quantum communication. However, most existing continuous-variable quantum secret sharing (CV-QSS) schemes rely on fiber channels. In this paper, we present a CV-QSS protocol designed for free-space transmission and construct models of crucial parameters, including channel transmittance, excess noise, and interruption probability, thus deriving the bound of the secret key rate. In particular, we provide a multi-source excess noise model for free-space CV-QSS based on the local local oscillator (LLO) scheme and a straightforward optimization of the noise. Furthermore, our research considers the impact of atmospheric turbulence on the protocol. Simulation results demonstrate that the intensification of atmospheric turbulence adversely affects the aforementioned crucial parameters, leading to a significant reduction in the key rate. However, our protocol shows its capability to securely share secrets over a distance of exceeding 60 km among five participants in the presence of turbulence with Cn2 = 1 × 10−15m−2/3, while maintaining a high key rate of approximately 0.55 bit/pulse over a distance of 10 km across twenty participants. These findings suggest that efficient CV-QSS in free space is indeed achievable. This research may serve as a reference for the design and optimization of the practical CV-QSS system. Copyright © 2025, The Authors. All rights reserved.

关键词： Atmospheric turbulence

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BLOCK-TO-SCENE PRE-TRAINING FOR POINT CLOUD HYBRID-DOMAIN MASKED AUTOENCODERS

arXiv

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arXiv 2024年

作者： Zha, Yaohua Dai, Tai Wang, Yanzi Guo, Hang Zhang, Taolin Ouyang, Zhihao Fan, Chunlin Chen, Bin Chen, Ke Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Institute of Visual Intelligence Pengcheng Laboratory China College of Computer Science and Software Engineering Shenzhen University China Harbin Institute of Technology Shenzhen China Aitist.ai

Point clouds, as a primary representation of 3D data, can be categorized into scene domain point clouds and object domain point clouds based on the modeled content. Masked autoencoders (MAE) have become the mainstream paradigm in point clouds self-supervised learning. However, existing MAE-based methods are domain-specific, limiting the model’s generalization. In this paper, we propose to pre-train a general Point cloud Hybrid-Domain Masked AutoEncoder (PointHDMAE) via a block-to-scene pre-training strategy. We first propose a hybrid-domain masked autoencoder consisting of an encoder and decoder belonging to the scene domain and object domain, respectively. The object domain encoder specializes in handling object point clouds and multiple shared object encoders assist the scene domain encoder in analyzing the scene point clouds. Furthermore, we propose a block-to-scene strategy to pre-train our hybrid-domain model. Specifically, we first randomly select point blocks within a scene and apply a set of transformations to convert each point block coordinates from the scene space to the object space. Then, we employ an object-level mask and reconstruction pipeline to recover the masked points of each block, enabling the object encoder to learn a universal object representation. Finally, we introduce a scene-level block position regression pipeline, which utilizes the blocks’ features in the object space to regress these blocks’ initial positions within the scene space, facilitating the learning of scene representations. Extensive experiments across different datasets and tasks demonstrate the generalization and superiority of our hybrid-domain model. The code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

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computers, Materials & Continua 2021年第12期69卷 3549-3561页

作者： Wei Sun Xuan Chen Xiaorui Zhang Guangzhao Dai Pengshuai Chang Xiaozheng He Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Rensselaer Polytechnic Institute TroyNY12180USA

Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera *** has gradually become a core technology of intelligent transportation *** existing vehicle re-identification models adopt the joint learning of global and local ***,they directly use the extracted global features,resulting in insufficient feature ***,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional *** solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this *** model consists of global and local *** global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation *** addition,multi-scale pooling operations are used to obtain multiscale *** the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local *** features from both branches are combined to provide a more comprehensive and distinctive feature *** experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.

关键词： Vehicle re-identification region batch dropblock multi-feature learning local attention

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Large Language Models for Mobile GUI Text Input Generation: An Empirical Study

arXiv

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arXiv 2024年

作者： Cui, Chenhui Li, Tao Wang, Junjie Chen, Chunyang Towey, Dave Huang, Rubing School of Computer Science and Engineering Macau University of Science and Technology Taipa999078 China Institute of Software Chinese Academy of Sciences Beijing100190 China Department of Computer Science Technical University of Munich Heilbronn74076 Germany School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo315100 China Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute Guangdong Zhuhai519099 China

Mobile applications (apps) have become an essential part of our daily lives, making ensuring their quality an important activity. Graphical User Interface (GUI) testing is a quality assurance method that has frequently been used for mobile apps. When conducting GUI testing, it is important to generate effective text inputs for the text-input components. Some GUIs require these text inputs to be able to move from one page to the next: This can be a challenge to achieving complete UI exploration. Recently, Large Language Models (LLMs) have demonstrated excellent text-generation capabilities. To the best of our knowledge, there has not yet been any empirical study to evaluate different pre-trained LLMs’ effectiveness at generating text inputs for mobile GUI testing. This paper reports on a large-scale empirical study that extensively investigates the effectiveness of nine state-of-the-art (SOTA) LLMs in Android text-input generation for UI pages. We collected 114 UI pages from 62 open-source Android apps and extracted contextual information from the UI pages to construct prompts for LLMs to generate text inputs. The experimental results show that some LLMs can generate more effective and higher-quality text inputs, achieving a 50.58% to 66.67% page-pass-through rate (PPTR) — measured as the proportion of the LLM-generated text inputs that can result in moving the GUI to the next page. We also found that using more complete UI contextual information can increase the PPTRs of LLMs for generating text inputs. We conducted an experiment to evaluate the bug-detection capabilities of LLMs by directly generating invalid text inputs. We collected 37 real-world bugs related to text inputs. The experimental results show that using LLMs to directly generate invalid text inputs for bug detection is insufficient: The bug-detection rates of the nine LLMs are all less than 23%. Among them, GPT-4 achieves the highest bug-detection rate of 22.88%. In addition, we also describe six insi

关键词： Quality assurance

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FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

arXiv

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arXiv 2024年

作者： Wang, Tianfu Fan, Qilin Wang, Chao Yang, Long Ding, Leilei Yuan, Nicholas Jing Xiong, Hui School of Computer Science and Technology University of Science and Technology of China China School of Big Data and Software Engineering Chongqing University China Guangzhou HKUST Fok Ying Tung Research Institute China School of Artificial Intelligence Peking University China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at GitHub1 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models

Keeping Pace with Ever-Increasing Data: Towards Continual Le...

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International Conference on software engineering (ICSE)

作者： Shuzheng Gao Hongyu Zhang Cuiyun Gao Chaozheng Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Big Data and Software Engineering Chongqing University China

Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for providing up-to-date code intelligence services to developers. In this paper, we aim at the following problem: How to enable code intelligence models to continually learn from ever-increasing data? One major challenge here is catastrophic forgetting, meaning that the model can easily forget knowledge learned from previous datasets when learning from the new dataset. To tackle this challenge, we propose REPEAT, a novel method for continual learning of code intelligence models. Specifically, REPEAT addresses the catastrophic forgetting problem with representative exemplars replay and adaptive parameter regularization. The representative exemplars replay component selects informative and diverse exemplars in each dataset and uses them to re-train model periodically. The adaptive parameter regularization component recognizes important parameters in the model and adaptively penalizes their changes to preserve the knowledge learned before. We evaluate the proposed approach on three code intelligence tasks including code summarization, software vulnerability detection, and code clone detection. Extensive experiments demonstrate that REPEAT consistently outperforms baseline methods on all tasks. For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61, and 1.72 on code summarization, vulnerability detection and clone detection, respectively.

关键词：

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ASYMMETRIC LOSS FOR POSITIVE-UNLABELED LEARNING

ASYMMETRIC LOSS FOR POSITIVE-UNLABELED LEARNING

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Cong Pu, Jian Xu, Zhi Zhang, Junping Software Engineering Institute East China Normal University China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Computer Science Fudan University China

ISBN: (纸本)9781665438643

Positive-unlabeled (PU) learning is a learning paradigm when only positive and unlabeled data are available in the training stage. This paradigm is particularly useful for the applications that the negative samples are hard to define or expensive to obtain. We propose a simple yet effective and scalable method to address PU learning with a novel asymmetric loss. The proposed asymmetric loss behaviors differently for the prediction errors of the labeled and unlabeled samples, and thus encourages the identification of the negative examples from the unlabeled set. For the PU learning with SCAR assumption, neither hyper-parameter nor class prior is required to be tuned or known. For the situation with selection bias on the labeled samples, we propose a heuristic method to automatically choose the hyper-parameter according to the class prior on the training data. Compared with previous approaches, our method only requires a slight modification of the conventional cross-entropy loss and is compatible with various deep neural networks in an end-to-end way. Extensive experiments on synthetic and real-world datasets with and without SCAR assumption verify the superior performance of the proposed method. © 2021 IEEE

关键词： Deep neural networks

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Simulation on Molten Pool Characteristics of Al2O3 Laser Selective Melting Based on Energy Input Model

Simulation on Molten Pool Characteristics of Al2O3 Laser Sel...

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2019 International Conference on Optoelectronic Science and Materials, ICOSM 2019

作者： Chen, Xiaobing Sun, Quan Sun, Quanping Wu, Haibing Xu, Qian Key Lab of Advanced Manufacturing Technology of Jiangsu Province Huaiyin Institute of Technology Huai'an Jiangsu Province223003 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu Province223003 China

In order to obtain the formation rule of molten pool characteristics in SLM process of ceramics, an energy input model for SLM process of Al2O3 ceramics is established. The model is related to laser power P, scanning speed v, scanning spacing s and thickness h of powder layer. The influence of process parameters in the energy model on molten pool characteristics is analyzed by simulation of temperature field of multi-layer and multi-channel SLM process. The simulation results show that when the laser power P is 105W, the scanning speed v is 50mm/s, the initial temperature of powder is 330°C and the thickness of powder layer is 0.20mm, the molten pool formed is easy to form Al2O3 ceramic specimens. © Published under licence by IOP Publishing Ltd.

关键词： Selective laser melting

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