检索结果-内蒙古大学图书馆

GPT-Based Automated Induction: Vulnerability Detection in Medical software

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Ma, Jiating Zhou, Yipeng Li, Qi Sheng, Quan Z. Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen518060 China Macquarie University School of Computing Faculty of Science and Engineering Macquarie ParkNSW2119 Australia Tsinghua University Institute for Network Sciences and Cyberspace Beijing100190 China Beijing National Research Center for Information Science and Technology Beijing100190 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients' privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines. © 2004-2012 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Using the improved mask R-CNN and Softer-NMS for target segmentation of remote sensing image 4

Using the improved mask R-CNN and Softer-NMS for target segm...

引用

4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Wang, Yiwen Rao, Yunbo Huang, Cheng Yang, Yang Huang, Yuanyuan He, Qixue School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Institute of Computer Sciences Chengdu China School of Cybersecurity Chengdu University of Information Technology Chengdu China Chengdu Information Technology of the Chinese Academy of Sciences Co. Ltd Chengdu China

ISBN: (纸本)9781665413220

Recently, the combination of remote sensing image processing and deep learning methods is an increasingly popular trend. In this paper, we combine the existing instance segmentation model Mask R-CNN and the target detection algorithm Softer-NMS, and propose a model based on the improved Mask R-CNN. The model can capture the edges well, thereby segmenting the target more accurately. The experimental results also verify the effectiveness of our method. Compared with the prototype Mask R-CNN, our method improves the overall segmentation accuracy, and the loss function of the mask branch and the loss function of the candidate frame of the RPN drop faster than the original model. Incidentally, there is also a good improvement in the accuracy of detection. © 2021 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things

引用

International Journal of Communication Systems 2024年第15期37卷

作者： Zanbouri, Kouros Darbandi, Mehdi Nassr, Mohammad Heidari, Arash Navimipour, Nima Jafari Yalcın, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pôle Universitaire Léonard de Vinci Paris France Communication Technology Engineering Department Tartous University Tartus Syria Department of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Kuwait Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Turkey Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. © 2024 John Wiley & Sons Ltd.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

引用

26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

来源：评论

学校读者我要写书评

暂无评论

An Optimized Sensors Associated Forest Fire Prediction and Reporting System Based on Internet of Things Technology

An Optimized Sensors Associated Forest Fire Prediction and R...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： P. Deepak Nidamanuri Srinu Benisha Maria Xavier R. Sravani Nandurkar Y Suha Afaneh Dept of Computer Science and Engineering Einstein Engineering college Tirunelveli Department of CSE QIS College of Engineering and Technology AndhraPradesh Department of ECE Jeppiaar Institute of Technology Kunnam Chennai Tamilnadu India Department of S&H QIS College of Engineering and Technology AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of cybersecurity faculty of information technology Zarqa university Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Forest fires pose a significant threat to the environment, leading to the destruction of ecosystems and contributing to climate change. This paper presents an optimized sensor-based forest fire prediction and reporting system leveraging Internet of Things (IoT) technology. The system integrates Wireless Sensor Networks (WSNs) and IoT-enabled devices to monitor environmental parameters, including temperature, CO2, and smoke levels. The system demonstrated high sensor accuracy, with temperature sensors showing a maximum deviation of ±0.5°C and gas sensors displaying an accuracy of ±2 ppm for CO2. Smoke detection accuracy was within ±0.05 ppm, making the system reliable for early fire detection. The system's response time improved significantly with increasing temperatures, detecting fire events within 10 seconds for temperatures above 100°C. The Wireless Sensor Network maintained a packet delivery ratio (PDR) of over 99% and an average latency of 200ms under high node density, ensuring timely transmission of critical data. With power consumption optimized for long-term deployment, sensor nodes were estimated to operate for up to 24 months. Overall, this system offers a cost-effective, accurate, and efficient solution for real-time forest fire monitoring and response, with potential applications in large, remote forest areas.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Accuracy Forestry Real-time systems Internet of Things Time factors Reliability Monitoring

来源：评论

学校读者我要写书评

暂无评论

Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

来源：评论

学校读者我要写书评

暂无评论

ENHANCING NEURAL SPOKEN LANGUAGE RECOGNITION: AN EXPLORATION WITH MULTILINGUAL DATASETS

arXiv

引用

arXiv 2025年

作者： Anidjar, Or Haim Yozevitch, Roi Department of Computer Software & Engineering Ariel University Golan Heights 1 Ariel4077625 Israel Department of Electrical Engineering Ariel University Golan Heights 1 Ariel4077625 Israel Faculty of Computer Science College of Management Elie Wiesel 2 Rishon LeTsiyon7579806 Israel Ariel Cyber Innovation Center Ariel University Golan Heights 1 Ariel4077625 Israel

In this research, we advanced a spoken language recognition system, moving beyond traditional feature vector-based models. Our improvements focused on effectively capturing language characteristics over extended periods using a specialized pooling layer. We utilized a broad dataset range from Common-Voice, targeting ten languages across Indo-European, Semitic, and East Asian families. The major innovation involved optimizing the architecture of Time Delay Neural Networks. We introduced additional layers and restructured these networks into a funnel shape, enhancing their ability to process complex linguistic patterns. A rigorous grid search determined the optimal settings for these networks, significantly boosting their efficiency in language pattern recognition from audio samples. The model underwent extensive training, including a phase with augmented data, to refine its capabilities. The culmination of these efforts is a highly accurate system, achieving a 97% accuracy rate in language recognition. This advancement represents a notable contribution to artificial intelligence, specifically in improving the accuracy and efficiency of language processing systems, a critical aspect in the engineering of advanced speech recognition technologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

引用

Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

来源：评论

学校读者我要写书评

暂无评论

P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

P1AC: Revisiting Absolute Pose From a Single Affine Correspo...

引用

International Conference on computer Vision (ICCV)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：