检索结果-内蒙古大学图书馆

IPRPAS: A Dataset of Physical Adversarial Samples for Assessing Object Detection in Intelligent Vehicles

IEEE Internet of Things Journal 2025年第12期12卷 21255-21263页

作者： Safarzadehvahed, Mahdieh Zulkernine, Mohammad De Araujo, Paulo Ricardo Marques Givigi, Sidney The School of Computing Queen's University Kingston Canada Queen's University Department of Electrical and Computer Engineering Kingston Canada

Physical adversarial samples can deceive Object Detection Models (ODMs). Research on the real-world impacts of these samples on ODMs in intelligent vehicles and securing them requires a publicly available benchmark dataset. In this paper, we leverage our autonomous driving platform, equipped with a high-resolution video camera and a LiDAR, to create a novel and challenging benchmark for securing 2D and multi-sensor ODMs. We present Images and Point Clouds of Resembling Physical Adversarial Samples (IPRPAS), a collection of 3409 annotated paired images and point clouds of printed, displayed or projected Resembling Physical Adversarial Samples (RPASs) in both indoor and outdoor scenarios. IPRPAS includes various common driving objects such as cars, traffic signs, traffic lights, and persons under different weather conditions, positions, distances, lighting conditions, and viewing angles. Our experimental evaluation demonstrates that IPRPAS samples deceive state-of-the-art ODMs. Our benchmark is available online at: https://***/***?persistentId=doi:10.5683/SP3/O8DLSU. This dataset and the described experiments provide a benchmark for future research on the effectiveness of RPASs and defenses against RPASs in real-world environments. © 2014 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

引用

CAAI Transactions on Intelligence Technology 2023年第3期8卷 755-769页

作者： Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

来源：评论

学校读者我要写书评

暂无评论

Towards characterizing bug fixes through dependency-level changes in Apache Java open source projects

引用

science China(Information sciences) 2022年第7期65卷 102-120页

作者： Di CUI Lingling FAN Sen CHEN Yuanfang CAI Qinghua ZHENG Yang LIU Ting LIU School of Computer Science and Technology Xi'an Jiaotong University College of Cyber Science Nankai University College of Intelligence and Computing Tianjin University Department of Computer Science Drexel University School of Computer Science and Engineering Nanyang Technological University School of Cyber Science and Engineering Xi'an Jiaotong University

The complexity and diversity of bug fixes require developers to understand bug fixes from multiple perspectives in addition to fine-grained code changes. The dependencies among files in a software system are an important dimension to inform software quality. Recent studies have revealed that most bug-prone files are always architecturally connected with dependencies, and as one of the best practices in the industry,changes in dependencies should be avoided or carefully made during bug fixing. Hence, in this paper, we take the first attempt to understand bug fixes from the dependencies perspective, which can complement existing code change perspectives. Based on this new perspective, we conducted a systematic and comprehensive study on bug fixes collected from 157 Apache open source projects, involving 140456 bug reports and182621 bug fixes in total. Our study results show that a relatively high proportion of bug fixes(30%) introduce dependency-level changes when fixing the corresponding 33% bugs. The bugs, whose fixes introduce dependency-level changes, have a strong correlation with high priority, large fixing churn, long fixing time,frequent bug reopening, and bug inducing. More importantly, patched files with dependency-level changes in their fixes, consume much more maintenance costs compared with those without these changes. We further summarized three representative patch patterns to explain the reasons for the increasing costs. Our study unveils useful findings based on qualitative and quantitative analysis and also provides new insights that might benefit existing bug prediction techniques. We release a large set of benchmarks and also implement a prototype tool to automatically detect dependency-level changes from bug fixes, which can warn developers and remind them to design a better fix.

关键词： empirical software engineering software maintenance software evolution software architecture software design

来源：评论

学校读者我要写书评

暂无评论

Adaptive Hypergraph Network for Trust Prediction 40

Adaptive Hypergraph Network for Trust Prediction

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Xu, Rongwei Liu, Guanfeng Wang, Yan Zhang, Xuyun Zheng, Kai Zhou, Xiaofang School of Computing Macquarie University Australia School of Computer Science and Engineering University of Electronic Science and Technology of China China Hong Kong University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy. © 2024 IEEE.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Signup page using Face recognition authentication

Signup page using Face recognition authentication

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional Neural Networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Smart Farming: IoT-Driven Crop Yield Prediction for Rice Cultivation 4

Smart Farming: IoT-Driven Crop Yield Prediction for Rice Cul...

引用

4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Sumesh, Nandana Navaneeth, R. Raj, Vimal Rajesh, Vismaya Ani, R. Department of Computer Science Amrita School of Computing Amritapuri India

ISBN: (纸本)9798331515720

In recent years, the agricultural sector has faced challenges in achieving optimal crop yields due to complex environmental factors and limited access to data-driven decision-making tools. An innovative IoT-based approach to predict crop yields is presented, aiming to improve crop productivity for farmers. The system utilizes a network of IoT sensors, including DHT22 and rainfall sensors, to gather real-Time data on temperature, humidity, and rainfall. Soil-integrated sensors are also used to monitor specific soil properties such as nitrogen, phosphorus, potassium, pH, electrical conductivity, humidity, and temperature. This data is sent to a web interface for live display and stored for predictive modeling. Several regression models-decision tree, random forest, ridge regression, and linear regression-were implemented to predict crop yields, with the random forest regression model achieving the highest accuracy and an error rate of 6.46 percent. The stacking model was also analyzed, but the random forest model's MAE and R-metrics proved superior, leading to its selection for deployment. A user-friendly web-based interface has been developed to enable farmers to interact with the system and predict crop yields effectively. This implementation smoothly integrates IoT technology with advanced data analytics, providing valuable insights to optimize agricultural practices, which in turn enhances food security and improves livelihoods. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

An Optimized Hybrid Deep Learning Model for Appliance Energy Prediction in Smart Homes

引用

Journal of engineering science and Technology Review 2024年第3期17卷 151-158页

作者： Vidhya, S. Balaji, M. Kamaraj, V. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

The increased energy consumption due to industrial machines and household appliances affectenergy sustainability as well as economic stability. To meet the energy demand various practices are followed worldwide. Research on sustainable energy production is at peak meanwhile research towards reducing the cost of energy generation is gaining more interest in recent times. one of the key points considered to reduce the power generation cost is energy consumption prediction. Future demands can be predicted, and necessary power can be generated or delivered to meet the demand. Specifically, prediction model is essentially required for smart homes as they utilize multiple devices for smart connectivity. Machine learning algorithms are widely used for energy prediction applications. However, the performance of machine learning models needs manual selection of features and is still complex in data analysis and decision-making process. Recently deep learning (DL) techniques are used in various classification and prediction applications. DL superior performance is incorporated for energy prediction for smart home appliances. This research work presents a hybrid optimized DL model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network. Additionally, to improve the prediction performances, the parameters of the hybrid DL model is fine-tuned using canonical particle swarm optimization algorithm. Experiments of the proposed model utilizes UCI Household power consumption dataset to evaluate the performances in terms of RMSE, MAPE, MAE and R2-score. The proposed model attained RMSE of 26.2154W, MAPE of 2.965%, MAE of 18.65W and R2-Score of 0.9989 which is much better than existing linear regression, extreme learning machine and diverse LSTM models. © 2024 school of science, DUTH. All rights reserved

关键词： Domestic appliances

来源：评论

学校读者我要写书评

暂无评论

Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

引用

Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South UniversityChangsha410083China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin541000China The Department of Computer Science Universidad de ChileSantiagoChile

In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service *** this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking ***,a Bi-LSTM-based model is proposed to predict the trajectories of *** service area is divided into several equal-sized *** the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory ***,we propose a scheduling strategy for delay optimization based on the vehicle trajectory *** the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task *** results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

来源：评论

学校读者我要写书评

暂无评论

Large circuit models: opportunities and challenges

引用

science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

来源：评论

学校读者我要写书评

暂无评论

TeleForensic: Delving into Digital Evidence with CDR Forensics 3

TeleForensic: Delving into Digital Evidence with CDR Forensi...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Srish, N. Sridharan, S. Udhaya Kumar, S. Amrita School of Computing Computer Science Department Chennai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

Call Data Records (CDR) are important evidence in cases involving cybercrime and crimes such as murder and robbery. Nonetheless, analyzing CDR is arduous work because the amount of data is enormous, and if a person does not know where to look, they would overlook valuable information. In today's world, officials do their best to solve crimes, but as the phrase goes, every piece of evidence counts, and CDR is no exception. This research investigates the many processes and techniques used in the analysis of CDR data, particularly in the context of telecommunications, including data analytics, pattern matching, geographical mapping, and visualization methods. Furthermore, the paper covers problems and prospects in CDR forensic analysis, emphasizing the importance of improved tools and approaches to deal with growing communication technologies and increasing cyber threats. © 2024 IEEE.

关键词： Crime

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：