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IOT Based Smart Parking System Using Ensemble Learning

Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

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computer Systems science & engineering 2023年第11期47卷 2259-2274页

作者： Kaili Shao Hui Fu Ying Song Bo Wang Faculty of Engineering Huanghe Science and Technology UniversityZhengzhou 450003China Computer School Beijing Information Science and Technology UniversityBeijing100192China Software Engineering College Zhengzhou University of Light IndustryZhengzhou450001China

To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud *** nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user *** address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their *** paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the *** proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of *** proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance *** task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available *** simulated experiments are *** the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling.

关键词： Cloud computing edge computing edge cloud task scheduling task offloading particle swarm optimization

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BlockWhisper: A Blockchain-Based Hybrid Covert Communication Scheme with Strong Ability to Evade Detection 24th

BlockWhisper: A Blockchain-Based Hybrid Covert Communication...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Zehui Xu, Yuwei Huang, Ranfeng Fan, Xinhe Xu, Jingdong Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Software College Northeastern University Shenyang China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9789819615247

In light of the pervasive significance of privacy protection in contemporary society, the exploration of covert communication technology has been extensive, which is designed to safeguard communication behaviors and contents from disclosure. The decentralization, anonymity, and robustness of blockchain technology render it a more suitable medium for covert communication than traditional networks, which are vulnerable to tracing and destruction. Nevertheless, state-of-the-art blockchain-based covert communication schemes tend to focus on the concealment of a single type of constituent element in a blockchain transaction. These methods are able to resist detection methods for a particular element, yet are easily exposed under the joint detection of multiple classes of methods. The absence of resistance to joint detection methods targeting all elements presents a significant challenge that impedes the advancement of blockchain-based covert channels. Consequently, BlockWhisper is proposed as a blockchain-based hybrid covert channel that is capable of evading the recognition of various detection methods. Following the probability distribution model obtained from a blockchain transaction distribution analysis method, BlockWhisper is implemented using a top address list and a special hybrid coding method. The experiments demonstrated that Graph-based Detection (GD), Similarity-based Detection (SMD), Shape-based Detection (SPD), Entropy-based Detection (ETD), and Machine Learning-based Detection (MLD) yielded inferior results on this scheme, which substantiates the assertion that BlockWhisper possesses a strong ability to evade detection. Moreover, a comparison of BlockWhisper with other schemes reveals that it has a superior channel capacity on the basis of the guarantee of channel feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Covert communication Evading detection

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Deep Learning Based Open Set Recognition: A Survey

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 828-863页

作者： Zhang, Qin Liu, Zi-Qi Zhang, Xiao-Lin Zhang, Peng Liu, Han Chen, Xiao-Jun College of Computer Science and Software Engineering ShenZhen University Guangdong ShenZhen518060 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China

In recent years, machine learning research has continuously made breakthroughs, which has led to the maturity and implementation of a large number of intelligent systems. However, the current machine learning paradigm overly relies on complete prior knowledge, and its application scenarios are limited to static closed specialized systems. The emergence of new categories is one of the main challenges in the research of dynamic open environments, to which the conventional closed-world machine learning methods can not handle it well. In order to break the excessive dependence of existing machine learning on complete category information, the research on open set recognition problem has become a new trend, which extends the traditional closed-world classification methods to open-world applications. Open set recognition problem requires the classification models classify the learned known classes correctly, and effectively identify unknown class samples that appear in the testing phase simultaneously, to avoid the large number of misclassifications. This article systematically surveys the research on open set recognition in recent years, focusing on deep learning based open set recognition methods. It introduces the classic models by sorting out them into six categories based on the main ideas they employ, and horizontally compares various research achievements, and give the comparison details of their performances. Related paper and code links are collected and available online. © 2025 science Press. All rights reserved.

关键词： Deep learning

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Streamlining Classroom and Laboratory Management in University: Enhancing Problem Reporting Processes

Streamlining Classroom and Laboratory Management in Universi...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Qamar H. Naith Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Managing the classroom and lab activities in the university and reporting process for the problems that occur during lectures is a hard process that needs more consideration. This study addresses challenges faced by technicians and academic staff in university classrooms, emphasizing the need for a streamlined automated issue reporting system. This paper proposes a comprehensive system aimed at improving communication, efficiency, and effectiveness in addressing classroom issues, benefiting technicians, students, and lecturers. Quantitative Analysis and evaluation of the system's user experience from instructors and technicians showed highly positive outcomes, indicating its high effectiveness in managing classroom issues and improving overall classroom management. Moreover, the statistical test revealed no statistically significant differences (α= 0.05) in the willingness to use the classroom issue management system between technicians and academic staff, indicating similar user perceptions and readiness for both groups to utilize the system.

关键词： Surveys Productivity Bridges Statistical analysis User experience Stakeholders Usability Information technology

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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Mutual Reconstruction-based Linear Discriminant Hashing for Image Retrieval 7

Mutual Reconstruction-based Linear Discriminant Hashing for ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Huang, Danlu Lai, Zhihui Kong, Heng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Breast and Thyroid Surgery BaoAn Central Hospital of Shenzhen Shenzhen China

ISBN: (纸本)9781839539848

Recently, hash learning has attracted much attention due to its ability to convert high-dimensional data into compact binary codes for efficient retrieval and storage. However, most classification-based supervised hashing methods neglect the original goal of increasing the similarity of similar samples while reducing the similarity of different samples in the learned hash code. In this paper, we combine the idea of linear discriminant analysis with hash learning by designing a new scatter matrix and propose a supervised hash learning framework to learn optimal discriminative hash codes for image retrieval. To solve the problem of reducing information loss during the learning process, we design a novel method via mutual reconstruction between binary hash codes and original features. Thus, a method named Mutual Reconstruction-based Linear Discriminant Hashing (RDAH) is obtained, which further improves the discriminative ability of the learned hash code. We test the performance of RDAH on three image datasets to show the superiority on large-scale image retrieval. © The Institution of engineering & Technology 2023.

关键词： Image retrieval

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A software Execution Data Component Identification Algorithm Based on Spectral Clustering 10

A Software Execution Data Component Identification Algorithm...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Tang, Yahui Rao, Mengqi Wei, Qingjie Chen, Feng Liu, Xin Chongqing University of Posts and Telecommunications School of Software Engineering ChongQing China Chongqing University of Posts and Telecommunications College of Computer Science and Technology ChongQing China

ISBN: (纸本)9798350304770

In order to understand the architecture of software system and facilitate the future maintenance of the software, we can reconstruct the software system as a component-based software system. To obtain the component information of the software, it is necessary to identify components based on the information contained in the software execution data. This paper presents a software Execution data component identification algorithm based on Spectral Clustering (SESC). The input of the algorithm is the software execution event log, and the output is an evaluation report on the quality of the identified components, yielding the log of software execution events with component information. By using four software execution event logs, we compare the proposed SESC with five component identification algorithms based on the community detection algorithm. SESC takes the number of calls between classes into consideration, so it can identify components more accurately, and has certain advantages in time performance. © 2023 IEEE.

关键词： Clustering algorithms

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Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild 38

Locating What You Need: Towards Adapting Diffusion Models to...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Jianan Gao, Chenchao Xiao, Zhiqing Zhao, Junbo Wu, Sai Chen, Gang Wang, Haobo College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China School of Software Technology Zhejiang University China International School of Information Science and Engineering Dalian University of Technology China

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric. © 2024 Neural information processing systems foundation. All rights reserved.

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