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2024 International Conference on Signal Processing and Communication Security, ICSPCS 2024

作者： Jiang, Xuefeng Fang, Xianjin Xu, Liuquan School of Safety Science and Engineering Anhui University of Science and Technology Huainan232001 China School of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China

ISBN: (纸本)9781510681699

Early fire Due to the small target and the occurrence of early scene complexity, the existing fire detection methods make it very easy to miss the detection of false detection phenomena. To address the above problems, this study designs CAC-Net, a fire detection method based on residual feature extraction and a self-attention mechanism. The method utilizes an efficient two-step residual feature extraction module to finely tune the receptive field and enhance the extraction of complex features as well as small object features. Effective Pyramid Aggregation Module AF accurately aggregates task-related information. The CGD module uses group convolution and techniques to efficiently arrange convolution filters for efficient flow and integration of information between different feature map channels, achieving lower false detections. In this paper, we conduct detailed and rich experiments on a live-collected dataset and achieve 96.4% precision and 96.1% recall. The experimental results demonstrate that the CAC-Net model significantly reduces the misdetection and omission of fires and has efficient research value in the task of early fire detection in complex scenarios. © 2024 SPIE.

关键词： Feature extraction

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Scheduling a Single-Arm Multi-Cluster Tool With a Condition-Based Cleaning Operation

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1965-1983页

作者： Qinghua Zhu Hongpeng Li Cong Wang Yan Hou School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102-1982 USA

As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase scheduling complexity in cluster tools. In this paper, we focus on scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning ***, algorithms for a feasible schedule are also *** an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.

关键词： Chamber cleaning multi-cluster tools scheduling semiconductor manufacturing

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Differential privacy protection method for trip-oriented shared data

Differential privacy protection method for trip-oriented sha...

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作者： Du, Danlei Luo, Entao Yi, Yang Peng, Tao Li, Xubin Zhang, Shaobo Jiang, Xu Wang, Tian School of Information Engineering Hunan University of Science and Engineering YongZhou China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Department of Computer Science Northeastern Illinois University ChicagoIL United States School of Computer Science and Engineering Guangzhou University Guangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering Nanjing University of Science and Technology NanJing China Zhuhai China Zhuhai China

While location information sharing technology provides convenience for unmanned driving and journey navigation, user journey information sharing has also become a disaster for privacy information leakage. The traditional differential privacy method can only perturb the data entirely and cannot consider the design of data availability. In this paper, the difference privacy algorithm is improved by combining it with the Apriori algorithm, and the relevant perturbation is carried out after mining the associated data of the user's trip. In the face of possible data attacks, the privacy protection of the sensitive information of the user's actual data is ensured while the availability of the data is ensured. By testing 3000 trip data generated by experimental simulation, the results show that the correlation information between the original datasets is destroyed. However good availability is maintained after the Laplace data perturbation of the proposed algorithm for both simultaneous and multi-person trips. © 2022 John Wiley & Sons, Ltd.

关键词： Machine learning

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Intelligent surveillance support system

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Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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HiLo Optical Sectioning Algorithm Based on the Rolling Ball Method 5

HiLo Optical Sectioning Algorithm Based on the Rolling Ball ...

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5th International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Ma, Rui Jiang, Jingrui Tao, Yuchuan Liu, Jiang School of Information Engineering Southwest University of Science and Technology Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350368284

Traditional structured illumination microscopy techniques still face issues such as complex hardware structures, high optical path costs, low efficiency of optical sectioning reconstruction algorithms, and long image acquisition times. To address these problems, this text designs and builds a high-speed portable three-dimensional optical sectioning microscopy system based on DMD and LED, which greatly simplifies the optical path structure and reduces hardware costs. Additionally, through in-depth analysis of traditional optical sectioning reconstruction algorithms, a HiLo optical sectioning reconstruction algorithm based on the rolling ball method (RB-HiLo) is proposed. Experimental results show that compared to the traditional RMS algorithm, this new algorithm improves image acquisition efficiency by 22.8%, enhances optical sectioning reconstruction efficiency by 20.7%, and demonstrates lower background intensity, more effective deblurring, and higher signal-To-noise ratio in the reconstructed optical sectioning images as indicated by the normalized intensity distribution curves. © 2024 IEEE.

关键词： Image reconstruction

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Natural language requirements testability measurement based on requirement smells

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Neural Computing and Applications 2024年第21期36卷 13051-13085页

作者： Zakeri-Nasrabadi, Morteza Parsa, Saeed School of Computer Engineering Iran University of Science and Technology Hengam St. Resalat Square Tehran Tehran Iran

Requirements form the basis for defining software systems’ obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despite the importance of measuring and quantifying requirements testability, no automatic approach for measuring requirements testability has been proposed based on the requirements smells, which are at odds with the requirements testability. This paper presents a mathematical model to evaluate and rank the natural language requirements testability based on an extensive set of nine requirements smells, detected automatically, and acceptance test efforts determined by requirement length and its application domain. Most of the smells stem from uncountable adjectives, context-sensitive, and ambiguous words. A comprehensive dictionary is required to detect such words. We offer a neural word embedding technique to generate such a dictionary automatically. Using the dictionary, we could automatically detect Polysemy smell (domain-specific ambiguity) for the first time in 10 application domains. Our empirical study on nearly 1000 software requirements from six well-known industrial and academic projects demonstrates that the proposed smell detection approach outperforms Smella, a state-of-the-art tool, in detecting requirements smells. The Precision and Recall of smell detection are improved with an average of 0.03 and 0.33, respectively, compared to the state-of-the-art. The proposed requirement testability model measures the testability of 985 requirements with a mean absolute error of 0.12 and a mean squared error of 0.03, demonstrating the model’s potential for practical use. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Embeddings

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Introduction

Signals and Communication Technology

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Signals and Communication technology 2024年 Part F1492卷 3-17页

作者： Tian, Yu-Chu Gao, Jing School of Computer Science Queensland University of Technology BrisbaneQLD Australia College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

The main theme of this book is network analysis and architecture design for network planning. While there are various types of networks, this book specifically focuses on computer networks for data communication. Therefore, it uses the term networks to specifically refer to computer networks unless specified explicitly otherwise. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Diff-OD: Object Detection with Text-to-Image Diffusion Models

Diff-OD: Object Detection with Text-to-Image Diffusion Model...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Chen, Baolin Li, Tianyu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331506797

Diffusion models have recently made revolutionary breakthroughs in the task of generating images from text, which has aroused many works in transferring implicit knowledge in diffusion models to visual tasks. Although these studies have found that the visual representations of the intermediate layers of diffusion models are distinguishable, they mainly focus on tasks such as semantic segmentation and depth estimation, and have not yet explored the basic visual task of object detection. Inspired by the above findings, we set out to explore the application prospects of diffusion models in visual feature object detection tasks. We found that the visual features of diffusion models are both semantic and positional, achieving excellent visual recognition results in object detection tasks. Inspired by this, we designed scene text prompts to introduce more semantic information to the diffusion model to further enhance its detection ability. Since there is a strong positional connection between the conditional text prompt and the feature map in the pre-trained text-to-image diffusion model, we concatenate the cross-attention maps with diffusion features to further enhance its representation ability. Our method surpasses the methods based on ResnetlOl and Clip as feature extractors, and achieves comparable performance with Convnext, which is currently the most advanced visual backbone. © 2025 IEEE.

关键词： computer vision diffusion models object detection representation learning

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Enhanced Structure-Aware Graph Embedding for Workflow Recommendation 3rd

Enhanced Structure-Aware Graph Embedding for Workflow Recomm...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Rui Zhao, Taiyin School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819624676

With the continuous development of informatization, the system operation in today's society is becoming increasingly efficient, which prompts individuals and teams to continuously optimize their workflow design and improve the efficiency of workflow modeling processes. To address these issues, we deeply explore the attribute information of workflows and apply it to workflow recommendations. This article proposes an enhanced Structure Aware Graph Embedding method (ESAGE) applied to workflow recommendation, which utilizes the node attribute information of the workflow graph to enhance the graph structure information, generate feature embeddings for the workflow to calculate the similarity between workflows. Based on this, we have achieved efficient recommendation in the workflow. In addition, this method is also applicable to generating feature embeddings for nodes in other graph-structured data. Finally, based on real datasets, we designed and conducted experiments, and the experimental results showed the effectiveness of this method in the practical process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph embeddings

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ScIAMC:Single-Cell Imputation via adaptive matrix completion 10

ScIAMC:Single-Cell Imputation via adaptive matrix completion

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Shuai Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts. © 2023 IEEE.

关键词： Cell proliferation

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