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Heterogeneous software Effort Estimation via Cascaded Adversarial Auto-Encoder 1

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

VRVideos: A flexible pipeline for Virtual Reality Video Creation

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VRVideos: A flexible pipeline for Virtual Reality Video Crea...

IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Anthony Dickson Jeremy Shanks Jonathan Ventura Alistair Knott Stefanie Zollmann Depart. of Computer Science University of Otago Dunedin New Zealand Depart. of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9781665457262

Recent advances in Neural Radiance Field (NeRF)-based methods have enabled high-fidelity novel view synthesis for video with dynamic elements. However, these methods often require expensive hardware, take days to process a second-long video and do not scale well to longer videos. We create an end-to-end pipeline for creating dynamic 3D video from a monocular video that can be run on consumer hardware in minutes per second of footage, not days. Our pipeline handles the estimation of the camera parameters, depth maps, 3D reconstruction of dynamic foreground and static background elements, and the rendering of the 3D video on a computer or VR headset. We use a state-of-the-art visual transformer model to estimate depth maps which we use to scale COLMAP poses and enable RGB-D fusion with estimated depth data. In our preliminary experiments, we rendered the output in a VR headset and visually compared the method against ground-truth datasets and state-of-the-art NeRF-based methods.

关键词： Headphones Visualization Solid modeling Three-dimensional displays Pipelines Estimation Virtual reality

SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems

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

作者： Qiao, Sibo Guo, Qiang Shi, Fengdong Wang, Min Zhu, Haohao Khan, Fazlullah Rodrigues, Joel J.P.C. Lyu, Zhihan School of Software Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China National Demonstration Center for Experimental Engineering Training Education Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China School of Control Sciences and Engineering Tiangong University Tianjin300387 China School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China Higher School of Technology Amazonas State University Amazonas Manaus69050-010 Brazil Department of Game Design Faculty of Arts Uppsala University Uppsala Sweden

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems. © 2013 IEEE All rights reserved.

关键词： Information leakage

A Real-Time Approach to Recognition of Kazakh Sign Language

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A Real-Time Approach to Recognition of Kazakh Sign Language

2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Zholshiyeva, Lazzat Zhukabayeva, Tamara Turaev, Sherzod Berdiyeva, Meruyert Kuanysbaevna, Sengirbaeva Raykhan High School Of Information Technology And Engineering Astana International University Nur-Sultan Kazakhstan College Of Information Technology United Arab Emirates University Department Of Computer Science & Software Engineering Al Ain United Arab Emirates South Kazakhstan Medical Academy Department Of Medical Biophysics And It Shymkent Kazakhstan Special Boarding School №1 For Children With Hearing Impairment Shymkent Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The interaction between man and computer is expanded with computer technology development. Hand gesture recognition is an essential technology for human-computer interaction, and it allows the recording and interpretation of hand gestures without some devices' use. Since the hand is a man's primary working tool, its position and orientation in space are crucial for many potential applications, such as interaction with virtual reality devices. Hand position estimation is a substantial process of hand gesture recognition. However, there are many difficulties in 3D hand pose estimating from a single RGB image. Media Pipe is a high-quality finger and hand-tracking framework. It uses in machine learning to detect 3D landmarks. The proposed paper provides a real-time approach to recognizing Kazakh gestures performance using a conventional camera and Media Pipe. © 2022 IEEE.

关键词： Virtual reality

Light-YOLOv8-Flame: A Lightweight High-Performance Flame Detection Algorithm

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arXiv 2025年

作者： Lan, Jiawei Tao, Ye Wang, Zhibiao Yu, Haoyang Cui, Wenhua School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Fire detection algorithms, particularly those based on computer vision, encounter significant challenges such as high computational costs and delayed response times, which hinder their application in real-time systems. To address these limitations, this paper introduces Light-YOLOv8-Flame, a lightweight flame detection algorithm specifically designed for fast and efficient real-time deployment. The proposed model enhances the YOLOv8 architecture through the substitution of the original C2f module with the FasterNet Block module. This new block combines Partial Convolution (PConv) and Convolution (Conv) layers, reducing both computational complexity and model size. A dataset comprising 7,431 images, representing both flame and non-flame scenarios, was collected and augmented for training purposes. Experimental findings indicate that the modified YOLOv8 model achieves a 0.78% gain in mean average precision (mAP) and a 2.05% boost in recall, while reducing the parameter count by 25.34%, with only a marginal decrease in precision by 0.82%. These findings highlight that Light-YOLOv8-Flame offers enhanced detection performance and speed, making it well-suited for real-time fire detection on resource-constrained devices. © 2025, CC BY.

关键词： Response time (computer systems)

User and Interaction Both Matter: Social Relationship Mining Via Interaction Graph Propagating

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User and Interaction Both Matter: Social Relationship Mining...

IEEE International Conference on Communications (ICC)

作者： Yilong Zang Ruimin Hu Xixi Li Zheng Wang Dengshi Li National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Cyber Engineering Xidian University Xi'an China School of Artificial Intelligence Jianghan University Wuhan China

Social relationship mining benefits many applications such as leadership analysis and advisor recommendation. Existing methods focus on mining user relationships only from the perspective of user-level. To our knowledge, from this perspective, representing the user interactions by edges is not sufficient for the complex information about interactions between users. In addition, mining users' relationship independently ignores the propagation of social interaction across networks. In this paper, we investigate social relationship mining from a new perspective of interaction-level. We propose an Interaction Graph Propagating(IGP) model which constructs an interaction graph. It not only captures the user interaction information as the union but also exploits the propagation between user interactions. In particular, we utilize the graph attention mechanism to distinguish the contributions of each neighbor union. Experimental results on several public datasets demonstrate that IGP achieves significant improvements over state-of-the-art methods.

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CSL: A Large-scale Chinese Scientific Literature Dataset

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arXiv 2022年

作者： Li, Yudong Zhang, Yuqing Zhao, Zhe Shen, Linlin Liu, Weijie Mao, Weiquan Zhang, Hui School of Information Engineering China Shenzhen University School of Computer Science and Software Engineering China Tencent AI Lab United States Information Technology Center for National Science and Technology Infrastructure Beijing China

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code are available at https://***/ydli-ai/CSL . © 2022, CC BY-NC-SA.

关键词： Benchmarking

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds

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arXiv 2024年

作者： Zhang, Shuhai Yang, Jiahao Luo, Hui Chen, Jie Wang, Li Liu, Feng Han, Bo Tan, Mingkui The School of Software Engineering South China University of Technology China the Pazhou Laboratory Guangzhou China The National Key Laboratory of Optical Field Manipulation Science and Technology CAS China the Institute of Optics and Electronics CAS Chengdu China the School of Electronic and Computer Engineering Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518066 China The Department of Mathematics The Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX76019 United States University of Melbourne Australia Department of Computer Science Hong Kong Baptist University Hong Kong

Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions. Adversarial purification has been an effective means to improve DNNs robustness by removing these perturbations before feeding the data into the model. However, it faces significant challenges in preserving key structural and semantic information of data, as the imperceptible nature of adversarial perturbations makes it hard to avoid over-correcting, which can destroy important information and degrade model performance. In this paper, we break away from traditional adversarial purification methods by focusing on the clean data manifold. To this end, we reveal that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones. Leveraging this insight, we propose Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring clean data. Specifically, 1) we propose a Perceptual consistency restoration mechanism by minimizing the discrepancy between generated samples and input samples in both pixel and perceptual spaces. 2) To maintain the optimized latent vectors within the valid data manifold, we introduce a Latent distribution consistency constraint strategy to align generated samples with the clean data distribution. 3) We also apply a Latent vector consistency prediction scheme via an ensemble approach to enhance prediction reliability. CMAP fundamentally addresses adversarial perturbations at their source, providing a robust purification. Extensive experiments on CIFAR-10 and ImageNet-100 show that our CMAP significantly enhances robustness against strong adversarial attacks while preserving high natural accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

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arXiv 2022年

作者： Wu, Chao Ji, Cheng Yuan, Geng Pan, Riwei Guo, Weichao Yu, Chao Zhu, Zongwei Wang, Yanzhi Department of Electrical and Computer Engineering Northeastern University United States School of Computer Science Nanjing University of Science and Technology China Department of Computer Science City University of Hong Kong Hong Kong Guangdong Oppo Mobile Telecommunications Corp. Ltd China School of Software Engineering University of Science and Technology of Chinese China

Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over conventional compression methods. However, this method suffers from non-trivial system overheads incurred by delta maintenance and read penalty, which prevents its applicability on mobile devices. To this end, this paper proposes DeltaFS, a metadata-enabled Delta compression on log-structured File System for mobile devices, to achieve utmost compressing efficiency and zero hardware costs. DeltaFS smartly exploits the out-of-place updating ability of Log-structured File System (LFS) to alleviate the problems of write amplification, which is the key bottleneck for delta compression implementation. Specifically, DeltaFS utilizes the inline area in file inodes for delta maintenance with zero hardware cost, and integrates an inline area manage strategy to improve the utilization of constrained inline area. Moreover, a complimentary delta maintenance strategy is incorporated, which selectively maintains delta chunks in the main data area to break through the limitation of constrained inline area. Experimental results show that DeltaFS substantially reduces write traffics by up to 64.8%, and improves the I/O performance by up to 37.3%.1 © 2022, CC BY.

关键词： Metadata