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Formalization of Android Activity-Fragment Multitasking Mechanism and Static Analysis of Mobile Apps

Formal Aspects of Computing 2025年第2期37卷 1-86页

作者： He, Jinlong Wu, Zhilin Chen, Taolue Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computing and Mathematical Sciences Birkbeck University of London Northern Ireland London United Kingdom

The multitasking mechanism between activities and fragments plays a fundamental role in the Android operating system, which involves a wide range of features, including launch modes, intent flags, task affinities, and structured activities containing fragments. All of them are being widely used in Android apps, both open source and commercial ones. In this article, we present a formal semantics of the Android multitasking mechanism between activities and fragments, which accommodates all the important features and gives insofar the most comprehensive and accurate formalization. In particular, our semantics is formulated based on multi-stack systems, and fully captures the behavior of task stacks and activity stacks regarding fragments. Based on the semantics, we provide new static analysis algorithms, which are both multi-stack aware and fragment sensitive, thus achieve more precise static analysis for Android apps. We validate our approach by extensive experiments on both open source and commercial Android apps. The results highlight the benefits of the considering the semantics of the multitasking mechanism between activities and fragments in static analysis, and confirm the efficacy of our approach. © 2025 Copyright held by the owner/author(s).

关键词： Android multitasking mechanism activities and fragments formal semantics stack unboundedness static analysis

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Automated Detection of Multi-type Small Lesions in CT Images Based on Deep Learning 9

Automated Detection of Multi-type Small Lesions in CT Images...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Li, Shuhua Samonte, Mary Jane C. Yan, Fenglong School of Information Technology Mapua University Manila Philippines School of Computer Science and Technology Guangdong University of Science and Technology Dongguan China School of Computer & Software Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9798350385991

With the rapid development of deep learning, the medical image analysis method has become one of the most concerned issues in the field of medical imaging diagnosis. Medical image analysis and processing are essential for effective treatment and excellent prognosis of medical lesions. In this paper, High-definition computed tomography (CT) images of multi-type lesions are analyzed. To solve the problem of insufficient medical images, Mosaic is used within DeepLesion database for data combination and enhancement. In addition, images from COVID-CT are incorporated into the dataset. Concerning data noise, different kinds of filtering methods are tested and noise removal is performed using non-local mean filtering. Based on deep separable convolution (DSC), the accuracy of the training network is improved by using the enhanced dataset. The experimental results show that the average accuracy of the model is 80.5% at 29.23 frames per second, which satisfies the real-time detection. It provides a feasibility study for the practical application of computer-aided diagnosis. © 2024 IEEE.

关键词： computerized tomography

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Combining Interval Type-2 Fuzzy Clustering Method with Preprocessing Model for High-Resolution Remote Sensing Images 24

Combining Interval Type-2 Fuzzy Clustering Method with Prepr...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Wang, Chunyan Chen, Junzheng Wang, Xiang School of Software Liaoning Technical University Huludao China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798400718199

Aiming at the problem that the classification accuracy decreases due to the increase of heterogeneity in homogeneous areas of high-resolution remote sensing images and the increase of similarity in different areas, an interval type-2 fuzzy clustering method combined with the image preprocessing model is proposed. First, the spatial information and grayscale information of adjacent pixels in the image are used to filter the image to reduce the impact of outlier pixels. Then, the double fuzzy factor is used to construct an interval type two fuzzy clustering model to improve the algorithm's ability to deal with uncertainty. Finally, the maximum membership criterion is used to defuzzify the fuzzy membership matrix to obtain clear classification results. Experimental results on WorldView-2 and QuickBird high-resolution remote sensing images show that the proposed algorithm can effectively suppress the influence of regional noise and achieve higher classification accuracy. © 2024 ACM.

关键词： Fuzzy clustering

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua science and technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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Traffic sign detection algorithm based on improved YOLOv5 4

Traffic sign detection algorithm based on improved YOLOv5

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Ma, Chi Li, Qinrong Hu, Hui Li, Jingyan Guo, Qiang School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510682528

This paper introduces an enhanced YOLOv5 algorithm tailored for real-world traffic sign detection applications. Through the incorporation of Coordinate Attention after the SPPF module of the YOLOv5 backbone, the YOLOv5 neck pays more attention to key areas in the image and preserves accurate positional information. In response to the fact that traffic signs are mostly small targets, the PAN and FPN in the original algorithm's neck network are upgraded by substituting BiFPN for the previous feature fusion method, improving the algorithm's ability to detect traffic signs of different sizes, specifically targeting improved detection accuracy for small-scale targets. To validate the effectiveness of these modifications, we compared our improved model with other object detection model on the TT100K dataset and conducted ablation experiments. The experiments result revealed that the enhanced algorithm achieved an mAP of 94.2%, surpassing the original YOLOv5 model by 6.9%. The detection speed was 49.2 FPS, meeting the real-time requirements. © 2024 SPIE.

关键词： Traffic signals

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Application of Static Virus Spread Algorithm in Base-Balanced DNA Fragment Optimization

Application of Static Virus Spread Algorithm in Base-Balance...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yao, Yao Zheng, Yanfen Zhang, Xun Liu, Xin Zhang, Xiaokang Wang, Bin Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University School of Software Engineering Dalian China

ISBN: (纸本)9798350386226

DNA has found applications in a diverse array of fields such as computing, medical diagnosis, and circuits. Designing DNA fragments that meet specific requirements is crucial for ensuring the smooth execution of tasks in these domains. However, the conventional approach of combining constraints with evolutionary algorithms often encounters challenges like orthogonality and thermodynamic instability. This poses risks to the control of reaction processes. To tackle these challenges, we have undertaken two tasks in this study: expanding constraint sets and innovating evolutionary algorithms. Firstly, we propose a base balance strategy aimed at enhancing the thermodynamic properties within DNA fragment groups by diversifying neighboring base combinations. The addition of this strategy achieves a minimum variance of 0.03 for the melting temperature while ensuring orthogonality. It represents a significant improvement compared to previous results and reduces the complexity of controlling reactions. Secondly, our static virus spread algorithm optimizes target DNA fragments by base mutations and virus amplification. Simultaneously,it demonstrates good performance across 23 benchmark functions, highlighting its optimization potential. This work is expected to further refine theoretical shortcomings and offer a convenient tool for DNA fragment optimization. © 2024 IEEE.

关键词： Diagnosis

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MILG:Realistic lip-sync video generation with audio-modulated image inpainting

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Visual Informatics 2024年第3期8卷 71-81页

作者： Han Bao Xuhong Zhang Qinying Wang Kangming Liang Zonghui Wang Shouling Ji Wenzhi Chen School of Software Technology Zhejiang UniversityHangzhouChina College of Computer Science Zhejiang UniversityHangzhouChina College of Engineering Zhejiang UniversityHangzhouChina

Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated ***,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,background and other parts of a face,which decreases the overall visual *** solve these problems,we innovatively introduce diverse image inpainting to lip-sync *** propose Modulated Inpainting Lip-sync GAN(MILG),an audio-constraint inpainting network to predict synchronous *** utilizes prior knowledge of RONI and audio sequences to predict lip shape instead of image generation,which can keep the RONI ***,we integrate modulated spatially probabilistic diversity normalization(MSPD Norm)in our inpainting network,which helps the network generate fine-grained diverse mouth movements guided by the continuous audio ***,to lower the training overhead,we modify the contrastive loss in lipsync to support small-batch-size and few-sample *** experiments demonstrate that our approach outperforms the existing state-of-the-art of image quality and authenticity while keeping lip-sync.

关键词： Lip-sync Image inpainting Face generation Modulated SPD normalization

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A Combinatorial Interaction Testing Method for Multi-Label Image Classifier 35

A Combinatorial Interaction Testing Method for Multi-Label I...

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35th IEEE International Symposium on software Reliability Engineering, ISSRE 2024

作者： Wang, Peng Hu, Shengyou Wu, Huayao Niu, Xintao Nie, Changhai Chen, Lin Nanjing University State Key Laboratory for Novel Software Technology and School of Computer Science China

ISBN: (纸本)9798350353884

Multi-label image classification is a critical task in computer vision, in which the correlations between labels are typically exploited by modern classifiers for an effective classification. In this study, we propose LV-CIT, a black-box testing method that applies Combinatorial Interaction Testing (CIT) to systematically test the ability of classifiers to handle such correlations. Specifically, LV-CIT views each label of the label space as an input-parameter taking binary values (indicating whether an object appears in an image), and manages to generate a label value covering array as the set of test cases to cover certain combinations of label values. Then, for each test case, LV-CIT relies on an object library to generate composite test images that perfectly match the specified labels, and reports classification errors if such labels cannot be correctly recognised. The experimental results on two popular datasets with six state-of-The-Art image classifiers show that LV-CIT is more efficient than the existing CIT tools in generating label value covering arrays. LV-CIT is also effective in errors revelation, as it can find 111% more errors by using 20% fewer test images than the existing methods for testing multi-label image classifiers. © 2024 IEEE.

关键词： Black-box testing

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Anomaly Detection in Spatial Information Networks: A Security Knowledge Graph-based Approach

Anomaly Detection in Spatial Information Networks: A Securit...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Tan, Yayue Zhang, Yaqi Liu, Hui Tang, Yongqing College of Computer and Information Science School of Software Southwest University Chongqing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350372274

Anomaly detection stands as a critical element in securing space information networks (SINs). This paper delves into the realm of anomaly detection within dynamic networks, shedding light on established methodologies. The focus lies on scrutinizing various anomaly detection strategies in space information networks, conducting a comprehensive assessment of their practical applications, advantages, and drawbacks. Moreover, it introduces a novel anomaly detection approach driven by deep learning principles. This innovative method merges graph convolution techniques with a network securityoriented knowledge graph, presenting a cutting-edge solution. As a final contribution, the paper extends its purview to the future landscape of anomaly detection within space information networks, offering valuable insights into potential advancements and forthcoming trends in this domain. This holistic investigation seeks to enhance the understanding of anomaly detection paradigms within the unique context of space information networks, aiming to fortify their security measures and operational resilience. ©2024 IEEE.

关键词： Knowledge graph

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RandoMix: a mixed sample data augmentation method with multiple mixed modes

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Multimedia Tools and Applications 2025年第8期84卷 4343-4359页

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior erformance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data augmentation Diversity Mixed-sample RandoMix Robustness

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