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检索条件"丛书名=Lecture Notes in Computer Science"

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Mining Rare Temporal Pattern in Time Series 35th

Mining Rare Temporal Pattern in Time Series

引用

35th Australasian Database Conference

作者： Long Van Ho Ho, Nguyen Cong Trinh Le Anh-Vu Dinh-Duc Quach, Khang Ngoc Tu Nguyen Int Univ Sch Comp Sci & Engn Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Loyola Univ Maryland Dept Comp Sci Baltimore MD USA Quy Nhon Univ Dept Math Quy Nhon Vietnam Griffith Univ Nathan Qld Australia TVT Corp Res Ho Chi Minh City Vietnam

ISBN: (纸本)9789819612413;9789819612420

Time series data from various domains is continuously growing, and extracting and analyzing temporal patterns within these series can provide valuable insights. Temporal pattern mining (TPM) extends traditional pattern mining by incorporating event time intervals into patterns, making them more expressive but also increasing the computational complexity in terms of time and space. One important type of temporal pattern is the rare temporal pattern (RTP), which occurs infrequently but with high confidence. Mining these rare patterns poses several challenges, for example, the low support threshold can lead to a combinatorial explosion and the generation of many irrelevant patterns. To address this, an efficient approach to mine rare temporal patterns is essential. This paper introduces the Rare Temporal Pattern Mining from Time Series (RTPMfTS) method, designed to discover rare temporal patterns. The key contributions of this work are as follows: (1) An end-to-end RTPMfTS process that takes time series data as input and outputs rare temporal patterns. (2) A highly efficient Rare Temporal Pattern Mining (RTPM) algorithm, which leverages optimized data structures for fast event and pattern retrieval, as well as effective pruning techniques to accelerate the mining process. (3) A comprehensive experimental evaluation of RTPM, demonstrating that it outperforms the baseline in both runtime and memory efficiency.

关键词： Pattern Mining Rare Temporal Patterns Time Series

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Adversarial Malware Detection Official Work-in-Progress Paper 14th

Adversarial Malware Detection Official Work-in-Progress Pape...

引用

14th International Conference on Security, Privacy and Applied Cryptographic Engineering (SPACE)

作者： Vishwakarma, Ashish Kashyap, Umesh Ali, Sk Subidh Indian Inst Technol Bhilai 491002 India

ISBN: (纸本)9783031804076;9783031804083

Malware detection is one of the most challenging tasks in the domain of Cybersecurity. The use of machine learning models as detectors significantly improved the performance. At the same time, adversarial modeling of malware can introduce a small noise into the malware data to generate adversarial malware, which can evade the machine learning based detector. In this work, we have proposed a detection framework for adversarial malware. We have considered SLIPNER and MalGAN datasets to generate adversarial malware using an adversarial-based attack algorithm. Our findings show that our approach is extremely effective in detecting and eliminating adversarial malware ! - Query ID="Q1" Text="Please check and confirm if the authors given and family names have been correctly identified." -.

关键词： GAN Malware Exciter network Denoiser network neural networks perturbation

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Supporting the Development of Oracle APEX Low-Code Applications with Large Language Models 25th

Supporting the Development of Oracle APEX Low-Code Applicati...

引用

25th International Conference on Product Focused Software Process Improvement

作者： Gorissen, Simon C. Sauer, Stefan Beckmann, Wolf G. TEAM GmbH Software & Consulting Dept D-33104 Paderborn Germany Paderborn Univ Dept Comp Sci D-33095 Paderborn Germany Paderborn Univ Software Innovat Lab SICP D-33095 Paderborn Germany

ISBN: (纸本)9783031783852;9783031783869

Large Language Models (LLMs) and low-code development platforms (LCDPs) have shown potential to fundamentally change the way how software products and applications are developed and to both combat the shortage of skilled software developers by increasing developer efficiency and lower the entry barrier for citizen developers to develop and maintain software on their own. This paper investigates whether these technologies can be combined to be even more powerful. To do this, a solution concept and prototype implementation were developed. Using the prototype, one can describe desired changes in an Oracle Application Express (APEX) low-code application to the GPT4 Turbo LLM in a chat. The LLM then performs the changes for the user by calling specific edit functions that the system offers and generates replies to the user. This solution enables citizen developers to edit a fully functional Web application through natural language without help from a professional software developer. We also present a qualitative user study that we performed with ten Oracle APEX customers. It showed that participants have a rather positive opinion of both the fundamental concept and the prototype, liking aspects like its time-savings and ease of use. The study also uncovered some problems like a lack of a common vocabulary or technical understanding between the LLM and some users. However, participants already suggested ways to remedy such problems like integrating an element inspector into the prototype. Overall, the study highlights the feasibility and potential of the system and outlines multiple directions for its further development.

关键词： Application programs

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Generalizing Soft Actor-Critic Algorithms to Discrete Action Spaces 7th

Generalizing Soft Actor-Critic Algorithms to Discrete Action...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Zhang, Le Gu, Yong Zhao, Xin Zhang, Yanshuo Zhao, Shu Jin, Yifei Wu, Xinxin Beijing Elect Sci & Technol Inst Dept Cybersecur Beijing Peoples R China JiangXi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9789819784868;9789819784875

ATARI is a suite of video games used by reinforcement learning (RL) researchers to test the effectiveness of the learning algorithm. Receiving only the raw pixels and the game score, the agent learns to develop sophisticated strategies, even to the comparable level of a professional human games tester. Ideally, we also want an agent requiring very few interactions with the environment. Previous competitive model-free algorithms for the task use the valued-based Rainbow algorithm without any policy head. In this paper, we change it by proposing a practical discrete variant of the soft actor-critic (SAC) algorithm. The new variant enables off-policy learning using policy heads for discrete domains. By incorporating it into the advanced Rainbow variant, i.e., the "bigger, better, faster" (BBF), the resulting SAC-BBF improves the previous state-of-the-art interquartile mean (IQM) from 1.045 to 1.088, and it achieves these results using only replay ratio (RR) 2. By using lower RR 2, the training time of SAC-BBF is strictly one-third of the time required for BBF to achieve an IQM of 1.045 using RR 8. As a value of IQM greater than one indicates super-human performance, SAC-BBF is also the only model-free algorithm with a super-human level using only RR 2. The code is publicly available on GitHub at https://***/lezhang-thu/bigger-better-faster-SAC.

关键词： Sample-efficient RL ATARI 100K Soft actor-critic

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Left Alone Facing a Difficult Choice: An Expert Analysis of Websites Promoting Selected Privacy-Enhancing Technologies 29th

Left Alone Facing a Difficult Choice: An Expert Analysis of ...

引用

29th Nordic Conference on Secure IT Systems

作者： Shams, Shirin Reinke, Sebastian Reinhardt, Delphine Univ Gottingen Inst Comp Sci Gottingen Germany Univ Gottingen Campus Inst Data Sci Gottingen Germany

ISBN: (纸本)9783031790065;9783031790072

The privacy community has invested considerable effort in understanding why motivated individuals do not adopt available solutions. As a result, several factors supporting individuals in adopting Privacy Enhancing Technologies (PETs) have been revealed. However, the adoption rate remains low. To contribute in changing this current state, we adopt an unexplored approach by analysing 69 online websites promoting four selected PETs: Virtual Private Network (VPN), Tor, private browser, and private search engine. In addition to considering the accessibility of these websites, we consider a set of 24 selected criteria grounded in the factors identified in the literature as supporting individuals in adopting PETs. These criteria aim to explore the presentation of PETs to individuals. For example, we consider the presentation of aspects of PET technology, such as coverage, limitations, and speed. Our results show that, on average, only about one-third of our criteria set are fulfilled by the analysed websites. Furthermore, our accessibility audit reveals issues that could create significant obstacles for impaired users. As a result, these websites miss their primary goal of effectively informing individuals about PETs by not utilising the identified supporting factors. These results indicate that individuals are left alone in making privacy choices and do not have sufficient support for adopting PETs. To address this situation, we propose two design templates incorporating supporting factors, offering a foundation for presenting PETs to individuals to assist them in adoption.

关键词： Usable Privacy Presentation of PETs Accessibility Audit

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ImgQuant: Towards Adversarial Defense with Robust Boundary via Dual-Image Quantization 7th

ImgQuant: Towards Adversarial Defense with Robust Boundary v...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Lv, Huanhuan Jiang, Songru Wan, Tuohang Chen, Lijun Nanjing Univ Dept Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9789819785049;9789819785056

Deep neural networks (DNNs) are vulnerable to adversarial example attacks, which pose a potential threat to safety-sensitive autonomous driving. Previous research on adversarial defense mainly concentrates on modifying DNN models, preprocessing adversarial examples, and detecting adversarial examples. However, these methods usually suffer from limited defense effectiveness, lacking a well-defined and robust boundary. To tackle these problems, we propose a novel adversarial defense mechanism, ImgQuant, which enhances model adversarial robustness through dual-image quantization. Compared with existing methods, ImgQuant has two competitive advantages. First, it diminishes the adversary's search space by squeezing unnecessary input details, thereby shrinking the living space of adversarial examples and improving the adversarial robustness of the model. Second, we implement dual-image quantization through a client-server communication model to establish a robust security boundary for ImgQuant. This ensures the elimination of adversarial noise as long as the perturbation magnitude epsilon does not exceed the boundary. Our proposed ImgQuant is comprehensively verified on universal datasets and extended to real road sign recognition. Extensive experiments show that ImgQuant has an identical high accuracy within the robust security boundary under various attacks, and can also be used to improve the performance of adversarial training.

关键词： Adversarial defense Robustness Quantization Image classification

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Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound 15th

Geo-UNet: A Geometrically Constrained Neural Framework for C...

引用

15th International Workshop on Machine Learning in Medical Imaging

作者： Chen, Yiming D'Souza, Niharika S. Mandepally, Akshith Henninger, Patrick Kashyap, Satyananda Karani, Neerav Dey, Neel Zachary, Marcos Rizq, Raed Chouinard, Paul Golland, Polina Syeda-Mahmood, Tanveer F. MIT Boston MA USA IBM Res San Jose CA 95120 USA Boston Sci Corp Maple Grove MN USA

ISBN: (纸本)9783031732836;9783031732843

Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segmentation networks like the UNet lack the precision needed for clinical adoption in IVUS workflows. This arises due to the difficulty of automatically learning accurate lumen contour from limited training data while accounting for the radial geometry of IVUS imaging. We propose the Geo-UNet framework to address these issues via a design informed by the geometry of the lumen contour segmentation task. We first convert the input data and segmentation targets from Cartesian to polar coordinates. Starting from a convUNet feature extractor, we propose a two-task setup, one for conventional pixel-wise labeling and the other for single boundary lumencontour localization. We directly combine the two predictions by passing the predicted lumen contour through a new activation (named CDFeLU) to filter out spurious pixel-wise predictions. Our unified loss function carefully balances area-based, distance-based, and contour-based penalties to provide near clinical-grade generalization in unseen patient data. We also introduce a lightweight, inference-time technique to enhance segmentation smoothness. The efficacy of our framework on a venous IVUS dataset is shown against state-of-the-art models.

关键词： Lumen Segmentation Intravascular Ultrasound Geometric Contour Modeling CDF Error Linear Units

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Synthetic Augmentation for Anatomical Landmark Localization Using DDPMs 9th

Synthetic Augmentation for Anatomical Landmark Localization ...

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9th International Workshop on Simulation and Synthesis in Medical Imaging

作者： Hadzic, Arnela Bogensperger, Lea Joham, Simon Johannes Urschler, Martin Med Univ Graz Inst Med Informat Stat & Documentat Graz Austria Graz Univ Technol Inst Comp Graph & Vis Graz Austria

ISBN: (纸本)9783031732805;9783031732812

Deep learning techniques for anatomical landmark localization (ALL) have shown great success, but their reliance on large annotated datasets remains a problem due to the tedious and costly nature of medical data acquisition and annotation. While traditional data augmentation, variational autoencoders (VAEs), and generative adversarial networks (GANs) have already been used to synthetically expand medical datasets, diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images. In this study, we explore the use of denoising diffusion probabilistic models (DDPMs) for generating medical images and their corresponding heatmaps of landmarks to enhance the training of a supervised deep learning model for ALL. Our novel approach involves a DDPM with a 2-channel input, incorporating both the original medical image and its heatmap of annotated landmarks. We also propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility, before we evaluate the DDPM-augmented dataset in the context of an ALL task involving hand X-Rays.

关键词： Anatomical landmark localization Heatmap regression Diffusion models Data augmentation

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Code-Level Safety Verification for Automated Driving: A Case Study 26th

Code-Level Safety Verification for Automated Driving: A Case...

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26th International Symposium on Formal Methods (FM)

作者： Nenchev, Vladislav Imrie, Calum Gerasimou, Simos Calinescu, Radu BMW Grp Petuelring 130 D-80809 Munich Germany Univ York Dept Comp Sci York N Yorkshire England

ISBN: (纸本)9783031711763;9783031711770

The formal safety analysis of automated driving vehicles poses unique challenges due to their dynamic operating conditions and significant complexity. This paper presents a case study of applying formal safety verification to adaptive cruise controllers. Unlike the majority of existing verification approaches in the automotive domain, which only analyze (potentially imperfect) controller models, employ simulation to find counter-examples or use online monitors for runtime verification, our method verifies controllers at code level by utilizing bounded model checking. Verification is performed against an invariant set derived from formal specifications and an analytical model of the required behavior. For neural network controllers, we propose a scalable three-step decomposition, which additionally uses a neural network verifier. We show that both traditionally implemented as well as neural network controllers are verified within minutes. The dual focus on formal safety and implementation verification provides a comprehensive framework applicable to similar cyber-physical systems.

关键词： Model-based verification Formal safety verification Deep neural network verification daptive cruise control Automated driving

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Multi-dimensional Information Awareness Residual Network for Lightweight Image Super-Resolution 7th

Multi-dimensional Information Awareness Residual Network for...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Wei, Ziyan Guo, Zhiqing Wang, Liejun Xinjiang Univ Sch Comp Sci & Technol Urumqi Peoples R China

ISBN: (纸本)9789819786848;9789819786855

In recent years, Lightweight image super-resolution technology has achieved good performance. However, many models struggle to effectively capture and process global information, leading to problems such as loss of detail and unnatural texture in reconstructed images. To solve these problems, we propose a Multi-Dimensional Information Awareness Residual Network (MIAN), which adopts a lightweight design to ensure efficient image reconstruction performance. Firstly, our MIAN effectively aggregates multi-scale context information through multi-layer channel distillation blocks (MCDB), which helps to extract important features layer by layer and reconstruct high-frequency details more accurately. Secondly, we design hierarchical spatial amplification attention (HSAA) to further enhance attention to key areas of the image and significantly improve the ability to capture and reconstruct details by layering the importance of different areas. Thirdly, we propose rapid channel perception attention (RCPA), which makes the network more focused on the useful information of the current task by optimizing the information interaction between feature channels. Finally, we introduce lightweight deepwise global self-attention (LDGA), which can identify and utilize similar features in a wide range and effectively keep the details and texture information in the reconstruction process. Extensive experiments show that our MIAN significantly improves the quality of image super-resolution, reduces the parameters and calculation cost, and achieves state-of-the-art super-resolution reconstruction performance.

关键词： Super-resolution Information fusion Lightweight network

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