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IEEE Conference on Decision and Control

作者： Yanfang Mo Jiaxin Yu S. Joe Qin Hong Kong Institute for Data Science City University of Hong Kong Hong Kong School of Data Science and Hong Kong Institute for Data Science City University of Hong Kong Hong Kong Department of Computing and Decision Science Institute of Data Science Lingnan University Hong Kong

In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximization (EM) and a statistical constraint. In addition, the noise covariance matrices are estimated as a natural outcome of the EM method. A simulation case study of the nonlinear Lorenz oscillation system illustrates the advantages of the proposed approach over two alternatives.

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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Hydrodynamic Optimization Design of Underwater Glider Wings Based on Biomimetic Principles 24

Hydrodynamic Optimization Design of Underwater Glider Wings ...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and data Analysis

作者： Hong He Tianding Chen Hui Wang Ao Chen Xin Yu Yimei Fan School of Physics and Information Engineering Minnan Normal University Zhangzhou Fujian China School of Data and Computer Science Xiamen Institute of Technology Xiamen Fujian China School of Computing and Information Science Fuzhou Institute of Technology Fuzhou Fujian China

ISBN: (纸本)9798400711831

This paper designs a bionic sea turtle fin, which mimics the shape of the sea turtle's forelimbs and maintains an overall streamlined shape to reduce the resistance received by the outer side of the fin. This is of great significance and practical value for exploring new underwater propulsion methods and enriching and improving underwater propulsion technology. In terms of shape design, this paper uses Solid Works software to design the shape according to the contour of the sea turtle's forelimbs, and based on bionic principles, analyzes the flapping motion rules and propulsion mechanism of the sea turtle. To verify that the efficiency of the biomimetic turtle hydrofoil traction machine is higher than that of traditional traction machines, this study focuses on the optimization of the external shape configuration. The Flow Simulation plugin within the Solid Works software is applied to perform computational fluid dynamics CFD) simulations on the biomimetic turtle hydrofoil traction machine. Analyze the impact of different distances and different pitch angles between the fins on the motion performance; set the calculation domain, boundary conditions, residuals, inlet velocity, and sea conditions, etc., and perform simulation experiments on the bionic sea turtle fin tractors with different distances and different angles; analyze the obtained velocity cloud diagram and pressure cloud diagram. The results show that the ocean current resistance received by the bionic sea turtle fin tractor is significantly less than that of the traditional tractor, which verifies the feasibility of the bionic fin and its good application prospects.

关键词： Bionic Design

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Characterization of Western US hydrologic processes linked to atmospheric rivers in two sets of seasonal global retrospective forecasts

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Climate Dynamics 2025年第6期63卷 1-26页

作者： Zavadoff, Breanna Kirtman, Ben Cooperative Institute for Marine and Atmospheric Studies University of Miami Miami USA Rosenstiel School of Marine Atmospheric and Earth Sciences University of Miami Miami USA Frost Institute for Data Science and Computing University of Miami Miami USA

Atmospheric rivers (ARs) are narrow filaments of high water vapor content that have considerable influence on the western United States (US) hydroclimate. ARs provide significant amounts of annual precipitation and snowfall and affect mountain snowpack via snow water equivalent (SWE) accumulation and ablation. With ARs projected to become increasingly key players in western US hydrology, water resource managers will rely progressively more on AR seasonal forecasts to infer flood/drought risks and make informed decisions about water supply allocation. However, precisely how well current seasonal climate prediction systems capture ARs and their associated hydrologic variables is still an open question. Here, we evaluate the ability of high (HR) and low resolution (LR) CCSM4 and CESM1 seasonal global retrospective forecasts to characterize precipitation, snowfall, and SWE changes associated with western US landfalling ARs. HR forecasts more accurately represent hydrologic variables than LR forecasts, however, CCSM4-HR underestimates AR-related snowfall, causing enhanced AR-related SWE ablation. Further investigation reveals amplified onshore positive temperature advection by south-southwesterly biased AR winds causes ARs in CCSM4-HR to be embedded within thicker air columns, yielding increased freezing level heights, reduced snowfall, and increased SWE loss. Results suggest both HR and LR global seasonal forecast models are capable of characterizing AR distribution and frequency, but HR models are needed for proper precipitation, snowfall, and SWE representation. Furthermore, models used to assess AR-related hydrological processes must contain accurate wind fields, as even minor biases can have a profound effect on a model’s ability to simulate AR precipitation and SWE accumulation/ablation rates.

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DEEP ORTHOGONAL HYPERSPHERE COMPRESSION FOR ANOMALY DETECTION

arXiv

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arXiv 2023年

作者： Zhang, Yunhe Sun, Yan Cai, Jinyu Fan, Jicong School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces. In this paper, we first propose a novel deep anomaly detection model that improves the original hypersphere learning through an orthogonal projection layer, which ensures that the training data distribution is consistent with the hypersphere hypothesis, thereby increasing the true positive rate and decreasing the false negative rate. Moreover, we propose a bi-hypersphere compression method to obtain a hyperspherical shell that yields a more compact decision region than a hyperball, which is demonstrated theoretically and numerically. The proposed methods are not confined to common datasets such as image and tabular data, but are also extended to a more challenging but promising scenario, graph-level anomaly detection, which learns graph representation with maximum mutual information between the substructure and global structure features while exploring orthogonal single- or bi-hypersphere anomaly decision boundaries. The numerical and visualization results on benchmark datasets demonstrate the superiority of our methods in comparison to many baselines and state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA SHARPNESS

arXiv

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arXiv 2024年

作者： Zou, Yingtian Kawaguchi, Kenji Liu, Yingnan Liu, Jiashuo Lee, Mong-Li Hsu, Wynne School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Department of Computer Science & Technology Tsinghua University China

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Regression analysis

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Generative AI: A Review on Models and Applications 2

Generative AI: A Review on Models and Applications

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Malik, Kiran Baliyan, Anupam School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Matu Ram Institute of Engineering and Management Computer Science and Engineering Haryana Rohtak India Chandigarh University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350369960

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape. © 2023 IEEE.

关键词： Advanced Hybrid Architectures Flow-Based Models Generative Adversarial Networks (GANs) Generative Reinforcement Learning (GRL) Variational Autoencoders (VAEs)

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Mobile-enabled Prosthetic System with Machine Learning Support

Mobile-enabled Prosthetic System with Machine Learning Suppo...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Williams, Christian Lee, Andrew Tolah, Ahmed Einafshar, Mohammadjavad Massaad, Elie Kiapour, Ali Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States Department of Biomedical Engineering Wentworth Institute of Technology BostonMA United States Department of Materials and Production Aalborg University Aalborg Denmark Massachusetts General Hospital Harvard Medical School BostonMA United States College of Engineering Northeastern University BostonMA United States

ISBN: (纸本)9798350350548

Machine learning (ML) and its applications have expanded over the past few years. ML inevitably makes its way to the world of prosthetics and amputees. Smart prosthetics have been studied and growing recently. Through the collection of data from these devices, results can help the user in numerous ways. On the other hand, mobile devices and applications are widely used. However, how to combine mobile applications and ML to enhance the prosthetic device was less addressed. In this research, we studied ML with an IoT-based prosthetic device paired with a mobile application. The hypothesis was that the ML methods could help evaluate the user’s status. The study results showed that some evaluated ML methods were able to see through the average temperature, humidity and contraction percentage of the people who wear a designed prosthetic device. The results also indicated that the users could tell if the contractions reached a concerning level. © 2024 IEEE.

关键词： Adversarial machine learning

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HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

HiSMatch: Historical Structure Matching based Temporal Knowl...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Zixuan Hou, Zhongni Guan, Saiping Jin, Xiaolong Peng, Weihua Bai, Long Lyu, Yajuan Li, Wei Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Baidu Inc

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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