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Multimodal Imaging Classification Based on Mixture Model Networks 15th

Multimodal Imaging Classification Based on Mixture Model Net...

引用

15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Li, Xuan Yu, Fuyuan Cui, Jiachen Liu, Ju Wu, Qiang Shandong Univ Sch Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9789819771837;9789819771844

In medical imaging, different modality reflect different clinical information due to their different imaging principles. Compared with single-modality imaging analysis, multimodal fusion technology can make use of complementary information among modalities. To capture the potential relationships of different modalities, this paper proposes a multimodal imaging classification framework based on Mixture model Networks (MoNet). Firstly, from the perspective of graph structure, the feature is regarded as a node. The K-nearest neighbor algorithm is used to construct initial image representation from the features extracted from multimodal imaging, and the Graclus algorithm is used to achieve multiple feature dimensionality reduction. Then, MoNet is used to realize the adaptive updating of edge weights and mine the potential connections between features. Finally, the Fully Connected Network is employed to complete the prediction classification. Comparative experiments are carried out on the BRATS2017 dataset, and the results show that our method provides better performance than the baseline.

关键词： Multimodal fusion Mixture model networks Brain magnetic resonance imaging

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CD-Font: One-Shot Font Generation via Conditional Diffusion Model with Disentangled Guidance 1

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20th International Conference on Intelligent Computing (ICIC)

作者： Chen, Siyi Li, Zhenhua Liang, Dong Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (数字)9789819756001

ISBN: (纸本)9789819755998;9789819756001

One-shot font generation aims to create a new font. library by extracting style information from the reference font. Most existing font generation methods rely on GAN-based image-to-image translation frameworks, which still suffer from unstable training and imprecise character structure generation due to the nature of adversarial training. In this paper, we propose a one-shot font generation framework named CD-Font, based on a conditional diffusion model with style-content disentangled guidance. Unlike existing methods, we use two different encoders to separate the representations for styles and contents and fuse them as conditions of the diffusion model. Specifically, we concatenate the content image with the noisy image throughout all denoising steps to improve the integrity of character structures. During inference, we present a disentangled guidance sampling strategy to enable the generated font images to exhibit strong correlations with both the reference image and the target character. Extensive experiments and user studies demonstrate that our CD-Font outperforms current methods in one-shot font generation. Furthermore, we apply our method to cross-lingual font generation, showing its promising cross-lingual capability.

关键词： Automatic Font Generation Diffusion Model One-Shot Image Generation

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Safe and Infinite Resource Scheduling Using Energy Timed Automata 18th

Safe and Infinite Resource Scheduling Using Energy Timed Aut...

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18th International Conference on Theoretical Aspects of Software Engineering (TASE)

作者： Cuijpers, Pieter J. L. Hansen, Jonas Larsen, Kim G. Tech Univ Eindhoven Eindhoven Netherlands Radboud Univ Nijmegen Nijmegen Netherlands Aalborg Univ Aalborg Denmark

ISBN: (纸本)9783031646256;9783031646263

We study the existence of infinite and safe schedules for resource-aware timed systems, in the setting of multiple continuous resources. Specifically, we explore the multi-variable extension of Energy Timed Automata, where variables are constrained by polyhedra in Rn. We ask the question of whether there exist infinite runs satisfying such boundary constraints and show how schedules can be synthesized by characterising these runs as limit sets using quantifier elimination for linear real arithmetic. Additionally, we show that this existential problem is decidable under the assumption that limit sets are linear, we relate this to an earlier decidability result for single-variable Energy Timed Automata that are flat and segmented, and show constructively that there exist flat and segmented multi-variable Energy Timed Automata that give rise to nonlinear limit sets.

关键词： Energy Timed Automata Infinite Constrained Runs Quantifier Elimination Multiple Continuous Variables

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Feature Activation-Driven Zero-Shot NAS: A Contrastive Learning Framework 33rd

Feature Activation-Driven Zero-Shot NAS: A Contrastive Learn...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Wang, Di Xiang, Xunzhi Jing, Kun Xu, Jungang Univ Chinese Acad Sci Beijing Peoples R China Anhui Univ Hefei Anhui Peoples R China

ISBN: (纸本)9783031723315;9783031723322

The main bottleneck in current neural architecture search (NAS) algorithms is the inability to efficiently evaluate various neural architectures. While existing performance predictor-based approaches have significantly reduced the time required to assess alternative architectures, they still necessitate the training of a substantial number of architectures. Unfortunately, this training process consumes considerable computational resources. To address this challenge, we introduce a novel training-free evaluation metric rooted in the principles of contrastive learning. This innovative metric evaluates architecture performance by analyzing the differential responses elicited from positive and negative samples within the deep architecture. By leveraging the insights of contrastive learning, it offers a more resource-efficient solution for assessing neural architectures. We prove the superiority of our method with evaluation benchmarks such as NAS-bench-101 and NAS-bench-201. This method combined with the search strategy finally achieved an accuracy of 97.5 % on CIFAR-10. The code for our work is available at https://***/wdi-nancy/FADZS- NAS.

关键词： Neural architecture search Training-free proxies Deep feature activation Contrastive learning

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Scheduling Distributed I/O Resources in HPC Systems 30th

Scheduling Distributed I/O Resources in HPC Systems

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30th European Conference on Parallel and Distributed Processing (Euro-Par)

作者： Bandet, Alexis Boito, Francieli Pallez, Guillaume Univ Bordeaux CNRS Bordeaux INP InriaLaBRIUMR 5800 F-33400 Talence France INRIA Rennes France

ISBN: (纸本)9783031695766;9783031695773

This paper presents a comprehensive investigation on optimizing I/O performance in the access to distributed I/O resources in high-performance computing (HPC) environments. I/O resources, such as the I/O forwarding nodes and object storage targets (OST), are shared by applications. Each application has access to a subset of them, and multiple applications can access the same resources. We propose heuristics to schedule these distributed I/O resources in two steps: for each application, determining how many (allocation) and which (placement) resources to use. We discuss a wide range of information about applications' characteristics that can be used by the scheduling algorithms. Despite the fact that a higher level of application knowledge is associated with better performance, we demonstrate the robustness of our solutions in scenarios where information is limited or inaccurate. This research provides insights into the trade-offs between the depth of application characterization and the practicality of scheduling I/O resources.

关键词： HPC parallel I/O parallel file system object storage targets I/O forwarding scheduling resource allocation

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Network-Agnostic Multi-party Computation Revisited 27th

Network-Agnostic Multi-party Computation Revisited

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27th IACR International Conference on Practice and Theory of Public Key Cryptography (PKC)

作者： Bhimrajka, Nidhish Choudhury, Ashish Varadarajan, Supreeth Int Inst Informat Technol Bangalore Bengaluru India

ISBN: (纸本)9783031577215;9783031577222

We study network-agnostic secure multi-party computation (MPC) in the presence of computationally-bounded adversaries. A network-agnostic protocol provides the best possible security guarantees, irrespective of the type of underlying communication network. Previous MPC protocols in this regime either assume a setup for a common reference string (CRS) and a threshold additively homomorphic encryption (Blum et al. CRYPTO 2020) or a plain public-key infrastructure (PKI) setup (Bacho et al. CRYPTO 2023). Both these MPC protocols perform circuit-evaluation over encrypted data and also deploy different forms of zero-knowledge (ZK) proofs, along with other computationallyexpensive cryptographic machinery. We aim to build an MPC protocol based on circuit evaluation on secret-shared data, avoiding ZK proofs and other computationally-expensive cryptographic machinery and based on a plain PKI setup. To achieve our goal, we present the first network-agnostic verifiable secret sharing (VSS) protocol with the optimal threshold conditions, which is of independent interest. Previously, network-agnostic VSS is known either with perfect security (Appan et al. IEEE IT 2023) where the threshold conditions are not known to be optimal or with statistical security (Appan et al. TCC 2023) where the threshold conditions are optimal, but the parties need to perform exponential amount of computation and communication. Although our proposed MPC protocol incurs higher communication complexity compared to state-of-the-art networkagnostic MPC protocols, it offers valuable insights and motivates alternative directions for designing computationally inexpensive MPC protocols, based on a plain PKI setup, which has not been explored in the domain of network-agnostic MPC.

关键词： Cryptographic security Multiparty Computation Verifiable Secret Sharing Network-agnostic Public Key Infrastructure

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Natively Neuromorphic LMU Architecture for Encoding-Free SNN-Based HAR on Commercial Edge Devices 33rd

Natively Neuromorphic LMU Architecture for Encoding-Free SNN...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Fra, Vittorio Leto, Benedetto Pignata, Andrea Macii, Enrico Urgese, Gianvito Politecn Torino Turin Italy

ISBN: (纸本)9783031723582;9783031723599

Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL) solutions. The scarce availability of dedicated hardware able to actualize the emulation of brain-inspired computation, which is otherwise only simulated, yet still hinders the wide adoption of neuromorphic computing for edge devices and embedded systems. With this premise, we adopt the perspective of neuromorphic computing for conventional hardware and we present the L2MU, a natively neuromorphic Legendre Memory Unit (LMU) which entirely relies on Leaky Integrate-and-Fire (LIF) neurons. Specifically, the original recurrent architecture of LMU has been redesigned by modelling every constituent element with neural populations made of LIF or Current-Based (CuBa) LIF neurons. To couple neuromorphic computing and off-the-shelf edge devices, we equipped the L2MU with an input module for the conversion of real values into spikes, which makes it an encoding-free implementation of a Recurrent Spiking Neural Network (RSNN) able to directly work with raw sensor signals on non-dedicated hardware. As a use case to validate our network, we selected the task of Human Activity Recognition (HAR). We benchmarked our L2MU on smartwatch signals from hand-oriented activities, deploying it on three different commercial edge devices in compressed versions too. The reported results remark the possibility of considering neuromorphic models not only in an exclusive relationship with dedicated hardware but also as a suitable choice to work with common sensors and devices.

关键词： Neuromorphic computing Edge devices IoT Encoding-free LIF neurons Legendre memory unit

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OntoEditor: Real-Time Collaboration via Distributed Version Control for Ontology Development 21st

OntoEditor: Real-Time Collaboration via Distributed Version ...

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21st International Conference on The Semantic Web (ESWC)

作者： Hemid, Ahmad Shabbir, Waleed Khiat, Abderrahmane Lange, Christoph Quix, Christoph Decker, Stefan Fraunhofer FIT Data Sci & Artificial Intelligence St Augustin Germany Fraunhofer IAIS Enterprise Informat Syst St Augustin Germany Hsch Niederrhein Krefeld Germany Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9783031606250;9783031606267

In today's remote work environment, the demand for real-time collaborative tools has surged. Our research targets efficient collaboration among knowledge engineers and domain experts in Ontology development. We developed a web-based tool for real-time collaboration, compatible with GitLab, GitHub, and Bitbucket. To tackle the challenge of concurrent modifications leading to potential inconsistencies, we integrated an Operational Transformation-based real-time database. This integration enables multiple users to concurrently collaborate to build and edit their ontologies, ensuring both consistency and atomicity. Furthermore, our tool enhances user experience by providing meaningful syntax error messages for ontologies expressed in various RDF serialization formats. This streamlined the manual correction process. Additionally, we established a reliable synchronization channel for users to allow pulling and committing changes to distributed repositories for their developed ontologies. Yielding promising results, our evaluation focused on two key aspects: first, assessing the tool's collaborative editing consistency via an automated typing script;second, conducting a comprehensive user study to evaluate its features and compare its functionalities with similar tools.

关键词： Real-time collaboration RDF serialization Version Control Systems Git integration Error detection Syntax validation Ontology development

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ProvIoT: Detecting Stealthy Attacks in IoT through Federated Edge-Cloud Security 2nd

ProvIoT: Detecting Stealthy Attacks in IoT through Federated...

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22nd International Conference on Applied Cryptography and Network Security (ACNS)

作者： Mukherjee, Kunal Wiedemeier, Joshua Wang, Qi Kamimura, Junpei Rhee, John Junghwan Wei, James Li, Zhichun Yu, Xiao Tang, Lu-An Gui, Jiaping Jee, Kangkook Univ Texas Dallas Richardson TX 75083 USA Univ Illinois Champaign IL USA Stellar Cyber San Jose CA USA Univ Cent Oklahoma Edmond OK USA NEC Labs Amer Inc Princeton NJ USA Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031547751;9783031547768

Internet of Things (IoT) devices have increased drastically in complexity and prevalence within the last decade. Alongside the proliferation of IoT devices and applications, attacks targeting them have gained popularity. Recent large-scale attacks such as Mirai and VPNFilter highlight the lack of comprehensive defenses for IoT devices. Existing security solutions are inadequate against skilled adversaries with sophisticated and stealthy attacks against IoT devices. Powerful provenancebased intrusion detection systems have been successfully deployed in resource-rich servers and desktops to identify advanced stealthy attacks. However, IoT devices lack the memory, storage, and computing resources to directly apply these provenance analysis techniques on the device. This paper presents ProvIoT, a novel federated edge-cloud security framework that enables on-device syscall- level behavioral anomaly detection in IoT devices. ProvIoT applies federated learning techniques to overcome data and privacy limitations while minimizing network overhead. Infrequent on-device training of the local model requires less than 10% CPU overhead;syncing with the global models requires sending and receiving similar to 2MB over the network. During normal offline operation, ProvIoT periodically incurs less than 10% CPU overhead and less than 65MB memory usage for data summarization and anomaly detection. Our evaluation shows that ProvIoT detects fileless malware and stealthy APT attacks with an average F1 score of 0.97 in heterogeneous real-world IoT applications. ProvIoT is a step towards extending provenance analysis to resource-constrained IoT devices, beginning with well-resourced IoT devices such as the RaspberryPi, Jetson Nano, and Google TPU.

关键词： Provenance graph analysis anomaly detection dynamic malware analysis federated learning deep learning privacy

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SecGraph: Towards SGX-based Efficient and Confidentiality-Preserving Graph Search 29th

SecGraph: Towards SGX-based Efficient and Confidentiality-Pr...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Wang, Qiuhao Yang, Xu Qi, Saiyu Qi, Yong Xi An Jiao Tong Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9789819755615;9789819755622

Graphs have more expressive power and are widely researched in various search demand scenarios, compared with traditional relational and XML models. Today, many graph search services have been deployed on a third-party server, which can alleviate users from the burdens of maintaining large-scale graphs and huge computation costs. Nevertheless, outsourcing graph search services to the third-party server may invade users' privacy. PeGraph was recently proposed to achieve the encrypted search over the social graph. The main idea of PeGraph is to maintain two data structures XSet and TSet motivated by the OXT technology to support encrypted conductive search. However, PeGraph still has some limitations. First, PeGraph suffers from high communication and computation costs in search operations. Second, PeGraph cannot support encrypted search over dynamic graphs. In this paper, we propose an SGX-based efficient and confidentiality-preserving graph search scheme SecGraph that can support insertion and deletion operations. We first design a new proxy-token generation method to reduce the communication cost. Then, we design an LDCF-encoded XSet based on the Logarithmic Dynamic Cuckoo Filter to reduce the computation cost. Finally, we design a new dynamic version of TSet named Twin-TSet to enable encrypted search over dynamic graphs. We have demonstrated the confidentiality preservation property of SecGraph through rigorous security analysis. Experiment results show that SecGraph yields up to 208x improvement in search time compared with PeGraph and the communication cost in PeGraph is up to 540x larger than that in SecGraph.

关键词： Cost reduction

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