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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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Graph-Augmented Contrastive Clustering for Time Series

SSRN

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SSRN 2023年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Zhang, Peng Chen, Junyang Chen, Xiaojun Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

The recent emergence of time series contrastive clustering methods can be categorized into two classes. The first class uses contrastive learning for universal representations, which can be effective in various downstream tasks, but overlooks important categorical information and clustering objectives, leading to unsuitable models. The second class incorporates contrastive learning into clustering objectives, but fails to fully explore potential connections and structures between time series data. To this end, we propose the "Time Series Graph-augmented Contrastive Clustering (TSGCC) method", a graph-augmented contrastive learning framework aiming to create cluster-friendly features and assignments by iteratively optimizing contrastive loss. The method uses a weighted KNN graph to build positive and negative sample pairs, projecting the instances into a feature space with the dimensionality number of clusters. Experimental results demonstrate that TSGCC outperforms 18 advanced time-series clustering methods on the 36 representatives out of the 128 UCR benchmarks, achieving the best clustering results on 12 datasets, with the highest average rank (3.22) of RI and the second-highest average rank (3.89) of NMI among 18 clustering methods. We further compared our method with recent approaches on the entire set of 128 UCR datasets, demonstrating our method's broad applicability and effectiveness. © 2023, The Authors. All rights reserved.

关键词： Time series

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Alternating Refinement Relations for Fuzzy Concurrent Game Structures

SSRN

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SSRN 2024年

作者： Zhang, Shasha Zhou, Jie Ma, Yanfang Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Mathematics and Science College Shanghai Normal University Shanghai200234 China Computer Science and Information Engineering College Changzhou Institute of Technology Jiangsu Changzhou213032 China

Fuzzy concurrent game structures (FCGSs), an extension of concurrent game structures in the fuzzy setting, combine both fuzzy and nondeterministic choices. They are therefore a powerful semantic foundation for modeling fuzzy systems that interact with their environments. In recent years, refinement relations from the field of formal verification, including trace containment and simulation, have been successfully introduced to special classes or related ones of FCGSs, such as fuzzy transition systems and fuzzy automata, and have been developed quickly. However, these refinement relations are not directly applicable to FCGSs as they constrain the possible behaviors of the environment. To further satisfy the need for analysis and verification of fuzzy systems, this paper introduces alternating refinement relations for FCGSs. First, we present two general notions of alternating trace containment for FCGSs. Although these notions coincide with the corresponding classical ones, we show that they do not coincide within FCGSs. Then, we provide algorithms to solve the problems of deciding two types of alternating trace containments in finite FCGSs under memoryless strategies. On the other hand, we propose two types of alternating simulations for FCGSs via weight functions and closed sets, and discover a novel result that they have different discriminatory abilities, with one alternating simulation implying the other. Finally, we investigate the relationships between two types of alternating trace containments and two types of alternating simulations. © 2024, The Authors. All rights reserved.

关键词： Game theory

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Explaining Model Overfitting in CNNS Via GMM Clustering

SSRN

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SSRN 2024年

作者： Dou, Hui Mu, Xinyu Yi, Mengjun Han, Feng Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology China Department of Computer Science and Technology Nanjing University Nanjing210023 China School of Electronic Science and Engineering Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China

Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challenges for practical applications. In this study, we provide quantitative metrics for assessing CNN filters by clustering the feature maps corresponding to individual filters in the model via Gaussian Mixture Model (GMM). By analyzing the clustering results, we screen out some anomaly filters associated with outlier samples. We further analyze the relationship between the anomaly filters and model overfitting, proposing three hypotheses. This method is universally applicable across diverse CNN architectures without modifications, as evidenced by its successful application to models like AlexNet and LeNet-5. We present three meticulously designed experiments demonstrating our hypotheses from the perspectives of model behavior, dataset characteristics, and filter impacts. Through this work, we offer a novel perspective for evaluating the CNN performance and gain new insights into the operational behavior of model overfitting. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Explaining Model Overfitting in CNNs via GMM Clustering

arXiv

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arXiv 2024年

关键词： Convolutional neural networks

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QuIP: A P4 Quantum Internet Protocol Prototyping Framework

arXiv

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arXiv 2024年

作者： Kozlowski, Wojciech Kuipers, Fernando A. Smets, Rob Turkovic, Belma QuTech Delft University of Technology Delft2628 CD Netherlands The Networked Systems Group Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands SURF Utrecht3511 EP Netherlands TNO The Hague2595 DA Netherlands

Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed;one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facilitate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusability between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid. © 2024, CC BY.

关键词： Network architecture

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HC-MAE: Hierarchical Cross-attention Masked Autoencoder Integrating Histopathological Images and Multi-omics for Cancer Survival Prediction

HC-MAE: Hierarchical Cross-attention Masked Autoencoder Inte...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Suixue Wang Xiangjun Hu Qingchen Zhang School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Technology Hainan University Haikou China Software College Shenyang Normal University Shenyang China

Accurate cancer survival prediction enables clinicians to tailor treatment regimens based on individual patient prognoses, effectively mitigating over-treatment and inefficient medical resource allocation. Recently, the integration of histopathological images and multi-omics data, together with deep learning, has become increasingly applied to predict cancer survival. However, current deep learning-based integration methods ignore the spatial relationships across various fields of view within gigapixel histopathological images, since they mainly focus on a specific field of view. Inspired by the hierarchical image pyramid transformer (HIPT), we propose a hierarchical cross-attention masked autoencoder (HC-MAE) to integrate histopathological images and multi-omics data for cancer survival prediction. Specifically, HC-MAE aggregates the representations learned from different fields of view, effectively capturing the fine-grained details and the spatial relationships within histopathological images. We conduct experiments to compare the HC-MAE method with current state-of-the-art methods on six cancer datasets sourced from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that HC-MAE achieves superior performance on five out of six cancer datasets, significantly outperforming the compared methods. The code is available at https://***/SuixueWang/HC-MAE.

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A Survey on Federated Learning in Intelligent Transportation Systems

arXiv

引用

arXiv 2024年

作者： Zhang, Rongqing Wang, Hanqiu Li, Bing Cheng, Xiang Yang, Liuqing School of Software Engineering Tongji University Shanghai201804 China School of Electronics Peking University Beijing100871 China Guangzhou511458 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR5999077 Hong Kong

The development of Intelligent Transportation System (ITS) has brought about comprehensive urban traffic information that not only provides convenience to urban residents in their daily lives but also enhances the efficiency of urban road usage, leading to a more harmonious and sustainable urban life. Typical scenarios in ITS mainly include traffic flow prediction, traffic target recognition, and vehicular edge computing. However, most current ITS applications rely on a centralized training approach where users upload source data to a cloud server with high computing power for management and centralized training. This approach has limitations such as poor real-time performance, data silos, and difficulty in guaranteeing data privacy. To address these limitations, federated learning (FL) has been proposed as a promising solution. In this paper, we present a comprehensive review of the application of FL in ITS, with a particular focus on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. For each scenario, we provide an in-depth analysis of its key characteristics, current challenges, and specific manners in which FL is leveraged. Moreover, we discuss the benefits that FL can offer as a potential solution to the limitations of the centralized training approach currently used in ITS applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Sakib, Nazmus Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480451

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2022 IEEE.

关键词： Supply chains

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Modular Verification of SPARCv8 Code

引用

Journal of computer Science & technology 2020年第6期35卷 1382-1405页

作者： Jun-Peng Zha Xin-Yu Feng Lei Qiao Department of Computer Science and Technology Nanjing UniversityNanjing 210023China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Beijing Institute of Control Engineering Beijing 100080China

Inline assembly code is common in system software to interact with the underlying hardware platforms. The safety and correctness of the assembly code is crucial to guarantee the safety of the whole system. In this paper, we propose a practical Hoare-style program logic for verifying SPARC (Scalable Processor Architecture) assembly code. The logic supports modular reasoning about the main features of SPARCv8 ISA (instruction set architecture), including delayed control transfers, delayed writes to special registers, and register windows. It also supports relational reasoning for refinement verification. We have applied it to verify that there is a contextual refinement between a context switch routine in SPARCv8 and a switch primitive. The program logic and its soundness proof have been mechanized in Coq.

关键词： Scalable Processor Architecture Version 8(SPARCv8) assembly code verification context switch Coq refinement verification

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