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  • 1.
    Ennadir, Sofiane
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Abbahaddou, Yassine
    DaSciM, LIX, Ecole Polytechnique, Institut Polytechnique de Paris, France.
    Lutzeyer, Johannes F.
    DaSciM, LIX, Ecole Polytechnique, Institut Polytechnique de Paris, France.
    Vazirgiannis, Michalis
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. DaSciM, LIX, Ecole Polytechnique, Institut Polytechnique de Paris, France.
    Boström, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    A Simple and Yet Fairly Effective Defense for Graph Neural Networks2024In: AAAI Technical Track on Safe, Robust and Responsible AI Track, Association for the Advancement of Artificial Intelligence (AAAI) , 2024, Vol. 38, p. 21063-21071, article id 19Conference paper (Refereed)
    Abstract [en]

    Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs to small adversarial perturbations. Existing defense methods against such perturbations suffer from high time complexity and can negatively impact the model's performance on clean graphs. To address these challenges, this paper introduces NoisyGNNs, a novel defense method that incorporates noise into the underlying model's architecture. We establish a theoretical connection between noise injection and the enhancement of GNN robustness, highlighting the effectiveness of our approach. We further conduct extensive empirical evaluations on the node classification task to validate our theoretical findings, focusing on two popular GNNs: the GCN and GIN. The results demonstrate that NoisyGNN achieves superior or comparable defense performance to existing methods while minimizing added time complexity. The NoisyGNN approach is model-agnostic, allowing it to be integrated with different GNN architectures. Successful combinations of our NoisyGNN approach with existing defense techniques demonstrate even further improved adversarial defense results. Our code is publicly available at: https://github.com/Sennadir/NoisyGNN.

  • 2.
    Hasselberg, Adam
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. RISE, Gothenburg, Sweden.
    Timoudas, Thomas Ohlson
    RISE, Gothenburg, Sweden..
    Carbone, Paris
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Dán, György
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Cliffhanger: An Experimental Evaluation of Stateful Serverless at the Edge2024In: 2024 19th Wireless On-Demand Network Systems and Services Conference, IEEE, 2024, p. 41-48Conference paper (Refereed)
    Abstract [en]

    The serverless computing paradigm has transformed cloud service deployment by enabling automatic scaling of resources in response to varying demand. Building on this, stateful serverless computing introduces critical capabilities for data management, fault tolerance, and consistency, which are particularly relevant in the context of distributed deployments, notably in edge computing environments. In this work, we explore the feasibility of stateful serverless computing in resource-limited edge environments through an empirical study utilizing a multi-view object tracking application. Our results show that while these systems perform well in cloud environments, their effectiveness is severely affected at the edge due to state, application, and resource management solutions optimized for cloud environments. Existing solutions are most detrimental to applications with intermittent workloads, as typical combinations of concurrency handling and resource reservation can lead to minutes of unstable system behavior due to cold starts. Our results highlight the need for a tailored approach in stateful serverless systems for edge computing scenarios.

  • 3.
    Horchidan, Sonia-Florina
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Chen, Po Hao
    Brown University, Brown University.
    Kritharakis, Emmanouil
    Boston University, Boston University.
    Carbone, Paris
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Kalavri, Vasiliki
    Brown University, Brown University.
    Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems2024In: Advances in Database Technology - EDBT, Open Proceedings.org , 2024, Vol. 27, p. 676-689, article id 3Conference paper (Refereed)
    Abstract [en]

    As Machine Learning predictions are increasingly being used in business analytics pipelines, integrating stream processing with model serving has become a common data engineering task. Despite their synergies, separate software stacks typically handle streaming analytics and model serving. Systems for data stream management do not support ML inference out-of-the-box, while model-serving frameworks have limited functionality for continuous data transformations, windowing, and other streaming tasks. As a result, developers are left with a design space dilemma whose trade-offs are not well understood. This paper presents Crayfish, an extensible benchmarking framework that facilitates designing and executing comprehensive evaluation studies of streaming inference pipelines. We demonstrate the capabilities of Crayfish by studying four data processing systems, three embedded libraries, three external serving frameworks, and two pre-trained models. Our results prove the necessity of a standardized benchmarking framework and show that (1) even for serving tools in the same category, the performance can vary greatly and, sometimes, defy intuition, (2) GPU accelerators can show compelling improvements for the serving task, but the improvement varies across tools, and (3) serving alternatives can achieve significantly different performance, depending on the stream processors they are integrated with.

1234567 1 - 3 of 1092
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