Computer Systems (VT 2025)

• Distributed Computing
• Distributed Systems
• Artificial Intelligence
• Machine Learning

I urval från Stockholms universitets publikationsdatabas

• Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems

  2025. Alfreds Lapkovskis (et al.). 2025 IEEE International Conference on Edge Computing and Communications (EDGE), 93-102

  Konferens

  Ensuring Service Level Objectives (SLOs) in large-scale architectures, such as Distributed Computing Continuum Systems (DCCS), is challenging due to their heterogeneous nature and varying service requirements across different devices and applications. Additionally, unpredictable workloads and resource limitations lead to fluctuating performance and violated SLOs. To improve SLO compliance in DCCS, one possibility is to apply machine learning; however, the design choices are often left to the developer. To that extent, we provide a benchmark of Active Inference—an emerging method from neuroscience—against three established reinforcement learning algorithms (Deep Q-Network, Advantage Actor-Critic, and Proximal Policy Optimization). We consider a realistic DCCS use case: an edge device running a video conferencing application alongside a WebSocket server streaming videos. Using one of the respective algorithms, we continuously monitor key performance metrics, such as latency and bandwidth usage, to dynamically adjust parameters, including the number of streams, frame rate, and resolution, to optimize service quality and user experience. To test algorithms’ adaptability to constant system changes, we simulate dynamically changing SLOs and both instant and gradual data-shift scenarios, such as network bandwidth limitations and fluctuating device thermal states. Although the evaluated algorithms all showed advantages and limitations, our findings demonstrate that Active Inference is a promising approach for ensuring SLO compliance in DCCS, offering lower memory usage, stable CPU utilization, and fast convergence.

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• Automatic fused multimodal deep learning for plant identification

  2025. Alfreds Lapkovskis, Natalia Nefedova, Ali Beikmohammadi. Frontiers in Plant Science 16, 1-17

  Artikel

  Introduction: Plant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The advent of deep learning (DL) techniques has revolutionized this field by enabling autonomous feature extraction, significantly reducing the dependence on manual expertise. However, conventional DL models often rely solely on single data sources, failing to capture the full biological diversity of plant species comprehensively. Recent research has turned to multimodal learning to overcome this limitation by integrating multiple data types, which enriches the representation of plant characteristics. This shift introduces the challenge of determining the optimal point for modality fusion.

  Methods: In this paper, we introduce a pioneering multimodal DL-based approach for plant classification with automatic modality fusion. Utilizing the multimodal fusion architecture search, our method integrates images from multiple plant organs—flowers, leaves, fruits, and stems—into a cohesive model. To address the lack of multimodal datasets, we contributed Multimodal-PlantCLEF, a restructured version of the PlantCLEF2015 dataset tailored for multimodal tasks.

  Results: Our method achieves 82.61% accuracy on 979 classes of Multimodal-PlantCLEF, outperforming late fusion by 10.33%. Through the incorporation of multimodal dropout, our approach demonstrates strong robustness to missing modalities. We validate our model against established benchmarks using standard performance metrics and McNemar’s test, further underscoring its superiority.

  Discussion: The proposed model surpasses state-of-the-art methods, highlighting the effectiveness of multimodality and an optimal fusion strategy. Our findings open a promising direction in future plant classification research.

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