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Resilience Week (RWS)

作者： Sharon A. Boamah Reynold Mathieu Victor Faillace Dennis Agnew Janise McNair Arturo Bretas Department of Electrical and Computer Engineering University of Florida Gainesville FL Distributed Systems Group Sandia National Laboratory

ISBN: (数字)9798350388985

ISBN: (纸本)9798350388992

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid.

关键词： Simulation Data integration Data models Stability analysis Smart grids Communication networks Security Reliability State estimation Resilience

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Person-Independent Hand Gesture Recognition Using MediaPipe and Multi-layer Perceptron

Person-Independent Hand Gesture Recognition Using MediaPipe ...

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International Conference on Smart systems and Wireless Communication, SSWC 2024

作者： Barman, Subhajyoti Majumdar, Sejuti Department of Computer Science and Business Systems Techno India University West Bengal WB Kolkata India Department of Computing and Analytics NSHM College of Management and Technology Durgapur Group of Institutions Durgapur India

ISBN: (纸本)9789819613472

Hand gesture recognition has become increasingly essential, not only for facilitating communication for hearing-impaired individuals but also for implementing automation that minimizes human contact with surfaces, especially in the post-pandemic era. However, the implementation of accurate recognition systems faces several challenges due to variations in human hand size, varying distances from the camera, and hand orientations, making it difficult to establish a direct correspondence between reference gestures and target gestures. To address these challenges, this research employs a machine learning (ML)-based technique that emulates the human brain’s ability to identify gestures in a user-independent manner, regardless of distance, palm size, and orientation. In this research work, the pre-trained MediaPipe hand model has been used to extract hand landmark points from the hand data corpus. These landmark points, along with hand-face position, were utilized to develop feature vectors for the classification process. After normalization, these features exhibited a high correlation within the same category of hand gestures. Initially, a spot-checking activity has been done to recognize the machine learning model that can accurately identify gestures from the normalized feature vectors. The spot-checking process uses eight different machine learning models and they have been fed with a subset of total available hand gestures from the corpus. It has been identified that the multi-layer perceptron (MLP) ensures 100% classification accuracy that motivates the development of the final model using the MLP classification algorithm with 25 different hand gestures. This developed model was able to ensure a 99.62% accuracy level when it was tested with data collected from more numbers of hand profiles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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A Finite Memory Approach Applied to Verified Pseudo State Estimation of Fractional Models of Lithium-Ion Batteries

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IFAC-PapersOnLine 2024年第12期58卷 185-190页

作者： Andreas Rauh Marit Lahme Carl von Ossietzky Universität Oldenburg Department of Computing Science Group: Distributed Control in Interconnected Systems D-26111 Oldenburg Germany

Fractional equivalent circuit models have been used in numerous publications to represent long-term memory phenomena of the charging/discharging behavior of Lithium-ion batteries. However, despite the fact that fractional models allow for capturing (infinite horizon) memory properties, the same feature also complicates the numerical evaluation. This is especially critical in cases, in which state estimation procedures are implemented that need to be executed in real time. For this type of applications, it is necessary that the execution time in each time step is bounded a-priori and that it does not grow over time. For that purpose, classical simulation approaches exploit, for example, Grünwald-Letnikov schemes with a finite length of previous state information. However, truncating the memory window without considering the arising errors may lead to wrong state estimates. This is especially critical if interval observers are designed which are meant to include the sets of reachable states with certainty.

关键词： Interval observer design fractional differential equations Grünwald-Letnikov discretization quantification of temporal truncation errors

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments

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Procedia computer science 2025年 257卷 452-459页

作者： Mahtab Shahin Saeed Rahimpour Tara Ghasempouri Sanja Bauk Thomas Fahringer Dirk Draheim Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems.

关键词： Cloud-Native Architectures Serverless Computing Data Mining Apriori Algorithm distributed Computing Big Data Maritime

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Ephemeral Per-query Engines for Serverless Analytics 49

Ephemeral Per-query Engines for Serverless Analytics

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Joint Workshops at the 49th International Conference on Very Large Data Bases, VLDBW 2023

作者： Wawrzoniak, Michael Bruno, Rodrigo Klimovic, Ana Alonso, Gustavo Systems Group Computer Science Department ETH Zürich Switzerland INESC-ID Técnico U. Lisboa Portugal

We challenge the common assumption that queries are submitted to a pre-configured, already running engine and put forward the idea of dynamically instantiating a chosen data processing engine upon query submission by leveraging Function-as-a-Service (FaaS) platforms. We demonstrate the idea by running unmodified data processing engines (we use Apache Drill as an initial example) on real-world serverless FaaS platforms and show that such engines can be instantiated on demand when a query arrives. We aim to eventually support a wide range of queries and workloads. Wide access to such functionality would be a game changer in data processing. First, it would enable pay-per-query models supporting sporadic, interactive data analysis on arbitrary engines. Second, it would significantly increase the flexibility for data processing by enabling the possibility of dynamically choosing the actual engine, its configuration, and the resource allocation on a per-query basis. Logically, this amounts to dynamically attaching a query engine to the query rather than sending the query to a pre-configured and already deployed engine. In this paper we elaborate on this vision, outline the design of the MetaQ prototype that we are building to explore the idea, demonstrate that it is realistic through initial experiments, and discuss its many exciting practical implications. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Engines

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Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

Synthetically Generating Human-like Data for Sequential Deci...

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Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications 2023

作者： Brandt, Bryan Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division Naval Research Laboratory WashingtonDC United States Computer Science Department University of Wisconsin-Whitewater WI United States

ISBN: (纸本)9781510661721

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks. © 2023 SPIE.

关键词： Decision making

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A Graph-based Approach to Human Activity Recognition

arXiv

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arXiv 2024年

作者： Peroutka, Thomas Murturi, Ilir Donta, Praveen Kumar Dustdar, Schahram Distributed Systems Group TU Wien Vienna1040 Austria Department of Computer and Systems Sciences Stockholm University Stockholm16425 Sweden

Advanced wearable sensor devices have enabled the recording of vast amounts of movement data from individuals regarding their physical activities. This data offers valuable insights that enhance our understanding of how physical activities contribute to improved physical health and overall quality of life. Consequently, there is a growing need for efficient methods to extract significant insights from these rapidly expanding real-time datasets. This paper presents a methodology to efficiently extract substantial insights from these expanding datasets, focusing on professional sports but applicable to various human activities. By utilizing data from Inertial Measurement Units (IMU) and Global Navigation Satellite systems (GNSS) receivers, athletic performance can be analyzed using directed graphs to encode knowledge of complex movements. Our approach is demonstrated on biathlon data and detects specific points of interest and complex movement sequences, facilitating the comparison and analysis of human physical performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Wearable sensors

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Efficient Tabular Data Preprocessing of ML Pipelines

arXiv

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arXiv 2024年

作者： Zhu, Yu Jiang, Wenqi Alonso, Gustavo Systems Group Department of Computer Science ETH Zurich Switzerland

Data preprocessing pipelines, which includes data decoding, cleaning, and transforming, are a crucial component of Machine Learning (ML) training. Thy are computationally intensive and often become a major bottleneck, due to the increasing performance gap between the CPUs used for preprocessing and the GPUs used for model training. Recent studies show that a significant number of CPUs across several machines are required to achieve sufficient throughput to saturate the GPUs, leading to increased resource and energy consumption. When the pipeline involves vocabulary generation, the preprocessing performance scales poorly due to significant row-wise synchronization overhead between different CPU cores and servers. To address this limitation, in this paper we present the design of Piper, a hardware accelerator for tabular data preprocessing, prototype it on FPGAs, and demonstrate its potential for training pipelines of commercial recommender systems. Piper achieves 4.7 ∼ 71.3× speedup in latency over a 128-core CPU server and outperforms a data-center GPU by 4.8∼ 20.3× when using binary input. The impressive performance showcases Piper’s potential to increase the efficiency of data preprocessing pipelines and significantly reduce their resource consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphics processing unit

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