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Multi scale histogram-based intrusion detection system for the MIL-STD-1553 protocol 3

Multi scale histogram-based intrusion detection system for t...

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3rd IEEE International Mediterranean conference on Communications and Networking, MeditCom 2023

作者： Baldini, Gianmarco Joint Research Centre European Commission Ispra Italy

ISBN: (纸本)9798350333732

In recent years, there is a growing interest by the research community in the development of countermeasures to cybersecurity attacks to serial bus networks in different domains including the industrial sector (e.g., SCADA), vehicular systems (CAN-BUS) and other domains. This paper focuses specifically to the protection of the serial buses used in the aerospace sector (e.g., aircrafts, satellite systems) and the MIL-STD-1553 in particular. The MIL-STD-1553 protocol is commonly used in the aerospace sector in order to communicate information across the components of aerospace systems and supports spacecraft on-board data handling (OBDH) subsystems, both military and civil. This paper proposes an intrusion detection systems (IDS) to detect attacks by analyzing the MIL-STD-1553 traffic using machine learning algorithms. Taking in consideration that the platforms, where the MIL-STD-1553 protocol is deployed, have limited computing resources in comparison to large ICT infrastructures where IDS is usually implemented, the objective is to design a IDS which is computing efficient. This paper proposes an IDS based on the specific coding of MIL-STD-1553 messages to words, whose frequency is analyzed using histograms. A novel multi-scale histogram IDS, where sequence of words are also analyzed, is used to widen the feature space, which is then optimized using the ReliefF algorithm to identify the most discriminating features. The approach manages to obtain a 96% attack detection accuracy on a recent MIL-STD-1553 public data set outperforming other IDS approaches from literature based on entropy measures. © 2023 IEEE.

关键词： Cybersecurity

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UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models 37

UniPC: A Unified Predictor-Corrector Framework for Fast Samp...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Zhao, Wenliang Bai, Lujia Rao, Yongming Zhou, Jie Lu, Jiwen Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781713899921

Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., <10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256x256 (conditional) with only 10 function evaluations. Code is available at https://***/wl-zhao/UniPC.

关键词： data handling

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A meal detection approach based on parity space to detect untreated meals in subjects with Type 1 diabetes∗ 63

A meal detection approach based on parity space to detect un...

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63rd IEEE conference on Decision and Control, CDC 2024

作者： Mongini, Paolo Alberto Mazzoleni, Mirko Ferramosca, Antonio Magni, Lalo Toffanin, Chiara University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy University of Bergamo Department of Management Information and Production Engineering Via Marconi 5 Dalmine24044 Italy University of Pavia Department of Civil and Architecture Engineering Pavia27100 Italy

ISBN: (纸本)9798350316339

In this paper a data-driven fault detection technique based on parity space is applied to the problem of detecting unannounced meals for type 1 diabetes patients. This method involves the generation and evaluation of a residual signal to detect faults (unannounced meals) acting on the system (patient). Insulin on Board (IOB), meal intake, glucose and its second derivative have been selected as input signals to generate the residuals, and the parity space matrices are estimated using 8-hour training in silico data generated by the UVA/Padova simulator. The residual evaluation module compares the residual with a threshold that is optimized for each patient using 2-day tuning data. The complete system is evaluated on 1-week scenario obtaining promising results (TPR =9 5 %, PPV=7 2 %) with a detection delay of 4 2 minutes for 8 0 % of patients. For the 20 outlier patients, a fine-tuning and patient-tailored input signal processing are proposed as a future development. © 2024 IEEE.

关键词： Biomedical signal processing

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DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation 37

DAW: Exploring the Better Weighting Function for Semi-superv...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Sun, Rui Mai, Huayu Zhang, Tianzhu Wu, Feng Univ Sci & Technol China Sch Informat Sci & Technol Deep Space Explorat Lab Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9781713899921

The critical challenge of semi-supervised semantic segmentation lies how to fully exploit a large volume of unlabeled data to improve the model's generalization performance for robust segmentation. Existing methods tend to employ certain criteria (weighting function) to select pixel-level pseudo labels. However, the trade-off exists between inaccurate yet utilized pseudo-labels, and correct yet discarded pseudo-labels in these methods when handling pseudo-labels without thoughtful consideration of the weighting function, hindering the generalization ability of the model. In this paper, we systematically analyze the trade-off in previous methods when dealing with pseudo-labels. We formally define the trade-off between inaccurate yet utilized pseudo-labels, and correct yet discarded pseudolabels by explicitly modeling the confidence distribution of correct and inaccurate pseudo-labels, equipped with a unified weighting function. To this end, we propose Distribution-Aware Weighting (DAW) to strive to minimize the negative equivalence impact raised by the trade-off. We find an interesting fact that the optimal solution for the weighting function is a hard step function, with the jump point located at the intersection of the two confidence distributions. Besides, we devise distribution alignment to mitigate the issue of the discrepancy between the prediction distributions of labeled and unlabeled data. Extensive experimental results on multiple benchmarks including mitochondria segmentation demonstrate that DAW performs favorably against state-of-the-art methods. Code is available at https://***/yuisuen/DAW.

关键词： Semantics

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Towards a Framework for Seismic data 1

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5th Iberoamerican and the 4th Indo-American Knowledge Graphs and Semantic Web conference, KGSWC 2023

作者： Mastoras, Valadis Vassiliades, Alexandros Rousi, Maria Diplaris, Sotiris Mavropoulos, Thanassis Gialampoukidis, Ilias Vrochidis, Stefanos Kompatsiaris, Ioannis Information Technologies Institute Center for Research and Technology Hellas Thessaloniki Greece

ISBN: (数字)9783031477454

ISBN: (纸本)9783031477447

Over the past decade, Knowledge Graphs (KGs) gained significant attention as a powerful method for knowledge representation. Driven by increasing interest, a paradigm shift has occurred, where the technology of KGs has transitioned from the research domain to the industry and public sector, with companies and organizations increasingly representing their data as Linked Open data, gaining in that way significant traction for this technology. This paper focuses on KGs in the context of environmental challenges. More specifically, this work concerns KGs that contain seismic event data, such as location, timestamp, magnitude, depth, target date, as well as images before and after the event occurrence. Moreover, a Natural Language processing (NLP) module is integrated to enhance the KG. That module enables users to query for seismic events in a free-text manner, before addressing them with a relevant response through a dedicated Information Retrieval (IR) component. The KG was constructed with data retrieved from the Instituto Nazionale di Geofisica e Vulcanologia, a rich resource that comes with earthquake-related information, such as magnitude, depth, occurrence location, and timestamp. Additionally, public APIs from the Copernicus Open Access data Hub and ONDA DIAS are leveraged to provide access to sentinel data, such as images of the event location before and after its occurrence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Location

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Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning 37

Disambiguated Attention Embedding for Multi-Instance Partial...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Tang, Wei Zhang, Weijia Zhang, Min-Ling Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Peoples R China Univ Newcastle Sch Informat & Phys Sci Callaghan NSW 2308 Australia

ISBN: (纸本)9781713899921

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.

关键词： data handling

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Fuzzy vs. Crisp in Uncertainty-aware Service Selection: Enabling Sustainability on Multimedia Event processing

Fuzzy vs. Crisp in Uncertainty-aware Service Selection: Enab...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE)

作者： Pontes, Felipe Arruda Schukat, Michael Curry, Edward Univ Galway Insight Ctr Data Analyt Galway Ireland Univ Galway Sch Comp Sci Galway Ireland

ISBN: (纸本)9798350332285

The high energy consumption projections of Cloud/Edge applications urge the development of ecologically sustainable Multimedia Event processing (MEP) systems. In these applications, a Multi-Criteria Decision Making (MCDM) problem must be solved when selecting the best service workers alternatives for processing user queries, according to the user-specific performance criteria requirements of energy, accuracy and speed. Moreover, fuzzy logic provides a well-established method, such as the fuzzy TOPSIS, for dealing with the uncertainties arising from real-world scenarios, where ambiguities of user requirement interpretations and imprecision of measurement of the computing devices may directly impact this decision-making process. However, this fuzzy method is more complex and computationally intensive than the original (crisp) TOPSIS. Therefore, it is crucial to understand to what degree the fuzzy and crisp methods may be used interchangeably and still get the same results to avoid unnecessary complexity in sustainable MEP solutions in a real-world context. In our work, we developed a fuzzy TOPSIS ranking method for handling the uncertainties of the user criteria weights and service worker alternative ratings. Contrary to a previous study, we provide evidence that replacing the fuzzy TOPSIS method with its crisp counterpart significantly affects the ranking results when applied to a real-world scenario, with contradiction rates higher than 60% in most scenarios explored, which suggests that it is not viable to interchange these methods without consequences to the sustainability efforts of an MEP application.

关键词： Sustainable development

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DIGITAL EARTH OBSERVATION INFRASTRUCTURES AND INITIATIVES: A REVIEW FRAMEWORK BASED ON OPEN PRINCIPLES

DIGITAL EARTH OBSERVATION INFRASTRUCTURES AND INITIATIVES: A...

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2023 Free and Open Source Software for Geospatial, FOSS4G 2023

作者： Di Leo, M. Minghini, M. Kona, A. Spadaro, N. Kotsev, A. Dusart, J. Lumnitz, S. Ilie, C.M. Kilsedar, C.E. Tzotsos, A. Arcadia Sit European Commission Jrc Ispra Italy Ispra Italy Fincons S.p.A. European Commission Jrc Ispra Italy Brussels Belgium Frascati Italy Terrasigna Bucharest Romania Athens Greece

Recent years have seen a tremendous increase of digital Earth Observation (EO) infrastructures, which provide web-based environments for accessing and processing data in a highly automated and scalable way. However, the current landscape of EO infrastructures and initiatives is fragmented, with various levels of user on-boarding and uptake success. The current work aims to make sense of this complex landscape by providing two main contributions. First, it offers a classification scheme used to review and analyse more than 150 EO infrastructures and initiatives. Then, adopting a user-centric perspective, the main limitations and obstacles currently faced by users when working with the existing EO platforms are identified. For each of these limitations, we propose a number of good practices that could benefit, from a user point of view, the design and functioning of EO platforms. Some technological enablers, i.e. specific resources (such as software components, standards and data encodings) that emerged from the analysis as holding a great potential for improving the usability of existing EO platforms, are finally listed. The work aims to provide a first scientific insight on how to best design and operate EO platforms to maximise the benefits of their user communities. © 2023 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： data handling

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ScOSA on the Way to Orbit: Reconfigurable High-Performance Computing for spacecraft 14

ScOSA on the Way to Orbit: Reconfigurable High-Performance C...

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14th IEEE space Computing conference (SCC)

作者： Luedtke, Daniel Firchau, Thomas Cortes, Carlos Gonzalez Lund, Andreas Nepal, Ayush Mani Elbarrawy, Mahmoud M. Hammadeh, Zain Haj Mess, Jan-Gerd Kenny, Patrick Broemer, Fiona Mirzaagha, Michael Saleip, George Kirstein, Hannah Kirchhefer, Christoph Gerndt, Andreas German Aerosp Ctr DLR Inst Software Technol Braunschweig Germany German Aerosp Ctr DLR Inst Space Syst Bremen Germany German Aerosp Ctr DLR Inst Software Technol Wessling Germany Univ Bremen Bremen Germany

ISBN: (纸本)9798350341430

The German Aerospace Center (DLR) is developing ScOSA (Scalable On-board Computing for space Avionics) as a distributed on-board computing architecture for future space missions. The ScOSA architecture consists of commercial offthe-shelf (COTS) and radiation-tolerant nodes interconnected by a spaceWire network. The system software provides services to enable parallel computing and system reconfiguration. This allows ScOSA to adapt to node errors and failures that COTS hardware is susceptible to in the space environment. In the ongoing ScOSA Flight Experiment project, a ScOSA system consisting of eight Xilinx Zynq systems-on-chip with dual-core ARM-based processors and a LEON3 radiation-tolerant processor is being built for launch on DLR's next CubeSat in late 2024. In this flight experiment, not only all 18 cores but also the programmable logic will be used for high performance on-board data processing. This paper presents the current hardware and software architecture of ScOSA. The scalability of ScOSA is highlighted from both hardware and software perspectives. We present benchmark results of the ScOSA system and experiments of the ScOSA system software on ESA's OPS-SAT in orbit in combination with a machine learning application for image classification.

关键词： COTS FPGA spaceWire Multi-core Computer Architecture Hybrid LEON3 Reconfiguration Benchmark

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Design, manufacture and characterisation of X-ray Computer Tomography (XCT) calibration artefacts for space hardware qualification 23

Design, manufacture and characterisation of X-ray Computer T...

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23rd International conference of the european Society for Precision Engineering and Nanotechnology, EUSPEN 2023

作者： Keen, Joel Tucker, Nick Chahid, Younes Atkins, Carolyn University of Lincoln School of Engineering Brayford Pool Lincoln LincolnshireLN6 7TS United Kingdom STFC UK Astronomy Technology Centre EdinburghEH9 3HJ United Kingdom

ISBN: (纸本)9781998999132

Additive manufacture (AM) has many potential space hardware applications, particularly to enable the manufacture of flexural and optical components with novel geometries to achieve optimal performance and reduced mass. The effect of unwanted cavities or inclusion defects on printed components presents the need to verify the absence of cavities by a non-destructive testing method prior to expensive and time-consuming finishing processes such as single point diamond turning. X-ray Computer Tomography (XCT) is proposed for this application. This paper describes the manufacture and characterisation of a calibration artefact to verify the XCT measurements. The artefact cavities are smaller than most previous attempts, ranging from 120 μm to 30 μm in diameter. Several iterations of the manufacturing process incorporated certain mechanical design improvements. High precision machining centres from Kern Microtechnik Gmbh were used for the manufacture of the calibration artefacts. Commercially available solid carbide drill bits are produced in the required sizes but require sympathetic usage and careful handling techniques to achieve the required results. High magnification video equipment was used to monitor the drilling process in real time. Several techniques were compared to characterise the calibration artefacts including high magnification stereo microscopy, high depth-of-field optical microscopy, multisensor coordinate measuring machine and scanning electron microscopy. The characterisation of each hole is key to its final use for calibration. Finally, the calibration artefacts were pressed into a precisely matched hole in a sample AM flexure piece. The assembly was then scanned by XCT to quantify the difference between the direct characterisation and in-direct estimation of the artefact cavities. © 2023 Euspen Headquarters.

关键词： Calibration

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