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Extending Actor Models in data spaces

Extending Actor Models in Data Spaces

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32nd World Wide Web conference (WWW)

作者： zum Felde, Hendrik Meyer Bellebaum, Thomas Brost, Gerd Kollenstart, Maarten Dalmolen, Simon Fraunhofer AISEC Garching Germany TNO Groningen Netherlands

ISBN: (纸本)9781450394161

In today's internet almost any party can share sets of data with each other. However, creating frameworks and regulated realms for the sharing of data is very complex when multiple parties are involved and complicated regulation comes into play. As solution data spaces were introduced to enable participating parties to share data among themselves in an organized, regulated and standardized way. However, contract data processors, acting as data space participants, are currently unable to execute data requests on behalf of their contract partners. Here we show that an on-behalf-of actor model can be easily added to existing data spaces. We demonstrate how this extension can be realized using verifable credentials. We provide a sample use case, a detailed sequence diagram and discuss necessary architectural adaptations and additions to established protocols. Using the extensions explained in this work numerous real life use cases which previously could technically not be realized can now be covered. This enables future data spaces to provide more dynamic and complex real world use cases.

关键词： data spaces Contract data processing Actor Models Self-Sovereign Identities International data spaces Gaia-X On-behalf-of Model

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Improved Analysis of EGMS data for Displacement Monitoring: The Case Study of Regina Montis Regalis Basilica in Vicoforte, Italy 10

Improved Analysis of EGMS Data for Displacement Monitoring: ...

引用

10th International conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2024

作者： Lodigiani, Davide Franzini, Marica Casella, Vittorio Department of Civil Engineering and Architecture University of Pavia Via Ferrata 3 Pavia Italy

ISBN: (纸本)9789897586941

The Basilica of Vicoforte has always interested geotechnical engineers due to its location in a geologically complex area. One part of the Basilica is built on marl, while the other is built on clay. These two types of soil have different mechanical properties, which have caused the Basilica to experience various foundation failures over time. Monitoring is necessary to evaluate structural evolution and prevent further damage. Radar images are one of the geomatics techniques that can be used to perform these types of analyses;however, SAR data processing is challenging and requires specialized skills and software to monitor deformations using the PSInSAR approach. The european Ground Motion Service (EGMS) is useful for users and researchers, but analyzing specific buildings or monuments requires a more refined grid. The paper proposes a package of codes implemented using MATLAB release 2023b to manage grid spacing flexibly, customize it according to structure dimensions, and manage potential blunders. After a thorough data analysis, it was concluded that the monument exhibits no signs of subsidence trends. Instead, the analysis revealed that it undergoes seasonal fluctuations closely associated with temperature changes. The proposed approach enhances data accuracy and reliability, resulting in valuable insights and informed decisions. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： data handling

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Addressing the Need to Grow AUV Operator Workforce: Advanced Autonomy and Goal Based Mission Planning

Addressing the Need to Grow AUV Operator Workforce: Advanced...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Mills, Richard Sobin, Jacob Patterson, Richard Brodet, Steve Pilkington, Sarah Olsen, Arnt Kongsberg Underwater Technology Llc Kongsberg Discovery Lynnwood United States Kongsberg Gruppen Asa Kongsberg Discovery Horten Norway Kongsberg Mesotech Ltd Kongsberg Discovery Vancouver Canada

ISBN: (纸本)9798218142186

Marine robots have emerged as a key enabling technology for addressing complex and challenging missions at sea. They offer solutions that cannot be achieved through conventional methods. The maritime industry has developed advanced systems to help exploration, survey and inspection tasks, helping to advance a sustainable approach to expanding the blue economy. With these advancements, the question arises: do we have the workforce to handle the growing number of platforms entering the ocean space and are they sufficiently skilled enough to get the most out of them? One method to reduce the burden on operators and lower the entry level is the introduction of goal-based mission planning for autonomous underwater vehicles. This capability will enable more effective and efficient missions without the need for a high level of specialist knowledge on the part of the operator. Interoperability further increases effectiveness of uncrewed platforms, especially between surface and subsurface assets. The ability to rapidly transfer data between platforms to reduce latency between collection and utilization. This relies on the adoption of new technical capabilities such as automated subsurface launch and recovery, but also digital data distribution and processing in cloud-based platforms. © 2023 The Marine Technology Society (MTS).

关键词： data handling

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Blaze: Holistic Caching for Iterative data processing 24

Blaze: Holistic Caching for Iterative Data Processing

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19th european conference on Computer Systems, EuroSys 2024

作者： Song, Won Wook Eo, Jeongyoon Um, Taegeon Jeon, Myeongjae Chun, Byung-Gon FriendliAI Seoul National University Korea Republic of Samsung Research Korea Republic of Unist Korea Republic of FriendliAI Seoul National University Korea Republic of

ISBN: (纸本)9798400704376

Modern data processing workloads, such as machine learning and graph processing, involve iterative computations to converge generated models into higher accuracy. An effective caching mechanism is vital to expedite iterative computations since the intermediate data that needs to be stored in memory grows larger over iterations, often exceeding the memory capacity. However, existing systems handle intermediate data through separate operational layers (e.g., caching, eviction, and recovery), with each layer working independently in a greedy or cost-agnostic manner. These layers typically rely on user annotations and past access patterns, failing to make globally optimal decisions for the workload. To overcome these limitations, Blaze introduces a unified caching mechanism that integrates the separate operational layers. Blaze dynamically captures the workload structure and metrics using profiling and inductive regression, and automatically estimates the potential data caching efficiency associated with different operational decisions based on the profiled information. To achieve this goal, Blaze incorporates potential data recovery costs across stages into a single cost optimization function, which informs the optimal partition state and location. This approach reduces the significant disk I/O overheads caused by oversized partitions and the recomputation overheads for partitions with long lineages, while efficiently utilizing the constrained memory space. Our evaluations demonstrate that Blaze can accelerate end-to-end application completion time by 2.02 - 2.52× compared to recomputation-based MEM_ONLY Spark, and by 1.08 - 2.86× compared to checkpoint-based MEM+DISK Spark, while reducing the cache data stored on disk by 95% on average. © 2024 ACM.

关键词： Distributed computer systems

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Graph-Based Approach for european Law Classification

Graph-Based Approach for European Law Classification

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2023 IEEE International conference on Big data, Bigdata 2023

作者： Russo, Raffaele Giuseppe, Giuliano Di Vanacore, Alessandro Gatta, Valerio La Ferraro, Antonino Galli, Antonio Postiglione, Marco Moscato, Vincenzo University of Naples Federico II Department of Electrical Engineering and Information Technology Naples Italy

ISBN: (纸本)9798350324457

Deep learning, owing to its transformative influence across a myriad of sectors, has recently made its foray into the legal domain, instigated by the surge in digitization. Among the multitude of applications in this space, legal document classification emerges as a pivotal yet complex undertaking. Legal texts, characterized by unique domain-centric semantics and intricate linguistic patterns, necessitate precision-driven classification systems for numerous practical implications. This paper illuminates the challenges and opportunities in automating the classification of european Union (EU) legal documents, emphasizing the interrelationships among statutes and the hierarchical nature of legal references. In this context, we introduce a novel graph data modeling technique that adeptly marries content-centric indicators with the relational dynamics inherent among diverse legal documents. Central to our approach is a framework that melds text embeddings with graph neural networks for the classification of legal documents aligned with their subject-based directories. Empirical evaluations on the EU law dataset underline the efficacy of our model across varying granularities, from general thematic categories to intricate subtopics. This endeavor not only augments the comprehensibility and accessibility of EU jurisprudence but also holds significant implications across regulatory compliance, legal research, and policy formulation, underscoring the potential of deep learning in reshaping legal paradigms. © 2023 IEEE.

关键词： graph neural networks legal document classification natural language processing

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FPGA Implementation of Efficient 2D-FFT Beamforming for On-Board processing in Satellites 98

FPGA Implementation of Efficient 2D-FFT Beamforming for On-B...

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98th IEEE Vehicular Technology conference (VTC-Fall)

作者： Palisetty, Rakesh Eappen, Geoffrey Singh, Vibhum Socarras, Luis Manuel Garces Vu Nguyen Ha Vasquez-Peralvo, Juan A. Rios, Jorge Luis Gonzalez Duncan, Juan Carlos Merlano Martins, Wallace Alves Chatzinotas, Symeon Ottersten, Bjorn Coskun, Adem King, Stephen D'Addio, Salvatore Angeletti, Piero Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg Univ Toulouse Inst Super Aeronaut & Espace ISA SUPAERO Toulouse France European Space Agcy Toulouse France

ISBN: (纸本)9798350329285

On-board processing of digital beamforming in satellites is an efficient solution for the higher data rates, more capacity, and lower latency, but the available on-board limited power makes it impractical to digitally create thousands of beams at once. A significant portion of the analog hardware in a satellite communications payload can be replaced with highly integrated digital components, which are often more affordable, lighter, smaller, and reprogrammable by employing digital beamforming. In comparison to matrix-by-vector multiplication beamforming, the discrete Fourier transform (DFT) beamformer enables the finer realization of real-time beamformers with reduced circuit complexity and lower power consumption. Fast Fourier transform (FFT) methods can further reduce the computing cost of the DFT computation. Therefore, in this paper, area-power efficient two-dimensional (2D) FFT digital beamforming techniques are analyzed and implemented. The major implementation challenge is to produce N samples per cycle with lower area-power consumption. Fully unrolled 4-bit twiddle factor (TF) quantized FFT is proposed in this regard. The optimization techniques through quantization, truncation, and complex multipliers are thoroughly discussed for efficient implementation. The behavioral and post-route timing simulations are validated, and implementation results like area and power consumption are estimated and compared among conventional, fully unrolled, and the proposed 4-bit TF quantized 2D-FFT.

关键词： Beamforming fast Fourier transform look-up table power estimation quantization

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Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features 17

Fine-Tuning Deteriorates General Textual Out-of-Distribution...

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17th conference of the european-Chapter of the Association-for-Computational-Linguistics (EACL)

作者： Chen, Sishuo Yang, Wenkai Bi, Xiaohan Sun, Xu Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ Sch Comp Sci MOE Key Lab Computat Linguist Beijing Peoples R China

ISBN: (纸本)9781959429470

Detecting out-of-distribution (OOD) inputs is crucial for the safe deployment of natural language processing (NLP) models. Though existing methods, especially those based on the statistics in the feature space of fine-tuned pre-trained language models (PLMs), are claimed to be effective, their effectiveness on different types of distribution shifts remains under-explored. In this work, we take the first step to comprehensively evaluate the mainstream textual OOD detection methods for detecting semantic and non-semantic shifts. We find that: (1) no existing method behaves well in both settings;(2) fine-tuning PLMs on indistribution data benefits detecting semantic shifts but severely deteriorates detecting non-semantic shifts, which can be attributed to the distortion of task-agnostic features. To alleviate the issue, we present a simple yet effective general OOD score named GNOME that integrates the confidence scores derived from the task-agnostic and task-specific representations. Experiments show that GNOME works well in both semantic and non-semantic shift scenarios, and further brings significant improvement on two cross-task benchmarks where both kinds of shifts simultaneously take place. Our code is available at https://***/lancopku/GNOME.

关键词： Semantics

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Reproducible Query processing and data Citation of in Situ Soil Moisture data 19

Reproducible Query Processing and Data Citation of in Situ S...

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19th IEEE International conference on e-Science, e-Science 2023

作者： Staudinger, Moritz Hajszan, Tobias Miksa, Tomasz Himmelbauer, Irene Aberer, Daniel Rauber, Andreas Dorigo, Wouter Data Science Tu Wien Vienna Austria Sba Research Vienna Austria Climers Tu Wien Vienna Austria

ISBN: (纸本)9798350322231

data in today's dynamic world undergoes constant change and evolution, spanning various formats such as text, websites, tweets, and sensor readings. Storing and referencing these diverse data types pose significant challenges due to data movement, changes in content or structure, and limited availability. Efficient data identification is crucial for speeding up scientific discovery and result validation, especially when data accessibility is guaranteed. Recent years have witnessed progress in data citation practices, with conferences mandating the inclusion of utilized and generated data. However, existing solutions primarily cater to static datasets, rendering them ineffective for dynamically evolving ones. This paper addresses this gap by providing a tailored dynamic data citation prototype for the International Soil Moisture Network, one of the largest scientific in situ soil moisture databases. Our work encompasses the implementation and evaluation of different data versioning strategies and a query store architecture that enables the citation, reproducibility, and verification of large sets of SQL queries to recreate data requests by users. By applying the RDA Dynamic data Citation Guidelines, we assess the necessary needs for such a system and further measure the performance and storage impact of our proposed approaches. © 2023 IEEE.

关键词： Soil moisture

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Risk Control Method of Enterprise Social Network Marketing Based on Big data Fusion Technology 1

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6th european-Alliance-for-Innovation (EAI) International conference on Advanced Hybrid Information processing (ADHIP)

作者： Deng, Zhiyi Li, Jin China Univ Labor Relat Sch Labor Relat & Human Resource Beijing 100048 Peoples R China Univ Chinese Acad Social Sci Sch Journalism & Commun Beijing 102401 Peoples R China State Taxat Adm Beijing Municipal Tax Serv Miyun Branch Beijing 101599 Peoples R China

ISBN: (数字)9783031288678

ISBN: (纸本)9783031288661;9783031288678

The popularization and application of social network provides a broad platform and space for enterprises to carry out marketing activities, but at the same time, the virtualization of marketing environment increases the difficulty of marketing activities. Based on this, the enterprise social network marketing risk control method based on big data fusion technology is discussed, the enterprise social network marketing risk identification method is optimized, the enterprise social network marketing risk control evaluation index and evaluation algorithm are constructed, and the enterprise social network marketing risk control process is simplified. Finally, it is confirmed by experiments, The enterprise social network marketing risk control method based on big data fusion technology has high practicability in the process of practical application and fully meets the research requirements.

关键词： Big data fusion Social networks Enterprise marketing Risk control

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handling incomplete data using Radial basis Kernelized Intuitionistic Fuzzy C-Means 2

Handling incomplete data using Radial basis Kernelized Intui...

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2nd International conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Sethia, Kavita Singh, Jaspreeti Gosain, Anjana University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Sector-16C Dwarka New Delhi110078 India

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy C-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy C-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. © 2024 Elsevier B.V..

关键词： data mining

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