检索结果-内蒙古大学图书馆

Towards multiple ontology merging with CoMerger 19

Towards multiple ontology merging with CoMerger

19th International Semantic Web Conference on Demos and Industry Tracks: From Novel Ideas to Industrial Practice, ISWC-Posters 2020

作者： Babalou, Samira Konig-Ries, Birgitta Heinz-Nixdorf Chair for Distributed Information Systems Institute for Computer Science Friedrich Schiller University Jena Germany

To obtain a knowledge graph representing a domain of interest, it is often necessary to combine several, independently developed ontologies. Existing approaches are mostly limited to binary merge and lack scalability. This paper presents the development of an efficient multiple ontologies merging method, CoMerger. For efficient processing, rather than directly merging a large number of ontologies, we group related concepts across ontologies into partitions and merge first within and then across those partitions. Experiments on real-life datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. Our implementation is available through a live web portal. © 2020 CEUR-WS. All rights reserved.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Partitioned deep learning of fluid-structure interaction

arXiv

引用

arXiv 2021年

作者： Totounferoush, Amin Schumacher, Axel Schulte, Miriam Institute for Parallel and Distributed Systems Department of Computer Science University of Stuttgart Stuttgart70569 Germany

We present a partitioned neural network-based framework for learning of fluid-structure interaction (FSI) problems. We decompose the simulation domain into two smaller sub-domains, i.e., fluid and solid domains, and incorporate an independent neural network for each. A library is used to couple the two networks which takes care of boundary data communication, data mapping and equation coupling. Simulation data are used for training of the both neural networks. We use a combination of convolutional and recurrent neural networks (CNN and RNN) to account for both spatial and temporal connectivity. A quasi-Newton method is used to accelerate the FSI coupling convergence. We observe a very good agreement between the results of the presented framework and the classical numerical methods for simulation of 1d fluid flow inside an elastic tube. This work is a preliminary step for using neural networks to speed-up the FSI coupling convergence by providing an accurate initial guess in each time step for classical numerical solvers. © 2021, CC BY-NC-ND.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Thing

引用

IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Reputation systems for Supply Chains: The Challenge of Achieving Privacy Preservation

arXiv

引用

arXiv 2023年

作者： Bader, Lennart Pennekamp, Jan Thevaraj, Emildeon Spiß, Maria Kanhere, Salil S. Wehrle, Klaus Cyber Analysis & Defense Fraunhofer FKIE Germany Communication and Distributed Systems RWTH Aachen University Germany Institute for Industrial Management RWTH Aachen University Germany School of Computer Science and Eng. University of New South Wales Australia

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

A sustainable and secure load management model for green cloud data centres

引用

Scientific Reports 2023年第1期13卷 1-22页

作者： Saxena, Deepika Singh, Ashutosh Kumar Lee, Chung-Nan Buyya, Rajkumar Department of Computer Applications National Institute of Technology Haryana Kurukshetra 136119 India Department of Computer Science Goethe University Frankfurt Germany Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung 804201 Taiwan Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne 804201 Australia

The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%. © 2023, The Author(s).

关键词：

来源：评论

学校读者我要写书评

暂无评论

What to Do When the Users of an Ontology Merging System Want the Impossible? Towards Determining Compatibility of Generic Merge Requirements 22nd

What to Do When the Users of an Ontology Merging System Want...

引用

22nd International Conference on Knowledge Engineering and Knowledge Management, EKAW 2020

作者： Babalou, Samira Grygorova, Elena König-Ries, Birgitta Heinz-Nixdorf Chair for Distributed Information Systems Institute for Computer Science Friedrich Schiller University Jena Jena Germany

ISBN: (纸本)9783030612436

Ontology merging systems enable the reusability and interoperability of existing knowledge. Ideally, they allow their users to specify which characteristics the merged ontology should have. In prior work, we have identified Generic Merge Requirements (GMRs) reflecting such characteristics. However, not all of them can be met simultaneously. Thus, if a system allows users to select which GMRs should be met, it needs a way to deal with incompatible GMRs. In this paper, we analyze in detail which GMRs are (in-)compatible, and propose a graph based approach to determining and ranking maximum compatible supersets of user-specified GMRs. Our analysis shows that this is indeed feasible to detect the compatible supersets of the given GMRs that can be fulfilled simultaneously. This approach is implemented in the open source Merger tool. © 2020, Springer Nature Switzerland AG.

关键词： Reusability

来源：评论

学校读者我要写书评

暂无评论

Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

引用

IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Paulina Pawłowicz Vaidy Sunderam Che-Lun Hung Andrzej Kwiecień Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data.

关键词： Proteins Solid modeling Three-dimensional displays Pipelines Cluster computing Big Data Biology Organisms Decoding Sparks

来源：评论

学校读者我要写书评

暂无评论

Challenges and Opportunities of DevOps in Cyber-Physical Production systems Engineering

Challenges and Opportunities of DevOps in Cyber-Physical Pro...

引用

Industrial Cyber-Physical systems (ICPS)

作者： István Koren Felix Rinker Kristof Meixner Jasminka Matevska Jörg Walter Chair of Process and Data Science RWTH Aachen University Aachen Germany Christian Doppler Laboratory SQI Institute of Information Systems Engineering TU Wien Vienna Austria School of Electrical Engineering and Computer Science City University of Applied Sciences Bremen Germany Distributed Computing and Communication OFFIS - Institute for Information Technology Oldenburg Germany

DevOps is a set of practices that combines software development and operations to enable a continuous software product life cycle to improve the quality of software systems. Although DevOps is considered successful for typical software systems, it has not yet been widelyadopted in the area of - Physlcal Production systems (CPPSs) that integrate physical components with computer-based control and communication systems. Related to industrial settings, many new challenges arise, like long-term investments, missing flexibility in asset-heavy production environments, and the inherent physicality of hardware. This paper examines the use of the DevOps methodology in the manufacturing domain. It identifies and discusses the unique challenges and describes first solution proposals to overcome those challenges, based on literature and experiences from the industry. The article provides useful guidance to researchers and practitioners on potential pitfalls and exciting opportunities.

关键词： Production systems Data collection Software systems Hardware Safety Stakeholders Security

来源：评论

学校读者我要写书评

暂无评论

Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data

Semantic Map Learning of Traffic Light to Lane Assignment ba...

引用

International Conference on Intelligent Transportation

作者： Thomas Monninger Andreas Weber Steffen Staab Mercedes-Benz Research & Development North America Sunnyvale CA USA Mercedes-Benz AG Research & Development Stuttgart Germany University of Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany Electronics and Computer Science University of Southampton Southampton United Kingdom

Understanding which traffic light controls which lane is crucial to navigate intersections safely. Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes. The manual provisioning of this information is tedious, expensive, and not scalable. To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic. This works in an automated way and independently of the geometric arrangement. We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method. In addition, our novel rejection method includes accompanying safety considerations by leveraging statistical hypothesis testing. Finally, we propose a dataset transformation to re-purpose available motion prediction datasets for semantic map learning. Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches 5 5 https://***/map-learning/tl2la.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

引用

IEEE Transactions on Fuzzy systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：