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2nd International Workshop on knowledge Graph Construction, KGCW 2021

作者： Schröder, Markus Jilek, Christian Dengel, Andreas Smart Data and Knowledge Services Dept. DFKI GmbH Kaiserslautern Germany Computer Science Dept. TU Kaiserslautern Germany

The RDF Mapping Language (RML) enables, among other formats, the mapping of tabular data as Comma-Separated Values (CSV) files to RDF graphs. Unfortunately, the widely used spreadsheet format is currently neglected by its specification and well-known implementations. Therefore, we extended RML Mapper to support Microsoft Excel spreadsheet files and demonstrate its capabilities in an interactive online demo. Our approach allows to access various meta data of spreadsheet cells in typical RML maps. Some experimental features for more specific use cases are also provided. The implementation code is publicly available in a GitHub fork. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Spreadsheets

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A Human-in-the-Loop Approach for Personal knowledge Graph Construction from File Names 3

A Human-in-the-Loop Approach for Personal Knowledge Graph Co...

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3rd International Workshop on knowledge Graph Construction, KGCW 2022

作者： Schröder, Markus Jilek, Christian Dengel, Andreas Smart Data & Knowledge Services Dept. DFKI GmbH Kaiserslautern Germany Computer Science Dept. TU Kaiserslautern Germany

Users' personal and work related concepts (e.g. persons, projects, topics) are usually not sufficiently covered by knowledge graphs. Yet, already handmade classification schemes, prominently folder structures, naturally mention several of their concepts in file names. Thus, such data could be a promising source for constructing personal knowledge graphs. However, this idea poses several challenges: file names are usually noisy non-grammatical text snippets, while folder structures do not clearly define how concepts relate to each other. To cope with this semantic gap, we include knowledge workers as humans-in-the-loop to guide the building process with their feedback. Our semi-automatic personal knowledge graph construction approach consists of four major stages: domain term extraction, ontology population, taxonomic and non-taxonomic relation learning. We conduct a case study with four expert interviews from different domains in an industrial scenario. Results indicate that file systems are promising sources and, combined with our approach, already yield useful personal knowledge graphs with moderate effort spent. © 2021 Copyright for this paper by its authors.

关键词： knowledge graph

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arXiv

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arXiv 2025年

作者： Lee, Ming-Lun Chou, Han-Chang Chen, Yan-Ann Dept. of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan

Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge devices such as smartphones, wearable devices, and Internet of Things (IoT) devices which closely collect information from users. However, these devices are mostly battery-powered. The update procedure of federated learning will constantly consume the battery power and the transmission bandwidth. In this work, we propose an update control for federated learning, FedSAUC, by considering the similarity of users’ behaviors (models). At the server side, we exploit clustering algorithms to group devices with similar models. Then we select some representatives for each cluster to update information to train the model. We also implemented a testbed prototyping on edge devices for validating the performance. The experimental results show that this update control will not affect the training accuracy in the long run. © 2025, CC BY-NC-ND.

关键词： Edge computing

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Prediction and Classification of Brain Tumor using Modified Transfer Learning 18

Prediction and Classification of Brain Tumor using Modified ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Roy, Sukanya Mandal, Bijoy Kumar Khamaru, Koustov Bhattacharya, Arghya University of Engineering and Management Dept. of Computer Science Engineering Kolkata India Nshm Knowledge Campus Dept. of Computer Science Engineering Durgapur India

ISBN: (纸本)9789380544519

This study uses a novel method of segmenting and computing the tissue area in brain MRI images to predict the presence of brain tumors and classify the type of tumor into common types (pituitary, meningioma, and glioma). The tissues that make up the brain have intricate boundaries. In order to more accurately classify the different types of tumors, we have thus divided the brain tissues into three segments and examined for enlarged segments using Convolutional Neural Networks (CNNs) and a Modified CNN that uses transfer learning and VGGNet. The paper's methodology involves using CNNs with Transfer Learning for efficient image segmentation and clustering, followed by VGGNet for noise reduction and image segmentation refining. We are optimizing the internal parameters of VGGNet in order to outperform the competition. The volume and density of malignant clusters are computed through pixel counting. This kind of method works pretty well for figuring out tissue volume in a wide range of illnesses that affect people of all ages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Brain Tumor Glioma Image-segementation Meningioma Neural Networks. Image classification Pituitary ResNet Transfer Learning VGGNet

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Development of Prediction System for Ship Movements Using Machine Learning and Radar Images

Development of Prediction System for Ship Movements Using Ma...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Nishizaki, Chihiro Takenaka, Masako Hirai, Yurie Okazaki, Tadatsugi Kobayashi, Mitsuru Tokyo University of Marine Science and Technology Dept. of Maritime System Engineering Tokyo Japan National Maritime Research Institute Dept. of Knowledge and Data System Tokyo Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

To maintain ship navigation safety, the navigator must understand and predict the movements of other ships. Radar images include both ship (target) and non-ship images (noise), such as sea clutter. To understand the movements of other ships, navigators must detect ship images from radar images manually. To maintain the optimum situation awareness of navigators while reducing the workload, a function that can automatically detect and track ship images, including small ships, is desired on the radar. Therefore, a system to predict ship movements using machine learning and radar images is proposed in this study. The proposed prediction system is based on a learning model developed using a denoising convolutional autoencoder. The learning and validation data are radar images processed via image processing in advance. The prediction accuracy of ship movements in this study is 90.97%, and the loss is 0.0396. © 2022 IEEE.

关键词： Convolution

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The rewriting algorithm for disjunctive queries between peer ontologies

The rewriting algorithm for disjunctive queries between peer...

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3rd International Conference on Semantics, knowledge, and Grid, SKG 2007

作者： Qin, Biao Wang, Shan Dept. of Computer Science Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

ISBN: (纸本)0769530079

We study the problem of answering queries given a set of mappings between peer ontologies. In addition to the schema mapping between peer ontologies, there are axioms to give constraints to classes and properties. We propose a set of rules to build graphs for the axioms. Because the axioms have different properties, the generated graphs are classified into four sets. In each peer, its RDF/OWL query languages can support regular expressions. If it wants to be transitive along semantic paths in peer knowledge management systems, we must rewrite conjunctive and disjunctive queries between peers. Because conjunctive queries are well-understood, we focus on a novel algorithm to rewrite disjunctive queries along semantic paths based on the graphs. For all atoms of a disjunctive query, we consider its union as a set and find the maximum rewritings over peers through a graphical way. Finally we do extensive simulation experiments. The simulation results show our algorithm can generate more rewritings than the naive rewriting algorithm at each distance. © 2007 IEEE.

关键词： Ontology

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Newsalyze: Enabling news consumers to understand media bias 20

Newsalyze: Enabling news consumers to understand media bias

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2020 ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2020

作者： Hamborg, Felix Zhukova, Anastasia Donnay, Karsten Gipp, Bela Dept. of Computer and Information Science University of Konstanz Germany Data and Knowledge Engineering Group University of Wuppertal Wuppertal Germany Dept. of Political Science University of Zurich Switzerland Dept. of Politics and Public Administration University of Konstanz Germany

ISBN: (纸本)9781450375856

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of fake news, news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., freedom fighters vs. terrorists. At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view). © 2020. ACM ISBN.

关键词： news bias annotation newsroom frame analysis news articles news consumption content analysis media bias

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ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems 4

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Pradeep Kumar, D. Sowmya, B.J. Kanavalli, Anita Vincent, Annvin Dept. of Computer Science and Engineering Bangalore India Dept. of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350397475

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed. © 2022 IEEE.

关键词： Global optimization

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Refining formal specifications of human computer interaction by graph rewrite rules 1st

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1st International Conference on Fundamental Approaches to Software engineering, FASE 1998

作者： Sucrow, Bettina Eva Data Management Systems and Knowledge Representation Dept. of Mathematics and Computer Science University of Essen Essen45117 Germany

ISBN: (纸本)3540643036

Human computer interaction can be specified successfully using the concept of information resources and the formal notation of graph grammars. In order to achieve a precise and continuous specification process between the requirements and design stages, however, a suitable strategy for refining abstract specifications into more concrete ones correctly and consistently is highly necessary. In this paper it will be proposed to apply graph rewrite rules at a meta level to abstract graph grammar specifications of human computer interaction in order to achieve correct and consistent refinements of the specifications wrt important requirements. A safety-critical system concerning the interaction between the pilot and the flight management system on the flight deck of an aircraft will be used as an example. A graph grammar specification of this interaction at an abstract level will be refined wrt mode visualization by a graph rewrite rule at a meta level. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Formal specification

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An Architecture for Dynamic Load Balancing in Cloud Environment 2

An Architecture for Dynamic Load Balancing in Cloud Environm...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Lokesh, Gudivada Baseer, K.K. Dept. of Computer Science and Engineering Tirupati India Department of Data Science Tirupati India

ISBN: (纸本)9798350347579

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future. © 2023 IEEE.

关键词： Energy utilization

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