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Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research data Management 25

Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enab...

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18th international conference on Web Search and data Mining-WSDM

作者： Sakor, Ahmad Brunet, Mauricio Iglesias, Enrique Rivas, Ariam Rohde, Philipp D. Kraft, Angelina Vidal, Maria-Esther TIB Leibniz Informat Ctr Sci & Technol Hannover Germany Leibniz Univ Hannover Hannover Germany L3S Res Ctr Hannover Germany

ISBN: (纸本)9798400713293

Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. The Leibniz data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. This demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://***/ldmservice/.

关键词： data Science Digital Repositories Federated Search

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Advancing Manufacturing Energy Efficiency: The Role of AI and Web-Based Tools 6

Advancing Manufacturing Energy Efficiency: The Role of AI an...

引用

6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Lamsaf, Asmae Samale, Pranita Proenca, Hugo Neves, Joao C. Hambarde, Kailash Covilha Portugal

ISBN: (纸本)9798350306613

This paper introduces a web-based application that simplifies the data analysis processing chain by automating the analysis of arbitrary variables. In particular, our application allows users to easily upload and process data for the analysis of a target variable by exploiting machine learning and evo-lutionary algorithms for precise forecasting and optimization. We demonstrate the system's efficacy using a dataset from a textile company, where our application successfully predicted the target variables with a high level of R-squared of 0.78, using the best regression model. These results not only highlight its real-world applicability but also played an important role in enhancing sustainable manufacturing practices. This innovative application offers a significant step towards sustainable and efficient manufacturing, addressing the challenges of high energy consumption and environmental impact in the industry. © 2024 IEEE.

关键词： Artificial Intelligence data Analysis En-ergy Efficiency Genetic algorithms Industrial Energy Management Machine Learning Manufacturing Sector Optimization Sustainable Manufacturing Web-Based Application

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Object Detection in Maritime Surveillance: A data-Centric Comparison of YOLO and Transformer-Based models 8

Object Detection in Maritime Surveillance: A Data-Centric Co...

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8th IEEE international conference on Information and Communication Technology, CICT 2024

作者： Kapoor, Aman Sinha, Bam Bahadur Computer Science & Engineering Iiit Ranchi835217 India Computer Science & Engineering Nit Sikkim737139 India

ISBN: (纸本)9798331531539

This research focuses on improving maritime ob-ject detection by refining deep neural network models with the Roboflow ship detection dataset, which contains labeled images of ships. The main goal is to assess the performance of different YOLO architecture versions (YOLOv7 and YOLOv8) in comparison to the Detection Transformer (DETR), a model introduced by Facebook AI Research in 2020. Unlike traditional object detection approaches, which often utilize convolutional neural networks (CNNs) along with post-processing techniques such as anchor boxes or region proposal networks (RPNs), DETR simplifies the process by removing these additional components. In this study, both YOLO models and DETR were fine-tuned on the Roboflow dataset, and their performance was measured in detecting maritime objects. The findings indicate that while the YOLO models achieved strong results, DETR demonstrated competitive accuracy with a more streamlined process. This study underscores the promise of transformer-based models for specialized object recognition tasks, offering valuable insights for enhancing maritime detection systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Approximate Multicast Coflow Scheduling in Reconfigurable data Center Networks 1

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23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Wu, Yuhang Chen, Quan Liu, Jianglong Li, Fulong Cheng, Lianglun Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (数字)9789819707980

ISBN: (纸本)9789819707973;9789819707980

The emerging optical circuit technology, capable of establishing circuit connections among switches, has been proposed as a promising paradigm for data center networks. This paper investigates the problem of minimizing the completion time of multicast coflow in optical circuit switches (OCS)-based data center networks. The existing works either only focused on multicast coflow scheduling or focused solely on circuit scheduling in OCS-based networks, which greatly limits their performance. Hence, in this paper, we study how to reduce the completion time of multicast flows by considering circuit scheduling and coflow scheduling simultaneously. Firstly, We formulate the problem of multicast coflow scheduling, and prove it to be NP-hard. We propose a delay-efficient multicast coflow scheduling algorithm by integrating coflow scheduling with circuit scheduling. The proposed algorithm is proved to have an approximate ratio of at most 2 root n, where n represents the number of optical circuit switches. Through extensive simulations, it is shown that the proposed algorithm can achieve high performance compared to state-of-the-art methods.

关键词： data center networks Optical circuit switches Multicast coflow scheduling

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Dominance Analyses Reduction in Skyline Query processing over data Stream with data Mining Technique 24

Dominance Analyses Reduction in Skyline Query Processing ove...

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14th international conference on Information Communication and Management, ICICM 2024

作者： Mohamud, Mudathir Ahmed Ibrahim, Hamidah Sidi, Fatimah Rum, Siti Nurulain Mohd Department of Computer Science Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (纸本)9798400717475

The database community has observed in the past two decades, the growth of research interest in skyline queries, which aim to report to users interesting objects-commonly known as skylines-based on their preferences. The identification of skyline objects becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams. In this paper, we proposed the Dominance Analyses Reduction (DAR) framework, which aims at addressing the issues of redundant dominance analyses that arise while determining skylines over data stream. Dominance analyses are repeated for objects that are in the overlapped frames of two windows and for pairs of objects that later reappear in the stream. DAR employs the Apriori algorithm, one of the most prevalent data mining algorithms, to identify the frequently occurring dominance analyses. Instead of conducting the dominance analyses again, their results are stored and utilised in the subsequent derivation of skylines. The DAR framework has been validated through several experiments. Its results exhibit significant reduction in the number of pairwise comparisons at both object and dimension levels and execution time. © 2024 Copyright held by the owner/author(s).

关键词： data streams

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Modeling and analysis of entrepreneurs' behavior patterns under M&A

Modeling and analysis of entrepreneurs' behavior patterns un...

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2020 international conference on data processing algorithms and models, icdpam 2020

作者： Luo, Xiang Business School Sichuan University Sichuan China

For enterprise mergers and acquisitions impact on entrepreneurs' behavior patterns, in this paper, by establishing the model of transparent analysis of entrepreneur's behavior pattern, combined with the observation visualization of data, by introducing the concept of mergers and acquisitions, dynamic research points out for more than one time above the entrepreneur behavior patterns, so as to make the information as much as possible close to the reality, and analysis provides quantitative reference for the entrepreneur behavior patterns.. On this basis, this paper draw lessons from Arkadiyv. Sakhaetov and Timothy B. folta (2014), and other scholars research model, entrepreneurial behavior change (mainly for the innovation behavior) brought from mergers and acquisitions, established the mathematical model, analyzed the resources and the conflict of two kinds of variables influence on it, and the relationship between each variable has been carried on the theoretical deduction, to some extent the analysis confirms the rationality of the enterprise merger. © Published under licence by IOP Publishing Ltd.

关键词： data visualization

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11th Workshop on Business and IT Alignment, BITA 2020, 3rd Workshop on Blockchain and Smart Contract Technologies, BSCT 2020, 2nd international Workshop on transforming the Digital Customer Experience, DigEX 2020, 5th international Workshop on Intelligent data Analysis in Integrated Social CRM, iCRM 2020 and 3rd Workshop on Quality of Open data, QOD 2020 held at 23rd international conference on Business Information Systems, BIS 2020

11th Workshop on Business and IT Alignment, BITA 2020, 3rd W...

引用

11th Workshop on Business and IT Alignment, BITA 2020, 3rd Workshop on Blockchain and Smart Contract Technologies, BSCT 2020, 2nd international Workshop on transforming the Digital Customer Experience, DigEX 2020, 5th international Workshop on Intelligent data Analysis in Integrated Social CRM, iCRM 2020 and 3rd Workshop on Quality of Open data, QOD 2020 held at 23rd international conference on Business Information Systems, BIS 2020

ISBN: (纸本)9783030611453

The proceedings contain 26 papers. The special focus in this conference is on Business and IT Alignment. The topics include: Analysing and Predicting the Adoption of Anonymous Transactions in Cryptocurrencies;volatility and Value at Risk of Crypto Versus Fiat Currencies;how Much Identity Management with Blockchain Would Have Saved Us? A Longitudinal Study of Identity Theft;benefits of the Technology 4.0 Used in the Supply Chain - Bibliometric Analysis and Aspects Deferring Digitization;how Do Movie Preferences Correlate with e-Commerce Purchases? An Empirical Study on Amazon;financial Institutions and Use of Social Media: Analysis of the Largest Banks in the U.S. and Europe;Outsourcing of Social CRM Services in German SMEs;Customer-Focused Churn Prevention with Social CRM at Orange Polska SA (Research in Progress);Social CRM: A Literature Review Based on Keywords Network Analysis;digitalization of Small and Medium-Sized Enterprises: An Analysis of the State of Research;Social CRM Tools: A Systematic Mapping Study;Analyzing OpenStreetMap Contributions at Scale: Introducing OSM-Interactions Tilesets;enhancing the Interactive Visualisation of a data Preparation Tool from in-Memory Fitting to Big data Sets;materia: A data Quality Control Embedded Domain Specific Language in Python;models for Arabic Document Quality Assessment;open data Quality Dimensions and Metrics: State of the Art and Applied Use Cases;synthesizing Quality Open data Assets from Private Health Research Studies;incremental Modeling Method of Supply Chain for Decision-Making Support;impact of New Mobility Services on Enterprise Architectures: Case Study and Problem Definition;sharedWealth: Disincentivizing Mining Pools Through Burning and Minting.

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GDA: Grammar-based data Augmentation for Text Classification using Slot Information

GDA: Grammar-based Data Augmentation for Text Classification...

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conference on Empirical Methods in Natural Language processing (EMNLP)

作者： Hahn, Joonghyuk Cheon, Hyunjoon Orwig, Elizabeth Kim, Su-Hyeon Ko, Sang-Ki Han, Yo-Sub Yonsei Univ Seoul South Korea Kangwon Natl Univ Chuncheon Si Gangwon Do South Korea Univ Seoul Seoul South Korea

ISBN: (纸本)9798891760615

Recent studies propose various data augmentation approaches to resolve the low-resource problem in natural language processing tasks. data augmentation is a successful solution to this problem and recent strategies give variation on sentence structures to boost performance. However, these approaches can potentially lead to semantic errors and produce semantically noisy data due to the unregulated variation of sentence structures. In an effort to combat these semantic errors, we leverage slot information, the representation of the context of keywords from a sentence, and form a data augmentation strategy which we propose, called GDA. Our strategy employs algorithms that construct and manipulate rules of context-aware grammar, utilizing this slot information. The algorithms extract recurrent patterns by distinguishing words with slots and form the "rules of grammar"-a set of injective relations between a sentence's semantics and its syntactical structure-to augment the dataset. The augmentation is done in an automated manner with the constructed rules and thus, GDA is explainable and reliable without any human intervention. We evaluate GDA with state-of-the-art data augmentation techniques, including those using pre-trained language models, and the result illustrates that GDA outperforms all other data augmentation methods by 19.38%. Extensive experiments show that GDA is an effective data augmentation strategy that incorporates word semantics for more accurate and diverse data.

关键词： Semantics

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7th international conference on Future data and Security Engineering, FDSE 2020

7th International Conference on Future Data and Security Eng...

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7th international conference on Future data and Security Engineering, FDSE 2020

ISBN: (纸本)9783030639235

The proceedings contain 26 papers. The special focus in this conference is on Future data and Security Engineering. The topics include: A Comparative Study of Join algorithms in Spark;blockchain-Based Forward and Reverse Supply Chains for E-waste Management;a Pragmatic Blockchain Based Solution for Managing Provenance and Characteristics in the Open data Context;OAK: Ontology-Based Knowledge Map Model for Digital Agriculture;A Novel Approach to Diagnose ADHD Using Virtual Reality;a Three-Way Energy Efficient Authentication Protocol Using Bluetooth Low Energy;clustering-Based Deep Autoencoders for Network Anomaly Detection;flexible Platform for Integration, Collection, and Analysis of Social Media for Open data Providers in Smart Cities;post-quantum Digital-Signature algorithms on Finite 6-Dimensional Non-commutative Algebras;data Quality for Medical data Lakelands;malicious-Traffic Classification Using Deep Learning with Packet Bytes and Arrival Time;detecting Malware Based on Dynamic Analysis Techniques Using Deep Graph Learning;understanding the Decision of Machine Learning Based Intrusion Detection Systems;combining Support Vector Machines for Classifying Fingerprint Images;toward an Ontology for Improving Process Flexibility;sentential Semantic Dependency Parsing for Vietnamese;An In-depth Analysis of OCR Errors for Unconstrained Vietnamese Handwriting;authorization Policy Extension for Graph databases;A Model-Driven Approach for Enforcing Fine-Grained Access Control for SQL Queries;on Applying Graph database Time models for Security Log Analysis;integrating Web Services in Smart Devices Using Information Platform Based on Fog Computing Model;adaptive Contiguous Scheduling for data Aggregation in Multichannel Wireless Sensor Networks;relating Network-Diameter and Network-Minimum-Degree for Distributed Function Computation;growing Self-Organizing Maps for Metagenomic Visualizations Supporting Disease Classification.

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BitMatcher: Bit-level Counter Adjustment for Sketches 40

BitMatcher: Bit-level Counter Adjustment for Sketches

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40th IEEE international conference on data Engineering, ICDE 2024

作者： Shi, Qilong Jia, Chengjun Li, Wenjun Liu, Zaoxing Yang, Tong Ji, Jianan Xie, Gaogang Zhang, Weizhe Yu, Minlan Tsinghua University Peng Cheng Laboratory China Tsinghua University China Peng Cheng Laboratory Harvard University China University of Maryland United States Peking University Peng Cheng Laboratory China Peking University China Chinese Academy of Sciences China Peng Cheng Laboratory China Harvard University United States

ISBN: (纸本)9798350317152

Sketch has been widely used in the field of large-scale data stream processing. However, common fixed-counter algorithms such as Count-Min Sketch have to allocate larger counters, which wastes a lot of memory due to the high skewness of real-world data streams. To reduce memory usage, we propose to dynamically adjust the counter size that matches the distribution of the data stream. We introduce BitMatcher, a fast global-adjusting algorithm that automatically adjusts the counter to the appropriate size to match the data stream. During stream processing, BitMatcher identifies items hashed into a bucket based on isolated fingerprints. If it overflows, BitMatcher changes the flag bits in the bucket and dynamically increases or shrinks the size of some counters in a fine-grained manner. BitMatcher can also relocate a cold item in the bucket with the idea of cuckoo hashing to preserve the potential hot item while achieving global load balancing. Through the above way of dealing with overflow caused by skewed data, BitMatcher precisely manipulates allocated bits and maximizes memory utilization. The experiments show that BitMatcher has high throughput and can outperform SOTA by up to 4 orders of magnitude in terms of accuracy. We also deployed BitMatcher on several platforms, showing its software and hardware scalability. © 2024 IEEE.

关键词： data streams

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