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IEEE International Conference on Industrial Technology (ICIT)

作者： Sisay Chala Madjid Fathi Institute of Knowledge Based Systems and Knowledge Management University of Siegen Germany

ISBN: (纸本)9781509053216

Social network analysis is the investigation of social structures by using methods such as graph theory and machine learning. Social networks characterize networked structures in terms of nodes (i.e., individuals) and their relationships to each other as acquaintances, colleagues, collaborators and/or classmates. Through these relationships, one can find their ties with their connections, professions, and the degree of the ties. Networking sites such as LinkedIn ® and Researchgate ® also contain more information of the knowledge of connections about the skill of an individual. The purpose of this study is to identify methods that measure the skills, expertise and experience of a job seeker and to investigate importance of using social networking data as input to user modeling that determines the strength of skills to be used for recommending matching job vacancies. Result of preliminary experiment using social network data in skill measurement shows consistent improvement in accuracy of matching job seekers to vacancies.

关键词： Weight measurement Recruitment Feature extraction LinkedIn Qualifications

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Work in Progress – Establishing a Master Program in Cyber Physical systems: Basic Findings and Future Perspectives

Work in Progress – Establishing a Master Program in Cyber P...

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International Conference on Promising Electronic Technologies (ICPET)

作者： Isam Ishaq Rashid Jayousi Salaheddin Odeh Ezzaldeen Edwan Asmaa Shaheen Mohamed Elnaggar Ahmed Elagha Samy Salamah Christian Weber Hasan Abu Rasheed Raimund Kirner Martina Doolan Hamidreza Ahmadian Roman Obermaisser Dhiah el Diehn I. Abou-Tair Ala Khalifeh Sahel Alouneh Zaid Alhalhouli Khalid Alemerien Faiez Gargouri Bassem Bouaziz Nadia Aloui Mohammad Saleh Computer Engineering and Science Departments Al-Quds University Jerusalem Palestine Engineering Program and Computer Department Palestine Technical College - Deir El-Balah Deir El-Balah Palestine Institute of Knowledge Based Systems & Knowledge Management University of Siegen Siegen Germany University of Siegen Siegen Germany School of Engineering and Computer Science University of Hertfordshire Hatfield UK Chair of Embedded Systems University of Siegen Siegen Germany German Jordanian University Amman Jordan School of Electrical Engineering and Information Technology German Jordanian University Amman Jordan Computer and Information Technology Department Tafila Technical University Tafila Jordan Higher Institute of Computer Science and Multimedia University of Sfax Sfax Tunisia Higher Institute of Applied Sciences and Technology of Mateur University of Carthage Mateure Tunisia KTH Royal Institute of Technology Stockholm Sweden

This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.

关键词： Computer science Internet of Things Industries knowledge engineering Embedded systems Europe Collaboration

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Automated use case diagram generation from textual user requirement documents

Automated use case diagram generation from textual user requ...

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IEEE Canadian Conference on Electrical and Computer Engineering

作者： Sandeep Vemuri Sisay Chala Madjid Fathi Department of Mechatronics University of Siegen Germany Institute of Knowledge Based Systems and Knowledge Management University of Siegen Germany

With the current state of the art Natural language processing (NLP) technology, many researchers have proved that automatizing the requirement analysis process is possible which saves significant amount of time spent by the analysts. Numerous semi-automatic tools have been developed which assist the analyst in this process. However, a commonly used technique of using grammar in the elicited text as the basis of identifying useful information, has been facing scalability issues due to the fact that the textual format of requirements consists of unstructured natural language (NL). This paper is an attempt to approach this problem using a probabilistic technique to identify actors and use cases. The promising result demonstrates that, further improvements to this approach can completely automatize the analysis phase.

关键词： Feature extraction Training Tools Software Classification algorithms Testing Unified modeling language

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Active diagnosis automotive ontology for distributed embedded systems

Active diagnosis automotive ontology for distributed embedde...

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IEEE European Technology and Engineering management Summit (E-TEMS)

作者： Ahlam Mallak Christian Weber Madjid Fathi Alexander Holland Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany FOM University of Applied Sciences Dortmund Germany

Active diagnosis is the retrieval and analysis of the diagnostic information at runtime. In order to enhance the system ability for detecting faults, isolating them, and deciding the most suitable recovery action during the shortest possible completion period at runtime. Thus, it is necessary to build a precise and appropriate knowledge-based model that is able to serve the diagnosis and the fixing tasks efficiently and accurately. This paper presents an approach for an active ontology-based automotive diagnostic and repair for distributed embedded systems. This approach helps in creating better understanding and realization of the knowledge-based diagnostic concepts in automotive systems, as well as the integration of these concepts with distributed embedded systems, to achieve better diagnosis and repair for actual automotive systems at runtime when deployed in the future.

关键词： Ontologies Automotive engineering knowledge based systems Sensor systems Runtime Sensor phenomena and characterization

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Interdisciplinary Data Driven Production Process Analysis for the Internet of Production

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Procedia Manufacturing 2018年 26卷 1065-1076页

作者： R. Meyes H. Tercan T. Thiele A. Krämer J. Heinisch M. Liebenberg G. Hirt Ch. Hopmann G. Lakemeyer T. Meisen S. Jeschke Institute for Information Management in Mechanical Engineering RWTH Aachen University Dennewartstr. 27 52056 Aachen Germany Institute of Metal Forming RWTH Aachen University Intzestr. 10 52056 Aachen Germany Institute of Plastics Processing RWTH Aachen University Seffenter Weg 201 52056 Aachen Germany Knowledge-Based Systems Group RWTH Aachen University Ahornstraße 55 52056 Aachen Germany

Recent developments in the industrial field are strongly influenced by requirements of the fourth industrial revolution (I4.0) for modern Cyber-Physical Production systems (CPPS) and the coherent phenomenon of industrial big data (IBD). I4.0 is characterized by a growing amount of interdisciplinary work and cross-domain exchange of methods and knowledge. Similar to the development of the Internet of Things (IoT) for the consumer market, the emergence of an Internet of Production (IoP) in the industrial field is imminent. The future vision for an IoP is based on aggregated, multi-perspective and persistent data sets that can be seamlessly and semantically integrated to allow diagnosis and prediction in domain-specific real-time. In this paper, we demonstrate an exemplary scenario of collaborative cross-domain work, in which domain-experts from largely different fields of expertise, i.e. heavy plate rolling (HPR), injection molding (IM) and machine learning (ML), generate insights through data driven process analysis in two use cases. Specifically, in the HPR use case, reinforcement-learning was utilized to support the planning phase of the process aiming to reduce manual work load and to ultimately generate process plans that serve as a foundation for a simulation to calculate process results. On the contrary, in the IM use case, supervised-learning was utilized to learn a complex and computationally demanding finite element simulation model in order to predict process results for unknown process configurations, which can be used to optimize the process planning phase. While both use cases had the overall goal to utilize ML to gain new insights about the respective process, the actual ML application was utilized with reversed purpose. Particularly, in the HPR use case, ML was used to learn the process planning in order to calculate process results while in the IM use case, ML was used to predict process results in order to improve the process planning. We facilitate

关键词： Production Technology Industrial Big Data Internet of Production Heavy Plate Rolling Injection Molding Machine Lerarning

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AirScript - Creating documents in air

arXiv

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arXiv 2017年

作者： Dash, Ayushman Sahu, Amit Shringi, Rajveer Gamboa, John Afzal, Muhammad Zeshan Malik, Muhammad Imran Ahmed, Sheraz Dengel, Andreas Knowledge-Based Systems Group Department of Computer Science University of Kaiserslautern P.O. Box 3049 Kaiserslautern67653 Germany MindGarage University of Kaiserslautern Germany Knowledge Management Department Kaiserslautern Germany

This paper presents a novel approach, called AirScript, for creating, recognizing and visualizing documents in air. We present a novel algorithm, called 2-DifViz, that converts the hand movements in air (captured by a Myo-armband worn by a user) into a sequence of x, y coordinates on a 2D Cartesian plane, and visualizes them on a canvas. Existing sensor-based approaches either do not provide visual feedback or represent the recognized characters using prefixed templates. In contrast, AirScript stands out by giving freedom of movement to the user, as well as by providing a real-time visual feedback of the written characters, making the interaction natural. AirScript provides a recognition module to predict the content of the document created in air. To do so, we present a novel approach based on deep learning, which uses the sensor data and the visualizations created by 2-DifViz. The recognition module consists of a Convolutional Neural Network (CNN) and two Gated Recurrent Unit (GRU) Networks. The output from these three networks is fused to get the final prediction about the characters written in air. AirScript can be used in highly sophisticated environments like a smart classroom, a smart factory or a smart laboratory, where it would enable people to annotate pieces of texts wherever they want without any reference surface. We have evaluated AirScript against various well-known learning models (HMM, KNN, SVM, etc.) on the data of 12 participants. Evaluation results show that the recognition module of AirScript largely outperforms all of these models by achieving an accuracy of 91.7% in a person independent evaluation and a 96.7% accuracy in a person dependent evaluation. Copyright © 2017, The Authors. All rights reserved.

关键词： Visual communication

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Combined learning processes for injection moulding based on simulation and experimental data

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AIP Conference Proceedings 2019年第1期2139卷

作者： Christian Hopmann Sabina Jeschke Tobias Meisen Thomas Thiele Hasan Tercan Martin Liebenberg Julian Heinisch Matthias Theunissen 1Institute of Plastics Processing (IKV) at RWTH Aachen University Seffenter Weg 201 D-52074 Aachen Germany 2Institute of Information Management in Mechanical Engineering (IMA) - RWTH Aachen University Dennewart- straße 27 D-52068 Aachen Germany 3Knowledge-based Systems Group - RWTH Aachen University Ahornstraße 55 D-52056 Aachen Germany

Injection moulding enables the production of complex formed high-quality plastics parts in a single production step. To achieve a high and constant product quality, an appropriate process set-up with regards to product quality and process robustness is essential. A conventional process set-up requires expensive and time consuming experiments and know-how from the machine operator. One way to overcome this challenge is to make use of machine learning methods for process set-up. These methods can model the relationship between setting parameters and quality values and thus enable the identification of optimal working points. However, the training required for accurate modelling needs experimental data from extensive experiments for each process, too. Numerical simulation can predict quality values based on setting parameters without practical experiments. Whereas trends and the general dependencies between the parameters can be predicted with a satisfying accuracy, a certain discrepancy between the prediction of the simulation and the real process cannot be excluded. A combined approach using data from injection moulding simulations as well as experimental data appears promising to overcome the detriments of the solitary use of simulation for the training of machine learning algorithms. General dependencies could be gained from simulations without practical experiments and fine-tuning could be achieved by experimental trials with a minimal *** this paper, data obtained from a 2.5 D injection moulding simulation is compared with experimental data from a plate specimen and a complex formed injection moulded part. Therefore, central composed designs of experiments are used to identify differences in the effects and interdependencies of six setting parameters on quality values like part weight and dimensions. Furthermore, differences in the absolute values and the functionality of the effects are considered. On this basis, a combined machine learning concept using si

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Human behavior analysis for library and information science

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Library Hi Tech 2017年第4期35卷 442-444页

作者： Mu-Yen Chen Edwin David Lughofer Neil Y. Yen Chia-Chen Chen Department of Information Management National Taichung University of Science and Technology Taichung Taiwan Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz Austria School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Management Information Systems National Chung Hsing University Taichung City Taiwan

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Multidimensional knowledge representation of text analytics results in knowledge bases

Multidimensional knowledge representation of text analytics ...

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IEEE International Conference on Electro-Information Technology

作者： Johannes Zenkert Madjid Fathi Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management Germany

ISBN: (纸本)9781467399869

In the age of digitization, intelligent systems have to cope with an ever-growing amount of data. Therefore, knowledge representation plays a key-role for applications to handle continuously created data and to enable an access on flexible and extensively well-described data structures. This paper introduces a knowledge base design which has the capability of dimensional structuring of semantically-related data and explains how text analytic results can be integrated into a knowledge base. The paper discusses the main advantages of this design and shows how the data can be arranged in the knowledge base. The multidimensional structure of the knowledge base helps to resolve one of the main challenges of knowledge discovery which is the extraction of meaningful information from data in a context.

关键词： knowledge based systems Metadata Tagging Text analysis Hidden Markov models Organizations

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Standardizing health outcomes for people with rheumatoid arthritis receiving disease modifying drug therapy: A rapid review of patient-decision aids and preference studies to inform the development of OMERACT Health Outcome Descriptors

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Seminars in Arthritis and Rheumatism 2025年

作者： N. Raskin M. Hiligsmann A.R. Rebutoc N. Bansback A. Boonen R. Buchbinder M. Falahee L. Fraenkel D.A. Marshall L. Maxwell R. Nieuwlaat L. Proulx P. Saadat B. Shea P. Tugwell W. Wiercioch D. Beaton P. Richards H. Schünemann F. Guillemin G.S. Hazlewood Department of Health Services Research Care and Public Health Research Institute Maastricht University Maastricht the Netherlands Department of Data Analytics and Digitalization School of Business and Economics Maastricht University Maastricht The Netherlands Department of Medicine University of Calgary Alberta Canada School of Population and Public Health University of British Columbia Vancouver British Columbia Canada Arthritis Research Canada Vancouver British Columbia Canada Department of Internal Medicine Division of Rheumatology Maastricht University Medical Center The Netherlands Care and Public Health Research Institute Maastricht University Maastricht the Netherlands Musculoskeletal Health and Wiser Health Care Units School of Public Health and Preventive Medicine Monash University Melbourne Victoria Australia Rheumatology Research Group Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham Birmingham UK NIHR Birmingham Biomedical Research Centre University Hospitals Birmingham NHS Foundation Trust Birmingham UK Department of Internal Medicine Yale University New Haven USA Berkshire Health Systems Pittsfield Massachusetts USA Professor and Svare Chair Health Economics Value and Impact Cumming School of Medicine University of Calgary Alberta Canada Departments of Medicine and Community Health Sciences Cumming School of Medicine University of Calgary Calgary Alberta Canada Faculty of Medicine University of Ottawa Ottawa Ontario Canada Ottawa Hospital Research Institute Centre for Practice Changing Research Ottawa Ontario Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton Ontario Canada Michael G. DeGroote Cochrane Canada & McMaster GRADE Centres McMaster University Hamilton Ontario Canada Managing Director Canadian Arthritis Patient Alliance Ottawa Ontario Canada Patient Author Ottawa Canada Institute of Health Policy Management and Evaluatio

Background Interest in standardizing descriptions of health outcomes is increasing. In a Health Outcome Descriptor (HOD), outcomes are systematically described covering four domains: Symptoms, Time horizon, Testing and Treatment, and Consequences. Given the lack of HODs for Rheumatoid Arthritis (RA), the aim of this study was to review published RA outcome descriptions and map them to the HOD framework. Methods We conducted a rapid review of patient-decision aids (PtDAs) and patient preference studies to assess how seven RA outcomes have been described in English to patients. These outcomes were selected by author consensus, from a living systematic review of RA drug therapy. After data extraction and a thematic content analysis, a narrative summary for each outcome was provided. Results We included 11 PtDAs and 27 patient preference studies. Overall, the descriptions of the same health outcome varied widely across studies. Adverse events were described in most cases (N=26/38). For both PtDAs and preference studies, few provided a description for patient-important outcomes like remission (N=2/11 and N=1/27 respectively) and pain (N=2/11 and N=6/27 respectively). From an HOD perspective, the descriptions focused primarily on symptoms patients may experience (95%), and less on the other domains (18-39%). Conclusion There is wide variability in the content of the published RA outcome descriptions, as well as a lack of descriptions regarding common patient-important outcomes. As this study provides a detailed overview of existing descriptions, it may inform future development of HODs for RA.

关键词： Rheumatoid Arthritis Health States Standardization Health Outcome Descriptors Rapid Review OMERACT ACR American College of Rheumatology AEs Adverse Events DMARDs Disease-Modifying Antirheumatic Drugs HOD Health Outcome Descriptor MDIC Medical Device Innovation Consortium MeSH Medical Subject Headings PREFER Patient Preferences in Benefit-Risk Assessment during the Drug Life Cycle PPRCTS Patient Preferences to Value Health Outcomes in Rheumatology Clinical Trials PtDAs Patient-Decision Aids RA Rheumatoid Arthritis SDM Shared-Decision Making

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