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ACM/IEEE international conference on Model Driven engineering Languages and systems (MODELS)

作者： Staudinger, Simon Schuetz, Christoph G. Schrefl, Michael Johannes Kepler Univ Linz Linz Austria

ISBN: (纸本)9798350324983

Analytics projects often follow a generic process model, which maps out the main stages and tasks for conducting an analytics project while granting leeway to the project manager regarding the specific execution. A generic process model is instantiated by various organizations for projects applying different types of analytics-descriptive, predictive, prescriptive, etc.-on different use cases in various domains, using vastly different data. Each organization, each type of analytics, and each individual project thus requires a customized process tailored to the specific needs of the organization, type of analytics, and individual project. At each stage of a data analytics project, the project team has to assess the use case (analytics problem) and determine the course of action. Proper documentation of assessment and course of action, i.e., the design decisions and the underlying motivations, facilitates development in the subsequent stages and tasks as well as after deployment when using the developed system. In this paper, we present a use case for multilevel modeling, namely the documentation of knowledge related to analytics projects and data analyses, which are processes aimed at finding patterns in data. We employ the concept of multilevel business artifact, which allows for the representation of data and life cycle models in a single object at multiple levels of abstraction while granting the flexibility to specialize models in objects at lower levels. We use the real-world problem of flight delay prediction as a running example to illustrate the use of multilevel business artifacts for knowledge management in analytics projects.

关键词： multilevel modeling knowledge management multilevel-objects data analysis

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Medical Diagnostic System using Embedding JADE in jCOLIBRI

Medical Diagnostic System using Embedding JADE in jCOLIBRI

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2024 international conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Singh Dhatterwal, Jagjit Malik, Kiran Das, Purushottam Singh Kaswan, Kuldeep Koneru Lakshmaiah Education Foundation Department of Artificial Intelligenceand Data Science AP Vaddeswaram India Greater Noida India Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Department of Computer Scienceand Engineering Dehradun India Galgotias University Department of Computer Scienceand Engineering Greater Noida203201 India

ISBN: (纸本)9798350378092

Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g., the problems of knowledge acquisition, remembering, robustness and maintenance. This paper discusses the CBR methodology, the research issues and the technical aspects of implementing intelligent medical diagnoses systems. It develops a celebrative framework of MAS-CBR to criticize the medical problem to get the best treatment against dengue disease. This paper also embedding the framework of JADE and jCOLIBRI to improve the quality of decision- making in the medical diagnosis field. © 2024 IEEE.

关键词： knowledge acquisition

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Towards a Reference Software Architecture for Human-AI Teaming in Smart manufacturing 44

Towards a Reference Software Architecture for Human-AI Teami...

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44th ACM/IEEE international conference on Software engineering: New Ideas and Emerging Results, ICSE-NIER 2022

作者： Haindl, Philipp Buchgeher, Georg Khan, Maqbool Moser, Bernhard Software Competence Center Hagenberg Hagenberg Austria Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Mang Haripur Pakistan

ISBN: (纸本)9781665495967

With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded *** project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture product and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project. © 2022 IEEE.

关键词： Machine learning

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Realization of Network Security knowledge Base with knowledge Graph 3rd

Realization of Network Security Knowledge Base with Knowledg...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Ouyang, Zepeng Li, Xiangyang Wang, Bing Ji, Zhenyan School of Software Engineering Beijing Jiaotong University No. 3 Shangyuan Village Beijing China Cyberspace Security Department China Railway Information Technology Group Co. Ltd. No. 10 Fuxing Avenue Beijing China

ISBN: (纸本)9789819616978

In the era of digital transformation, the sheer volume of network security data poses significant challenges in terms of organization and retrieval. Traditional search engines fail to provide contextual answers, necessitating the development of more advanced systems. This paper presents the design and implementation of a network security knowledge base enhanced with a knowledge graph. The proposed system aims to improve the efficiency of information management and retrieval for network security professionals. We discuss the multi-layer architecture of the knowledge base, which includes the UI, service, data access, and data storage layers. The knowledge graph is constructed through a four-layer architecture comprising the data, extraction, knowledge, and application layers. Using the Scrapy framework, we efficiently crawl and extract data from various online sources. This data is processed with the Jena tool and stored in Neo4j as structured triples, enabling complex queries and advanced analytic. Our system supports semantic queries, knowledge association discovery, and intelligent question answering, improving user experience and decision-making in network security management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： knowledge graph

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ML Techniques for Crop Demand Prediction- A Study

ML Techniques for Crop Demand Prediction- A Study

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Shyamala, Pachila Deepa, K. Tresa Sangeetha, S.V. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences AlMusannah Department of Engineering Oman

ISBN: (纸本)9798350359688

The challenge that is being faced by modern agriculture is to reduce the environmental impact of food production while providing food for an expanding world population. One essential instrument for tackling this issue is the ability to estimate crop demand accurately. Precise forecasting of crop demand is crucial for mitigating the effects of climate change and global market dynamics while optimizing resource allocation and minimizing waste. The agricultural sector can quickly adjust to changing conditions by utilizing machine learning techniques and combining varied data sources. This adaptation also applies to hazards associated with climate change to ensure resilience in the face of erratic weather patterns. Moreover, the deliberate application of predictive models facilitates proactive decision-making, augmenting the agricultural sector's overall sustainability and bolstering its ability to fulfill the dietary requirements of an expanding world population. This paper employs multiple machine learning (ML) approaches to predict the best-fit model for crop predictions. Gradient Boost (GB) is the best-fit model for this problem. © 2024 IEEE.

关键词： Classification Decision Tree Gradient Boost K-Means KNN LSTM Naïve Bayes Random Forest Regression Supervised learning SVM

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Research on Higher Education Information Technology in China : Based on CiteSpace knowledge graph analysis 14

Research on Higher Education Information Technology in China...

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14th international conference on Information Technology in Medicine and Education, ITME 2024

作者： Kong, Lingshuai Xu, Nengwu Li, Shu National University of Defense Technology China Beijing University of Civil Engineering and Architecture China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356670

This study examines the evolution of higher education informatisation in China using bibliometric analysis and CiteSpace knowledge graph creation. Analyzing data from the China National knowledge Infrastructure (CNKI), it identifies key developmental phases from 2000 to 2024, aligned with major educational reforms and technological advances. The research outlines three distinct stages: an exploratory phase (2000-2009), a rapid growth phase (2010-2020), and a current phase focusing on deep integration and advanced research (2021-2024). Findings emphasize the importance of inter-institutional collaboration and the shift towards intelligent education systems incorporating technologies like AI and big data. This study provides insights into the dynamic changes in higher education informatisation in China, offering a foundation for future policy and innovation. © 2024 IEEE.

关键词： knowledge graph

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Applying the LOT Methodology to Enhance the Cinematic Heritage Archives 16

Applying the LOT Methodology to Enhance the Cinematic Herita...

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16th international conference on knowledge engineering and Ontology Development, KEOD 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Cosentino, Alessandro Araújo, Webert Júnio Koch, Inês Faculty of Informatics Università della Svizzera Italiana Lugano Switzerland Escola de Ciência da Informação Universidade Federal de Minas Gerais Belo Horizonte Brazil Faculty of Engineering The University of Porto INESC TEC Porto Portugal

ISBN: (纸本)9789897587160

The Locarno Film Festival (LFF) archives represent a valuable collection of cinematic history, providing essential resources for research, education, and the promotion of international film culture. To ensure these resources are easily accessible, it is crucial to develop advanced methods for managing and linking the information they contain. This work focuses on creating a shared way for organizing information, transforming the LFF archives into dynamic, interconnected resources. This transformation is essential for preserving cinematic heritage, improving discoverability, promoting digital transformation, and efficiently managing archives. Using an interdisciplinary approach, we developed the OntoFest following the Linked Open Terms (LOT) Methodology. Significant outcomes of this project include the successful reuse of existing ontologies to manage heterogeneous information, which has improved our ability to understand and retrieve relevant data. This work demonstrates the potential of digital archives in the cinematic field and provides a foundation for future initiatives in digitizing cinematic heritage archives. OntoFest not only contributes to preserving the cinematic cultural heritage of the LFF but also lays the groundwork for new research and creative applications in the digital transformation of film festival archives. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Ontology

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A Code Caching Method for Industrial Software Services 2

A Code Caching Method for Industrial Software Services

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2nd IEEE international conference on Software Services engineering (SSE)

作者： Deng, Lua Li, Congfei Qi, Zhang Liu, Pingwei Qian, Wei Dai, Jing Guodian Nanjing Automat Co Ltd Nanjing Peoples R China

ISBN: (纸本)9798350368529;9798350368512

In the context of the Industrial Internet of Things (IIoT), industrial software services have significantly revolutionized conventional industrial manufacturing processes by integrating cutting-edge IIoT technologies. The Programmable Logic Controller (PLC) serves as the core execution unit, facilitating the fusion of conventional industrial systems with IIoT technologies. In PLC environments, domain-specific translation tools can convert advanced PLC languages, such as Structured Text (ST), into C source code, thereby streamlining the integration of control systems. However, throughout the translation endeavor, certain code segments may necessitate recurrent translation, leading to sub-optimal resource utilization. In view of this challenge, in this paper, we propose a Recommendation-based Code Caching Method (RCCM) for industrial software services to reduce the repetitive translation results and improve the overall efficiency of translation processes. Through the analysis of user access history data, the recommendation algorithm offers suitable policies for cache replacement. Finally, the experimental results demonstrate that the RCCM outperforms existing caching policies in improving system throughput significantly.

关键词： Industrial software services software engineering code caching context-aware recommendation model programmable logic controllers

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A Blockchain-Enabled Cyber-Physical Space for Enhancing Security and Decentralization in Smart Factory 51

A Blockchain-Enabled Cyber-Physical Space for Enhancing Secu...

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51st international conference on Computers and Industrial engineering, CIE 2024

作者： Guo, Wenyou Qu, Ting Zhang, Zhongfei Zhang, Kai Huang, George Q. Li, Jiayin School of Management Jinan University Guangzhou510632 China Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China Institute of Physical Internet Jinan University Zhuhai519070 China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Guangdong Sampu Garage Co. Ltd. Zhaoqing526238 China

The adoption of Industry 4.0 technologies is profoundly reshaping the landscape of production operations management. These new technologies facilitate the coordination and data sharing among various manufacturing resources, eliminating data silos. However, this also imposes new requirements on the security and efficiency of production operations. This study proposes a Cyber-Physical Space architecture centered on blockchain, combined with other Industry 4.0 technologies, aimed at empowering the production operations of smart factories. Based on this architecture, we design a relatively comprehensive blockchain solution incorporating consensus mechanisms, smart contracts, and multi-chain collaboration mechanism. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Smart manufacturing

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A knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals data

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data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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