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Towards A knowledge Graph-based Exploratory Search for Privacy engineering 8

Towards A Knowledge Graph-based Exploratory Search for Priva...

8th International Workshop on the Visualization and Interaction for Ontologies, Linked data and knowledge Graphs, VOILA! 2023

作者： Herwanto, Guntur Budi Ekaputra, Fajar J. Piroi, Florina Sabou, Marta Faculty of Computer Science University of Vienna Austria Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia Institute of Information Systems Engineering Faculty of Informatics TU Wien Austria Institute of Data Process and Knowledge Management WU Wien Austria

The concept of privacy-by-design has gained considerable attention in the wake of legal requirements that software systems should fulfill. The interconnected knowledge on privacy concepts, legal requirements, and software development artifacts required to implement this concept can be a major burden for organizations. To address this challenge, we propose a knowledge graph-based Exploratory Search system to help organizations complete privacy engineering tasks. We identify major requirements of such systems and develop an Exploratory Search prototype built on privacy engineering knowledge that integrates different information sources. While still preliminary, this work serves as a foundation for future research on integrating privacy knowledge into software development and demonstrates the potential of Exploratory Search Systems to support the cognitive process in privacy tasks such as privacy threat identification. © 2023 Copyright for this paper by its authors.

关键词： knowledge graph

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Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning

arXiv

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arXiv 2024年

作者： Milena, Chadimová Eduard, Jurášek Tomáš, Kliegr Department of Information and Knowledge Engineering Faculty of informatics and Statistics Prague University of Economics and Business nám. Winstona Churchilla Prague Czech Republic

This paper introduces a novel method, referred to as "hashing", which involves masking potentially bias-inducing words in large language models (LLMs) with hash-like meaningless identifiers to reduce cognitive biases and reliance on external knowledge. The method was tested across three sets of experiments involving a total of 490 prompts. Statistical analysis using chi-square tests showed significant improvements in all tested scenarios, which covered LLama, ChatGPT, Copilot, Gemini and Mixtral models. In the first experiment, hashing decreased the fallacy rate in a modified version of the "Linda" problem aimed at evaluating susceptibility to cognitive biases. In the second experiment, it improved LLM results on the frequent itemset extraction task. In the third experiment, we found hashing is also effective when the Linda problem is presented in a tabular format rather than text, indicating that the technique works across various input representations. Overall, the method was shown to improve bias reduction and incorporation of external knowledge. Despite bias reduction, hallucination rates were inconsistently reduced across types of LLM models. These findings suggest that masking bias-inducing terms can improve LLM performance, although its effectiveness is model- and task-dependent. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Creating an ATC knowledge graph in support of the artificial situational awareness system

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Transportation Research Procedia 2022年 64卷 328-336页

作者： Michael Schrefl Bernd Neumayr Sebastian Gruber Marlene Hartmann Ivan Tukarić Tomislav Radišić Institute of Business Informatics - Data and Knowledge Engineering Johannes Kepler University of Linz Linz Austria Department of Aeronautics Faculty of Transport and Traffic Sciences University of Zagreb Zagreb Croatia

Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.

关键词： knowledge graph air traffic control AIXM FIXM artificial situational awareness hybrid system

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

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E-Learning: A Successful Learning Environment 1

E-Learning: A Successful Learning Environment

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Momani, Fayiz Hatamleh, Amer Ahmad Amir Fazamin Wan Hamzah, Wan Mohd Jusoh, Julaily Aida Zakaria, Muhammad D. Das, Sunanda Faculty of Management and Financial Sciences E-business & Commerce Department Jordan MIS Bi and Data Analysis Irbid National University Faculty of Business and Financial Science Irbid Jordan Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain University Cyber Security Bangalore India

ISBN: (纸本)9798350341737

Education is developing with the economy. This led to the creation of e-Iearning tools for instructors and students. In e-learning, electronic resources are used to teach. E-learning can be taught in or out of school, but computers and the Internet are essential. That is, e-learning involves delivering education to a big group of people at once or over time. So, overall, in this research is about E-learning that make a successful environment. We chose this term since it is widely used in E-learning research to improve student academic achievement. Due to the difficulties of accessing adequate study materials during the Covid-19 epidemic, distance learning has grown more popular among students. This study proposes a unique learning technique that involves choosing and organizing important learning items using a recommender system. We also studied our method's efficacy. Using a recommended system to help online learning activities was proven to be effective. Software development methodology is a collection of principles and processes used to build software. Systematic software development is the goal. The research approach utilized for this topic is Lean Development Methodology. We also examine the advantages and disadvantages of e-learning. This research also introduces various new strategies and methodologies that may be employed in the future to improve system-learning. It also presents system that we choose and why we choose it. So, it aims to eliminate waste and increase production. Following the principles can help developers avoid nonproductive chores while still producing high-quality results. The ultimate objective is to develop an efficient and error-free system. Finally, E-learning is a social revolution. It's part of a bigger rethinking of how we teach future employees and students. The eLearning platform enabled the learning data visualization dashboard beautifully. User-centered design lets the dashboard adapt to its users' demands. © 2023 IEEE.

关键词： Learning systems

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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A Novel Approach to Abstractive Summarization Based on LOF, Sentence-BERT and T5 – with Fact Checking Use Case

SSRN

引用

SSRN 2023年

作者： Vajdečka, Peter Svátek, Vojtěch Víta, Martin Faculty of Informatics and Statistics Prague University of Economics and Business Nám. Winstona Churchilla 4 Prague130 67 Czech Republic Department of Information and Knowledge Engineering

We propose a novel hybrid approach to text summarization. First, the Local Outlier Factor method is applied to generate an extractive summary, using sentence vectorization via a fine-tuned Sentence-BERT encoder. Then the T5 transformer is fine-tuned using these extractive summaries to generate the final abstractive summaries. On English fact-checks from *** the new method outperformed all state-of-the-art methods in terms of the ROUGE metric. We were also, to our knowledge, the first to apply automatic summarization to Czech fact-check data (***). Beyond the fact-checking domain, the method was applied on a (Czech) news dataset, SumeCzech;although we only used 10 % of the initial training data for model fine-tuning, we overcame most of state-of-the-art results. © 2023, The Authors. All rights reserved.

关键词： Statistics

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A Personalized Blood Glucose Prediction Model Using Random Forest Regression

A Personalized Blood Glucose Prediction Model Using Random F...

引用

2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Syafrudin, Muhammad Alfian, Ganjar Fitriyani, Norma Latif Fahrurrozi, Imam Anshari, Muhammad Rhee, Jongtae Sejong University Department of Artificial Intelligence Seoul Korea Republic of Universitas Gadjah Mada Vocational College Department of Electrical Engineering and Informatics Yogyakarta Indonesia Sejong University Department of Data Science Seoul Korea Republic of Universiti Brunei Darussalam School of Business and Economics Brunei Dongguk University Department of Industrial and Systems Engineering Seoul Korea Republic of

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Predicting future blood glucose (BG) values for diabetic patients, particularly for type 1 diabetes (T1D), remains an important and challenging issue. To overcome it, several well-known machine learning models have been used in recent years. Thus, a personalized model based by using random forest (RF) regression is implemented to forecast the future BG level of T1D patients. To create the models and forecast the BG value, a clinical dataset of T1D patients is employed. In this study, the future BG value, or prediction horizon (PH), is used for the next 15 and 30 minutes. Several performance metrics, including coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RMSE), are measured during the experiments to calculate the prediction models' performances. The results revealed that the proposed BG prediction model outperformed other models including Multi-Layer Perceptron, Support Vector Regression, Decision Tree, K-Nearest Neighbour, with an average RMSE, MAPE, and R2 of 15.54 mg/dL, 8.94%, 0.88, and 27.61 mg/dL, 15.52%, and 0.66 for PH of 15 and 30 minutes, respectively. In addition, the RMSE score was reduced by around 1.14 mg/dL and 1.32 mg/dL for the next 15 and 30 minutes, respectively, after adding statistical-extracted data as additional features for the regression models, compared to regression models without statistical data. Ultimately, it is expected that the results of the present study could be used to improve diabetes care. © 2022 IEEE.

关键词： Decision trees

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Towards Extraction of Validation Rules from OPC UA Companion Specifications

Towards Extraction of Validation Rules from OPC UA Companion...

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Nilay Tufek Aparna Sai Sree Thuluva Valentin Philipp Just Marta Sabou Technology Department Siemens AG Munich Germany Inst. Computer Engineering Vienna University of Technology Inst. Data Process Knowledge Management Vienna University of Economics and Business

Interoperability is the key to industrial automation. OPC UA aims to provide interoperability between industrial machines at the network, protocol, and semantic layers. In order to achieve it, the implementation of a machine and its nodeset file should comply with the OPC UA standard. However, there are a few areas for improvement in technology to automatically check compliance since the compliance rules/validation rules are expressed in textual format in specification documents. In other words, they are not machine-interpretable. Converting the text-based specification into machine-readable form is a complex problem since each companion specification is domain-specific and written by a set of industry experts from all over the world from diverse language backgrounds. The specifications are majorly unique, with little commonality between them. Because of these reasons, it is challenging to develop a generic information extraction approach to retrieve rules that work for all specifications. In this study, we aim to handle this challenge to extract the textual rules from specifications automatically by applying Natural Language Processing and Machine Learning technologies. We present our methodology based on Named Entity Recognition for OPC UA documents for information extraction and text classification to identify rules. To achieve this goal, we created five named entity data sets from five selected OPC UA companion specifications. The sentences in the data set and the entities in the sentences were annotated by two OPC UA experts. We point out a deeper analysis of the data sets to highlight common and unique entities in them and show their usage in identifying the rules in the specifications.

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Mining Frequent Structures in Conceptual Models

arXiv

引用

arXiv 2024年

作者： Fumagalli, Mattia Sales, Tiago Prince Barcelos, Pedro Paulo F. Micale, Giovanni Glaser, Philipp-Lorenz Bork, Dominik Zaytsev, Vadim Calvanese, Diego Guizzardi, Giancarlo KRDB Research Centre on Knowledge and Data Free University of Bozen-Bolzano Bolzano Italy University of Twente Enschede Netherlands Department of Clinical and Experimental Medicine University of Catania Catania Italy University of Twente Enschede Netherlands TU Wien Business Informatics Group Vienna Austria

The challenge of using structured methods to represent knowledge is a well-documented issue in conceptual modeling and has been the focus of extensive research. It is widely recognized that adopting modeling patterns offers an effective structural approach for designing conceptual models. Patterns, in this context, refer to generalizable, recurring structures that provide solutions to common design problems. They significantly enhance both the understanding and improvement of the modeling process. Numerous experimental studies have demonstrated the undeniable value of using patterns in conceptual modeling. Despite this, the task of identifying patterns in conceptual models remains highly complex, and there is currently no systematic method for pattern discovery. To address this gap, this paper proposes a general approach for discovering frequent structures in conceptual modeling languages as a means to support pattern identification. Specifically, we focus on uncovering recurring structures that reflect the usage patterns of a given conceptual modeling language. As proof of concept, we implement our approach by focusing on two widely-used conceptual modeling languages. This implementation includes an exploratory tool that integrates a frequent subgraph mining algorithm with graph manipulation techniques. The tool processes multiple conceptual models and identifies recurrent structures based on various criteria. We validate the tool using two state-of-the-art curated datasets: one consisting of models encoded in OntoUML and the other in ArchiMate. The primary objective of our approach is to provide a support tool for language engineers. This tool can be used to identify both effective and ineffective modeling practices, enabling the refinement and evolution of conceptual modeling languages. Furthermore, it facilitates the reuse of accumulated expertise, ultimately supporting the creation of higher-quality models in a given language. © 2024, CC BY-NC-ND.

关键词： Modeling languages

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