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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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Plus ça change, plus c'est la même chose – The Australian Records Management Case 18th

Plus ça change, plus c'est la même chose – The Australian...

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Xie, Sherry L. Gao, Yubao Ma, Linqing School of Information Resource Management Renmin University of China Beijing100872 China Center for Digital Records Management Research Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Data Knowledge of the Ministry of Education of China Beijing100872 China Center for Archival Undertakings Renmin University of China Beijing100872 China

ISBN: (纸本)9783031280344

Intrigued by the information management replacing records management phenomenon, this study aimed at shedding light on it. Relying on preserved websites, the study examined relevant contents from 2007 to early 2022. It discovered the battle between records and information, and later on information asset. This battle incurred dazzling changes in managerial considerations, yet the challenges to management – either towards records, information, information and records, or information asset – remained the same. So were the unsatisfactory outcomes. A case seemed perfect to tell the plus ça change, plus c'est la même chose story. It is our hope that future studies can gather mighty strengthen and thus settle down the records-information battle once for all. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

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Automated Identification of Food Substitutions Using knowledge Graph Embeddings 13

Automated Identification of Food Substitutions Using Knowled...

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13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022

作者： Loesch, Julie Meeckers, Louis Van Lier, Ilse De Boer, Alie Dumontier, Michel Celebi, Remzi Data Science and Knowledge Engineering Maastricht University Netherlands Food Claims Centre Venlo Maastricht University Campus Venlo Venlo Netherlands Youth Food and Health Maastricht University Campus Venlo Netherlands Institute of Data Science Maastricht University Netherlands

Healthy eating is a daily challenge for many, which is in uenced by various factors such as taste, accessibility, price, and the food environment. Consumers often are insufficiently informed about healthier options for the foods they consume. Being able to identify healthy alternatives for foods according to similarities in nutritional value will help consumers choose products that they prefer. This work aims to identify healthy alternatives to foods that also have similar nutritional characteristics through the use of knowledge graph embeddings (KGEs). The quality of the KGEs is assessed against a newly created ground truth, which is verified by two domain experts. Hence, this work presents a newly created ground truth food substitution data set and describes the development of a food recommender system that identifies healthier alternatives to foods. Copyright © 2022 for this paper by its authors.

关键词： knowledge graph

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Assisted Diagnostic Learning Based on Concept Lattice

Assisted Diagnostic Learning Based on Concept Lattice

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2021 International Conference on Signal Processing and Communication Technology, SPCT 2021

作者： Shen, Xiajiong Li, Xingchen Han, Daojun Zhang, Yanfeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the continuous development of education, online education is becoming more and more popular with learners because of its convenience. However, online education has the problem of insufficient interaction with students. Students still need to rely on the students themselves to judge the lack of knowledge in practice tasks, which will greatly affect the learning efficiency. In this paper, we propose a method based on formal concept analysis to analyze the knowledge that may not be mastered, and a method of intensive practice for students. First of all, establishing the formal context of student error test questions, construct the concept lattice to calculate the degree of importance of the knowledge concept that may not be mastered, judge the student's mastery of the knowledge concept, and give feedback. Secondly, construct the concept lattice of the unpractised test set, and calculate the similarity of the concept extension set that meets the threshold, find similar topics and push them, and realize targeted training for students who have not mastered the knowledge concepts. © 2022 SPIE.

关键词： Students

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TABLELLM: Enabling Tabular data Manipulation by LLMs in Real Office Usage Scenarios

arXiv

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arXiv 2024年

作者： Zhang, Xiaokang Luo, Sijia Zhang, Bohan Ma, Zeyao Zhang, Jing Li, Yang Li, Guanlin Yao, Zijun Xu, Kangli Zhou, Jinchang Zhang-Li, Daniel Yu, Jifan Zhao, Shu Li, Juanzi Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China Computer Science Anhui University China

We introduce TABLELLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TABLELLM, we have crafted benchmarks tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TABLELLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction. Copyright © 2024, The Authors. All rights reserved.

关键词： Spreadsheets

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WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

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Journal of Computer Science & Technology 2021年第4期36卷 741-761页

作者： Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China

Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are performed artificially(e.g.,by database administrators),but it is hard for them to achieve high performance through tuning in various types of systems and in various *** recent years,there have been some studies on tuning traditional database systems,but all these methods have some *** this article,we put forward a tuning system based on attention-based deep reinforcement learning named WATuning,which can adapt to the changes of workload characteristics and optimize the system performance efficiently and ***,we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of *** algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems,and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate ***,WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of ***,WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make *** experimental results show that the throughput and the latency of WATuning are improved by 52.6%and decreased by 31%,respectively,compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.

关键词： attention mechanism auto-tuning system reinforcement learning(RL) workload-aware

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Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Yuxin Zhang, Lizhong Zhu, Tao Luo, Wenjian School of Computer Science and Information Engineering Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230027 China School of Computer Science and Information Engineering University of South China Hunan Hengyang421200 China School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9798350337020

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to 'track the moving optima', rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://***/DMiC-Lab-HFUT/SMDE-Transfer. © 2023 IEEE.

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LibriS2S: A German-English Speech-to-Speech Translation Corpus

arXiv

引用

arXiv 2022年

作者： Jeuris, Pedro Niehues, Jan Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English. For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information. We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input. © 2022, CC BY-NC-SA.

关键词： Speech synthesis

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AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks

arXiv

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arXiv 2022年

作者： Yang, Yimin Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper introduces a novel data-driven predictive model based on TransUNet for precipitation nowcasting task. The TransUNet model which combines the Transformer and U-Net models has been previously successfully applied in medical segmentation tasks. Here, TransUNet is used as a core model and is further equipped with Convolutional Block Attention Modules (CBAM) and Depthwise-separable Convolution (DSC). The proposed Attention Augmented TransUNet (AA-TransUNet) model is evaluated on two distinct datasets: the Dutch precipitation map dataset and the French cloud cover dataset. The obtained results show that the proposed model outperforms other examined models on both tested datasets. Furthermore, the uncertainty analysis of the proposed AA-TransUNet is provided to give additional insights on its predictions. © 2022, CC BY-SA.

关键词： Convolution

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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...

引用

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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