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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Lee, Sunghyeon Song, Junsup Karagiannis, Dimitri Lee, Moonkun Department of Computer Science and Engineering Jeonbuk National University Jeonju Korea Republic of Research Group Knowledge Engineering University of Vienna Vienna Austria

ISBN: (纸本)9781665445115

Process algebra is one of the most suitable formal methods to model Smart IoT Systems to specify their probabilistic behavior, since process algebra may include probability property for choice operation as shown in PAROMA and PACSR, which, however, do not have enough analytic methods to control the degree of nondeterminism for the operation. In order to overcome the limitation, the paper presents a new formal method, called dTP-Calculus, and an analytic method to control the nondeterminism in order to satisfy safety and security requirements of the IoT systems. To prove the concept of the approach in the paper, the SAVE tool suite has been developed on the ADOxx and is used to demonstrate its applicability with a Smart EMS (Emergence Medical Service) Example. © 2021 IEEE.

关键词： Internet of things

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DOGHED: A template-based generator for multimodal dialog systems targeting heterogeneous devices

DOGHED: A template-based generator for multimodal dialog sys...

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2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2003

作者： Channarukul, Songsak McRoy, Susan W. Ali, Syed S. Natural Language and Knowledge Representation Research Group Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee United States

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A Consensus Privacy Metrics Framework for Synthetic data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of computer science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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A direct algorithm for computing the transitive closure of a two-dimensionally structured file 3rd

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3rd Symposium on Mathematical Fundamentals of database and knowledge Base Systems, MFDBS 1991

作者： Taylor, Stephen Hachem, Nabil I. Data General Corporation WestboroMA01531 United States Data/Knowledge Base Research Group Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (纸本)9783540540090

It is well-known that the computation cost to find the transitive closure (TC) of a graph stored as an adjacency matrix is the same, to within a constant factor, as matrix multiplication. In this paper, we present a new TC algorithm based on double hashing and two-dimensionally organized files. We show that, when using this algorithm, the computation and i/o costs of finding the TC of a database relation is like that of performing a relational composition operation. For sparse closures, sparse compositions will be performed, which may be significantly more thrifty than the corresponding matrix operations, which must be at least O(n2), and for which most algorithms are O(n3). © Springer-Verlag Berlin Heidelberg 1991.

关键词： Matrix algebra

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Forest fire area estimation using support vector machine as an approximator 10th

Forest fire area estimation using support vector machine as ...

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Nittaya Poomka, Pumrapee Chuaybamroong, Paradee Kerdprasop, Kittisak Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

Forest fire is critical environmental issue that can cause severe damage. Fast detection and accurate estimation of forest fire burned area can help firefighters to effectively control damage. Thus, the purpose of this paper is to apply state of the art data modeling method to estimate the area of forest fire burning using support vector machine (SVM) algorithm as a tool for area approximation. The dataset is real forest fires data from the Montesinho natural park in the northeast region of Portugal. The original dataset comprises of 517 records with 13 attributes. We randomly sample the data 10 times to obtain 10 data-subsets for building estimation models using two kinds of SVM kernel: radial basis function and polynomial function. The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics: mean absolute error (MAE) and root mean square error (RMSE). The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65, respectively. These errors are less than other techniques reported in the literature. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Fires

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Modeling method for temperature anomaly analysis 10th

Modeling method for temperature anomaly analysis

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Kittisak Chuaybamroong, Paradee Kerdprasop, Nittaya Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

This study applies intelligent analytical methods to analyze temperature anomaly events during the past seven centuries of countries in the Southeast Asia including Thailand, Malaysia, Myanmar, and Cambodia. The temperature reconstruction during the years 1300 to 1999 were used as data source for anomaly analysis. In the analytical process, correlation analysis was applied to initially investigate the temperature variability concordance among the Southeast Asian countries. The results are that temperature variability patterns in Thailand, Myanmar, and Cambodia are moderately correlated to each other. On the contrary, the temperature variation patterns of Malaysia do not correlate to other countries in the same region. The further in-depth analysis focuses on the temperature anomaly of Thailand that shows high variability from the 14th to 16th centuries. Several machine learning algorithms had been applied to estimate the temperature anomaly of Thailand based on the anomaly events among the neighbors. The learned models reveal that Myanmar temperature anomaly most associate to the Thailand’s temperature variation. The performance of each model had been assessed and the results reveal that the chi-squared automatic interaction detection, or CHAID, is the best one with 0.624 correlation coefficient and relative error around 0.611. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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A fast and simple graph kernel for RDF

A fast and simple graph kernel for RDF

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International Workshop on data Mining on Linked data, with Linked data Mining Challenge, DMoLD 2013 - Collocated with the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECMLPKDD 2013

作者： De Vries, Gerben Klaas Dirk De Rooij, Steven System and Network Engineering Group Informatics Institute University of Amsterdam Netherlands Knowledge Representation and Reasoning Group Department of Computer Science VU University Amsterdam Netherlands

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state-of-the-art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag-of-words feature vectors, which increases performance in two of the four experiments.

关键词： Forestry

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Sequential clustering and contextual importance measures for incremental update summarization 26

Sequential clustering and contextual importance measures for...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Zopf, Markus Mencía, Eneldo Loza Fürnkranz, Johannes Research Training Group AIPHES Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64289 Germany

ISBN: (纸本)9784879747020

Unexpected events such as accidents, natural disasters and terrorist attacks represent an information situation where it is crucial to give users access to important and non-redundant information as early as possible. Previous work uses either a fast but inaccurate pipeline approach or a precise but slow clustering approach. Instead, we propose to use sequential clustering for grouping information so that we are able to publish sentences at each time step. By doing so, we combine the best of both clustering and pipeline approaches and create a fast and precise real-time system. Experiments on the TREC Temporal Summarization 2015 shared task dataset show that our system achieves better results compared to the state-of-the-art. © 1963-2018 ACL.

关键词： Real time systems

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SeqCluSum: Combining Sequential Clustering and Contextual Importance Measuring to Summarize Developing Events over Time 24

SeqCluSum: Combining Sequential Clustering and Contextual Im...

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24th Text REtrieval Conference, TREC 2015

作者： Zopf, Markus Research Training Group AIPHES Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64293 Germany

Unexpected events such as accidents, natural disasters and terrorist attacks represent an information situation where it is essential to give users access to important and non-redundant information as fast as possible. In this paper, we introduce SeqCluSum, a temporal summarization system which combines sequential clustering to cluster sentences and a contextual importance measurement to weight the created clusters and thereby to identify important sentences. We participated with this system in the TREC Temporal Summarization track where systems have to generate extractive summaries for developing events by publishing sentence-length updates extracted from web documents. Results show that our approach is very well suited for this task by achieving best results. We furthermore point out several improvement possibilities to show how the system can further be enhanced. © 2015 24th Text REtrieval Conference, TREC 2015 - Proceedings. All Rights Reserved.

关键词： Disasters

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