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License and template access control for geospatial linked data 3

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License and template access control for geospatial linked da...

3rd International Workshop on Geospatial Linked data and the 2nd Workshop on Querying the Web of data, GeoLD-QuWeDa 2018

作者： Meehan, Alan Fatema, Kaniz Brennan, Rob Clinton, Éamonn McNerney, Lorraine O'Sullivan, Declan ADAPT Centre Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland Department of Electronics Computing and Mathematics University of Derby Kedleston Road Derby United Kingdom Ordnance Survey Ireland Phoenix Park Dublin Ireland

As Linked data technologies mature geospatial data institutions are seeking to exploit these advantages and selectively serve their commercially valuable data to customers on the web. An access control solution is required to ensure customers are not accessing unauthorized data. In this paper we present a fine grained and flexible access control method for geospatial Linked data. Our declarative approach provides a vocabulary to model data licenses and templates. A license models what a customer is allowed to access at a fine granularity and a template models how data is allowed to be accessed. Each template contains variables and the values that these variables are allowed to contain are specified. This provides great flexibility and allows the creation of templates for a range of different data access use cases. We propose an implementation architecture for our approach which proposes a license and template-based access control web service that sits on top of a triple store, SPARQL processor and a RESTful API for access. We also present a case study where a prototype implementation of our access control approach was applied to Ordnance Survey Ireland's controlled access geospatial Linked data. © CEUR-WS. All rights reserved.

关键词： Access control

Exploiting the power of levenberg-marquardt optimizer with anomaly detection in time series

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arXiv 2021年

作者： Wang, Wenyi Taylor, John Bala, Biswajit Defence Science and Technology Group Australia CSIRO Data61 Australia Australian National University School of Computing

The Levenberg-Marquardt (LM) optimization algorithm has been widely used for solving machine learning problems. Literature reviews have shown that the LM can be very powerful and effective on moderate function approximation problems when the number of weights in the network is not more than a couple of hundred. In contrast, the LM does not seem to perform as well when dealing with pattern recognition or classification problems, and inefficient when networks become large (e.g. with more than 500 weights). In this paper, we exploit the true power of LM algorithm using some real-world aircraft datasets. On these datasets most other commonly used optimizers are unable to detect the anomalies caused by the changing conditions of the aircraft engine. The challenging nature of the datasets are the abrupt changes in the time series data. We find that the LM optimizer has a much better ability to approximate abrupt changes and detect anomalies than other optimizers. We compare the performance, in addressing this anomaly/change detection problem, of the LM and several other optimizers. We assess the relative performance based on a range of measures including network complexity (i.e. number of weights), fitting accuracy, over fitting, training time, use of GPU’s and memory requirement etc. We also discuss the issue of robust LM implementation in MATLAB and TensorFlow for promoting more popular usage of the LM algorithm and potential use of LM optimizer for large-scale problems. © 2021, CC BY.

关键词： Aircraft engines

The Conflict Between Explainable and Accountable Decision-Making Algorithms

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arXiv 2022年

作者： Lima, Gabriel Grgić-Hlača, Nina Jeong, Jin Keun Cha, Meeyoung School of Computing KAIST Data Science Group IBS Korea Republic of Max Planck Institute for Software Systems Max Planck Institute for Research on Collective Goods Germany Kangwon National University Korea Republic of Data Science Group IBS School of Computing KAIST Korea Republic of

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility. © 2022, CC BY.

关键词： Decision making

Hierarchical representation of time-varying volume data with /sup 4//spl radic/2 subdivision and quadrilinear B-spline wavelets

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Hierarchical representation of time-varying volume data with...

Pacific Conference on Computer Graphics and Applications

作者： L. Linsen V. Pascucci M.A. Duchaineau B. Hamann K.I. Joy Center for Image Processing and Integrated Computing (CIPIC) University of California Davis USA Center for Applied Scientific Computing (CASC) Data Science Group Lawrence Livermore National Laboratory USA Center for Applied Scientific Computing (CASC) Data Science Group Lawrence Livermore National Laboratory Center for Image Processing and Integrated Computing (CIPIC) University of California Davis

Multiresolution methods for representing data at multiple levels of detail are widely used for large-scale two- and three-dimensional data sets. We present a four-dimensional multiresolution approach for time-varying volume data. This approach supports a hierarchy with spatial and temporal scalability. The hierarchical data organization is based on /sup 4//spl radic/2 subdivision. The /sup n//spl radic/2-subdivision scheme only doubles the overall number of grid points in each subdivision step. This fact leads to fine granularity and high adaptivity, which is especially desirable in the spatial dimensions. For high-quality data approximation on each level of detail, we use quadrilinear B-spline wavelets. We present a linear B-spline wavelet lifting scheme based on /sup n//spl radic/2 subdivision to obtain narrow masks for the update rules. Narrow masks provide a basis for out-of-core data exploration techniques and view-dependent visualization of sequences of time steps.

关键词： Spline Large-scale systems Spatial resolution data visualization Image processing Scientific computing Computer science Laboratories Image resolution Large scale integration

Classification of goods using text descriptions with sentences retrieval

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arXiv 2021年

作者： Lee, Eunji Kim, Sundong Kim, Sihyun Park, Sungwon Cha, Meeyoung Jung, Soyeon Yang, Suyoung Choi, Yeonsoo Ji, Sungdae Song, Minsoo Kim, Heeja School of Computing KAIST Data Science Group Institute for Basic Science ICT and Data Policy Bureau Korea Customs Service Customs Valuation and Classification Institute Korea Customs Service

The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office. This decision is crucial to importers and exporters, as it determines the tariff rate. However, similar to court decisions made by judges, the task can be non-trivial even for experienced customs officers. The current paper proposes a deep learning model to assist this seemingly challenging HS code classification. Together with Korea Customs Service, we built a decision model based on KoELECTRA that suggests the most likely heading and subheadings (i.e., the first four and six digits) of the HS code. Evaluation on 129,084 past cases shows that the top-3 suggestions made by our model have an accuracy of 95.5% in classifying 265 subheadings. This promising result implies algorithms may reduce the time and effort taken by customs officers substantially by assisting the HS code classification task. © 2021, CC BY-NC-SA.

关键词： Decision support systems

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation 28

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Cross-lingual Machine Reading Comprehension with Language Br...

28th International Conference on Computational Linguistics, COLING 2020

作者： Liu, Junhao Shou, Linjun Pei, Jian Gong, Ming Yang, Min Jiang, Daxin Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Microsoft STCA NLP Group Beijing China School of Computing Science Simon Fraser University Canada

ISBN: (纸本)9781952148279

Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous. In this paper, we tackle this challenge and enhance the cross-lingual transferring performance by a novel augmentation approach named Language Branch Machine Reading Comprehension (LBMRC). A language branch is a group of passages in one single language paired with questions in all target languages. We train multiple machine reading comprehension (MRC) models proficient in individual language based on LBMRC. Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages. Combining the LBMRC and multilingual distillation can be more robust to the data noises, therefore, improving the model’s cross-lingual ability. Meanwhile, the produced single multilingual model can apply to all target languages, which saves the cost of training, inference, and maintenance for multiple models. Extensive experiments on two CLMRC benchmarks clearly show the effectiveness of our proposed method. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Distillation

database Systems for Advanced Applications 1

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丛书名： Lecture Notes in Computer science

1000年

作者： Weiyi Meng Ling Feng Stéphane Bressan Werner Winiwarter Wei Song

Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

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Parallel Image Encryption Algorithm Using Partitioned Cellul...

2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Fazlali, Mahmood Alihoseini, Amirhossein Zarei, Ebrahim Tabani, Myasar Pourmoafi, Seyedali University of Hertfordshire Cybersecurity and Computing Systems Research Group Hatfield United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Dept. of Data and Computer Sciensce Tehran Iran Khansar Campus University of Isfahan Dep. of Computer Science Isfahan Iran

ISBN: (纸本)9798350346473

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm. © 2023 IEEE.

关键词： Graphics processing unit

Validating the predictions of mathematical models describing tumor growth and treatment response

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arXiv 2025年

作者： Lorenzo, Guillermo Hormuth, David A. Wu, Chengyue Pash, Graham Chaudhuri, Anirban Lima, Ernesto A.B.F. Okereke, Lois C. Patel, Reshmi Willcox, Karen Yankeelov, Thomas E. Group of Numerical Methods in Engineering Department of Mathematics University of A Coruña Spain Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX United States Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Breast Imaging The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biostatistics The University of Texas MD Anderson Cancer Center HoustonTX United States Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Texas Advanced Computing Center The University of Texas at Austin AustinTX United States Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX United States Department of Oncology The University of Texas at Austin AustinTX United States

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient’s cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them. © 2025, CC BY-NC-ND.

关键词： Prediction models