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11th IEEE International Conference on Data science and Advanced Analytics, DSAA 2024

作者： Ullah, Farhan Srivastava, Gautam Mostarda, Leonardo Cacciagrano, Diletta University of Camerino Division of Computer Science Camerino Italy Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada University of Perugia Department of Mathematics and Computer Science Perugia Italy School of Software Northwestern Polytechnical University Xian China

ISBN: (纸本)9798350364941

Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance. © 2024 IEEE.

关键词： Federated learning

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Improving the performance of MPI derived datatypes by optimizing memory-access cost

Improving the performance of MPI derived datatypes by optimi...

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IEEE International Conference on Cluster Computing, CLUSTER 2003

作者： Byna, Surendra Gropp, William Sun, Xian-He Thakur, Rajeev Department of Computer Science Illinois Institute of Technology ChicagoIL60616 United States Math. and Comp. Science Division Argonne National Laboratory ArgonneIL60439 United States

ISBN: (纸本)0769520669

The MPI Standard supports derived datatypes, which allow users to describe noncontiguous memory layout and communicate noncontiguous data with a single communication function. This feature enables an MPI implementation to optimize the transfer of noncontiguous data. In practice, however, few MPI implementations implement derived datatypes in a way that performs better than what the user can achieve by manually packing data into a contiguous buffer and then calling an MPI function. In this paper, we present a technique for improving the performance of derived datatypes by automatically using packing algorithms that are optimized for memory-access cost. The packing algorithms use memory-optimization techniques that the user cannot apply easily without advanced knowledge of the memory architecture. We present performance results for a matrix-transpose example that demonstrate that our implementation of derived datatypes significantly outperforms both manual packing by the user and the existing derived-datatype code in the MPI implementation (MPICH). © 2003 IEEE.

关键词： Memory architecture

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Differentially private synthesization of multi-dimensional data using Copula functions 17

Differentially private synthesization of multi-dimensional d...

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17th International Conference on Extending Database Technology, EDBT 2014

作者： Li, Haoran Xiong, Li Jiang, Xiaoqian Math and Computer Science Department Emory University AtlantaGA United States Biomedical Informatics Division UC San Diego San DiegoCA United States

ISBN: (纸本)9783893180653

Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Most of the existing techniques that generate differentially private histograms or synthetic data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. In this paper, we propose DPCopula, a differentially private data synthesization technique using Copula functions for multi-dimensional data. The core of our method is to compute a differentially private copula function from which we can sample synthetic data. Copula functions are used to describe the dependence between multivariate random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. We present two methods for estimating the parameters of the copula functions with differential privacy: maximum likelihood estimation and Kendall's t estimation. We present formal proofs for the privacy guarantee as well as the convergence property of our methods. Extensive experiments using both real datasets and synthetic datasets demonstrate that DPCopula generates highly accurate synthetic multidimensional data with significantly better utility than stateof-the-art techniques.

关键词： Maximum likelihood estimation

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Build less code, deliver more science: An experience report on composing scientific environments using component-based and commodity software platforms

Build less code, deliver more science: An experience report ...

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16th ACM SIGSOFT Symposium on Component Based Software Engineering, CBSE 2013

作者： Gorton, Ian Liu, Yan Lansing, Carina Elsethagen, Todd Van Dam, Kerstin Kleese Computational Sciences and Math Division Pacific Northwest National Laboratory Richland WA United States Faculty of Engineering and Computer Science Concordia University Montreal QC Canada

ISBN: (纸本)9781450321228

Modern scientific software is daunting in its diversity and complexity. From massively parallel simulations running on the world's largest supercomputers, to visualizations and user support environments that manage ever growing complex data collections, the challenges for software engineers are plentiful. While high performance simulators are necessarily specialized codes to maximize performance on specific supercomputer architectures, we argue the vast majority of supporting infrastructure, data management and analysis tools can leverage commodity open source and component-based technologies. This approach can significantly drive down the effort and costs of building complex, collaborative scientific user environments, as well as increase their reliability and extensibility. In this paper we describe our experiences in creating an initial user environment for scientists involved in modeling the detailed effects of climate change on the environment of selected geographical regions. Our approach composes the user environment using the Velo scientific knowledge management platform and the MeDICi Integration Framework for scientific workflows. These established platforms leverage component-based technologies and extend commodity open source platforms with abstractions and capabilities that make them amenable for broad use in science. Using this approach we were able to deliver an operational user environment capable of running thousands of simulations in a 7 month period, and achieve significant software reuse. Copyright © ACM.

关键词： Middleware

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Algorithmic Learning Theory 1

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丛书名： Lecture Notes in computer science

1000年

作者： Yoav Freund László Györfi György Turán Thomas Zeugmann

ISBN: (数字)9783540879879

ISBN: (纸本)9783540879862

This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13–16, 2008. The conference was co-located with the 11th - ternational Conference on Discovery science (DS 2008). The technical program of ALT 2008 contained 31 papers selected from 46 submissions, and 5 invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2008 was the 19th in the ALT conference series, established in Japan in 1990. The series Analogical and Inductive Inference is a predecessor of this series: it was held in 1986, 1989 and 1992, co-located with ALT in 1994, and s- sequently merged with ALT. ALT maintains its strong connections to Japan, but has also been held in other countries, such as Australia, Germany, Italy, Sin- pore, Spain and the USA. The ALT conference series is supervised by its Steering Committee: Naoki Abe (IBM T. J.

关键词： Data Mining and Knowledge Discovery Artificial Intelligence Natural Language Processing (NLP) computer Appl. in Arts and Humanities

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DPSynthesizer: Differentially private data synthesizer for privacy preserving data sharing 40th

DPSynthesizer: Differentially private data synthesizer for p...

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Proceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014

作者： Li, Haoran Xiong, Li Zhang, Lifan Jiang, Xiaoqian Math and Computer Science Department Emory University Atlanta GA United States Biomedical Informatics Division UC San Diego La Jolla CA United States

Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Releasing synthetic data that mimic original data with differential privacy provides a promising way for privacy preserving data sharing and analytics while providing a rigorous privacy guarantee. However, to this date there is no open-source tools that allow users to generate differentially private synthetic data, in particular, for high dimensional and large domain data. Most of the existing techniques that generate differentially private histograms or synthetic data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. We propose DPSynthesizer, a toolkit for differentially private data synthesization. The core of DPSynthesizer is DPCopula designed for highdimensional and large-domain data. DPCopula computes a differentially private copula function from which synthetic data can be sampled. Copula functions are used to describe the dependence between multivariate random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. DPSynthesizer also implements a set of state-of-the-art methods for building di fferentially private histograms, suitable for low-dimensional data, from which synthetic data can be generated. We will demonstrate the system using DPCopula as well as other methods with various data sets and show the feasibility, utility, and efficiency of various methods. © 2014 VLDB Endowment 2150-8097/14/08.

关键词： Graphic methods

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The orbit method and gelfand pairs, associated with nilpotent Lie groups

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Journal of Geometric Analysis 1999年第4期9卷 569-582页

作者： Benson, Chal Jenkins, Joe Ratcliff, Gail Department of Math and Computer Sciences University of Missouri-St. Louis St. Louis MO 63121 United States Division of Math Sciences National Science Foundation Arlington VA 22203-9966 4210 Wilson Blvd United States

Let K be a compact Lie group acting by automorphisms on a nilpotent Lie group N. One calls (K, N) a Gelfand pair when the integrable K-invariant functions on N form a commutative algebra under convolution. We prove that in this case the coadjoint orbits for G := K |× N which meet the annihilator l-fraktur sign^⊥ of the Lie algebra l-fraktur sign of K do so in single K-orbits. This generalizes a result of the authors and R. Lipsman concerning Gelfand pairs associated with Heisenberg groups. © 1999 The Journal of Geometric Analysis.

关键词： Gelfand pairs Nilpotent groups Orbit method

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Work in Progress - Computing and Undergraduate Engineering: A Collaborative Process to Align Computing Education with Engineering Workforce Needs (CPACE)

Work in Progress - Computing and Undergraduate Engineering: ...

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IEEE Frontiers in Education Conference 2008

作者： Vergara, Claudia E. Urban-Lurain, Mark Briedis, Daina Buch, Neeraj LaPrad, Jeannine Paquette, Louise Sticklen, Jon Wolff, Thomas F. Center for Research on College Science Teaching and Learning MSU United States Instructional Technology Research and Development Division Science and Mathematics Education College of Natural Science MSU United States Chemical Engineering and Materials Science MSU United States Civil and Environmental Engineering MSU United States CSW United States Math of Computer Science Department LCC United States Computer Science and Engineering MSU United States College of Engineering MSU United States

ISBN: (纸本)9781424419692

This NSF-funded community-building (CB) project brings together Michigan State University (MSU), Lansing Community College (LCC), and the Corporation for a Skilled Workforce (CSW) to design and implement a process to create a collaboratively-defined undergraduate computing education within the engineering and technology fields in alignment with the computational problem-solving abilities needed to transform mid-Michigan's economy and workforce. In this WIP we outline the process we are developing to ensure that a wide variety of stakeholders - business, community leaders and post secondary educators collaborate to identify workforce computational skills, define how these skills can be integrated across a curriculum, and develop revised curricula that integrate computational problem-solving across engineering departmental courses. By documenting, evaluating and making the process explicit, this process can serve as a model for national efforts to revitalize undergraduate computing education in engineering, and should be extensible to other computing education reform efforts.

关键词： Computational problem-solving curricula Curicular reform Undergraduate engineering and technology Workforce computational skills

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Implementation of distributed loop scheduling schemes on the TeraGrid

Implementation of distributed loop scheduling schemes on the...

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21st International Parallel and Distributed Processing Symposium, IPDPS 2007

作者： Penmatsa, Satish Chronopoulos, Anthony T. Karonis, Nicholas T. Toonen, Brian R. University of Texas at San Antonio Dept. of Computer Science San Antonio TX 78249 Northern Illinois University Dept. of Computer Science DeKalb IL 60115 Argonne National Laboratory Math and CS Division Argonne IL 60439 IEEE

ISBN: (纸本)1424409101

Grid computing can be used for high performance computations. However, a serious difficulty in concurrent programming of such heterogeneous systems is how to deal with scheduling and load balancing of such systems which may consist of heterogeneous computers on different sites. Distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been proposed and analyzed in the past. Here, we implement the previous schemes in MPICH-G2 and MPIg on the TeraGrid. We present performance results for three loop scheduling schemes on single and multi-site TeraGrid clusters. © 2007 IEEE.

关键词： Grid computing

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A scientific data management system for irregular applications 15

A scientific data management system for irregular applicatio...

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15th International Parallel and Distributed Processing Symposium, IPDPS 2001

作者： No, Jaechun Thakur, R. Kaushik, D. Freitag, L. Choudhary, A. Math. and Computer Science Division Argonne National Laboratory ArgonneIL60439 United States Dept. of Elec. and Computer Eng. Northwestern University EvanstonIL60208 United States

ISBN: (纸本)0769509908

Many scientific applications are I/O intensive and generate large data sets, spanning hundreds or thousands of "files." Management, storage, efficient access, and analysis of this data present an extremely challenging task. We have developed a software system, called Scientific Data Manager (SDM), that uses a combination of parallel file I/O and database support for high-performance scientific data management. SDM provides a high-level API to the user and, internally, uses a parallel file system to store real data and a database to store application-related metadata. In this paper, we describe how we designed and implemented SDM to support irregular applications. SDM can efficiently handle the reading and writing of data in an irregular mesh, as well as the distribution of index values. We describe the SDM user interface and how we have implemented it to achieve high performance. SDM makes extensive use of MPI-IO's noncontiguous collective I/O functions. SDM also uses the concept of a history file to optimize the cost of the index distribution using the metadata stored in database. We present performance results with two irregular applications, a CFD code called FUN3D and a Rayleigh-Taylor instability code, on the SGI Origin2000 at Argonne National Laboratory. © 2001 IEEE.

关键词： Metadata

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