SHF: Small: Program Analysis for Dependable Clustering
SHF:小型:可靠集群的程序分析
基本信息
- 批准号:2007730
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-10-01 至 2024-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Cluster analysis, a.k.a. Clustering, is a machine-learning technique used to group together entities that are related or share similar characteristics. Clustering has applications in medicine, biology, social sciences, robotics, and earth sciences, including high-stakes domains such as medical image processing or medical diagnosis, predicting disease-related genes, or resource allocation in acute disease. However, currently, users or developers of applications that incorporate clustering have no assurances that the applications are reliable; this calls into question results or diagnoses obtained with the use of clustering, and discourages researchers or practitioners from using clustering applications. This project will make clustering implementations more reliable, easier to develop, and easier to fix. Software engineers will benefit from implementations that are easier to program/fix/check. In turn, the resulting software will be more reliable, benefiting end-users. The project will introduce students and IT professionals to challenges in, as well as approaches for, dependable machine learning; this will make students and professionals better equipped for tackling emerging software research and development challenges. Ongoing outreach and support efforts, to minorities and underrepresented groups, will continue. Clustering use will expand with the increasing interest in machine learning in general, and increased adoption of machine-learning implementations (hardware or software) in software-driven products. Hence it is imperative that clustering implementations be dependable. Researchers in this space lack definitions of basic clustering-correctness properties and effective/efficient analyses for verifying clustering implementations' properties. This project will address the aforementioned issues by defining clustering correctness starting from first principles, e.g., program determinism; and constructing approaches for verifying correctness via program analysis. Approaches will include differential execution, white-box as well as black-box techniques, dynamic slicing, and symbolic execution. The scope of this work includes "purely software" clustering implementations as well as implementations that use hardware acceleration. Using these tools, developers and researchers will be able to gain effective insights into clustering-implementation behavior and dependability. Researchers will be able to use the general principles introduce developed in this work to construct program analyses in other domains, e.g., scientific computing, numerical computing, high-performance computing, and use the tools/approaches for lockstep-executing, slicing, or symbolically executing other categories of data-intensive applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
聚类分析(又称聚类)是一种机器学习技术,用于将相关或具有相似特征的实体组合在一起。聚类在医学,生物学,社会科学,机器人技术和地球科学中都有应用,包括医学图像处理或医学诊断等高风险领域,预测与疾病相关的基因或急性疾病中的资源分配。但是,目前,合并聚类的应用程序的用户或开发人员尚无保证应用程序可靠;这会引起疑问结果或通过使用聚类获得的诊断,并阻止研究人员或从业者使用聚类应用程序。该项目将使聚类实现更加可靠,易于开发,并且更易于修复。软件工程师将受益于更易于编程/修复/检查的实施。反过来,最终的软件将更加可靠,使最终用户受益。该项目将向学生和IT专业人员介绍可靠的机器学习的挑战以及方法;这将使学生和专业人员更好地解决新兴软件研发挑战。持续的宣传和支持努力,对少数群体和代表性不足的团体将继续进行。聚类的使用将随着对机器学习的日益兴趣而扩展,并增加了软件驱动产品中机器学习实现(硬件或软件)的采用。因此,必须可靠地进行聚类实现。 该空间中的研究人员缺乏对基本聚类校正属性的定义和用于验证聚类实现属性的有效/有效分析。该项目将通过定义从第一原则开始的群集正确性来解决上述问题,例如计划确定性;以及通过程序分析验证正确性的构建方法。方法将包括差分执行,白色框以及黑框技术,动态切片和符号执行。这项工作的范围包括“纯粹的软件”聚类实现以及使用硬件加速的实现。使用这些工具,开发人员和研究人员将能够获得对聚类实践行为和可靠性的有效见解。研究人员将能够使用这项工作中开发的一般原则在其他领域中构建程序分析,例如,科学计算,数值计算,数值计算,高性能计算,并使用工具/方法来锁定锁定,切片,切片,或象征性地通过数据进行宣传。基金会的智力优点和更广泛的影响评论标准。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
DEANOMALYZER: Improving Determinism and Consistency in Anomaly Detection Implementations
DEANOMALYZER:提高异常检测实施中的确定性和一致性
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Ahmed, Muyeed;Neamtiu, Iulian
- 通讯作者:Neamtiu, Iulian
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Iulian Neamtiu其他文献
Algebraic-datatype taint tracking, with applications to understanding Android identifier leaks
代数数据类型污点跟踪,利用应用程序了解 Android 标识符泄漏
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Sydur Rahaman;Iulian Neamtiu;Xin Yin - 通讯作者:
Xin Yin
Scraping Sticky Leftovers: App User Information Left on Servers After Account Deletion
清除粘性残留物:帐户删除后留在服务器上的应用程序用户信息
- DOI:
10.1109/sp46214.2022.9833720 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Preethi Santhanam;Hoang Dang;Zhiyong Shan;Iulian Neamtiu - 通讯作者:
Iulian Neamtiu
Statistically Rigorous Testing of Clustering Implementations
对集群实现进行严格的统计测试
- DOI:
10.1109/aitest.2019.000-1 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Xin Yin;Vincenzo Musco;Iulian Neamtiu;Usman Roshan - 通讯作者:
Usman Roshan
Improving Smartphone Security and Reliability
提高智能手机的安全性和可靠性
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Iulian Neamtiu;Xuetao Wei;M. Faloutsos;L. Gomez;Tanzirul Azim;Yongjian Hu;Zhiyong Shan - 通讯作者:
Zhiyong Shan
Implementation-induced Inconsistency and Nondeterminism in Deterministic Clustering Algorithms
确定性聚类算法中由实现引起的不一致和不确定性
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Xin Yin;Iulian Neamtiu;Saketan Patil;Sean T. Andrews - 通讯作者:
Sean T. Andrews
Iulian Neamtiu的其他文献
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{{ truncateString('Iulian Neamtiu', 18)}}的其他基金
Collaborative Research: SHF: Medium: Precise Static Analysis of Event-based Systems
合作研究:SHF:中:基于事件的系统的精确静态分析
- 批准号:
2106710 - 财政年份:2021
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
TWC: Small: Collaborative: Improving Android Security with Dynamic Slicing
TWC:小:协作:通过动态切片提高 Android 安全性
- 批准号:
1617584 - 财政年份:2016
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
CAREER: Differential Types and Declarative Hypothesis Testing for Software Evolution
职业:软件演化的差异类型和声明性假设检验
- 批准号:
1629186 - 财政年份:2015
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
TC: Medium: Collaborative Research: Program Analysis for Smartphone Application Security
TC:媒介:协作研究:智能手机应用程序安全的程序分析
- 批准号:
1630037 - 财政年份:2015
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
PLDI'12 and Trends in Concurrency'12 Travel Support
PLDI12 和并发趋势12 差旅支持
- 批准号:
1160282 - 财政年份:2012
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
CAREER: Differential Types and Declarative Hypothesis Testing for Software Evolution
职业:软件演化的差异类型和声明性假设检验
- 批准号:
1149632 - 财政年份:2012
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
TC: Medium: Collaborative Research: Program Analysis for Smartphone Application Security
TC:媒介:协作研究:智能手机应用程序安全的程序分析
- 批准号:
1064646 - 财政年份:2011
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
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