SHF: Small: Program Analysis for Dependable Clustering

SHF：小型：可靠集群的程序分析

• 批准号: 2007730
• 负责人: Iulian Neamtiu
• 金额: $ 40万
• 依托单位: New Jersey Institute of Technology
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007730&HistoricalAwards=false
• 关键词: SHF; Small; Program; Analysis; Dependable

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，又名聚类，是一种机器学习技术，用于将相关或共享相似特征的实体分组在一起。聚类在医学、生物学、社会科学、机器人学和地球科学中都有应用，包括医学图像处理或医学诊断、预测疾病相关基因或急性疾病资源分配等高风险领域。然而，目前，包含集群的应用程序的用户或开发人员无法保证应用程序的可靠性；这让人对使用聚类获得的结果或诊断产生疑问，并阻止研究人员或从业者使用聚类应用程序。该项目将使集群实现更加可靠、更容易开发、更容易修复。软件工程师将受益于更容易编程/修复/检查的实现。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 专业人员介绍可靠机器学习的挑战和方法；这将使学生和专业人士更好地应对新兴的软件研究和开发挑战。将继续对少数群体和代表性不足的群体进行持续的外展和支持工作。随着人们对机器学习的兴趣日益浓厚，以及软件驱动产品中机器学习实现（硬件或软件）的采用增多，集群的使用将会扩大。因此，集群实现必须可靠。 该领域的研究人员缺乏基本聚类正确性属性的定义以及用于验证聚类实现属性的有效/高效分析。该项目将通过从首要原则（例如程序决定论）开始定义聚类正确性来解决上述问题；并构建通过程序分析验证正确性的方法。方法将包括差异执行、白盒和黑盒技术、动态切片和符号执行。这项工作的范围包括“纯软件”集群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有效地了解集群实现行为和可靠性。研究人员将能够使用本工作中介绍的一般原理来构建其他领域的程序分析，例如科学计算、数值计算、高性能计算，并使用锁步执行、切片或符号执行的工具/方法该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

• 发表时间: 2023
• 期刊: IEEE AITest Conference 2023
• 作者: Ahmed, Muyeed;Neamtiu, Iulian
• 通讯作者: Neamtiu, Iulian