On Provably Correct Decision-Making for Automated Driving

The introduction of driving automation in road vehicles can potentially reduce road traffic crashes and significantly improve road safety. Automation in road vehicles also brings several other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Many different hardware and software components (e.g. sensing, decision-making, actuation, and control) interact to solve the autonomous driving task. Correctness of such automated driving systems is crucial as incorrect behaviour may have catastrophic consequences. Autonomous vehicles operate in complex and dynamic environments, which requires decision-making and planning at different levels. The aim of such decision-making components in these systems is to make safe decisions at all times. The challenge of safety verification of these systems is crucial for the commercial deployment of full autonomy in vehicles. Testing for safety is expensive, impractical, and can never guarantee the absence of errors. In contrast, formal methods, which are techniques that use rigorous mathematical models to build hardware and software systems can provide a mathematical proof of the correctness of the system. The focus of this thesis is to address some of the challenges in the safety verification of decision-making in automated driving systems. A central question here is how to establish formal verification as an efficient tool for automated driving software development.A key finding is the need for an integrated formal approach to prove correctness and to provide a complete safety argument. This thesis provides insights into how three different formal verification approaches, namely supervisory control theory, model checking, and deductive verification differ in their application to automated driving and identifies the challenges associated with each method. It identifies the need for the introduction of more rigour in the requirement refinement process and presents one possible solution by using a formal model-based safety analysis approach. To address challenges in the manual modelling process, a possible solution by automatically learning formal models directly from code is proposed

Deductive Verification of Unmodified Linux Kernel Library Functions

This paper presents results from the development and evaluation of a deductive verification benchmark consisting of 26 unmodified Linux kernel library functions implementing conventional memory and string operations. The formal contract of the functions was extracted from their source code and was represented in the form of preconditions and postconditions. The correctness of 23 functions was completely proved using AstraVer toolset, although success for 11 functions was achieved using 2 new specification language constructs. Another 2 functions were proved after a minor modification of their source code, while the final one cannot be completely proved using the existing memory model. The benchmark can be used for the testing and evaluation of deductive verification tools and as a starting point for verifying other parts of the Linux kernel.Comment: 18 pages, 2 tables, 6 listings. Accepted to ISoLA 2018 conference. Evaluating Tools for Software Verification trac

Spartan Daily, April 4, 1949

Volume 37, Issue 107https://scholarworks.sjsu.edu/spartandaily/11220/thumbnail.jp

Clovis News, 05-15-1913

https://digitalrepository.unm.edu/clovis_news/1032/thumbnail.jp

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces

Spartan Daily, April 4, 1949

Volume 37, Issue 107https://scholarworks.sjsu.edu/spartandaily/11220/thumbnail.jp

1971

Shop Talk by Frank Santos (page 1) You\u27ve Come a Long Way, Lawn-Mower Pusher by Alan B. Albin (2) In The Eyes of the Laymen by Eugene P. Elcik (2) The Importance of Water Management by Fred V. Grau (A-1) Automatic Irrigation Systems Integrated with Pumping Systems by Michael O. Mattwell (A-5) Installation of a Complete Water Source and Automatic System by Richard C. Blake (A-19) How the Soil Conservation Service Can Help in Golf Course Management by Christopher G. Mousitakis (A-22) Our Shrinking Environment by Haim B. Gunner (A-24) Pesticides\u27 Dilemma - Emotion vs. Science by Allen H. Morgan (A-28) Effects of Turf Grasses and Trees in Neutralizing Waste Water by William E. Sopper (A-34) Unsolved and New Problems Developing in Golf Course Management by Alexander M. Radko (A-44) Coming of the Conglomerate Director of Golf Courses by Edmund B. Ault (A-48) Aquatic Weed Control by John E. Gallagher (A-52) What Project Apollo Has Done for Golf and Golf Course Architecture by Mal Purdy (A-54) Maintenance of Grass Tennis Courts by Wayne Zoppo (A-59) Diseases of Ornametnals Growing in Turf Areas by R.E. Partyka (A-62) Control of Turf Insects by John C. Schread (A-65) Lime for Turf by Henry W. Indyk (A-68) How to Stop Guessing When You Buy Seed by Dale Kern (A-71) Broad Aspects of Turf Grass Culture Other Than Golf Courses by Geoffrey S. Cornish (A-79) Establishing and Maintaining Turf int he national Capitol Parks by Alton E. Rabbitt (A-81) Preventive Maintenance on Small One Cylinder Air Cooled Engine by F. W. Hazle (A-85) Top Fairway Mower Performance by James R. Maloney (A-95) Grinding Reel Type Mowers by Ray Christopherson (A-99