Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Stochastic Differential Equations

A type system is a syntactic method for automatically checking the absence of certain erroneous behaviors by classifying program phrases according to the kinds of values they compute. The study of type systems -- and of programming languages from a type-theoretic perspective -- has important applications in software engineering, language design, high-performance compilers, and security.This text provides a comprehensive introduction both to type systems in computer science and to the basic theory of programming languages. The approach is pragmatic and operational; each new concept is motivated by programming examples and the more theoretical sections are driven by the needs of implementations. Each chapter is accompanied by numerous exercises and solutions, as well as a running implementation, available via the Web. Dependencies between chapters are explicitly identified, allowing readers to choose a variety of paths through the material.The core topics include the untyped lambda-calculus, simple type systems, type reconstruction, universal and existential polymorphism, subtyping, bounded quantification, recursive types, kinds, and type operators. Extended case studies develop a variety of approaches to modeling the features of object-oriented languages.

https://www.seas.upenn.edu/~bcpierce/tapl/contents.pdf

Types and Programming Languages

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Complete formal verification is the only known way to guarantee that a system is free of programming errorsWe present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation It also proves much more: we can predict precisely how the kernel will behave in every possible situationseL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler Its performance is comparable to other high-performance L4 kernels

/pdf/sel4-formal-verification-of-an-os-kernel-20d8n4rs7p.pdf

seL4: formal verification of an OS kernel

Elementary Techniques.- 1. The Basics.- 2. Functional Programming in HOL.- 3. More Functional Programming.- 4. Presenting Theories.- Logic and Sets.- 5. The Rules of the Game.- 6. Sets, Functions, and Relations.- 7. Inductively Defined Sets.- Advanced Material.- 8. More about Types.- 9. Advanced Simplification, Recursion, and Induction.- 10. Case Study: Verifying a Security Protocol.

Isabelle/HOL: A Proof Assistant for Higher-Order Logic

Foundations.- Getting started with Isabelle.- Advanced methods.- Basic use of Isabelle.- Proof management: The subgoal module.- Tactics.- Tacticals.- Theorems and forward proof.- Theories, terms and types.- Defining logics.- Syntax transformations.- Substitution tactics.- Simplification.- The classical reasoner.- Basic concepts.- First-order logic.- Zermelo-Fraenkel set theory.- Higher-order logic.- First-order sequent calculus.- Constructive Type Theory.- Syntax of Isabelle Theories.

Isabelle: A Generic Theorem Prover

Informal arguments that cryptographic protocols are secure can be made rigorous using inductive definitions. The approach is based on ordinary predicate calculus and copes with infinite-state systems. Proofs are generated using Isabelle/HOL. The human effort required to analyze a protocol can be as little as a week or two, yielding a proof script that takes a few minutes to run.

Protocols are inductively defined as sets of traces. A trace is a list of communication events, perhaps comprising many interleaved protocol runs. Protocol descriptions incorporate attacks and accidental losses. The model spy knows some private keys and can forge messages using components decrypted from previous traffic. Three protocols are analyzed below: Otway-Rees (which uses shared-key encryption), Needham-Schroeder (which uses public-key encryption), and a recursive protocol (Bull and Otway, 1997) (which is of variable length).

One can prove that event ev always precedes event ev' or that property P holds provided X remains secret. Properties can be proved from the viewpoint of the various principals: say, if A receives a final message from B then the session key it conveys is good.

/pdf/the-inductive-approach-to-verifying-cryptographic-protocols-45jaqp4cpt.pdf

The inductive approach to verifying cryptographic protocols

From the Publisher:
In teaching the methods of functional programming--in particular, how to program in Standard ML, a functional language recently developed at Edinburgh University, the author shows how to use such concepts as lists, trees, higher-order functions and infinite data structures.

ML for the working programmer

Isabelle is a generic theorem prover, designed for interactive reasoning in a variety of formal theories. At present it provides useful proof procedures for Constructive Type Theory, various first-order logics, Zermelo-Fraenkel set theory, and higher-order logic. This survey of Isabelle serves as an introduction to the literature. It explains why generic theorem proving is beneficial. It gives a thorough history of Isabelle, beginning with its origins in the LCF system. It presents an account of how logics are represented, illustrated using classical logic. The approach is compared with the Edinburgh Logical Framework. Several of the Isabelle object-logics are presented.

/pdf/isabelle-the-next-700-theorem-provers-51kaqgk3xb.pdf

Lawrence C. Paulson

Papers

Isabelle/HOL: A Proof Assistant for Higher-Order Logic

Isabelle: A Generic Theorem Prover

The inductive approach to verifying cryptographic protocols

ML for the working programmer

Isabelle: The Next 700 Theorem Provers