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

Security protocols aim to protect the honest users of a network from the dishonest ones. Asymmetric (public key) cryptography is valuable, though it is normally used in conjunction with symmetric cryptography, where two users share a secret key, Asymmetric cryptography is typically used to securely exchange symmetric keys, which carry the bulk of the traffic. This mode of operation is faster than using expensive public-key encryption exclusively. It is also more secure, since the symmetric keys can be changed frequently. However, the protocol used to set up of this communication must be designed with care. For example, each message typically includes a nonce: a freshly-generated number that the other party must include in his response; the first party then knows that the response was not an old message replayed by an intruder. Many flaws have been discovered in security protocols [5]. Security protocol verification technologies have progressed in recent years. A variety of tools are available for analyzing protocols. Model checking is excellent for debugging a protocol, finding attacks in seconds [6,7]. Theorem proving is valuable too: it can analyze protocols in more detail and handles the protocols that are too big for model checking. Subgoals presented to the user suggest possible failure modes and give insights into how the protocol operates. Past work on protocol verification has focused on protocols arising from the academic community. Only seldom have deployed protocols been investigated, such as Kerberos [3], SSL [8] and SSL’s successor, TLS [12]. Past work has largely focused on key exchange protocols. Such protocols allow two participants (invariably called Alice and Bob) to agree on a session key : a short-term symmetric key. In this paper, I would like to describe a project, joint with Bella, Massacci and Tramontano, to verify a very large commercial protocol: SET, or Secure Electronic Transactions [15].

/pdf/set-cardholder-registration-the-secrecy-proofs-extended-3uzspvs2dt.pdf

SET Cardholder Registration: the Secrecy Proofs (Extended Abstract)

The reflection theorem has been proved using Isabelle/ZF. This theorem cannot be expressed in ZF, and its proof requires reasoning at the meta-level. There is a particularly elegant proof that reduces the meta-level reasoning to a single induction over formulas. Each case of the induction has been proved with Isabelle/ZF, whose built-in tools can prove specific instances of the reflection theorem upon demand.

The reflection theorem: A study in meta-theoretic reasoning

Note: this document is part of the earlier Isabelle documentation, which is largely superseded by the Isabelle/HOL Tutorial [9]. It describes the oldstyle theory syntax and shows how to conduct proofs using the ML top level. This style of interaction is largely obsolete: most Isabelle proofs are now written using the Isar language and the Proof General interface. However, this paper contains valuable information that is not available elsewhere. Its examples are based on first-order logic rather than higher-order logic.

/pdf/old-introduction-to-isabelle-u6zpbm67ch.pdf

Old Introduction to Isabelle

Proceedings of the Automated Reasoning Workshop 2008

ML is a functional language like Scheme Very different from Scheme in syntax and use Can be pure – no assignment ML stands for Meta Language Originally developed for theorem proving (1978, revised 1997) Very popular in Europe ML characteristics Static type checking – types are very important Strong type checking – program is "safe" if interpreter accepts "variables" must be declared Syntax more like C than Scheme Fall 2012 CIS 425-ML 2

Lawrence C. Paulson

Papers

SET Cardholder Registration: the Secrecy Proofs (Extended Abstract)

The reflection theorem: A study in meta-theoretic reasoning

Old Introduction to Isabelle

Proceedings of the Automated Reasoning Workshop 2008

Functional Programming in ML