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

Isabelle [28, 30] is an interactive theorem prover that supports a variety of logics. It represents rules as propositions (not as functions) and builds proofs by combining rules. These operations constitute a meta-logic (or ‘logical framework’) in which the object-logics are formalized. Isabelle is now based on higher-order logic-a precise and well-understood foundation. Examples illustrate the use of this meta-logic to formalize logics and proofs. Axioms for first-order logic are shown to be sound and complete. Backwards proof is formalized by meta-reasoning about object-level entailment. Higher-order logic has several practical advantages over other meta-logics. Many proof techniques are known, such as Huet's higher-order unification procedure.

/pdf/the-foundation-of-a-generic-theorem-prover-1iccqnv2jc.pdf

The foundation of a generic theorem prover

Informal justifications of security protocols involve arguing backwards that various events are impossible. Inductive definitions can make such arguments rigorous. The resulting proofs are complicated, but can be generated reasonably quickly using the proof tool Isabelle/HOL. There is no restriction to finite state systems and the approach is not based on belief logics. Protocols are inductively defined as sets of traces, which may involve many interleaved protocol runs. Protocol descriptions model accidental key losses as well as attacks. The model spy can send spoof messages made up of components decrypted from previous traffic. Several key distribution protocols have been studied, including Needham-Schroeder, Yahalom and Otway-Rees. The method applies to both symmetric key and public key protocols. A new attack has been discovered in a variant of Otway-Rees (already broken by W. Mao and C. Boyd (1993)). Assertions concerning secrecy and authenticity have been proved.

/pdf/proving-properties-of-security-protocols-by-induction-26cllquy4n.pdf

Proving properties of security protocols by induction

From the Publisher:
This study of techniques for formal theorem-proving focuses on the applications of Cambridge LCF (Logic for Computable Functions), a computer program for reasoning about computation.

Logic and computation : interactive proof with Cambridge LCF

Internet browsers use security protocols to protect sensitive messages. An inductive analysis of TLS (a descendant of SSL 3.0) has been performed using the theorem prover Isabelle. Proofs are based on higher-order logic and make no assumptions concerning beliefs of finiteness. All the obvious security goals can be proved; session resumption appears to be secure even if old session keys are compromised. The proofs suggest minor changes to simplify the analysis.TLS, even at an abstract level, is much more complicated than most protocols verified by researchers. Session keys are negotiated rather than distributed, and the protocol has many optional parts. Netherless, the resources needed to verify TLS are modest: six man-weeks of effort and three minutes of processor time.

/pdf/inductive-analysis-of-the-internet-protocol-tls-2idpovo0pu.pdf

Inductive analysis of the Internet protocol TLS

Isabelle is an interactive theorem prover that supports a variety of logics. It represents rules as propositions (not as functions) and builds proofs by combining rules. These operations constitute a meta-logic (or `logical framework') in which the object-logics are formalized. Isabelle is now based on higher-order logic -- a precise and well-understood foundation. Examples illustrate use of this meta-logic to formalize logics and proofs. Axioms for first-order logic are shown sound and complete. Backwards proof is formalized by meta-reasoning about object-level entailment. Higher-order logic has several practical advantages over other meta-logics. Many proof techniques are known, such as Huet's higher-order unification procedure.

Lawrence C. Paulson

Papers

The foundation of a generic theorem prover

Proving properties of security protocols by induction

Logic and computation : interactive proof with Cambridge LCF

Inductive analysis of the Internet protocol TLS

The Foundation of a Generic Theorem Prover