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

Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

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Principles of Model Checking

SPIN is an efficient verification system for models of distributed software systems. It has been used to detect design errors in applications ranging from high-level descriptions of distributed algorithms to detailed code for controlling telephone exchanges. The paper gives an overview of the design and structure of the verifier, reviews its theoretical foundation, and gives an overview of significant practical applications.

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The model checker SPIN

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

MD5 is one of the most widely used cryptographic hash functions nowadays. It was designed in 1992 as an improvement of MD4, and its security was widely studied since then by several authors. The best known result so far was a semi free-start collision, in which the initial value of the hash function is replaced by a non-standard value, which is the result of the attack. In this paper we present a new powerful attack on MD5 which allows us to find collisions efficiently. We used this attack to find collisions of MD5 in about 15 minutes up to an hour computation time. The attack is a differential attack, which unlike most differential attacks, does not use the exclusive-or as a measure of difference, but instead uses modular integer subtraction as the measure. We call this kind of differential a modular differential. An application of this attack to MD4 can find a collision in less than a fraction of a second. This attack is also applicable to other hash functions, such as RIPEMD and HAVAL.

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How to break MD5 and other hash functions

RC6 has been submitted as a candidate for the Advanced Encryption Standard (AES). Two important features of RC6 that were absent from its predecessor RC5 are a quadratic function and a fixed rotation. By examining simplified variants that omit these features we clarify their essential contribution to the overall security of RC6.

Fast Software Encryption

Concurrent systems and semantics.- Using partial orders to tackle state explosion.- Persistent sets.- Sleep sets.- Verification of safety properties.- Model checking.- Experiments.- Conclusions.

Partial-Order Methods for the Verification of Concurrent Systems: An Approach to the State-Explosion Problem

An object-oriented framework for temporal data models.- An architecture for supporting interoperability among temporal databases.- Extended update functionality in temporal databases.- On transaction management in temporal databases.- Implementation options for time-series data.- Expressive power of temporal relational query languages and temporal completeness.- Transitioning temporal support in TSQL2 to SQL3.- Valid time and transaction time proposals: Language design aspects.- Point-based temporal extensions of SQL and their efficient implementation.- Applicability of temporal data models to query multilevel security databases: A case study.- An architecture and construction of a business event manager.- Discovering unexpected patterns in temporal data using temporal logic.- Querying the uncertain position of moving objects.- Temporal database bibliography update.- The consensus glossary of temporal database concepts - February 1998 version.- A glossary of time granularity concepts.

Temporal Databases: Research and Practice

A quantum jump in computer science.- Artificial life and real world computing.- Recurrent neural networks.- Scalable computing.- Efficient use of parallel & distributed systems: From theory to practice.- Experimental validation of models of parallel computation.- Quo vadetis, parallel machine models?.- Templates for linear algebra problems.- The ART behind IDEAS.- Algorithmic number theory and its relationship to computational complexity.- Edge-coloring algorithms.- Towards a computational theory of genome rearrangements.- Algebraic topology and distributed computing a primer.- Differential BDDs.- Algorithmic techniques for geometric optimization.- All the needles in a haystack: Can exhaustive search overcome combinatorial chaos?.- Fundamental limitations on search algorithms: Evolutionary computing in perspective.- Mathematical system models as a basis of software engineering.- Formulations and formalisms in software architecture.- The Oz Programming Model.- Standard Generalized Markup Language: Mathematical and philosophical issues.- Avoiding the undefined by underspecification.- Towards a theory of recursive structures.- Chu spaces and their interpretation as concurrent objects.- Abstracting unification: A key step in the design of logic program analyses.- Programming Satan's computer.- Petri Net models of distributed algorithms.- Symmetry and induction in model checking.- Alternating automata and program verification.- Reasoning about actions and change with ramification.- Trends in active vision.- Computational machine learning in theory and praxis.- Fuzzy sets as a tool for modeling.- Information retrieval and informative reasoning.- Database transaction models.- Multimedia authoring tools: State of the art and research challenges.- Computational models for distributed multimedia applications.- Hypermedia systems as internet tools.

Computer Science Today: Recent Trends and Developments

The World Wide Web promises to transform human society by making virtually all types of information instantly available everywhere. Two prerequisites for this promise to be realized are a universal markup language and a universal query language. The power and flexibility of XML make it the leading candidate for a universal markup language. XML provides a way to label information from diverse data sources including structured and semi-structured documents, relational databases, and object repositories. Several XML-based query languages have been proposed, each oriented toward a specific category of information. Quilt is a new proposal that attempts to unify concepts from several of these query languages, resulting in a new language that exploits the full versatility of XML. The name Quilt suggests both the way in which features from several languages were assembled to make a new query language, and the way in which Quilt queries can combine information from diverse data sources into a query result with a new structure of its own.

Gerhard Goos

Papers

Fast Software Encryption

Partial-Order Methods for the Verification of Concurrent Systems: An Approach to the State-Explosion Problem

Temporal Databases: Research and Practice

Computer Science Today: Recent Trends and Developments

The World Wide Web and Databases