The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

/pdf/machine-learning-in-automated-text-categorization-e4pkjhyj3r.pdf

Machine learning in automated text categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.

Machine Learning in Automated Text Categorization

1. Introduction to text mining 2. Core text mining operations 3. Text mining preprocessing techniques 4. Categorization 5. Clustering 6. Information extraction 7. Probabilistic models for Information extraction 8. Preprocessing applications using probabilistic and hybrid approaches 9. Presentation-layer considerations for browsing and query refinement 10. Visualization approaches 11. Link analysis 12. Text mining applications Appendix Bibliography.

/pdf/the-text-mining-handbook-advanced-approaches-in-analyzing-ge55wa29fm.pdf

The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

Smart contracts are computer programs that can be correctly executed by a network of mutually distrusting nodes, without the need of an external trusted authority. Since smart contracts handle and transfer assets of considerable value, besides their correct execution it is also crucial that their implementation is secure against attacks which aim at stealing or tampering the assets. We study this problem in Ethereum, the most well-known and used framework for smart contracts so far. We analyse the security vulnerabilities of Ethereum smart contracts, providing a taxonomy of common programming pitfalls which may lead to vulnerabilities. We show a series of attacks which exploit these vulnerabilities, allowing an adversary to steal money or cause other damage.

A Survey of Attacks on Ethereum Smart Contracts SoK

Conditional rewriting logic as a unified model of concurrency

In this position paper, we consider some foundational topics regarding smart contracts (such as terminology, automation, enforceability, and semantics) and define a smart contract as an automatable and enforceable agreement. We explore a simple semantic framework for smart contracts, covering both operational and non-operational aspects, and describe templates and agreements for legally-enforceable smart contracts, based on legal documents. Building upon the Ricardian Contract, we identify operational parameters in the legal documents and use these to connect legal agreements to standardised code. We also explore the design landscape, including increasing sophistication of parameters, increasing use of common standardised code, and long-term research.

pdf/smart-contract-templates-foundations-design-landscape-and-hybdotgkvg.pdf

Smart Contract Templates: foundations, design landscape and research directions.

GRIP is a high-performance parallel machine designed to execute functional programs using supercombinator graph reduction. It uses a high-bandwidth bus to provide access to a large, distributed shared memory, using intelligent memory units and packet-switching protocols to increase the number of processors which the bus can support. GRIP is also being programmed to support parallel Prolog and DACTL.

/pdf/grip-a-high-performance-architecture-for-parallel-graph-207xymgdte.pdf

GRIP—A high-performance architecture for parallel graph reduction

Significant improvements in performance arise if we can arrange for parallel execution of programs. The absence of side effects in functional languages allows concurrent evaluation of the program, but in lazy implementations this risks wasting work by evaluating expressions which are subsequently discarded. We discuss the use of strictness analysis to determine at compile time which parts of program evaluation can safely be carried out concurrently. We give a practical explanation of this technique, concentrating particularly on the problem of finding fixed points.

https://link.springer.com/content/pdf/10.1007%2F3-540-15975-4_28.pdf

Strictness analysis—a practical approach

The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: the crossover operator. The motivation for this study is to provide an adaptive crossover operator that gives best overall performance on a large set of problems. A new adaptive crossover operator “selective crossover” is proposed and is compared with two-point and uniform crossover on a problem generator where epistasis can be varied and on trap functions where deception can be varied. We provide empirical results which show that selective crossover is more efficient than two-point and uniform crossover across a representative set of search problems containing epistasis.

/pdf/selective-crossover-in-genetic-algorithms-an-empirical-study-21yqpn5vbe.pdf

Selective Crossover in Genetic Algorithms: An Empirical Study

The Smart Contract Templates project supports legally enforceable smart contracts, using operational parameters to connect legal agreements to standardised code. The standardised code is derived from legal documentation and performs some or all of the provisions of that contract. For financial contracts such as derivatives agreements, the legal documentation may be extensive, and the standardised code to perform the contract may be substantial. An important issue is how to validate whether the smart contract code will correctly perform the provisions of the legal contract. This requires an understanding of the semantics of legal text and is an important step towards supporting industry adoption of legally enforceable high-value smart contracts.

Smart Contract Templates: Legal semantics and code validation

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