Towards the Automatic Classification of Documents in User-generated Classifications

There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing

ooRexx 5 Yielding Swiss Army Knife Usability

The new version 5.0 of the message based object-oriented programming language ooRexx ("open object-oriented REXX") is easy to learn, yet powerful. This article introduces some of the new language features with nutshell examples that at the same time demonstrate its power when deployed in different operating system environments. The modern native API of ooRexx makes it in addition very easy to extend the language with new functionality and deploy it as a macro language for any C++-based application

Analyzing the Adoption Rate of Local Variable Type Inference in Open-source Java 10 Projects

Type Inference is used in programming languages to improve writability. In this paper, we will be looking more specifically at Local Variable Type Inference (LVTI). For those unfamiliar with LVTI, we will also give an in-depth explanation of what it is and how it works. There is a lot of debate surrounding Type Inference in modern day programming languages. More specifically, whether the costs associated with LVTI outweigh the benefits. It has found its way into many higher-level languages including C#, C++, JavaScript, Swift, Kotlin, Rust, Go, etc. In this paper, we will look at the usefulness of LVTI and its popularity since the release of Java 10. Our study will show that LVTI in Java has not received widespread adoption. We will also explain a possible reason for this is based on the information we have gather from our empirical study which involved statically analyzing 6 popular open source Java 10 projects. We will also discuss different scenarios in which Type Inference can obscure different programming errors

Programming Language Feature Agglomeration

Feature-creep is a well-known phenomenon in software systems. In this paper, we argue that feature-creep also occurs in the domain of programming languages. Recent languages are more expressive than earlier languages. However recent languages generally extend rather than replace the syntax (sometimes) and semantics (almost always) of earlier languages. We demonstrate this trend of agglomeration in a sequence of languages comprising Pascal, C, Java, and Scala. These are all block-structured Algol-derived languages, with earlier languages providing explicit inspiration for later ones. We present empirical evidence from several language-specific sources, including grammar definitions and canonical manuals. The evidence suggests that there is a trend of increasing complexity in modern languages that have evolved from earlier languages