Data Mining Practical Machine Learning Tools and Techniques

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

/pdf/multiagent-systems-a-modern-approach-to-distributed-5fon0donq1.pdf

Multiagent systems: a modern approach to distributed artificial intelligence

The letter and/or sound combinations that make up a human language are limited by the human's ability to pronounce tnese sounds° Therefore, the standard library search, which as a rule looks for all possible combinations of letters to find a word, is wasteful. Certain letters simply cannot be followed by certain other letters and a search for them is senseless. Following this same line of reasoning, letters very frequently occur in the combinations that are germane to the particular language. The growing amount of alphanumeric information presently being stored on magnetic tape presents increasingly difficult problems in both the number of tape reels used and the time necessary to search this mass of information in order to extract pertinent literature. At the present time most of this literature on tape utilizes the standard IBM 6-bit code to express alphanumeric symbols. ~t is entirely feasible to record standard English literature on tape -be it professional abstracts or novels -using only approximately two-thirds of the binary bits utilized to represent the same piece of written material in the conventional code. This can be accomplished by setting up, in a 9-bit code, the 400-odd letter combinations occurring most frequently. A 9-bit representation allows the programmer to set up as many as 512 symbols, thus leaving sufficient leeway to assign symbols to the most frequentlyused words, mathematical symbols, professional expressions, that are expected to be encountered in the literature to be recorded. In addition, these relatively short 9-bit symbols can be assigned to all key words that it may be necessary to look for later, thereby accelerating any future library search.

Information storage and retrieval

This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.

https://dl.acm.org/doi/pdf/10.1145/3345317

Academic Plagiarism Detection: A Systematic Literature Review

This paper presents an overview of different graph-based representations proposed to solve text classification tasks. The core of this manuscript is to highlight the importance of enriched/non-enriched co-occurrence graphs as analternative to traditional features representation models like vector representation, where most of the time these models can not map all the richness of text documents that comes from the web (social media, blogs, personalweb pages, news, etc). For each text classification task the type of graph created as well as the benefits of using it are presented and discussed. In specific, the type of features/patterns extracted, the implemented classification/similarity methods and the results obtained in datasets are explained. The theoretical and practical implications of using co-occurrence graphs are also discussed, pointing out the contributions and challenges of modeling text document as graphs.

/pdf/text-analysis-using-different-graph-based-representations-1dtfohutkk.pdf

Text Analysis Using Different Graph-Based Representations

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Text Analysis Using Different Graph-Based Representations