Four Reasons to be Happy about Internet Plagiarism

Russell Hunt wrote an essay on this theme, pointing out four different reasons why plagiarism should not be seen as evil in all cases and how education should be reconsidered in the light of technological advancements.

1. The institutional rhetorical writing environment (the “research paper,” the “literary essay,” the “term paper”) is challenged by this.

The assumption that a student’s learning is accurately and readily tested by her ability to produce, in a completely arhetorical situation, an artificial form that she’ll never have to write again once she’s survived formal education (the essay examination, the formal research paper), is questionable on the face of it, and is increasingly untenable.

2. The institutional structures around grades and certification are challenged by this.

University itself, as our profession has structured it, is the most effective possible situation for encouraging plagiarism and cheating. If I wanted to learn how to play the guitar, or improve my golf swing, or write HTML, “cheating” would be the last thing that would ever occur to me. It would be utterly irrelevant to the situation.

3. The model of knowledge held by almost all students, and by many faculty — the tacit assumption that knowledge is stored information and that skills are isolated, asocial faculties — is challenged by this.

Partisans of active learning, of problem- and project-based learning, of cooperative learning, and of many other “radical” educational initiatives, all contend that information and ideas are not inert masses to be shifted and copied in much the way two computers exchange packages of information, but rather need to be continuously reformatted, reconstituted, restructured, reshaped and reinvented and exchanged in new forms — not only as learning processes but as the social basis of the intellectual enterprise.

4. by facing this challenge we will be forced to help our students learn what I believe to be the most important thing they can learn at university: just how the intellectual enterprise of scholarship and research really works.

The clearest difference between the way undergraduate students, writing essays, cite and quote and the way scholars do it in public is this: typically, the scholars are achieving something positive; the students are avoiding something negative.

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Some consideration about clustering and information retrieval

In a recent conversation with Lorenzo Viscanti, I was raising the following points:

1. Using a supervised method for clustering can bias the results you get. As far as I know, every supervised method is based on a training set that has been prepared by an expert or that is taken from a specific and known context. This may yield some conflict witht the datasets because: a- the training set is epistemologically different from the dataset; b- the expert is not generalizable.

2. The definition of what a good cluster is cannot be mathematically determined. I partially agree on this. It is known that in Information Retrieval exists a bounch of methods for proofing the efficiency and efficacy of the retrieval functionalities.

However, the definition of what is a good cluster is based partially on human perception, which does not follow a proper logic. A couple of technique are therefore possible to apply, namely from cognitive psychology, to translate the variability of human cognition into quantitative data that is possible to comment on.

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Mental model

Humans establish mental models of how things work, or how they would behave in a particular situation. For example, having been a student at a university for a while, a student can establish a “mental model” of attending a university. That is, he goes to classes, talks to his classmates about how to accomplish certain homeworks, he knows how to interact with his professors, and etc. Suppose now a virtual university is being offered to students for online courses, and a website is to be constructed for the virtual university. This website should understand and respect the “mental models” of targeted students in order to avoid confusion for the students to find his way around at the virtual university.

wxPython: a GUI toolkit for python

wxPython is a GUI toolkit for the Python programming language. It allows Python programmers to create programs with a robust, highly functional graphical user interface, simply and easily. It is implemented as a Python extension module (native code) that wraps the popular wxWidgets cross platform GUI library, which is written in C++.

Like Python and wxWidgets, wxPython is Open Source which means that it is free for anyone to use and the source code is available for anyone to look at and modify. Or anyone can contribute fixes or enhancements to the project.

wxPython is a cross-platform toolkit. This means that the same program will run on multiple platforms without modification. Currently supported platforms are 32-bit Microsoft Windows, most Unix or unix-like systems, and Macintosh OS X.

Pyshell

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Probabilistic Part-of-Speech Tagging Using Decision Trees

H. Schmid. Probabilistic part-of-speech tagging using decision trees. In Proceedings of International Conference on New Methods in Language Processing, September 1994.Pdf Logo[url]

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In this paper, a new probabilistic tagging method is presented which avoids problems that Markov models based tagger face, when they have to estimate transition probabilities from sparse data.

In this tagging method, transition probabilities are estimate using a decision tree. Based on this method, a part-of-speech tagger (called TreeTagger) has been implemented which achieves 96.36% of accuracy on Penn-Treebank data which is better than that of a trigram tagger on the same data.

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Petri net: a graphical and mathematical modeling tool

A Petri net is a graphical and mathematical modeling tool. It consists of places, transitions, and arcs that connect them. Input arcs connect places with transitions, while output arcs start at a transition and end at a place. There are other types of arcs, e.g. inhibitor arcs. Places can contain tokens; the current state of the modeled system (the marking) is given by the number (and type if the tokens are distinguishable) of tokens in each place. Transitions are active components. They model activities which can occur (the transition fires), thus changing the state of the system (the marking of the Petri net). Transitions are only allowed to fire if they are enabled, which means that all the preconditions for the activity must be fulfilled (there are enough tokens available in the input places). When the transition fires, it removes tokens from its input places and adds some at all of its output places. The number of tokens removed / added depends on the cardinality of each arc. The interactive firing of transitions in subsequent markings is called token game

Petrieleme

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A spreading activation network model for information retrieval

S. E. Preece. A Spreading Activation Network Model for Information Retrieval. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, October 1981. [url]

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Despite being an outdated work on information retrieval, this thesis present an innovative associative retrieval method that demonstrate interesting results, and that aims at improving the search process itself.

The basic idea is to use coincidence matrix, whose rows correnspod to the document and whose columns correspond to attributes. On top of this representation of the dataset, the author builds an associative retrieval scheme, which generate a second order level of associations between the terms and that is based on a very simple process of diffusion through the network.

Each node of this connectivity matrix can be marked as active or inactive (or any real value between 0 and 1). Now a simple iterative process of spreading can trigger all the nodes connected to an initial one.

The author propose several types of processing within this framework: Cumulative processing (which deals only with the current level of activation);  Coincidence processing (which deals with activation trails); and Context processing (which uses geometric properties of the pattern of activation).

Chapter three describe the model in details. What follows are some random notes.

Parameters of processing determine how the activation spreads among the nodes. Processing is iterative, consisting of a sequence of spreading pulses. The author propose several kinds of spreafing methods, one is the scource node distribution, which determines the general pattern of spreading; the full strenght spreading (each link attached to an active node receives the full strenght of the node); an inverse source frequency spreading (each link receives an equal share of the node’s strenght). Thresholds may be applied to the links to act as a noise filters. The activation may also assume negative values.

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