Search results for "Software"
showing 10 items of 7396 documents
Intruder Pattern Identification
2008
This paper considers the problem of intrusion detection in information systems as a classification problem. In particular the case of masquerader is treated. This kind of intrusion is one of the more difficult to discover because it may attack already open user sessions. Moreover, this problem is complex because of the large variability of user models and the lack of available data for the learning purpose. Here, flexible and robust similarity measures, suitable also for non-numeric data, are defined, they will be incorporated on a one-class training K N N and compared with several classification methods proposed in the literature using the Masquerading User Data set (www.schonlau.net) repr…
The Orchestra of Multiple Model Repositories
2013
This paper motivates and encourages the simultaneous use of multiple model repositories in model-driven software. A multi-repository mechanism is proposed for that. With this mechanism, multiple model repositories residing in the same or different technical spaces can be mounted into a single repository (in the same sense as file systems are mounted in UNIX) and accessed in a uniform way. Relations (including generalizations) between elements from the mounted repositories are supported. Some of the repositories may be “computable” (virtual), which leads to the on-the-fly model transformation concept.
Bot recognition in a Web store: An approach based on unsupervised learning
2020
Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning stra…
Global scripts and local translations
2014
Author's version of an article in the journal: City, Culture and Society. Also available from the publisher at: http://dx.doi.org/10.1016/j.ccs.2014.05.007
OH_FET: a computer application for analysing urban dynamics over long time spans
2015
International audience; OH_FET formalism has been designed to analyse urban fabric over long time spans (Lefebvre et al., 2008; Rodier and Saligny, 2010; Rodier et al., 2010). The conceptual model is founded on the idea of historical object (OH – objet historique) which is defined as the Cartesian product of three elementary dimensions: social use (what), space (where) and time (when). Processes are performed in Python programming language and data are stored in various kinds of spatial database. With a minimum of required data from archaeologists’ observations, the application allows users to automate the calculation of standardised indicators about urban transformations and to generate gr…
Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods
2009
Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by m…
A Composition Approach to Design of Educational Animations
2017
International audience; The educational effectiveness of conventionally designed animations that portray complex, unfamiliar subject matter in a behaviorally realistic fashion too often falls well short of expectations. Previous research indicates that the veridical dynamic properties of these comprehensive animations make a major contribution to their lack of effectiveness. Due to these dynamics, there is a substantial mismatch between the processing demands these animations impose on learners and the characteristics of the human information processing system. As a result, the quality of the mental models that learners are able to construct is compromised. Interventions intended to improve…
Supporting Relational Processing in Complex Animated Diagrams
2008
The psychological utility of different approaches to cueing key information within complex animated diagrams remains relatively unexplored. Because of the varied processing demands required to comprehend such representations, it is likely that providing a range of different cue types would be best for supporting effective user information extraction. Traditional cueing approaches used with static diagrams may need to be reconsidered in order to meet the new design challenges associated with their animated counterparts.
OLAP Personalization with User-Describing Profiles
2010
In this paper we have highlighted five existing approaches for introducing personalization in OLAP: preference constructors, dynamic personalization, visual OLAP, recommendations with user session analysis and recommendations with user profile analysis and have analyzed research papers within these directions. We have pointed out applicability of personalization to OLAP schema elements in these approaches. The comparative analysis has been made in order to highlight a certain personalization approach. A new method has been proposed, which provides exhaustive description of interaction between user and data warehouse, using the concept of Zachman Framework [1, 2], according to which a set of…
Extracting Features from Social Media Networks Using Semantics
2016
This paper focuses on the analysis of social media content generated by social networks (e.g. Twitter) in order to extract semantic features. By using text categorization to sort text feeds into categories of similar feeds, it has been proved to reduce the overhead that is required to retrieve these feeds and at the same time, it provides smaller pools in which further investigations can be made easier. The aim of this survey is to draw a user profile, by analysing his or her tweets. In this early stage of research, being a pre-processing phase, a dictionary based approach is considered. Moreover, the paper describes an algorithm used in analysing the text and its preliminary results. This …