The effects of self-awareness on body movement indicators of the intention to deceive

A study was conducted to investigate the body movements of participants waiting to be interviewed in one of two conditions: preparing to answer questions truthfully or preparing to lie. The effects of increased self-awareness were also investigated, with half of the participants facing a mirror; the other half facing a blank wall. Analysis of covertly obtained video footage showed a significant interaction for the duration of hand/arm movements between deception level and self-awareness. Without a mirror, participants expecting to lie spent less time moving their hands than those expecting to tell the truth; the opposite was seen in the presence of a mirror. Participants expecting to lie also had higher levels of anxiety and thought that they were left waiting for less time than those expecting to tell the truth. These findings led to the identification of further research areas with the potential to support deception detection in security applications.     

Culture-based artefacts to inform ICT design: foundations and practice

AI & SOCIETY - Cultural aspects frame our perception of the world and direct the many different ways people interact with things in it. For this reason, these aspects should be considered when...     

Reusable abstractions for modeling languages

Model-driven engineering proposes the use of models to describe the relevant aspects of the system to be built and synthesize the final application from them. Models are normally described using Domain-Specific Modeling Languages (DSMLs), which provide primitives and constructs of the domain. Still, the increasing complexity of systems has raised the need for abstraction techniques able to produce simpler versions of the models while retaining some properties of interest. The problem is that developing such abstractions for each DSML from scratch is time and resource consuming.     

Two-space variability compensation technique for speaker verification in short length and reverberant environments

The performance of state-of-the-art speaker verification in uncontrolled environment is affected by different variabilities. Short duration variability is very common in these scenarios and causes the speaker verification performance to decrease quickly while the duration of verification utterances decreases. Linear discriminant analysis (LDA) is the most common session variability compensation algorithm, nevertheless it presents some shortcomings when trained with insufficient data. In this paper we introduce two methods for session variability compensation to deal with short-length utterances on i-vector space. The first method proposes to incorporate the short duration variability information in the within-class variance estimation process. The second proposes to compensate the session and short duration variabilities in two different spaces with LDA algorithms (2S-LDA). First, we analyzed the behavior of the within and between class scatters in the first proposed method. Then, both proposed methods are evaluated on telephone session from NIST SRE-08 for different duration of the evaluation utterances: full (average 2.5 min), 20, 15, 10 and 5 s. The 2S-LDA method obtains good results on different short-length utterances conditions in the evaluations, with a EER relative average improvement of 1.58%, compared to the best baseline (WCCN[LDA]). Finally, we applied the 2S-LDA method in speaker verification under reverberant environment, using different reverberant conditions from Reverb challenge 2013, obtaining an improvement of 8.96 and 23% under matched and mismatched reverberant conditions, respectively.     

Meta-modelling and graph grammars for multi-paradigm modelling in AToM<Superscript>3</Superscript>

This paper presents the combined use of meta-modelling and graph grammars for the generation of visual modelling tools for simulation formalisms. In meta-modelling, formalisms are described at a meta-level. This information is used by a meta-model processor to generate modelling tools for the described formalisms. We combine meta-modelling with graph grammars to extend the model manipulation capabilities of the generated modelling tools: edit, simulate, transform into another formalism, optimize and generate code. We store all (meta-)models as graphs, and thus, express model manipulations as graph grammars.We present the design and implementation of these concepts in AToM³ (A_To_ol for M_ulti-formalism, M_eta-M_odelling). AToM³ supports modelling of complex systems using different formalisms, all meta-modelled in their own right. Models in different formalisms may be transformed into a single common formalism for further processing. These transformations are specified by graph grammars. Mosterman and Vangheluwe [18] introduced the term multi-paradigm modelling to denote the combination of multiple formalisms, multiple abstraction levels, and meta-modelling. As an example of multi-paradigm modelling we present a meta-model for the Object-Oriented Continuous Simulation Language OOCSMP, in which we combine ideas from UML class diagrams (to express the OOCSMP model structure), Causal Block Diagrams (CBDs), and Statecharts (to specify the methods of the OOCSMP classes). A graph grammar is able to generate OOCSMP code, and then a compiler for this language (C-OOL) generates Java applets for the simulation execution.     

Quick fixing ATL transformations with speculative analysis

Model transformations are central components of most model-based software projects. While ensuring their correctness is vital to guarantee the quality of the solution, current transformation tools provide limited support to statically detect and fix errors. In this way, the identification of errors and their correction are nowadays mostly manual activities which incur in high costs. The aim of this work is to improve this situation. Recently, we developed a static analyser that combines program analysis and constraint solving to identify errors in ATL model transformations. In this paper, we present a novel method and system that uses our analyser to propose suitable quick fixes for ATL transformation errors, notably some non-trivial, transformation-specific ones. Our approach supports speculative analysis to help developers select the most appropriate fix by creating a dynamic ranking of fixes, reporting on the consequences of applying a quick fix, and providing a pre-visualization of each quick fix application. The approach integrates seamlessly with the ATL editor. Moreover, we provide an evaluation based on existing faulty transformations built by a third party, and on automatically generated transformation mutants, which are then corrected with the quick fixes of our catalogue.     

A method for outlier detection based on cluster analysis and visual expert criteria

Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering-based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance-related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.