The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

Development of a systematic computer vision-based method to analyse and compare images of false identity documents for forensic intelligence purposes-Part I: acquisition, calibration and validation issues

Following their detection and seizure by police and border guard authorities, false identity and travel documents are usually scanned, producing digital images. This research investigates the potential of these images to classify false identity documents, highlight links between documents produced by a same modus operandi or same source, and thus support forensic intelligence efforts. Inspired by previous research work about digital images of Ecstasy tablets, a systematic and complete method has been developed to acquire, collect, process and compare images of false identity documents. This first part of the article highlights the critical steps of the method and the development of a prototype that processes regions of interest extracted from images. Acquisition conditions have been fine-tuned in order to optimise reproducibility and comparability of images. Different filters and comparison metrics have been evaluated and the performance of the method has been assessed using two calibration and validation sets of documents, made up of 101 Italian driving licenses and 96 Portuguese passports seized in Switzerland, among which some were known to come from common sources. Results indicate that the use of Hue and Edge filters or their combination to extract profiles from images, and then the comparison of profiles with a Canberra distance-based metric provides the most accurate classification of documents. The method appears also to be quick, efficient and inexpensive. It can be easily operated from remote locations and shared amongst different organisations, which makes it very convenient for future operational applications. The method could serve as a first fast triage method that may help target more resource-intensive profiling methods (based on a visual, physical or chemical examination of documents for instance). Its contribution to forensic intelligence and its application to several sets of false identity documents seized by police and border guards will be developed in a forthcoming article (part II).