Routinisation and memorisation of tasks inside a workshop: the case of the introduction of ISO norms

Changing routines and creating new routinization processes are difficult tasks involving both cognitive and political mechanisms. In this paper we use Defial- a French meet proessing firm- in order to illustrate some of the problems involved in creating a new procedural memory in a workshop and in applying the concept of 'routine'. We discuss some methodological implications resulting from our various observations and the choice we made. In our case study, the complexity arose partly from the many different factors that affect the production process, such as stress and the overload syndrome. We show that time and hierachical pressure cannot alone ensure the success of memorization of a task. The routinization process is only truly sucessful when a new state of condidence towards management has been established, a confidence that helps overcome the socio-emotional issues arising from the changes that are taking place and that paves the way for the acceptance of change in both declarative and procedural memory.

Information technology and efficiency in trucking

In this paper, we develop an econometric model to estimate the impacts of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks using data at the operational level. This technology is supposed to improve capacity utilization by reducing coordination costs between demand and supply. The model is estimated on a subsample of the 1999 National Roadside Survey, covering heavy trucks travelling in the province of Quebec. The LF is explained as a function of truck, trip and carrier characteristics. We show that the use of EVMS results in a 16 percentage points increase of LF on backhaul trips. However, we also find that the LF of equipped trucks is reduced by about 7.6 percentage points on fronthaul movements. This last effect could be explained by a rebound effect: higher expected LF on the returns lead carriers to accept shipments with lower fronthaul LF. Overall, we find that this technology has increased the tonne-kilometers transported of equipped trucks by 6.3% and their fuel efficiency by 5%.Information and Communication Technology, Efficiency, Load factor, Trucking, Energy Efficiency

Information technology and efficiency in trucking.

In this paper, we develop an econometric model to estimate the impacts of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks using data at the operational level. This technology is supposed to improve capacity utilization by reducing coordination costs between demand and supply. The model is estimated on a subsample of the 1999 National Roadside Survey, covering heavy trucks travelling in the province of Quebec. The LF is explained as a function of truck, trip and carrier characteristics. We show that the use of EVMS results in a 16 percentage points increase of LF on backhaul trips. However, we also find that the LF of equipped trucks is reduced by about 7.6 percentage points on fronthaul movements. This last effect could be explained by a rebound effect: higher expected LF on the returns lead carriers to accept shipments with lower fronthaul LF. Overall, we find that this technology has increased the tonne-kilometers transported of equipped trucks by 6.3% and their fuel efficiency by 5%.

Saisir l'affectif urbain. Proposition originale par la cartographie de réactivation des discours

International audienceDans cette communication, les auteurs exposent en quoi les principales techniques visant à recueillir l'information auprès de personnes interviewées ne sont pas spécifiquement adaptées lorsqu'il s'agit de débusquer le rapport affectif des individus aux lieux ou à la ville, bien que chacune (observation in situ, carte mentale, entretien semi-directif, parcours commenté, discours réactivé) apportent des éléments relatifs à l'affectif ou révélateur de celui-ci (affects, repères spatio-temporels, représentations mentales, comportements). Les auteurs proposent alors une technique originale, par une approche cartographique, suivie d'une phase de réactivation qui permet à l'interviewé de se livrer plus avant dans la sphère de l'intime

Who are you, you who speak? Transducer cascades for information retrieval

International audienceThis paper deals with a survey corpus. We present information retrieval about the speaker. We used finite state transducer cascades and we present here detailed results with an evaluation. This work is part of a French project to enhance the corpus ESLO (sociolinguistic survey taken in the city of Orléans). This survey has been realized in 1968 and the project is to save records in computer format, to transcribe them and to increase the transcription with annotations in XML format. This work was supported by a French ANR contract (ANR-06-CORP-023) and by European fund from Région Centre (FEDER). The corpus represent a collection of 200 interviews with the questions about the life in the city of Orléans: How long have you lived in Orléans for?, What led you to live in Orléans?, Do you like living in Orléans?, etc. and questions about the occupation or the family of the speaker, completed by recordings within a professional or private context. The recording situations are different: interviews, discussions between friends, recordings in microphone hidden, interviews with the political, academic and religious personalities, conversations between a social worker and parents in Psycho Medical Center of Orleans. In total, we have 300 hours of speech estimated to 4,500,000 words. More precisely, we worked on almost 120 transcribed hours representing 112 Transcriber XML files and 32 577 Kb. We worked on 105 files (31 004 Kb) and we evaluated the results on 7 files (1 573 Kb-5.1%). The transcription files have no punctuation marks, but the first letter of proper names is capitalized and acronyms are fully capitalized. We used the CasSys system (Friburger, Maurel, 2004) that computes texts with transducer cascades (Abney, 1996). The cascades we used are hand built: each transducer describes a local grammar for the recognition of some entities. Some times this recognition needs the succession of two or more transducers, in a specific order. More precisely, we used two cascades; the first one, for named entity recognition, was built some years ago for a newspaper corpus and we adapted it to oral corpus in the project; the second one aimed at discovering information about the speaker in three domains: origin (is he/she Orléans city native or where he/she comes from?), family (is he/she married, with children or not?) and occupation (what is his/her occupation? where does he/she work?). We called this information designating entities. This second cascade was specifically built for the project. CasSys computes transducers with Unitex software (Paumier, 2003) that needs to segment the text by preprocessing. For written text, this segmentation usually uses sentence boundary detection (Friburger and al., 2000). In our corpus there is no punctuation. So we have chosen to use XML Transcriber tags to do the segmentation and also to hide the inside of the tag for the named entity task, sometimes ambiguous with context entities (Dister, 2007)

Enrichment of Renaissance texts with proper names

International audienceThe Renom project proposes to enrich Renaissance texts by proper names. These texts present two new challenges: great diversity due to various spellings of words; numerous XML-TEI tags to save the exact format of original edition. The task consisted to add Named Entity tags to this format tagging with generally the left context and sometimes the right context of a name. To do that, we improved the free and open source program CasSys to parse texts with Unitex graph cascades and we built dictionaries and specific cascades. The slot error rate was 6.1%. Proper Names and maps. were to allow navigating into. So, this paper deals with Named Entity Recognition in Renaissance texts

Does color influence eye movements while exploring videos?

Although visual attention studies consider color as one of the most important features in guiding visual attention, few studies have investigated how color influences eye movements while viewing natural scenes without any particular task. To better understand the visual features that drive attention, the aim of this paper was to quantify the influence of color on eye movements when viewing dynamic natural scenes. The influence of color was investigated by comparing the eye positions of several observers eye-tracked while viewing video stimuli in two conditions: color and grayscale. The comparison was made using the dispersion between the eye positions of observers, the number of attractive regions measured with a clustering method applied to the eye positions, and by comparing eye positions to the predictions of a saliency model. The mean amplitude of saccades and the mean duration of fixations were compared as well. Globally, a slight influence of color on eye movements was measured; only the number of attractive regions for color stimuli was slightly higher than for grayscale stimuli. However, a luminance-based saliency model predicts the eye positions for color stimuli as efficiently as for grayscale stimuli

Color Information in a Model of Saliency

International audienceBottom-up saliency models have been developed to predict the location of gaze according to the low level features of visual scenes, such as intensity, color, frequency and motion. We investigate in this paper the contribution of color features in computing the bottom-up saliency. We incorporated a chrominance pathway to a luminance-based model (Marat et al.). We evaluated the performance of the model with and without chrominance pathway. We added an efficient multi-GPU implementation of the chrominance pathway to the parallel implementation of the luminance-based model proposed by Rahman et al., preserving real time solution. Results show that color information improves the performance of the saliency model in predicting eye positions