Inférences réflexives dans la publicité

Advertisements are so ubiquitous nowadays that capturing the addressee’s attention and maintaining it long enough for them to be fully processed have become fundamental objectives for advertisers. Employing specific strategies in the design of the advertisement contributes efficiently to achieving these goals, getting the audience not only to attend the stimulus but also to process it in certain ways favourable for the advertiser. We argue that Relevance theory, an approach to communication built on a massively modular view of cognition, offers the right tools to explain the nature of the interpretative processes in verbal comprehension. Knowledge of the relevance-based reflexive inferential procedures involved in utterance interpretation allows advertisers to foresee the addressee’s processing behaviour, giving them the possibility to control it in a such a way that the intended interpretative effects are achieved in the desired way

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training

Hump Yard Track Allocation with Temporary Car Storage

In rail freight operation, freight cars need to be separated and reformed into new trains at hump yards. The classification procedure is complex and hump yards constitute bottlenecks in the rail freight network, often causing outbound trains to be delayed. One of the problems is that planning for the allocation of tracks at hump yards is difficult, given that the planner has limited resources (tracks, shunting engines, etc.) and needs to foresee the future capacity requirements when planning for the current inbound trains. In this paper, we consider the problem of allocating classification tracks in a rail freight hump yard for arriving and departing trains with predetermined arrival and departure times. The core problem can be formulated as a special list coloring problem. We focus on an extension where individual cars can temporarily be stored on a special subset of the tracks. An extension where individual cars can temporarily be stored on a special subset of the tracks is also considered. We model the problem using mixed integer programming, and also propose several heuristics that can quickly give feasible track allocations. As a case study, we consider a real-world problem instance from the Hallsberg Rangerbangård hump yard in Sweden. Planning over horizons over two to four days, we obtain feasible solutions from both the exact and heuristic approaches that allow all outgoing trains to leave on time

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Efficient Image Gallery Representations at Scale Through Multi-Task Learning

Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.Comment: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieva

When emotional intelligence affects peoples' perception of trustworthiness

By adopting social exchange theory and the affect-infusion-model, the hypothesis is made that emotional intelligence (EI) will have an impact on three perceptions of trustworthiness – ability, integrity and benevolence – at the beginning of a relationship. It was also hypothesized that additional information would gradually displace EI in forming the above perceptions. The results reveal that EI initially does not contribute to any of the perceptions of trustworthiness. As more information is revealed EI has an impact on the perception of benevolence, but not on the perceptions of ability and integrity. This impact was observed to be negative when the nature of the information was negative. On the other hand, information alone was shown to have a significant impact on the perceptions of ability and integrity, but not on the perception of benevolence. Theoretical and practical implications of the findings are addressed