Search results for "Heuristic"

Lost or not? : designing and evaluating user interfaces of mobile map services : the viewpoint of supporting users' location awareness

2016

The motivation for this thesis arose from the problem of people getting lost, both with and without mobile maps. I will answer a primary research question: 1) How can we support users’ location awareness with mobile map applications? As an ad-dition to this, I have the following sub-questions: a) Why do people get lost even when using a mobile map application? b) What are the best practices to support navigation? c) How can we research what the important objects in the natural environment are that should be emphasized in mobile maps? d) How do we prevent the user from focusing on the map service at the expense of perceiving the location in the real environment? e) What would a good mobile m…

Perfect Hashing Structures for Parallel Similarity Searches

2015

International audience; Seed-based heuristics have proved to be efficient for studying similarity between genetic databases with billions of base pairs. This paper focuses on algorithms and data structures for the filtering phase in seed-based heuristics, with an emphasis on efficient parallel GPU/manycores implementa- tion. We propose a 2-stage index structure which is based on neighborhood indexing and perfect hashing techniques. This structure performs a filtering phase over the neighborhood regions around the seeds in constant time and avoid as much as possible random memory accesses and branch divergences. Moreover, it fits particularly well on parallel SIMD processors, because it requ…

Context-Aware Adaptive System For M- Learning Personalization

2014

International audience; Context-aware mobile learning is becoming important because of the dynamic and continually changing learning settings in learner's mobile environment, giving rise to many different learning contexts that are difficult to apprehend. To provide personalization of learning content, we aim to develop a recommender system based on semantic modeling of learning contents and learning context. This modeling is complemented by a behavioral part made up of rules and metaheuristics used to optimize the combination of pieces of learning contents according to learner's context. All these elements form a new approach to mobile learning.

SMART NANOSPONGE-BASED SYSTEMS FOR ADVANCED APPLICATIONS

2023

Metaheuristics meet metamodels : a modeling language and a product line architecture for route optimization systems

2011

SEMANTIC AND CONTEXTUAL APPROACH FOR THE RECOMMENDATION OF LEARNING MODULES IN MOBILITY

2012

International audience; Many researchers argue that mobile learning is just an adaptation of e-learning on mobile technology, but far from a simple extension of e-learning, m-learning raises original issues in technological and pedagogical terms. M-learning is usually based on the consideration of a context rich on information and interactions. The challenge of m-learning is therefore, not simply to transfer on mobile content designed primarily for e-learning. This concept implies that we must rethink the entire process of the learning experience in mobility to maximize its efficiency.

Recommender system for combination of learning elements in mobile environment

2012

5 pages; International audience; The paper presents an ongoing research about the development of a new recommender system dedicated to m-learning. This system is an extension of content based recommender system proposals. It's made of three levels architecture: 1/ a domain model describing the knowledge of teaching, 2/ a user model defining learner's profile and learning's context, 3/ an adaptation model containing rules and metaheuristics, which aims at combining learning modules. Our system takes into account the spatio-temporal context of the learners, the evolution of learner's profile and the dynamic adaptation of modules during the learning process in a mobile environment. The result …

Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

2013

A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat - a perennial problem in genetic …

Design time, run time, and artificial intelligence techniques for mobility of user interface

2011

Abstract Advancement in technology provides opportunities to user as well as challenges for application development organization. User interfaces which were design for specific device tend to be developed for various devices. Users are busy people, when they move among different context would like to move application with them. The current trend of users demanding mobile graphic user interface to support their daily life and work has led to a new generation of techniques. Design time technique provides better usability as compare to run time technique. On the other hand artificial intelligence technique like agent provides better flexibility and usability as compare to others. In this paper…

Feature Extractors for Describing Vehicle Routing Problem Instances

2016

The vehicle routing problem comes in varied forms. In addition to usual variants with diverse constraints and specialized objectives, the problem instances themselves – even from a single shared source - can be distinctly different. Heuristic, metaheuristic, and hybrid algorithms that are typically used to solve these problems are sensitive to this variation and can exhibit erratic performance when applied on new, previously unseen instances. To mitigate this, and to improve their applicability, algorithm developers often choose to expose parameters that allow customization of the algorithm behavior. Unfortunately, finding a good set of values for these parameters can be a tedious task that…