Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

A context-based navigation paradigm for accessing web data.

This paper presents a context-based navigation paradigm, so as to overcome the phenomenon of user disorientation in a Web environment. Conventional navigation along static links is complemented by run-time generated guided tours, which are derived dynamically from the context of a user's information requirements. The result is a two-dimensional navigation paradigm, which reconciles complete navigational freedom and flexibility with a measure of linear guidance. Consequently, orientation is improved through reduced cognitive overhead and an increased sense of document coherence.Information; Requirements; Cognitive;

Enhancing allocentric spatial recall in pre-schoolers through navigational training programme

Unlike for other abilities, children do not receive systematic spatial orientation training at school, even though navigational training during adulthood improves spatial skills. We investigated whether navigational training programme (NTP) improved spatial orientation skills in pre-schoolers. We administered 12-week NTP to seventeen 4- to 5-year-old children (training group, TG). The TG children and 17 age-matched children (control group, CG) who underwent standard didactics were tested twice before (T0) and after (T1) the NTP using tasks that tap into landmark, route and survey representations. We determined that the TG participants significantly improved their performances in the most demanding navigational task, which is the task that taps into survey representation. This improvement was significantly higher than that observed in the CG, suggesting that NTP fostered the acquisition of survey representation. Such representation is typically achieved by age seven. This finding suggests that NTP improves performance on higher-level navigational tasks in pre-schooler

MESH: an object-oriented approach to hypermedia modeling and navigation.

This paper introduces the MESH approach to hypermedia modeling and navigation, which aims at relieving the typical drawbacks of poor maintainability and user disorientation. The framework builds upon two fundamental concepts. The data model combines established entity-relationship and object-oriented abstractions with proprietary concepts into a formal hypermedia data model. Uniform layout and link typing specifications can be attributed and inherited in a static node typing hierarchy, whereas both nodes and links can be submitted dynamically to multiple complementary classifications. In the context-based navigation paradigm, conventional navigation along static links is complemented by run-time generated guided tours, which are derived dynamically from the context of a user's information requirements. The result is a two-dimensional navigation paradigm, which reconciles complete navigational freedom and flexibility with a measure of linear guidance. These specifications are captured in a high-level, platform independent implementation framework.Data; Model; Specifications; Classification; Information; Requirements;

Autonomous Navigation in (the Animal and) the Machine

Understanding the principles underlying autonomous navigation might be the most enticing quest the computational neuroscientist can undertake. Autonomous operation, also known as voluntary behavior, is the result of higher cognitive mechanisms and what is known as executive function in psychology. A rudimentary knowledge of the brain can explain where and to a certain degree how parts of a computation are expressed. However, achieving a satisfactory understanding of the neural computation involved in voluntary behavior is beyond today’s neuroscience. In contrast with the study of the brain, with a comprehensive body of theory for trying to understand system with unmatched complexity, the field of AI is to a larger extent guided by examples of achievements. Although the two sciences differ in methods, theoretical foundation, scientific vigour, and direct applicability, the intersection between the two may be a viable approach toward understanding autonomy. This project is an example of how both fields may benefit from such a venture. The findings presented in this thesis may be interesting for behavioral neuroscience, exploring how operant functions can be combined to form voluntary behavior. The presented theory can also be considered as documentation of a successful implementation of autonomous navigation in Euclidean space. Findings are grouped into three parts, as expressed in this thesis. First, pertinent back- ground theory is presented in Part I – collecting key findings from psychology and from AI relating to autonomous navigation. Part II presents a theoretical contribution to RL theory developed during the design and implementation of the emulator for navigational autonomy, before experimental findings from a selection of published papers are attached as Part III. Note how this thesis emphasizes the understanding of volition and autonomous navigation rather than accomplishments by the agent, reflecting the aim of this project – to understand the basic principles of autonomous navigation to a sufficient degree to be able to recreate its effect by first principles

Learning to Predict Navigational Patterns from Partial Observations

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enables our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception. Code released upon publication.Comment: Under revie

Category-Specific Item Recognition and the Medial Temporal Lobe

Much neuropsychological and neuroimaging research has been focused on the contributions of different medial temporal lobe (MTL) structures to recognition memory. The majority of these studies have linked perirhinal cortex (PrC) to item recognition, whereas the hippocampus and parahippocampal cortex (PhC) have primarily been associated with the recollection of contextual detail pertaining to a specific prior stimulus encounter. Here, I report results from three fMRI studies that examined the neural correlates of item recognition with a specific focus on the relationship between such signals and category-specific effects in the MTL. In Chapter 2, I reveal that category-specific representations in both PrC and PhC can be brought to bear on item recognition decisions. In Chapter 3, I examined the specific stimulus properties that determine the relative contributions of PrC and PhC to item recognition, with a focus on landmark suitability. The results from this study revealed item recognition signals for non-landmark objects in PrC and landmarks in PhC. In Chapter 4, I focused specifically on face recognition to characterize the manner in which PrC codes item-recognition signals and to further explore the issue of category-specificity with independent functional localizer data. Results from this study indicate that item recognition signals in PrC can be distributed across voxels with directionally heterogeneous response profiles. Further, these data also revealed that the voxels comprising these patterns respond preferentially to faces under passive viewing conditions. Taken together, these findings suggest that item recognition signals are represented in a distributed, category-specific manner within both PrC and PhC