Location-based indexing for mobile context-aware access to a digital library

Mobile information systems need to collaborate with each other to provide seamless information access to the user. Information about the user and their context provides the points of contact between the systems. Location is the most basic user context. TIP is a mobile tourist information system that provides location-based access to documents in the digital library Greenstone. This paper identifies the challenges for providing effcient access to location-based information using the various access modes a tourist requires on their travels. We discuss our extended 2DR-tree approach to meet these challenges

A Survey on Spatial Indexing

Spatial information processing has been a centre of attention of research in the previous decade. In spatial databases, data related with spatial coordinates and extents are retrieved based on spatial proximity. A large number of spatial indexes have been proposed to make ease of efficient indexing of spatial objects in large databases and spatial data retrieval. The goal of this paper is to review the advance techniques of the access methods. This paper tries to classify the existing multidimensional access methods, according to the types of indexing, and their performance over spatial queries. K-d trees out performs quad tress without requiring additional memory usage

Advance of the Access Methods

The goal of this paper is to outline the advance of the access methods in the last ten years as well as to make review of all available in the accessible bibliography methods

Reinforcement Learning and Advanced Reinforcement Learning to Improve Autonomous Vehicle Planning

Planning for autonomous vehicles is a challenging process that involves navigating through dynamic and unpredictable surroundings while making judgments in real-time. Traditional planning methods sometimes rely on predetermined rules or customized heuristics, which could not generalize well to various driving conditions. In this article, we provide a unique framework to enhance autonomous vehicle planning by fusing conventional RL methods with cutting-edge reinforcement learning techniques. To handle many elements of planning issues, our system integrates cutting-edge algorithms including deep reinforcement learning, hierarchical reinforcement learning, and meta-learning. Our framework helps autonomous vehicles make decisions that are more reliable and effective by utilizing the advantages of these cutting-edge strategies.With the use of the RLTT technique, an autonomous vehicle can learn about the intentions and preferences of human drivers by inferring the underlying reward function from expert behaviour that has been seen. The autonomous car can make safer and more human-like decisions by learning from expert demonstrations about the fundamental goals and limitations of driving. Large-scale simulations and practical experiments can be carried out to gauge the effectiveness of the suggested approach. On the basis of parameters like safety, effectiveness, and human likeness, the autonomous vehicle planning system's performance can be assessed. The outcomes of these assessments can help to inform future developments and offer insightful information about the strengths and weaknesses of the strategy

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Food Distribution in Ant Colonies: Trophallaxis and Self-Organization

Roughly one hundred million years ago, solitary insect species evolved social interactions that enabled the formation of colonies. A main reason for this advance was their ability to feed each other with previously ingested food. Among other things, this allowed them to develop the well-known division of labor: groups or castes of individuals specializing in certain tasks. This social organization reached its climax in the evolution of non-reproductive castes, sacrificing their own reproduction to the benefit of the colony. The mutual feeding technique that supported this social evolution is called ‘trophallaxis’. This thesis is based on the question how ant colonies use trophallaxis to supply their members with food. The main goal of this thesis is to understand the collective properties of the food distribution in ant colonies with the simplest possible computational and analytical models. To this end, we construct a series of biophysically motivated simulation models and analytical descriptions of trophallaxis that include all its essential features. Our models are the first complete theoretical description of the physical mechanisms behind the self-organized food distribution in ant colonies. Despite our reductionist approach, the models exhibit a number of interesting properties that reproduce some of the behaviors seen in real ant colonies. We are confident that our models can serve as benchmarks for the behavior of real ant colonies or more biologically detailed models. As statistical null models, they can be used to assess to what extent an observed behavior is due to non-random strategies or due to the collective properties of a stochastic system. We find and analytically predict the characteristic time scales of trophallaxis for both well-mixed colonies and colonies with small spatial fidelity zones. We even successfully cover the range between these two limits with semi-analytic predictions. These newly discovered relationships between individual behavior and global food distribution dynamics provide microscopic explanations of experimental observations and phenomenological theories that were unknown so far

Unsupervised discovery of relations for analysis of textual data in digital forensics

This dissertation addresses the problem of analysing digital data in digital forensics. It will be shown that text mining methods can be adapted and applied to digital forensics to aid analysts to more quickly, efficiently and accurately analyse data to reveal truly useful information. Investigators who wish to utilise digital evidence must examine and organise the data to piece together events and facts of a crime. The difficulty with finding relevant information quickly using the current tools and methods is that these tools rely very heavily on background knowledge for query terms and do not fully utilise the content of the data. A novel framework in which to perform evidence discovery is proposed in order to reduce the quantity of data to be analysed, aid the analysts' exploration of the data and enhance the intelligibility of the presentation of the data. The framework combines information extraction techniques with visual exploration techniques to provide a novel approach to performing evidence discovery, in the form of an evidence discovery system. By utilising unrestricted, unsupervised information extraction techniques, the investigator does not require input queries or keywords for searching, thus enabling the investigator to analyse portions of the data that may not have been identified by keyword searches. The evidence discovery system produces text graphs of the most important concepts and associations extracted from the full text to establish ties between the concepts and provide an overview and general representation of the text. Through an interactive visual interface the investigator can explore the data to identify suspects, events and the relations between suspects. Two models are proposed for performing the relation extraction process of the evidence discovery framework. The first model takes a statistical approach to discovering relations based on co-occurrences of complex concepts. The second model utilises a linguistic approach using named entity extraction and information extraction patterns. A preliminary study was performed to assess the usefulness of a text mining approach to digital forensics as against the traditional information retrieval approach. It was concluded that the novel approach to text analysis for evidence discovery presented in this dissertation is a viable and promising approach. The preliminary experiment showed that the results obtained from the evidence discovery system, using either of the relation extraction models, are sensible and useful. The approach advocated in this dissertation can therefore be successfully applied to the analysis of textual data for digital forensics CopyrightDissertation (MSc)--University of Pretoria, 2010.Computer Scienceunrestricte

Turing pattern formation on the sphere for a morphochemical reaction-diffusion model for electrodeposition

The present paper deals with the pattern formation properties of a specific morpho- electrochemical reaction-diffusion model on a sphere. The physico-chemical background to this study is the morphological control of material electrodeposited onto spherical parti- cles. The particular experimental case of interest refers to the optimization of novel metal- air flow batteries and addresses the electrodeposition of zinc onto inert spherical supports. Morphological control in this step of the high-energy battery operation is crucial to the energetic efficiency of the recharge process and to the durability of the whole energy- storage device. To rationalise this technological challenge within a mathematical modeling perspective, we consider the reaction-diffusion system for metal electrodeposition intro- duced in [Bozzini et al., J. Solid State Electr.17, 467–479 (2013)] and extend its study to spherical domains. Conditions are derived for the occurrence of the Turing instability phe- nomenon and the steady patterns emerging at the onset of Turing instability are investi- gated. The reaction-diffusion system on spherical domains is solved numerically by means of the Lumped Surface Finite Element Method (LSFEM) in space combined with the IMEX Euler method in time. The effect on pattern formation of variations in the domain size is investigated both qualitatively, by means of systematic numerical simulations, and quan- titatively by introducing suitable indicators that allow to assign each pattern to a given morphological class. An experimental validation of the obtained results is finally presented for the case of zinc electrodeposition from alkaline zincate solutions onto copper spheres

Mustang Daily, March 3, 1994

Student newspaper of California Polytechnic State University, San Luis Obispo, CA.https://digitalcommons.calpoly.edu/studentnewspaper/5681/thumbnail.jp