Top-k Route Search through Submodularity Modeling of Recurrent POI Features

We consider a practical top-k route search problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination and limited in a cost budget, that maximally match her needs on feature preferences. One challenge is dealing with the personalized diversity requirement where users have various trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features). Another challenge is the large scale of the POI map and the great many alternative routes to search. We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints. We also present promising heuristic solutions and evaluate all the solutions on real life data.Comment: 11 pages, 7 figures, 2 table

Application of Genetic Algorithm in solving Tourist Routing Problem

Normally, tourist will experience dilemma in planning their tour route especially when they visited foreign country for the first time. Manually mapping the cities and searching the information on the Internet can be very exhaustive. Besides these, tourist also faced a dilemma on how to travel across different cities efficiently and at shortest distance. This can also be known as Tourist Routing Problem (TRP). TRP is a variance of Travelling Salesman Problem (TSP) which can defined by finding the optimal path to travel from point A to point B by going through the same place not more than twice at a shortest distance. After completing a thorough comparative study, the author decided to apply Genetic Algorithm (GA), which is one of the best heuristic solutions to date in solving TRP. A rapid-prototyping methodology had been chosen because the author can immediately alter the prototype if there are any changes in the requirements. An Android mobile application will be utilized as a platform to test the effectiveness of GA in solving TRP. To support this, simulation and experiments will be conducted to evaluate the performance and speedup of the algorithm. Besides focusing on finding the best shortest distance route to travel, this application will enable tourist to select places to visit according to their preferences and activities that will be happening at that particular place

SPETA: Social pervasive e-tourism advisor

Tourism is one of the major sources of income for many countries. Therefore, providing efficient, real-time service for tourists is a crucial competitive asset which needs to be enhanced using major technological advances. The current research has the objective of integrating technological innovation into an information system, in order to build a better user experience for the tourist. The principal strength of the approach is the fusion of context-aware pervasive systems, GIS systems, social networks and semantics. This paper presents the SPETA system, which uses knowledge of the user’s current location, preferences, as well as a history of past locations, in order to provide the type of recommender services that tourists expect from a real tour guide.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the GODO project (FIT-340000-2007-134), under the PIBES project of the Spanish Committee of Education and Science (TEC2006-12365-C02-01) and under the MID-CBR project of the Spanish Committee of Education and Science (TIN2006-15140-C03-02).Publicad

Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging

Any image recovery algorithm attempts to achieve the highest quality reconstruction in a timely manner. The former can be achieved in several ways, among which are by incorporating Bayesian priors that exploit natural image tendencies to cue in on relevant phenomena. The Hierarchical Bayesian MAP (HB-MAP) is one such approach which is known to produce compelling results albeit at a substantial computational cost. We look to provide further analysis and insights into what makes the HB-MAP work. While retaining the proficient nature of HB-MAP's Type-I estimation, we propose a stochastic approximation-based approach to Type-II estimation. The resulting algorithm, fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while retaining high reconstruction quality. We employ our fsHBMAP scheme towards the problem of tomographic imaging and demonstrate that fsHBMAP furnishes promising results when compared to many competing methods.Comment: 5 Pages, 4 Figures, Conference (Accepted to Asilomar 2017

Math and the Mouse: Explorations of Mathematics and Science in Walt Disney World

Math and the Mouse is an intensive, collaborative, project-driven, study away course that runs during the three-week May Experience term at Furman University and has many of the attributes of a course-based undergraduate research experience in mathematics. We take twelve students to Orlando, Florida to study the behind-the-scenes mathematics employed to make Walt Disney World operate efficiently. Students learn techniques of mathematical modeling (mostly resource allocation, logistics, and scheduling models), statistical analysis (mostly probability, clustering, data collection, and hypothesis testing), and ow management (queuing theory and some beginning ow dynamics) in an applied setting. Through planned course modules, collaborative activities, conversations with guest speakers, and three group projects, one of which is of the students\u27 choosing, this academic experience provides an engaged learning experience that shows how material from eleven academic courses comes together in connection with real-world applications

Congestion Alleviation Scheduling Technique for Car Drivers Based on Prediction of Future Congestion on Roads and Spots

ITSC2007 : IEEE Intelligent Transportation Systems Conference , Sep 30-Oct 3, 2007 , Bellevue, WA, USAIn arranging efficient touring to various areas in urban areas, taking into account potential congestion is needed in order to schedule the order of these visits it is important to on the roads used and at the places to be visited. A number of scheduling methods have been proposed for finding (1) a noncongested route by sharing route information among users, or (2) a schedule to alleviate congestion at specific places based on the latest congestion information. However, these methods do not suffice since they do not deal with, simultaneously, congestion on road and at sites visited. In this paper, we propose a method of finding schedules for thousands of users by predicting, in advance, both types of congestion. Using the predicted results, the method adjusts each user's provisional schedule by changing visiting order of places, and reducing their number in keeping with each user's preferences. We have implemented the proposed method and evaluated it by simulations. The results showed it to achieve higher user satisfaction than existing methods

2019 Furman University Faculty Scholarship Reception Program

On February 22, 2019, the Libraries and the Office of the Provost will host Furman’s third Faculty Scholarship Reception to recognize and celebrate the scholarly publications and creative works of Furman faculty members. The reception, held in the Blackwell Atrium of the James B. Duke Library, will showcase scholarship published by faculty members during the 2018 calendar year. The following faculty will provide four-minute presentations on their scholarly or creative works: Eunice Rojas. Associate Professor, Modern Languages and Literatures Omar Camenates. Associate Professor, Music Geoffrey Habron. Professor, Earth and Environmental Sciences Scott Henderson. Professor, Education Laura Leigh Morris. Assistant Professor, English M. Taha Kasim. Assistant Professor, Economic

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts

External-Memory Graph Algorithms

We present a collection of new techniques for designing and analyzing efficient external-memory algorithms for graph problems and illustrate how these techniques can be applied to a wide variety of specific problems. Our results include: Proximate-neighboring. We present a simple method for deriving external-memory lower bounds via reductions from a problem we call the “proximate neighbors” problem. We use this technique to derive non-trivial lower bounds for such problems as list ranking, expression tree evaluation, and connected components. PRAM simulation. We give methods for efficiently simulating PRAM computations in external memory, even for some cases in which the PRAM algorithm is not work-optimal. We apply this to derive a number of optimal (and simple) external-memory graph algorithms. Time-forward processing. We present a general technique for evaluating circuits (or “circuit-like” computations) in external memory. We also usethis in a deterministic list ranking algorithm. Deterministic 3-coloring of a cycle. We give several optimal methods for 3-coloring a cycle, which can be used as a subroutine for finding large independent sets for list ranking. Our ideas go beyond a straightforward PRAM simulation, and may be of independent interest. External depth-first search. We discuss a method for performing depth first search and solving related problems efficiently in external memory. Our technique can be used in conjunction with ideas due to Ullman and Yannakakis in order to solve graph problems involving closed semi-ring computations even when their assumption that vertices fit in main memory does not hold. Our techniques apply to a number of problems, including list ranking, which we discuss in detail, finding Euler tours, expression-tree evaluation, centroid decomposition of a tree, least-common ancestors, minimum spanning tree verification, connected and biconnected components, minimum spanning forest, ear decomposition, topological sorting, reachability, graph drawing, and visibility representation