Enhanced Prediction Models for Predicting Spatial Visualization (VZ) in Address Verification Task

Self Cite

An adaptive software system is known as an application that can adapt itself based on different conditions of users. There are multiple conditions/criteria that can be used to direct how an application would adapt. Spatial visualization (VZ) is one of several human spatial abilities that is used to predict human’s performance when using a computer application. Therefore, a difference in VZ level is a suitable choice as an adapting indicator, i.e., high VZ and low VZ users should get different features on a user interface (UI) to complete the same task. In this paper, we look at three studies where we asked participants to verify a set of housing addresses using a location-based application on an Android tablet with different versions of the application, especially, an adaptive version of the application was involved in the last study. We found that, for high VZ participants, the number of UI errors that participants created was significantly smaller when they were equipped with the adaptive software. We refer to a UI error (User Interface Error) as an error where a user tapped on a non-sensitive region of the screen. The results of the three studies and hypothesis tests for significance are reported.

Section: Related Workmentioning

confidence: 99%

UI Error Reduction for High Spatial Visualization Users when Using Adaptive Software to Verify Addresses

Patanasakpinyo¹,

Miller

Self Cite

“…An experiment of Batinov et al [1] implemented a location-based application for a purpose of census. Several studies also focused on the use of location-based application for an address verification task [10,11,13,14,15]. PatanasakPinyo et al [12] proposed a method to add an indexing ability to a location-based application using a raster map.…”

Section: Related Workmentioning

confidence: 99%

Ameliorating Accuracy of a Map Navigation When Dealing with Different Altitude Traffics that Share Exact Geolocation

Patanasakpinyo¹

Self Cite

Many users use a location-based application on a portable device to be a navigator when driving. However, there exists an incident that two roads are located on the same geolocation, i.e., same values of latitude and longitude but different altitude, for very long distance where one road is located on the ground level and another one is elevated. This incident mostly confuses a location-based application to precisely retrieve the actual road that a vehicle is currently on and, consequently, causes the application to either navigate incorrectly or suggest a route that is a detour. Calling an altitude from a GPS sensor might be a possible solution but it came with problems of accuracy, especially for mid-grade GPS sensors that equipped with most smartphone in today’s market. We proposed a concept of implementing a classification model that can classify whether a vehicle is on a ground road or an elevated road regardless of geolocation data. We trained and validated two models using a dataset that we had collected from actual driving on two roads in Thailand that fell under this condition. A data instance that we collected contained measurements related to driving or driving environment such as a real-time speed at any certain interval of time. We reported validation results of both models as well as other important statistics.

“…The example of such applications is designing a sequence of addresses for the address the verification task given the specific starting address [1,5,7,8]. Another good example is designing a matching problems for the paper folding test, which is a test of individual spatial visualization (VZ) [9,11,12,14].…”

Section: Conclusion and Feasible Future Applicationsmentioning

confidence: 99%

Alternative Approach to Achieve a Solution of Derangement Problems by Dynamic Programming

Patanasakpinyo¹,

Sulaiman²

Derangement is one well-known problem in the filed of probability theory. An in- stance of a derangement problem contains a finite collection C of n paired objects, C = {(x1 , y1 ), ..., (xn , yn )}. The derangement problem asks how many ways to gener- ate a new collection C′ ̸= C such that for each (xi,yj) ∈ C′,i ̸= j. We propose an efficient dynamic programming algorithm that divides an instance of the derangement problem into several subproblems. During a recursive process of unrolling a subproblem, there exists a repeated procedure that allows us to make a use of a subsolution that has already been computed. We present the methodology to formulate a concept of this subproblem as well as parts of designing and analyzing an efficiency of the proposed algorithm.