Landscape of Human Factor Issues in Ensemble Visualization: A Survey, Congrong Ren and Jian Chen, The Ohio State University


Ensembles are frequently generated by domain experts. However, the scale of ensemble can be super large, which makes it impossible to view all the members in ensemble. Ensemble visualization is an effective method to both communicate and explore information in ensembles without viewing the individuals, and thus plays an important role in ensemble data analysis. This work surveys ensemble visualization methods and provides design guidelines of them. Specifically, we start with defining ensemble and ensemble visualization in broader point of view. Then we introduce a new taxonomy to identify the factors affecting performance of an ensemble visualization which includes visual dimensions, mechanisms, ensemble tasks, and applications. Coding related publications based on this taxonomy, we figure out the ensemble visualization methods frequently used or studied in prior years and evaluate their performance, and summarize some guidelines of ensemble visualization design. 



Ultrasound Medical Imaging Systems Using Telemedicine and Blockchain for Remote Monitoring of Responses to Neoadjuvant Chemotherapy in Women’s Breast Cancer: Concept and Implementation, Safa Shubbar and Austin Melton, Kent State University


Breast cancer disease continues to be a worldwide concern and the most common cause of death among women. In general, the cause of breast cancer remains unknown. Early detection and diagnosis of the disease remain the only factors which contribute to the successful preservation of lives in both developed and developing countries. By using computer-aided diagnosis (CAD), experts can further manipulate and process the obtained breast images. In this work, image classification is implemented by using the support vector machine (SVM). The goal of this method is to eventually use different processed and scanned breast images which are derived from segmenting and processing each image to detect cancerous tumors. Remote health care is established to monitor women in remote areas who receive neoadjuvant cancerous treatment by using the modern telecommunication infrastructure and blockchain technology.                                                                                               


Using Google Cardboard to Explore Fitts' Law, Amanda Illig and Rachelle Hippler, Baldwin Wallace University


In a world filled with technology, users want the most efficient devices to complete tasks at hand.  The efficiency of devices has been studied since the 1950s and will continue to be an important aspect of human-computer interaction as the use of technology grows.  As low-cost virtual reality headsets have become more popular, it is important to understand the speed in which it takes to use these devices, and how they compare to those previously studied.  Fitts’ law is one method researchers use to compare and determine the efficiency of input devices.  Paul M. Fitts’ concluded in his 1954 study that there is a linear relationship between the index of difficulty (ID) of a target and the movement time.  The index of difficulty is a model in which to quantify the difficulty of a task.  This study utilizes a simulation tool built by Baldwin Wallace University Computer Science students to measure the accuracy of head movement in a virtual world and how it is influenced by the target’s distance and direction.  Traditionally, input devices studied using Fitts’ law are compared to a mouse.  Therefore, this study will compare the head movement of a Google Cardboard virtual reality headset to a mouse.  It is projected that Fitts’ law will apply to the head movement using a Google Cardboard virtual reality headset in three-dimensional space, focusing on the following three discoveries.  There was a linear relationship found for the distance between the start and the target and movement time.  Accuracy was also affected by target distance.  The size of the target also impacted movement time and accuracy.  Finally, it was found that the acceleration of the movement will be fast at first, slow in the middle and somewhat faster as the target is approached.