Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Machine perception and learning of complex social systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 125-136).The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months has generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify self- report survey data, and study social network dynamics. By combining theoretical models with rich and systematic measurements, we show it is possible to gain insight into the underlying behavior of complex social systems.by Nathan Norfleet Eagle.Ph.D

A New Generative Adversarial Network for Improving Classification Performance for Imbalanced Data

Data is a common issue in many industries, particularly in fields such as fraud detection and medical diagnosis. Imbalanced data refers to datasets where the distribution of classes is not equal, resulting in an over- representation of one class and an under-representation of another. This can lead to biassed and inaccurate machine learning models, as the algorithm may be inclined to favour the majority class and overlook important patterns in the minority class. Various sectors have utilised deep neural networks for data synthesis. However, according to research papers in these fields, balanced data outperforms imbalanced data when it comes to deep neural networks. Although deep generative approaches, such as Generative Adversarial Networks (GANs), are an efficient method of augmenting high-dimensional data, there is a lack of research on their effectiveness with credit card or breast cancer data and the current methods demonstrate limitations. Our research focuses on obtaining a great number of sets of data that are valid and resemble the minority class, in this case, fraudulent or malignant samples. Having more data like this can be used to train a binary classifier so it's effective against fraud or cancer diagnosis. To overcome challenges opposed to existing methods we have developed a novel GAN-based method called K-CGAN, which has been tested on credit card fraud and breast cancer data. K- CGAN is designed to generate synthetic data that resembles the minority class, effectively balancing the dataset and improving the performance of binary classifiers. Our research demonstrates the effectiveness of K-CGAN in handling complex data imbalance problems often encountered in practical applications. In addition, the experiments performed on different datasets indicate that K-CGAN can be used for various purposes. The application of machine learning algorithms in various industries has become increasingly popular in recent years. However, the quality and quantity of available data are crucial factors that directly impact the accuracy and reliability of these models. The scarcity and imbalance of datasets in certain domains pose challenges for researchers and practitioners, and the need for effective solutions is more pressing than ever. In this context, K- CGAN provides a promising approach to address data imbalance and improve the performance of machine learning models. Our results show that K-CGAN can be applied to different datasets with different characteristics, making it a valuable tool for data scientists and practitioners in various fields

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe