A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Impact of Rhabdomyosarcoma (RMS) Characteristics on Prognosis of Pediatric RMS: a SEER Database Large Population Study

To provide a better insight into the epidemiology, characteristics, therapeutics, and outcomes of pediatric RMS. Data of 1,623 pediatric RMS were acquired from the Surveillance, Epidemiology and End Results (SEER) database. Detailed information on demographics, primary site, size, subtype, stage, surgery, and survival had been recorded during 1975-2016. The most common subtype was embryonal RMS (64.9%) followed by alveolar RMS (29.9%). Additionally, the majority of RMS size was larger than 5 cm. Multivariable analysis exhibited that the age over 10, unfavorable primary site, distant metastasis was respectively correlated with the poor OS, whereas surgery could improve the outcomes of pediatric RMS. In conclusion, our large population-based analysis described that age, subtype, primary tumor sites, stage and surgery are all independent prognosis factors for RMS

Real-Time Sensing of Trust in Human-Machine Interactions

Human trust in automation plays an important role in successful interactions between humans and machines. To design intelligent machines that can respond to changes in human trust, real-time sensing of trust level is needed. In this paper, we describe an empirical trust sensor model that maps psychophysiological measurements to human trust level. The use of psychophysiological measurements is motivated by their ability to capture a human\u27s response in real time. An exhaustive feature set is considered, and a rigorous statistical approach is used to determine a reduced set of ten features. Multiple classification methods are considered for mapping the reduced feature set to the categorical trust level. The results show that psychophysiological measurements can be used to sense trust in real-time. Moreover, a mean accuracy of 71.57% is achieved using a combination of classifiers to model trust level in each human subject. Future work will consider the effect of human demographics on feature selection and modeling

Optimal Operator Training Reference Models for Human-in-the-loop Systems

The human operator is an integral part of a stable and safe power system. While there is increasing attention paid to automation improvements, the importance of understanding and training human operators may be understated. This paper discusses a project to enhance operator training programs by evaluating human performance relative to a reference operator model identified using optimal control theory. Along with establishing a simple computer-based operator workstation for future training purpose, this paper describes the optimal control response design methodology for a human-in-the-loop power system experiment. The overall system model is presented. An optimal controller synthesis methodology is applied to the model system and the optimal controller is designed. The performance of the optimal controller is then compared to human subject performance

Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In this work, we consider generating unseen future frames from previous obeservations, which is notoriously hard due to the uncertainty in frame dynamics. While recent works based on generative adversarial networks (GANs) made remarkable progress, there is still an obstacle for making accurate and realistic predictions. In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties. More specifically, our model is a multi-stage generative network, which is named the Difference Guided Generative Adversarial Netwok (DGGAN). The DGGAN learns to explicitly enforce future-frame predictions that is guided by synthetic inter-frame difference. Given a sequence of frames, DGGAN first uses dual paths to generate meta information. One path, called Coarse Frame Generator, predicts the coarse details about future frames, and the other path, called Difference Guide Generator, generates the difference image which include complementary fine details. Then our coarse details will then be refined via guidance of difference image under the support of GANs. With this model and novel architecture, we achieve state-of-the-art performance for future video prediction on UCF-101, KITTI.Comment: To appear in ACCV 201

Child population, economic development and regional inequality of education resources in China

There is great inequality of educational resources between different provinces in China due to unbalanced economic development. Despite continued redistribution of financial resources by the central government in favor of poorer provinces, educational inequality remains. In this paper, we argue that focusing on educational resources is far from sufficient. Poorer provinces do not only suffer from a lower level of educational resources, but they also have more children to educate, i.e. a greater need for education. Combining and analyzing the data in the Sixth National Population Census of China and the official statistics on education spending and resources, we found that provincial-level variations in the child population and the child dependency ratio have made access to educational resources even more unequal given the unequal financial capacity at the provincial level. Poorer provinces face a higher child dependency ratio and have lower economic development, and these two factors jointly lead to limited educational resources. Apart from a much higher level of redistribution in favor of less developed provinces, encouraging more balanced distribution of teachers and more broadly promoting economic equality are essential to reduce inequality in educational resources in China

Origin-Destination Travel Time Oracle for Map-based Services

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.Comment: 15 pages, 12 figures, accepted by SIGMOD International Conference on Management of Data 202