# Report of the 2017 IEEE Cyber Science and Technology Congress
## 2 Cyber Human Science and Computing  
This session included six papers. Four of them describe work pertinent to medicine [1; 2; 3; 4] such as overall survival rate prediction and differentiation of different skin diseases. A common theme is the use of machine learning algorithms to improve the state of the practice in medicine. One paper reported the implementation and evaluation of an architecture for modeling human behaviors [5]. The remaining paper presented the work on identifying image tags from Instagram hashtags [6], which could help develop training dataset automatically. [PERSON] et al. [1] reported their work on developing a computer-based method for automatic analysis of respiratory sounds captured using the stethoscope. In their study, they captured data from 60 patients on three types of respiratory sounds, namely, wheezes, crackles, and normal sounds. They proposed a deep Convolutional Neural Networks (CNN) model to automatically learn the features of the various respiratory sounds for classification. Their primary research contribution lies in the design of the CNN model, which consists of six convolutional layers, three max pooling layers, and three fully connected layers. [PERSON] et al. [2] proposed a novel discriminator for identifying two common skin diseases, Seborrheic keratosis (SK) and flat warts (FW) using deep convolution neural networks based on the confocal laser scanning microscope images. Clinically, it is critical to differentiate the two skin diseases because they require very different treatment plans. Unfortunately, it is quite difficult to reliably do so. The authors showed that the proposed method rivals the accuracy of that of dermatologists. In [3], [PERSON] at el. proposed a novel method to predict the overall survival rate of gastric cancer patients after the D2 gastrectomy. The overall survival rate estimation is an important topic for oncology. Existing statistical predication models do not function well with the high dimensional dataset. The authors proposed a multi-modal hypergraph learning framework to overcome the challenges caused by the high dimensionality of the dataset, and therefore, were able to improve the accuracy of prediction. Using a dataset obtained from West China Hospital of Sichuan University with 939 patients, the authors demonstrated the superiority of their method compared with random forest and support vector machine. In [4], [PERSON] and [PERSON] proposed to use crowdsensing to collect massive real-world health and physiological data. The authors made two very interesting observation regarding crowdsensing: (1) the data collected should be specific to the health conditions; and (2) a person would be more interested in learning about his or her health conditions and therefore is more willing to participate in crowdsensing. A key requirement for enabling crowdsensing is to incentivize participation from a large number of people. The authors introduced a taxonomy of inventive mechanisms for health crowdsensing, and provided a novel incentive mechanism for crowdsensing based on a game theoretical multi-window framework. In [5], [PERSON] and [PERSON] introduced a model describing a wide variety of human behaviors in terms of their personality as well as contextual information such as location and time. They refer to the digital model for a person as an ontic persona. They described an implementation architecture on producing such ontic personae based on the collected personal data. The authors evaluated their model and implementation using two case studies, one focused on the detection of scenarios, and the other on the classification of different personality characteristics such as openness, conscientiousness, extraversion, agreeableness, and neuroticism. In [6], [PERSON], [PERSON] and [PERSON] reported their work on using the Hyperlink-Induced Topic Search (HITS) algorithm [7] to identify image tags from Instagram hashtags. They created a bipartite graph with two types of nodes.