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Keynote Speakers

Prof. Chirag Shah
University of Washington, USA
Title Addressing Bias in Search and Fairness in Machine Learning
Abstract Bias is omnipresent — from data to algorithms, and from framing of a problem to interpreting its solution. In this talk, I will highlight how such bias in general with machine learning techniques, and in particular with search and recommender systems cause material problems for users, businesses, and society at large. The examples span areas of search, education, and health. I will then introduce the idea of marketplace as a way to find a balance or fairness in the system and address the issue of bias, among other things. I will draw specific examples from our work on search and recommendation systems to demonstrate that achieving fairness in a marketplace and addressing bias in data and algorithms are not just morally and ethically right things to do, but could also lead to a more sustainable growth for various industries, governments, and our scientific advancement.
Prof. Xiaobing Xian
Changshu Institute of Technology, China
Title The Collaboration application of Campus Hybrid Cloud Based on OpenID
Abstract The application of hybrid cloud has been a topic of concern on campus. Based on the Authentication technology of OpenID and OAuth2 authorization, we have built a mechanism that allows campus users using all kinds of public clouds by a much simpler faster way. Moreover, we have developed a set of authentication scheme which uses WeChat by scanning identify QR code. It has integrated the unified authentication entrance of PC and mobile applications and improved the service capability of the next generation information platform. On this basis,we have successively developed the Collaboration application of Campus Hybrid Cloud by using public clouds such as Mobile payment, Campus Chatterbot, Mobile learning ,Face recognition, IOT application, and so on. A low cost and high efficiency campus hybrid cloud is constructed. It has brought new start to the information construction of our campus.
Chief Hiroshi Nakamura
Kure Medical Center and Chugoku Cancer Center, Japan
Title Development of IoT-wearable Devices for the Purpose of Predicting the Cardiopulmonary Arrest Accident in the Citizens’ Marathon Competition
Abstract The cardiopulmonary arrest accident at the verge of the large-scale sports special events such as citizens’ marathons occurs at 1/50,000-100,000 runners. Though the outbreak frequency is low, the load to hang over the area when a cardiopulmonary arrest accident happened is big. In a risk foresight, electrocardiogram analysis is common, but the outdoors and the use by the sporting event have a limit. It is also unsuitable for the measurement in the outdoors including rainy weather in particular and the simultaneous record and analysis in the group. To solve many technical and environmental problems, we use a smartphone which has built-in a 3-axis gyroscope and a 3-axis accelerometer to measure both respiratory rate variabilities (RRV) and heart rate variabilities (HRV). Our newly proposed Stress-Index (SI) is possible to measure the stress objectively and quantitatively. SI can be derived with a simple expression: SI = RRV/HRV. By the analysis of the breathing pattern by machine learning to SI and Body mass index level, one high-risk runner that abnormality was recognized for circulation and breathing after Marathon completion. The possibility that this system was useful for a cardiopulmonary arrest predicting accident in the large-scale special event was suggested. We are now challenging to measure the SI during the human actions using the IoT-wearable devices to make the healthy world Society5.0 without stress and the pain in 2025.