Daqing Zhang

Bio: Daqing Zhang is an academic researcher from Peking University. The author has contributed to research in topics: Context (language use) & Mobile computing. The author has an hindex of 67, co-authored 331 publications receiving 16675 citations. Previous affiliations of Daqing Zhang include Institut Mines-Télécom & Institute for Infocomm Research Singapore.

Safety Issues Associated With the Clinical Application of PCSK9 Inhibitors: Current Findings

Abstract: Reduction in low-density lipoprotein cholesterol levels is the cornerstone of treatment and prevention of atherosclerotic cardiovascular diseases. Currently, high-intensity statins are being used as the first line therapy to lower low-density lipoprotein cholesterol levels, as they improve the prognosis of patients with atherosclerotic cardiovascular disease and those in high-risk groups. However, in some patients the expected reduction in cholesterol is not achieved despite aggressive treatment with statins. Moreover, some patients cannot tolerate the dosage or show poor response or compliance to statins. Therefore, combination therapies with statins and other medications should be considered. Recently, several clinical trials have shown that the use of proprotein convertase subtilisin/kexin type 9 inhibitors with or without statins and/or other lipid-lowering drugs can significantly reduce low-density lipoprotein cholesterol levels, sometimes to extremely low levels. Therefore, to facilitate appropriate prescription of these new lipid-lowering drugs, we systemically evaluated the safety issues associated with these inhibitors and extremely low low-density lipoprotein cholesterol levels.

A Training-Free Contactless Human Vitality Monitoring Platform Using Commodity Wi-Fi Devices

Abstract: Human vitality information is pivotal to many sensing applications. By vitality, we mean the status of a human target in a multi-room environment: whether he/she is still and which room he/she is located in. Continuous monitoring of human vitality helps us obtain important high-level contexts like one's emotions, living habits, and physical conditions. Unlike the most existing solutions that require human efforts in offline training or calibration, in this demo, we present WiVit, a training-free contactless Wi-Fi based sensing platform that can capture human vitality information in 7*24 hours. In typical indoor environments, WiVit can achieve 98% accuracy of vitality detection and nearly 100% accuracy of area detection.

Proceedings of 1st international symposium on From digital footprints to social and community intelligence

Abstract: It is our great pleasure to welcome you to the First International Symposium on Social and Community Intelligence (SCI), associated with the International Conference on Ubiquitous Computing (Ubicomp) 2011. The mission of the symposium is to provide an international forum for the discussion of challenges in the fields of SCI, including theoretical studies, practical issues, emerging technologies and applications. SCI gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of revealing the individual/group behaviors, social interactions, as well as community dynamics (e.g., city hot spots, traffic jams) by mining the digital traces left by people while interacting with cyber-physical spaces. The call for papers attracted 14 submissions from Asia, America and Europe. The program committee accepted 8 papers that cover a variety of topics, including Intelligent Social/ Community Services, Social Data Aggregation and Analysis, and Social Communication. In addition, the program includes two invited papers. We hope that these proceedings will serve as a valuable reference for researchers and developers in the SCI area.

Context Aware Computing for The Internet of Things: A Survey

Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

Membership Inference Attacks Against Machine Learning Models

Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.