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.

Spontaneous and Context-Aware Media Recommendation in Heterogeneous Spaces

Abstract: While mobile users move from one smart space to another, it is highly desirable for them to access the right media contents from the overabundant media information in the right form with their own devices This paper deals with two important issues in pervasive media access: one is the spontaneous media access in heterogeneous environments, the other is the context-aware media recommendation in different spaces A general platform for spontaneous media access and context-aware media recommendation has been proposed and implemented The proposed hybrid recommendation algorithm shows quite good performance in heterogeneous environments

A Context-Aware Framework for Reducing Bandwidth Usage of Mobile Video Chats

Abstract: Mobile video chat apps offer users an approachable way to communicate with others. As high-speed 4G networks are being deployed worldwide, the number of mobile video chat app users increases. However, video chatting on mobile devices brings users financial concerns, since streaming video demands high bandwidth and can use up a large amount of data in dozens of minutes. Lowering the bandwidth usage of mobile video chats is challenging since video quality may be compromised. In this paper, we attempt to tame this challenge. Technically, we propose a context-aware frame rate adaption framework, named low-bandwidth video chat (LBVC). It follows a sender-receiver cooperative principle that smartly handles the tradeoff between lowering bandwidth usage and maintaining video quality. We implement LBVC by modifying an open-source app–Linphone– and evaluate it with both objective experiments and subjective studies.

Toward Context-Aware Mobile Social Networks

Abstract: CA-MSNs are more intelligent and user-friendly than conventional online or mobile social networks. We first classify CA-MSNs into four categories, and divide their life cycle into four phases: discovery, connection, interaction, and organization. We then introduce personal and community context, and discuss the corresponding taxonomy. Subsequently, we elaborate how such context can be leveraged to enhance each life cycle phase. We also present our practices on designing various CA-MSN applications. Finally, future research directions are identified to shed light on the next generation MSNs from the context awareness perspective.

Introduction to the special section on intelligent systems for socially aware computing

Abstract: The recent advance of pervasive computing technologies promises to significantly enhance capabilities for data capture and data analysis. In this socially aware era, such technologies hold great promise and challenge for using the sensory data to understand human behavior, human mobility, human activities, and ultimately to help solve human social problems. The integration of pervasive computing and social computing has resulted in an emerging new research field in computer science—Socially Aware Computing. While the concept of social awareness has been developed in the field of Computer Supported Cooperative Work for decades, the notion of socially aware computation and communication has only recently been introduced by Alex Pentland [2005]. Socially Aware Computing brings light to the design of new software methodology, infrastructure, data analysis, and applications. This paradigm aims to leverage the large-scale and diverse sensing devices that can be deployed in human daily lives to recognize individual behaviors, discover group interaction patterns, and support communication and collaboration. Intelligent systems powered by artificial intelligence play an important role in realizing socially aware computing in various aspects, such as sensing, processing, and supporting human interaction. This special section aims to explore intelligent systems and related applications for socially aware computing. Submissions to this special issue came from an open call for papers. We received a total of 16 submissions of which 5 articles were accepted after three rounds of rigorous reviews. A large number of reviewers assisted us in the review process. In order to ensure high reviewing standards, three to four reviewers evaluated each article.

Social and Community Intelligence

Abstract: This paper introduces an emerging research area – Social and Community Intelligence (SCI). It aims at revealing the individual/group behaviors, social interactions as well as community dynamics by mining the digital traces left by people while interacting with cyber-physical spaces. The digital traces are generated mainly from three information sources: Internet and Web applications, static infrastructure, mobile devices and wearable sensors. The paper discusses the evolution, general framework, major applications, and research issues of Social and Community

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.