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Xuanzhe Liu

Bio: Xuanzhe Liu is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Android (operating system). The author has an hindex of 30, co-authored 191 publications receiving 3066 citations. Previous affiliations of Xuanzhe Liu include Information Technology Institute & Chinese Ministry of Education.


Papers
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Proceedings ArticleDOI
09 Jul 2007
TL;DR: The mashup architecture is proposed, the current SOA model is extended with mashup and a mashup component model is proposed to help developers leverage to create their own composite services.
Abstract: Mashup is a hallmark of Web 2.0 and attracts both industry and academia. It refers to an ad hoc composition technology of Web applications that allows users to draw upon content retrieved from external data sources to create entirely new services. Compared to traditional "developer-centric" composition technologies, e.g., BPEI and WSCI, mashup provides a flexible and easy-of-use way for service composition on Web. It makes the consumers free to compose services as they wish as well as simplifies the composition task. This paper makes two contributions. Firstly, we propose the mashup architecture, extend current SOA model with mashup and analyze how it facilitates service composition. Secondly, we propose a mashup component model to help developers leverage to create their own composite services. A case study is given to illustrate how to do service composition by mashup. This paper also discusses about some interesting topics about mashup.

262 citations

Proceedings ArticleDOI
Ziniu Hu1, Weiqing Liu1, Jiang Bian1, Xuanzhe Liu2, Tie-Yan Liu1 
02 Feb 2018
TL;DR: Wang et al. as mentioned in this paper designed a Hybrid Attention Networks (HAN) to predict the stock trend based on the sequence of recent related news, and applied the self-paced learning mechanism to imitate the third principle.
Abstract: Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.

229 citations

Proceedings ArticleDOI
15 Oct 2018
TL;DR: DeepCache as mentioned in this paper proposes a principled cache design for deep learning inference in continuous mobile vision, which benefits model execution efficiency by exploiting temporal locality in input video streams and propagates regions of reusable results by exploiting the model's internal structure.
Abstract: We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. It addresses a key challenge raised by mobile vision: the cache must operate under video scene variation, while trading off among cacheability, overhead, and loss in model accuracy. At the input of a model, DeepCache discovers video temporal locality by exploiting the video's internal structure, for which it borrows proven heuristics from video compression; into the model, DeepCache propagates regions of reusable results by exploiting the model's internal structure. Notably, DeepCache eschews applying video heuristics to model internals which are not pixels but high-dimensional, difficult-to-interpret data. Our implementation of DeepCache works with unmodified deep learning models, requires zero developer's manual effort, and is therefore immediately deployable on off-the-shelf mobile devices. Our experiments show that DeepCache saves inference execution time by 18% on average and up to 47%. DeepCache reduces system energy consumption by 20% on average.

174 citations

Proceedings ArticleDOI
12 Sep 2016
TL;DR: It is demonstrated that the categories and frequencies of emojis used by these users provide rich signals for the identification and the understanding of cultural differences of smartphone users.
Abstract: Emojis have been widely used to simplify emotional expression and enrich user experience. As an interesting practice of ubiquitous computing, emojis are adopted by Internet users from many different countries, on many devices (particularly popular on smartphones), and in many applications. The "ubiquitous" usage of emojis enables us to study and compare user behaviors and preferences across countries and cultures. We present an analysis on how smartphone users use emojis based on a very large data set collected from a popular emoji keyboard. The data set contains a complete month of emoji usage of 3.88 million active users from 212 countries and regions. We demonstrate that the categories and frequencies of emojis used by these users provide rich signals for the identification and the understanding of cultural differences of smartphone users. Users from different countries present significantly different preferences on emojis, which complies with the well-known Hofstede's cultural dimensions model.

161 citations

Proceedings ArticleDOI
Ying Zhang1, Gang Huang1, Xuanzhe Liu1, Wei Zhang1, Hong Mei1, Shun-Xiang Yang1 
19 Oct 2012
TL;DR: A tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability, and generates two artifacts to be deployed onto an Android phone and the server, respectively.
Abstract: Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a smartphone application by executing some parts of the application on a remote server. Supporting such capability is not easy for smartphone application developers due to (1) correctness: some code, e.g., that for GPS, gravity, and other sensors, can run only on the smartphone so that developers have to identify which parts of the application cannot be offloaded; (2) effectiveness: the reduced execution time must be greater than the network delay caused by computation offloading so that developers need to calculate which parts are worth offloading; (3) adaptability: smartphone applications often face changes of user requirements and runtime environments so that developers need to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, solutions applicable for legacy applications will be much more valuable. In this paper, we present a tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability. For a given Android application, DPartner first analyzes its bytecode for discovering the parts worth offloading, then rewrites the bytecode to implement a special program structure supporting on-demand offloading, and finally generates two artifacts to be deployed onto an Android phone and the server, respectively. We evaluated DPartner on three real-world Android applications, demonstrating the reduction of execution time by 46%-97% and battery power consumption by 27%-83%.

137 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.
Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

975 citations

01 Jan 2018
TL;DR: The conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona.
Abstract: Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

665 citations

Journal ArticleDOI
TL;DR: By consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.
Abstract: Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence , aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge , this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge , i.e., Edge DL.

611 citations

Journal ArticleDOI
TL;DR: In this paper, a survey on the relationship between edge intelligence and intelligent edge computing is presented, and the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework, challenges and future trends of more pervasive and fine-grained intelligence.
Abstract: Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

518 citations