Random sample partition (RSP) is a newly developed data management and processing model for Big Data processing and analysis. To apply the RSP model for Big Data computation tasks, it is very important to measure the ...
详细信息
Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Ori...
详细信息
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of th...
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.
Modern automation schemes have substantially improved by using standard communication protocols, upgraded technologies and user-friendly tools in power distribution domain. Nevertheless, this fundamental functionality...
详细信息
An important research branch of human-computer interaction(HCI) is to develop predictive models for human performance in fundamental interactions [1]. On today's graphical user interface(GUI), users often implicit...
An important research branch of human-computer interaction(HCI) is to develop predictive models for human performance in fundamental interactions [1]. On today's graphical user interface(GUI), users often implicitly perform various trajectory-based interactions, such as navigating through menus [2], entering the boundary of a button,
Current mobile applications(apps) have become increasingly complicated with increasing features that are represented on the graphical user interface associated with application programming interfaces(APIs) to access b...
详细信息
Current mobile applications(apps) have become increasingly complicated with increasing features that are represented on the graphical user interface associated with application programming interfaces(APIs) to access backend functionality and data. Meanwhile, apps suffer from the “software bloat” in volume. Some app features may be redundant, with respect to those features(from other apps) that the users already desirably and frequently use. However, the current app release model forces users to download and install a full-size installation package rather than optionally choosing only their desired features. Large-size apps can not only increase the local resource consumption, such as CPU, memory, and energy, but also inevitably compromise the user experience, such as the slow load time in the app. In this article, we first conduct an empirical study to characterize the app feature usage when users interact with Android apps,and surprisingly find that users access only a very small subset of app features. Based on these findings,we design a new approach named Lego Droid, which automatically decomposes an Android app for flexible loading and installation, while preserving the expected functionality with a fast and instant app load. With such a method, a slimmer bundle will be downloaded and host the target APIs inside the original app to satisfy users' requirements. We implement a system for Lego Droid and evaluate it with 1000 real-world Android apps. Compared to the original full-size apps, apps optimized by Lego Droid can substantially improve the load time by reducing the base bundle and feature bundles by 13.06% and 10.93%, respectively,along with the app-package installation size by 44.17%. In addition, we also demonstrate that Lego Droid is quite practical with evolving versions, as it can produce substantial reusable code to alleviate the developers' efforts when releasing new app versions.
The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network's decisions with a single ax...
详细信息
Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are propos...
详细信息
A large body of research effort has been dedicated to automated issue classification for Issue Tracking systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,incl...
详细信息
A large body of research effort has been dedicated to automated issue classification for Issue Tracking systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,including the different textual fields,feature representation methods and machine learning algorithms adopted by existing approaches,have not been comprehensively compared and *** fill this gap,we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification *** experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification,including:(1)Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification;(2)Word embedding with Long Short-Term Memory(LSTM)can better extract features from the textual fields in the issues,and hence,lead to better issue classification models;(3)There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues;(4)The performance of the issue classification model is not sensitive to the choices of ML *** on our study outcomes,we further propose an advanced issue classification approach,DEEPLABEL,which can achieve better performance compared with the existing issue classification approaches.
暂无评论