In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a temporarily connected UAV network such that the network throughput – the number of users served by the UAVs, is maximized, subject to the constraint that the number of people served by each UAV is no greater than its service capacity. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer with$1 \le s\le K$, e.g.,$s=3$. We also devise an improved heuristic, based on the approximation algorithm. We finally evaluate the performance of the proposed algorithms. Experimental results show that the numbers of users served by UAVs in the solutions delivered by the proposed algorithms are increased by 25% than state-of-the-arts.

In this paper we study the deployment of a UAV (unmanned aerial vehicle) network that consists of multiple UAVs to provide emergent communication services to people trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of users. Unlike most existing studies focusing on homogenous UAVs, we consider the deployment of heterogeneous UAVs, where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a connected UAV network such that the number of users served by the UAVs is maximized, subject to the constraint that the number of users served by each UAV is no greater than its service capacity, assuming that the maximum number of users can be served by a UAV is given. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer, e.g.,$s=3$. We finally evaluate the performance of the approximation algorithm. Experimental results show that the number of users served by all UAVs in the approximate solution is improved by 22% compared with the solutions delivered by state-of-the-arts.

A sparse label oriented semantic segmentation network (SL-SSNet) is proposed for classification of hyperspectral images in this paper. Since semantic segmentation network performs pixel-level classification and can extract long-range contextual information, we apply it to the task of hyperspectral image classification. To mitigate the small size sample problem, we not only design a lightweight fully convolutional network, but also explore the usefulness of unlabeled data by introducing two constraints. Firstly, an adversarial learning based multi-classifier consistency strategy is employed to improve the classification of unlabeled data. It constrains two different classifiers to have consistent prediction results on unlabeled data. As a result, the extracted features of unlabeled data can be more discriminative and the predictions are more reliable. Secondly, a manifold regularizer is applied to constrain the classification results of unlabeled data to be smooth with respect to the data manifold, which can further exploit the unlabeled data and alleviate the small size sample problem. The experimental results using multiple hyperspectral data demonstrate the efficiency of the proposed method.

Task-oriented virtual assistants are software systems that provide users with a natural language interface to complete domain-specific tasks. With the recent technological advances in natural language processing and machine learning, an increasing number of task-oriented virtual assistants have been developed. However, due to the well-known complexity and difficulties of the natural language understanding problem, it is challenging to manage a task-oriented virtual assistant software project. Meanwhile, the management and experience related to the development of virtual assistants are hardly studied or shared in the research community or industry, to the best of our knowledge. To bridge this knowledge gap, in this paper, we share our experience and the lessons that we have learned at managing a task-oriented virtual assistant software project at Microsoft. We believe that our practices and the lessons learned can provide a useful reference for other researchers and practitioners who aim to develop a virtual assistant system. Finally, we have developed a requirement management tool, named SpecSpace, which can facilitate the management of virtual assistant projects.

The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with GDPR. However, GDPR lists general rules rather than exact step-by-step guidelines about how to develop an app that fulfills the requirements. Therefore, there may exist GDPR compliance violations in existing apps, which would pose severe privacy threats to app users. In this paper, we take mobile health applications (mHealth apps) as a peephole to examine the status quo of GDPR compliance in Android apps. We first propose an automated system, named HPDROID, to bridge the semantic gap between the general rules of GDPR and the app implementations by identifying the data practices declared in the app privacy policy and the data relevant behaviors in the app code. Then, based on HPDROID, we detect three kinds of GDPR compliance violations, including the incompleteness of privacy policy, the inconsistency of data collections, and the insecurity of data transmission. We perform an empirical evaluation of 796 mHealth apps. The results reveal that 189 (23.7%) of them do not provide complete privacy policies. Moreover, 59 apps collect sensitive data through different measures, but 46 (77.9%) of them contain at least one inconsistent collection behavior. Even worse, among the 59 apps, only 8 apps try to ensure the transmission security of collected data. However, all of them contain at least one encryption or SSL misuse. Our work exposes severe privacy issues to raise awareness of privacy protection for app users and developers.

Record-and-replay tools are indispensable for quality assurance of mobile applications. Due to its importance, an increasing number of tools are being developed to record and replay user interactions for Android. However, by conducting an empirical study of various existing tools in industrial settings, researchers have revealed a gap between the characteristics requested from industry and the performance of publicly available record-and-replay tools. The study concludes that no existing tools under evaluation are sufficient for industrial applications. In this paper, we present a record-and-replay tool called SARA towards bridging the gap and targeting a wide adoption. Specifically, a dynamic instrumentation technique is used to accommodate rich sources of inputs in the application layer satisfying various constraints requested from industry. A self-replay mechanism is proposed to record more information of user inputs for accurate replaying without degrading user experience. In addition, an adaptive replay method is designed to enable replaying events on different devices with diverse screen sizes and OS versions. Through an evaluation on 53 highly popular industrial Android applications and 265 common usage scenarios, we demonstrate the effectiveness of SARA in recording and replaying rich sources of inputs on the same or different devices.