vMobiDesk: Desktop Virtualization for Mobile Operating System

The widespread use of smart mobile devices brings a lot of benefits to people’s lives and increases the popularity of Bring Your Own Device (BYOD). Along with BYOD, there are also several challenge issues (e.g., limited hardware capacity, frequent upgrades of applications and security and privacy concerns). In this paper, we propose Virtual Mobile Infrastructure (VMI), a general framework that provides more reliable and secure solution for BYOD. VMI is specifically designed for mobile users. The idea of VMI is to host a mobile Operating System (OS) on a remote server in a cloud data center, and run mobile applications on it. It enables mobile users to access the virtual mobile desktops via mobile optimized display protocols through the network. Particularly, we focus on the design and implementation of vMobiDesk, a prototype system for VMI. It provides an implementation of VMI desktop virtualization on Android OS which is one of the most popular mobile operating systems. vMobiDesk focuses on virtualizing the display of Android desktops, redirecting users’ input events, providing audio support and remote camera. The experimental results show that vMobiDesk has a low virtualization overhead, and meanwhile enables mobile users to obtain good user experience on remotely accessing Android virtual desktops

Data Aggregation Scheduling in Wireless Networks

Data aggregation is one of the most essential data gathering operations in wireless networks. It is an efficient strategy to alleviate energy consumption and reduce medium access contention. In this dissertation, the data aggregation scheduling problem in different wireless networks is investigated. Since Wireless Sensor Networks (WSNs) are one of the most important types of wireless networks and data aggregation plays a vital role in WSNs, the minimum latency data aggregation scheduling problem for multi-regional queries in WSNs is first studied. A scheduling algorithm is proposed with comprehensive theoretical and simulation analysis regarding time efficiency. Second, with the increasing popularity of Cognitive Radio Networks (CRNs), data aggregation scheduling in CRNs is studied. Considering the precious spectrum opportunity in CRNs, a routing hierarchy, which allows a secondary user to seek a transmission opportunity among a group of receivers, is introduced. Several scheduling algorithms are proposed for both the Unit Disk Graph (UDG) interference model and the Physical Interference Model (PhIM), followed by performance evaluation through simulations. Third, the data aggregation scheduling problem in wireless networks with cognitive radio capability is investigated. Under the defined network model, besides a default working spectrum, users can access extra available spectrum through a cognitive radio. The problem is formalized as an Integer Linear Programming (ILP) problem and solved through an optimization method in the beginning. The simulation results show that the ILP based method has a good performance. However, it is difficult to evaluate the solution theoretically. A heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. Finally, we investigate how to make use of cognitive radio capability to accelerate data aggregation in probabilistic wireless networks with lossy links. A two-phase scheduling algorithm is proposed, and the effectiveness of the algorithm is verified through both theoretical analysis and numerical simulations

An Exploration of Broader Influence Maximization in Timeliness Networks with Opportunistic Selection

The goal of classic influence maximization in Online Social Networks (OSNs) is to maximize the spread of influence with a fix budget constraint, e.g. the size of seed nodes is pre-determined. However, most existing works on influence maximization overlooked the information timeliness. That is, these works assume the influence will not decay with time and the influence could be accepted immediately, which are not practical. Secondly, even the influence could be passed to a special node in time, whether the influence could be delivered (influence take effect) or not is still an unknown question. Furthermore, if let the number of users who are influenced as the depth of influence and the area covered by influenced users as the breadth, most of research results are only focus on the influence depth instead of the influence breadth. Timeliness, acceptance ratio and breadth are three important factors neglected but strong affect the real result of influence maximization. In order to fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio and broad diffusion for influence breadth has been investigated in this paper. In our model, the breadth of influence is measured by the number of communities, and the tradeoff between depth and breadth of influence could be balanced by a parameter φ. Empirical studies on different large real-world social networks show that our model demonstrates that high depth influence does not necessarily imply broad information diffusion. Our model, together with its solutions, not only provides better practicality but also gives a regulatory mechanism for influence maximization as well as outperforms most of the existing classical algorithms

Area Query Processing Based on Gray Code in Wireless Sensor Networks

Area query processing is significant for various applications of wireless sensor networks since it can request information of particular areas in the monitored environment. Existing query processing techniques cannot solve area queries. Intuitively centralized processing on Base Station can accomplish area queries via collecting information from all sensor nodes. However, this method is not suitable for wireless sensor networks with limited energy since a large amount of energy is wasted for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the monitored area is partitioned into grids, and a unique gray code number is used to represent a Grid ID (GID), which is also an effective way to describe an area. Furthermore, a reporting tree is constructed to process area merging and data aggregations. Based on the properties of GIDs, subareas can be merged easily and useless data can be discarded as early as possible to reduce energy consumption. For energy-efficiently answering continuous queries, we also design an incremental update method to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is effective and energy-efficient

A partner-matching framework for social activity communities

A lot of daily activities require more than one person to participate and collaborate with each other; however, for many people, it is not easy to find good partners to engage in activities with one another. With the rapid growth of social network applications, more and more people get used to creating connections with people on the social network. Therefore, designing social network framework for partner-matching is significant in helping people to easily find good partners. In this paper, we proposed a framework which can match partners for an active community. In order to improve the matching performance, all users are divided into groups based on a specific classification tree that is built for a specific activity. The optimization goal of the partner-matching is to maintain as many stable partnerships as possible in the community. To achieve the goal, various factors are considered to design matching functions. The simulation results show that the proposed framework can help most people find stable partners quickly

PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud

The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing ~1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models.Comment: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023: 1298-130

Probabilistic Best Subset Selection via Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization problem aiming to recover the latent sparse pattern from all possible covariate combinations. In this paper, we propose a novel optimization method to solve the exact $L_0$-regularized regression problem (a.k.a. best subset selection). We reformulate the optimization problem from a discrete space to a continuous one via probabilistic reparameterization. Within the framework of stochastic gradient descent, we propose a family of unbiased gradient estimators to optimize the $L_0$-regularized objective and a variational lower bound. Within this family, we identify the estimator with a non-vanishing signal-to-noise ratio and uniformly minimum variance. Theoretically, we study the general conditions under which the method is guaranteed to converge to the ground truth in expectation. In a wide variety of synthetic and semi-synthetic data sets, the proposed method outperforms existing variable selection methods that are based on penalized regression and mixed-integer optimization, in both sparse pattern recovery and out-of-sample prediction. Our method can find the true regression model from thousands of covariates in a couple of seconds.