Robotic grasping faces new challenges in human-robot-interaction scenarios. We consider the task that the robot grasps a target object designated by human's language directives. The robot not only needs to locate a target based on vision-and-language information, but also needs to predict the reasonable grasp pose candidate at various views and postures. In this work, we propose a novel interactive grasp policy, named Visual-Lingual-Grasp (VL-Grasp), to grasp the target specified by human language. First, we build a new challenging visual grounding dataset to provide functional training data for robotic interactive perception in indoor environments. Second, we propose a 6- Dof interactive grasp policy combined with visual grounding and 6- Dof grasp pose detection to extend the universality of interactive grasping. Third, we design a grasp pose filter module to enhance the performance of the policy. Experiments demonstrate the effectiveness and extendibility of the VL-Grasp in real world. The VL-Grasp achieves a success rate of 72.5 % in different indoor scenes. The code and dataset is available at https://github.com/luyh20/VL-Grasp.

6-DoF grasp pose detection of multi-grasp and multi-object is a challenge task in the field of intelligent robot. To imitate human reasoning ability for grasping objects, data driven methods are widely studied. With the introduction of large-scale datasets, we discover that a single physical metric usually generates several discrete levels of grasp confidence scores, which cannot finely distinguish millions of grasp poses and leads to inaccurate prediction results. In this paper, we propose a hybrid physical metric to solve this evaluation insufficiency. First, we define a novel metric is based on the force-closure metric, supplemented by the measurement of the object flatness, gravity and collision. Second, we leverage this hybrid physical metric to generate elaborate confidence scores. Third, to learn the new confidence scores effectively, we design a multi-resolution network called Flatness Gravity Collision GraspNet (FGC-GraspNet). FGC-GraspNet proposes a multi-resolution features learning architecture for multiple tasks and introduces a new joint loss function that enhances the average precision of the grasp detection. The network evaluation and adequate real robot experiments demonstrate the effectiveness of our hybrid physical metric and FGC-GraspNet. Our method achieves 90.5% success rate in real-world cluttered scenes. Our code is available at https://github.com/luyh20IFGC-GraspNet.

The Mote architecture is the most popular platform used in wireless sensor network applications. In this architecture, microcontroller is responsible for all jobs, such as scheduling, sampling, computing, and communication. In the past one year, two practical applications: bridge structural health monitoring system and rare animal monitoring system are developed and deployed in Wuxi and Beijing, China. It is found that Mote architecture faces many problems in these applications. First, sampling, computing, and communication conflicts with each other if they are not carefully scheduled; second, some jobs are very difficult even impossible to be implemented in the microcontroller; third, low power, one of the most fundamental design principles in wireless sensor networks, is sometimes violated with all jobs implemented in the microcontroller. Software optimization is attempted to solve these problems. However, the effect is very limited. Application specific sensor node architecture is necessary for implementing these applications efficiently. In this paper, we propose new application specific sensor node architecture and corresponding design principles and then applied them in the field deployments. Experimental and field tests show that these architectures are more efficient than Mote architecture in these applications.

Network Coordinate (NC) systems provide an efficient and scalable mechanism to estimate latencies among hosts. However, many popular algorithms like Vivaldi suffer greatly from the existence of Triangle Inequality Violations (TIVs). Two-layer systems like Pharos and hierarchical Vivaldi have been proposed to remedy the impact of TIVs. They divide the whole space into several location-based clusters and run NC systems on both global layer and local layer. However, the two-layer model is only able to optimize the intra-cluster links relating to a limited portion of TIV triangles. In this paper, we propose a new NC system, Tarantula, which divides the space in a novel way. By categorizing the TIVs into three classes, we show that Tarantula handles a much larger portion of existing TIVs than two-layer systems. Moreover, we present two techniques to further strengthen the Tarantula system: 1) relate the updating step size in the Vivaldi algorithm used in Tarantula to ground-truth latency so as to improve the prediction for short links; 2) propose Dynamic Cluster Optimization to dynamically adjust clustering of hosts. Our experimental results show that Tarantula outperforms Pharos and Vivaldi significantly in terms of estimation accuracy. When implementing different NC systems in the application of server selection and detour finding, Tarantula again performs the best.

Computing the shortest-path distances between nodes is a key problem in analyzing social graphs. Traditional methods like breadth-first search (BFS) do not scale well with graph size. Recently, a Graph Coordinate System, called Orion, has been proposed to estimate shortest-path distances in a scalable way. Orion uses a landmark-based approach, which does not take account of the shortest-path distances between non-landmark nodes in coordinate calculation. Such biased input for the coordinate system cannot characterize the graph structure well. In this paper, we propose Pomelo, which calculates the graph coordinates in a decentralized manner. Every node in Pomelo computes its shortest-path distances to both nearby neighbors and some random distant neighbors. By introducing the novel partial BFS, the computational overhead of Pomelo is tunable. Our experimental results from different representative social graphs show that Pomelo greatly outperforms Orion in estimation accuracy while maintaining the same computational overhead.

Network coordinate (NC) systems provide a lightweight and scalable way for predicting the distances, i.e., round-trip latencies among Internet hosts. Most existing NC systems embed hosts into a low dimensional Euclidean space. Unfortunately, the persistent occurrence of Triangle Inequality Violation (TIV) on the Internet largely limits the distance prediction accuracy of those NC systems. Some alternative systems aim at handling the persistent TIV, however, they only achieve comparable prediction accuracy with Euclidean distance based NC systems. In this paper, we propose an NC system, so-called Phoenix, which is based on the matrix factorization model. Phoenix introduces a weight to each reference NC and trusts the NCs with higher weight values more than the others. The weight-based mechanism can substantially reduce the impact of the error propagation. Using the representative aggregate data sets and the newly measured dynamic data set collected from the Internet, our simulations show that Phoenix achieves significantly higher prediction accuracy than other NC systems. We also show that Phoenix quickly converges to steady state, performs well under host churn, handles the drift of the NCs successfully by using regularization, and is robust against measurement anomalies. Phoenix achieves a scalable yet accurate end-to-end distances monitoring. In addition, we study how well an NC system can characterize the TIV property on the Internet by introducing two new quantitative metrics, so-called RERPLand AERPL. We show that Phoenix is able to characterize TIV better than other existing NC systems.

Replicated distributed file systems are designed to store large file reliably across lots of machines, and it arouse the problem of selecting the nearest replica for clients. In this paper, we propose Rigel, a Network Coordinates (NC) based nearest replica selection service, which is an effective infrastructure to select the nearest replica for client in a scalable and lightweight way. Our simulation results have demonstrated that Rigel can at least reduce the read latency between clients and replicas by 20% when compared to the replica selection strategy in Hadoop Distributed File System.

Routing policies and the complexity of network give rise to violations of the Triangle Inequality with respect to delay (Round-Trip Time) in the Internet. Most of the network coordinate (NC) systems, for example Vivaldi, suffer from inaccurate distance estimation due to such Triangle Inequality Violations (TIVs). In this abstract, we propose a methodology to overcome TIVs by introducing medium in Euclidean distance based NC model.

Microblogging services, such as Twitter, are among the most important online social networks(OSNs). Different from OSNs such as Facebook, the topology of microblogging service is a directed graph instead of an undirected graph. Recently, due to the explosive increase of population size, graph sampling has started to play a critical role in measurement and characterization studies of such OSNs. However, previous studies have only focused on the unbiased sampling of undirected social graphs. In this paper, we study the unbiased sampling algorithm for directed social graphs. Based on the traditional Metropolis-Hasting Random Walk (MHRW) algorithm, we propose an unbiased sampling method for directed social graphs(USDSG). Using this method, we get the first, to the best of our knowledge, unbiased sample of directed social graphs. Through extensive experiments comparing with the ”ground truth ” (UNI, obtained through uniform sampling of directed graph nodes), we show that our method can achieve excellent performance in directed graph sampling and the error to UNI is less than 10%.

Previously, the network must be congested by probing flow before the congestion-related parameters (such as background flow and available bandwidth) can be estimated, which lead to the inaccuracy and inefficiency of today's Internet. In this work, the authors study how to estimate network state without saturating the network. By introducing the queueing theory, this study proposes a novel packet time series analysis (PTSA) framework model, which can be used to estimate the congestion-related parameters without saturating the network. The accuracy and efficiency of PTSA are validated under NS-2 simulation environment. The performance of PTSA methodology is evaluated in Schooner test-bed with a special scenario. The analytical, simulative and experimental results show that PTSA framework is more efficient to estimate network state with less aggression and higher sensitivity than those methods that need to saturate the network.

In practice, because of their saturating affects, potentially all traditional transmission control protocol (TCP) congestion estimators effectively degrade the operational performance and efficiency of virtually all high-speed networks. Here the authors adopt a novel idea of using queueing-based estimator to estimate the TCP operating points ahead of their occurrence of network congestion. For this purpose the authors employ recent work of packet time-series analysis. In order to demonstrate the effectiveness of the idea, the authors embed the new estimator into several TCP variants in NS-2 and Linux kernel. For this evaluation the authors have programmed the simulation system to simulate a wide range of classic operating environments and setup many practical test-bed emulators for measurements. The authors results show that the idea of using a queueing-based estimator provides us far more earlier, sensitive and consistent estimation for the operating point than the classic loss/delay-based TCP traffic estimators. The authors then show that the new approach improves the overall estimation efficiency and provides higher performance and fairness when monitoring TCP traffic.

In recent research work of securing Network Coordinate (NC) system, they concentrate on the passive security defense mechanisms. In this paper we propose SLINCS, a social link based evaluation security system that utilizes information from existing social relationship networks to implement proactive security mechanisms for NC systems. The key idea is to eliminate suspicious nodes before they launch potential attacks.

Broadcatching is a promising mechanism to improve the experience of BitTorrent users by automatically downloading files advertised through RSS feeds. However, though widely used, the mechanism itself has not been well studied. In this paper, we conducted extensive experiments on PlanetLab to evaluate the performance of Broadcatching under different typical scenarios. The results demonstrated the effectiveness of the broadcatching: it reduces the average completion time and downloading failure ratio. It also improves the overall fairness of the system: the subscribers are encouraged to share more while downloading faster, which results in the increased share ratio. Our study is the first work to systematically evaluate the benefit of broadcatching and sheds lights on how to improve performance of BitTorrrent by manipulating peer's behavior like Broadcatching.

Network coordinates (NC) system is an efficient mechanism for Internet distance prediction with scalable measurements. The intrinsical cause for the unsatisfactory accuracy of the simulation-based NC algorithms has been identified. Then Pharos, a fully decentralised and hierarchical scheme, is proposed to solve this problem. Pharos leverages multiple coordinate sets at different distance scales, with the right scale being chosen for prediction each time. We evaluate the performance of Pharos system with the King data set and latency data from PlanetLab, and compare it with the representative NC system, Vivaldi. The experimental results show that Pharos greatly outperforms Vivaldi in Internet distance prediction without adding any significant overhead. Our extensive evaluation results also demonstrate that Pharos can significantly improve the performance in distributed Internet applications, such as overlay multicast and server selection.

A Network Coordinate (NC) system is an efficient mechanism to predict Internet distance with scalable measurements. In this paper, we focus on the node churn problem – the continuous process of nodes arrival and departure – in distributed applications. Studies on Vivaldi, a representative distributed NC system, show that under node churn the prediction accuracy of the NC system will be seriously impaired. In this paper, we focus on how to handle the impact of node churn in Vivaldi. Firstly, we propose a simple solution by directly increasing the measurement frequency. Our experiments have demonstrated that this approach can reduce the harm of node churn. However, it increases the communication overhead as the measurement frequency grows. To avoid such expensive solution, we propose the design and implementation of Myth, a decentralised and fast convergence NC system. It introduces the merit of Landmark-based NC system to shorten convergence time in Vivaldi with slight extra overhead. Our experimental results show that Myth outperforms Vivaldi a lot under node churn, without compromising the performance under stable environment. Moreover, we have found that the use of Myth is a cost-effective way to achieve higher prediction accuracy; it will not only improve the prediction accuracy but also save the communication overhead.

Modeling the Internet delay structure is an important issue in designing large-scale distributed systems. However, linear models fail to characterize Triangle Inequality Violations (TIV), motivating us to research on nonlinear ones. In this paper, we propose the methodology and design of nonlinear modeling by utilizing Kernel Methods(KM), which is demonstrated effective by simulation. Moreover, our nonlinear model is easy to be applied without introducing any measurement overhead.

Network coordinates (NC) system is an efficient mechanism for Internet distance prediction with limited measurements. In this paper, we identify the intrinsical cause for the inadequate accuracy of the simulation based NC algorithms. We then propose Pharos, a fully decentralized and hierarchical scheme, to remedy this problem. Pharos leverages multiple coordinate sets at different distance scales, with the right scale being chosen for prediction each time. We evaluate the performance of Pharos system with the King data set and latency data from PlanetLab, and compare it with the representative NC system, Vivaldi. The experimental results show that Pharos outperforms Vivaldi much without adding any significant overhead.

A large number of overlay protocols have been developed, almost all of which assume each host has two-way communication capability. However, this does not hold as the deployment of firewalls and Network Address Translators (NAT) is widespread in the current Internet, which is a challenge to the design and implementation of overlay models and protocols. In this paper, we present Potential-based Multicast Tree Algorithm (PMTA) to enhance the multicast tree construction in presence of connectivity restricted hosts. We evaluate PMTA and previous multicast tree protocols based on real Internet end-to-end delay datasets. According to evaluation results, PMTA outperforms those protocols in terms of all metrics. PMTA reduces ARDP by 26%, and it also results in 23%-54% reduction in average overlay latencies. As the results suggest, PMTA can build efficient and effective multicast tree and is suitable for Internet multicast applications in the presence of connectivity restricted hosts.