Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Although such methods could be very accurate, it is usually very hard for developers to understand the context of the bug, given each bug location in isolation. In this study, we propose to model software executions with graphs at two levels of granularity: methods and basic blocks. An individual node represents a method or basic block and an edge represents a method call, method return or transition (at the method or basic block granularity). Given a set of graphs of correct and faulty executions, we propose to extract the most discriminative subgraphs which contrast the program flow of correct and faulty executions. The extracted subgraphs not only pinpoint the bug, but also provide an informative context for understanding and fixing the bug. Different from traditional graph mining which mines a very large set of frequent subgraphs, we formulate subgraph mining as an optimization problem and directly generate the most discriminative subgraph with a recently proposed graph mining algorithm LEAP. We further extend it to generate a ranked list of top-k discriminative subgraphs representing distinct locations which may contain bugs. Experimental results and case studies show that our proposed method is both effective and efficient to mine discriminative subgraphs for bug localization and context identification.

Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents efficiently to obtain context-appropriate information or gain resilience to sensor noise or failures. In this paper, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.

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Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand.

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Purpose This paper aims to develop a climbing robot to help people inspect lamps of high-mast lighting. Design/methodology/approach The robot consists of driving mechanism, suspension mechanism and compression mechanism. The driving mechanism is realized by link chains and sprockets, which are arranged opposite to each other, to form a dual caterpillar mechanism. The compression mechanism squeezes the caterpillar, and rubber feet “grasps” the steel rope to generate enough adhesion forces. The suspension mechanism is used to compensate the contraction or extension of the chains. The robot is equipped with a DC motor with a rated power of 250 W and a wireless module to communicate with the operator’s console. The dynamic model of the robot and the control strategy is derived, and the stability of the controller is proofed. Findings The payload experiment shows the robot can afford up to 3.7 times payload versus its own weight. Even when the payload is 30 kg, the robot can maintain a speed of the 1 m/s. The experiments also show that the tracking error of the robot reaches zero. Practical implications The proposed moving mechanism has a high load/weight ratio, which is a verified solution for the cable inspection purpose. Originality/value A rope climbing robot for high mast lighting inspection is proposed. The developed mechanism can reach a speed of 1 m/s with the payload of 30 kg, while its own weight is only 15.6 kg. The payload/weight ratio of the robot is 2.24; this value is rather good in many climbing robots reported in other renowned journal.

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