In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets.

Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method "SWAY", short for "the sampling way". This paper compares SWAY versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. For these models, the experiments of this paper show that SWAY is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations. Considering the simplicity and effectiveness of SWAY, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.

Crowdsourced testing is an emerging trend, in which test tasks are entrusted to the online crowd workers. Typically, a crowdsourced test task aims to detect as many bugs as possible within a limited budget. However not all crowd workers are equally skilled at finding bugs; Inappropriate workers may miss bugs, or report duplicate bugs, while hiring them requires nontrivial budget. Therefore, it is of great value to recommend a set of appropriate crowd workers for a test task so that more software bugs can be detected with fewer workers. This paper first presents a new characterization of crowd workers and characterizes them with testing context, capability, and domain knowledge. Based on the characterization, we then propose Multi-Objective Crowd wOrker recoMmendation approach (MOCOM), which aims at recommending a minimum number of crowd workers who could detect the maximum number of bugs for a crowdsourced testing task. Specifically, MOCOM recommends crowd workers by maximizing the bug detection probability of workers, the relevance with the test task, the diversity of workers, and minimizing the test cost. We experimentally evaluate MOCOM on 532 test tasks, and results show that MOCOM significantly outperforms five commonly-used and state-of-the-art baselines. Furthermore, MOCOM can reduce duplicate reports and recommend workers with high relevance and larger bug detection probability; because of this it can find more bugs with fewer workers.

With the emergence of audio-language models, constructing large-scale paired audio-language datasets has become essential yet challenging for model development, primarily due to the time-intensive and labour-heavy demands involved. While large language models (LLMs) have improved the efficiency of synthetic audio caption generation, current approaches struggle to effectively extract and incorporate detailed audio information. In this paper, we propose an automated pipeline that integrates audio-language models for fine-grained content extraction, LLMs for synthetic caption generation, and a contrastive language-audio pretraining (CLAP) model-based refinement process to improve the quality of captions. Specifically, we employ prompt chaining techniques in the content extraction stage to obtain accurate and fine-grained audio information, while we use the refinement process to mitigate potential hallucinations in the generated captions. Leveraging the AudioSet dataset and the proposed approach, we create AudioSetCaps, a dataset comprising 1.9 million audio-caption pairs, the largest audio-caption dataset at the time of writing. The models trained with AudioSetCaps achieve state-of-the-art performance on audio-text retrieval with R@1 scores of 46.3% for text-to-audio and 59.7% for audio-to-text retrieval and automated audio captioning with the CIDEr score of 84.8. As our approach has shown promising results with AudioSetCaps, we create another dataset containing 4.1 million synthetic audio-language pairs based on the Youtube-8 M and VGGSound datasets. To facilitate research in audio-language learning, we have made our pipeline, datasets with 6 million audio-language pairs,