Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 x 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.

Satellite-derived bathymetry (SDB) based on multi-spectral imagery data has been a critical tool for large-scale water depth in shallow water regions. Traditional SDB models primarily rely on known laws relating the exponential attenuation of light with the path length it traveled. In the past few years, deep computer vision models have emerged as valuable new technologies for bathymetry measurement. However, due to the black-box nature of these deep models, they may produce bathymetry results that are inconsistent with physical laws and exhibit limited generalizability across diverse areas. In this paper, we propose a novel hybrid architecture, HybridBathNet, that integrates UNet (extracting spatial and spectral feature) with a physical bathymetry network (ensuring physical relationships). By embedding physical constraints directly into the model architecture, HybridBathNet achieves improved bathymetric inversion accuracy while maintaining consistency with established optical attenuation laws. Experimental results demonstrate that the proposed model delivers high-quality bathymetric estimations across diverse island regions. Comparative evaluations against state-of-the-art methods further validate the superior accuracy and generalization capability of HybridBathNet. The code of HybridBathNet is available at https://github.com/qiushibupt/HybridBathNet.

Graph embedding aims at learning vertex representations in a low-dimensional space by distilling information from a complex-structured graph. Recent efforts in graph embedding have been devoted to generalizing the representations from the trained graph in a source domain to the new graph in a different target domain based on information transfer. However, when the graphs are contaminated by unpredictable and complex noise in practice, this transfer problem is quite challenging because of the need to extract helpful knowledge from the source graph and to reliably transfer knowledge to the target graph. This paper puts forward a two-step correntropy-induced Wasserstein GCN (graph convolutional network, or CW-GCN for short) architecture to facilitate the robustness in cross-graph embedding. In the first step, CW-GCN originally investigates correntropy-induced loss in GCN, which places bounded and smooth losses on the noisy nodes with incorrect edges or attributes. Consequently, helpful information are extracted only from clean nodes in the source graph. In the second step, a novel Wasserstein distance is introduced to measure the difference in marginal distributions between graphs, avoiding the negative influence of noise. Afterwards, CW-GCN maps the target graph to the same embedding space as the source graph by minimizing the Wasserstein distance, and thus the knowledge preserved in the first step is expected to be reliably transferred to assist the target graph analysis tasks. Extensive experiments demonstrate the significant superiority of CW-GCN over state-of-the-art methods in different noisy environments.

As one of the most biologically complex and valuable ecosystems on the planet, coral reefs also extremely susceptible to environmental and climate changes. The investigation, reservation, and restoration of coral reefs require accurate classifications of the specific species of corals that form those reefs through examining the underwater coral reef images. Conventionally, such classifications are performed by experts in a manual fashion. This paper proposes a cross-domain coral image classification approach using a novel structure of dual-stream hierarchical neural networks. Our extensive experiments demonstrate that the approach does not only achieve state-of-the-art performances in classifying coral species but also has the capability to realize comparable and robust performances in dealing with coral image classification tasks with data from different oceanic regions and different coral life-cycle without labelling the target domains. Therefore, our approach has the potential to reduce ocean scientists' manual efforts in recognizing different species of corals.