Globally distributed software development has been a mainstream paradigm in developing modern software systems. We have witnessed a fast-growing population of software developers from areas where English is not a native language in the last several decades. Given that English is still the de facto working language in most global software engineering teams, we need to gain more knowledge about the experiences of developers who are non-native English speakers. We conducted an empirical study to fill this research gap. In this study, we interviewed 27 Chinese developers in commercial software development and open source global software development teams and applied Bourdieu's capital-field-habitus framework in an abductive data analysis process. Our study reveals four types of capital (language, social, symbolic, and economic) involved in their experiences and examines the interrelations among them. We found that non-native speakers' insufficient language capital played an essential role in prohibiting them from accessing and accumulating other capital, thus reproducing the sustained and systematic disadvantaged positions of non-native English speakers in GSD teams. We further discussed the theoretical and practical implications of the study.

Cotton is a critical agricultural product and industrial raw material, playing a key role in the national economies and people's living conditions, particularly in developing countries. However, cotton picking and processing often result in the contamination with various foreign fibers, such as hair, hemp rope, plastic film, and polypropylene rope. These contaminants are difficult to remove during textile processing and tend to break into small fragments, significantly reducing the quality of cotton products and negatively impacting the cotton industry. In this paper, we present an AI-enabled hardware-software integrated system--XCotton, for identifying and removing foreign fibers. Our system has been deployed in actual cotton production environments in the multiple regions in China, Central Asia, and Africa. XCotton achieves a cleaning efficiency of 1000kg/h, representing a 43% improvement, with only 14 kWh energy consumption (63% less). Moreover, XCotton brings significant business values to its manufacturer and clients. XCotton not only enhances the quality of cotton products but also contributes to the value-adding and upgrading of the cotton industry in developing regions, supporting economic growth and improving living conditions.

Driving under the influence (DUI) is one of the main causes of traffic accidents, often leading to severe life and property losses. Setting up sobriety checkpoints on certain roads is the most commonly used practice to identify DUI-drivers in many countries worldwide. However, setting up checkpoints according to the police's experiences may not be effective for ignoring the strategic interactions between the police and DUI-drivers, particularly when inspecting resources are limited. To remedy this situation, we adapt the classic Stackelberg security game (SSG) to a new SSG-DUI game to describe the strategic interactions in catching DUI-drivers. SSG-DUI features drivers' bounded rationality and social knowledge sharing among them, thus realizing improved real-world fidelity. With SSG-DUI, we propose OPRADI, a systematic approach for advising better strategies in setting up checkpoints. We perform extensive experiments to evaluate it in both simulated environments and real-world contexts, in collaborating with a Chinese city's police bureau. The results reveal its effectiveness in improving police's real-world operations, thus having significant practical potentials.

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.

Accurately predicting ocean temperature, salinity, and velocity is fundamental to capture the dynamics of subsurface thermohaline structure and understanding its impacts on marine ecosystems and the Earth’s climate system. With the rise of Deep Learning (DL), researchers have explored its potential to capture complex spatiotemporal dependencies in ocean parameter prediction. However, a comprehensive understanding of the application of DL in this field remains in its early stage. To address this gap, we conduct a systematic review of 122 peer-reviewed articles published between 2013 and 2024, aiming to answer four key Research Questions (RQs). In RQ1, we examine how marine datasets are categorized and represented for ocean parameter prediction. RQ2 explores the spatial, temporal, and input-level patterns of these datasets as utilized across various DL models. In RQ3, we analyze the DL architectures adopted for forecasting temperature, salinity, and velocity, highlighting trends across model types. Finally, RQ4 investigates the optimization strategies and evaluation metrics employed to assess predictive performance. Based on our findings, we highlight underexplored challenges and outline promising directions for future research.