Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

人工智能在智慧海洋建设中的应用

围绕人工智能与智慧海洋建设这条主线，论述人工智能、智慧海洋的概念，列举当前较为成熟的人工智能与海洋科技在海洋观测方面融合的切入点，初步展现一种海洋技术与装备智能化的发展路径，提出加快人工智能技术向智慧海洋建设赋能的几点建议。     

基于数据运营安全的个人信息保护

个人信息因其自身携带隐私特性,与每个个体息息相关。个人信息保护不当,影响公众权益、企业利益以及社会秩序。在互联网、大数据、5G万物互联的时代,个人信息被广泛收集和使用,必须妥善解决个人信息保护问题,才能保障整个数据产业健康发展。而现有的个人信息保护方法或技术,不足以应对新形势下的保护诉求。基于数据运营安全的个人信息保护,针对当前个人信息保护的新形势和新诉求,提出结合人工智能,通过数据运营安全对结构化、半结构化、非结构化的个人信息流动的保护,涵盖从生产到运维,从采集、传输、存储、处理、分析、共享、销毁全生命周期保护,深入数据运营中内嵌防护,同时与业务解耦,达到保护个人信息安全的目标。     

新工科背景下电子通信专业“人工智能技术基础”课程改革实践

人工智能技术已经成为新一轮科技革命的重要驱动力量，这要求电子、通信等非计算机专业的人才培养引入针对性强的人工智能课程教学。本文针对新工科背景下电子、通信专业“人工智能技术基础”课程存在的问题进行课程改革，从授课和实验两个环节入手，分别从教学内容、教学方法和考核办法三个方面提出了具体的改革举措，并在“人工智能技术基础”课程中进行了实践，激发了学生的学习热情，拓展了学生的专业视野，提高了学生的实践创新能力，取得了良好的教学效果。为了进一步培养电子、通信学科交叉型AI人才，该课程选拔优秀学生开展丰富的人工智能课外实践活动，进一步提升学生的实践创新能力。     

Factors influencing the use of artificial intelligence in government: Evidence from China

Some studies have discussed the potential and challenges related to the use of artificial intelligence (AI) in government. However, there are few empirical studies that have examined factors that influence the use of AI in government. By collecting policy documents and empirical data from the government, IT enterprises, and the public in China, we identified the influencing factors in the three stages of government adoption, implementation, and decision-making. The research results show that the influencing factors of government application of AI are different at different stages and with different stakeholders’ backgrounds.     

Robustness of AI-based prognostic and systems health management

Prognostic and systems Health Management (PHM) is an integral part of a system. It is used for solving reliability problems that often manifest due to complexities in design, manufacturing, operating environment and system maintenance. For safety-critical applications, using a model-based development process for complex systems might not always be ideal but it is equally important to establish the robustness of the solution. The information revolution has allowed data-driven methods to diffuse within this field to construct the requisite process (or system models) to cope with the so-called big data phenomenon. This is supported by large datasets that help machine-learning models achieve impressive accuracy. AI technologies are now being integrated into many PHM related applications including aerospace, automotive, medical robots and even autonomous weapon systems. However, with such rapid growth in complexity and connectivity, a systems’ behaviour is influenced in unforeseen ways by cyberattacks, human errors, working with incorrect or incomplete models and even adversarial phenomena. Many of these models depend on the training data and how well the data represents the test data. These issues require fine-tuning and even retraining the models when there is even a small change in operating conditions or equipment. Yet, there is still ambiguity associated with their implementation, even if the learning algorithms classify accordingly. Uncertainties can lie in any part of the AI-based PHM model, including in the requirements, assumptions, or even in the data used for training and validation. These factors lead to sub-optimal solutions with an open interpretation as to why the requirements have not been met. This warrants the need for achieving a level of robustness in the implemented PHM, which is a challenging task in a machine learning solution.This article aims to present a framework for testing the robustness of AI-based PHM. It reviews some key milestones achieved in the AI research community to deal with three particular issues relevant for AI-based PHM in safety-critical applications: robustness to model errors, robustness to unknown phenomena and empirical evaluation of robustness during deployment. To deal with model errors, many techniques from probabilistic inference and robust optimisation are often used to provide some robustness guarantee metric. In the case of unknown phenomena, techniques include anomaly detection methods, using causal models, the construction of ensembles and reinforcement learning. It elicits from the authors’ work on fault diagnostics and robust optimisation via machine learning techniques to offer guidelines to the PHM research community. Finally, challenges and future directions are also examined; on how to better cope with any uncertainties as they appear during the operating life of an asset.     

市民社会思潮复苏下中国城市规划师的角色定位

随着中国经济的迅猛发展 ,越来越多的中国学者开始关注中国“市民社会”的构建问题 ,但是 ,对于城市规划师在市民社会中的作用还处于口号式的理解阶段 ,需要进一步的研究。本文从简述二战以来西方国家规划师角色的变迁历程入手 ,通过对“市民社会”概念以及中国市场经济条件下“市民社会”的构建特征的综述 ,指出在市民社会建构过程中 ,规划师应该利用注册规划师制度推行的契机走向社会 ,与非政府组织和社区组织一起协力参与城市的建设与发展     

Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory

With the rapid development of artificial intelligence (AI), AI anxiety has emerged and is receiving widespread attention, but research on this topic is not comprehensive. Therefore, we investigated the dimensions of AI anxiety using the theoretical model of integrated fear acquisition and a questionnaire survey. A total of 494 valid questionnaires were recovered. Through a first-order confirmatory factor analysis (CFA), a factor model of AI anxiety was constructed, and eight factors of AI anxiety were verified. Then, a second-order CFA was applied to verify the adaptation of the factor structure of AI anxiety to fear acquisition. We identified four dimensions of AI anxiety and proposed a theory of AI anxiety acquisition that illustrates four pathways of AI anxiety acquisition. Each pathway includes two factors that cause AI anxiety. We conclude by analyzing the limitations of current AI anxiety research and proposing a broader research agenda for AI anxiety.