Artificial Intelligence Security: Threats and Countermeasures

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed.

Challenges in Estuarine and Coastal Science

Estuarine and coastal waters are acknowledged centres for anthropogenic impacts. Superimposed on the complex natural interactions between land, rivers and sea are the myriad consequences of human activity – a spectrum ranging from locally polluting effluents to some of the severest consequences of global climate change. For practitioners, academics and students in the field of coastal science and policy, this book examines and exemplifies current and future challenges: from upper estuaries to open coasts and adjacent seas; from tropical to temperate latitudes; from Europe to Australia. This authoritative volume marks the 50th anniversary of the Estuarine and Coastal Sciences Association, and contains a prologue by founding member Professor Richard Barnes and a short history of the Association. Individual chapters then address coastal erosion and deposition; open shores to estuaries and deltas; marine plastics; coastal squeeze and habitat loss; tidal freshwaters – saline incursion and estuarine squeeze; restoration management using remote data collection; carbon storage; species distribution and non-natives; shorebirds; Modelling environmental change; physical processes such as sediments and modelling; sea level rise and estuarine tidal dynamics; estuaries as fish nurseries; policy versus reality in coastal conservation; developments in Estuarine, coastal and marine management.

A Review on Fact Extraction and Verification

We study the fact-checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset. The task consists of the subtasks of retrieving the relevant documents (and sentences) from Wikipedia and validating whether the information in the documents supports or refutes a given claim. This task is essential and can be the building block of applications such as fake news detection and medical claim verification. In this article, we aim at a better understanding of the challenges of the task by presenting the literature in a structured and comprehensive way. We describe the proposed methods by analyzing the technical perspectives of the different approaches and discussing the performance results on the FEVER dataset, which is the most well-studied and formally structured dataset on the fact extraction and verification task. We also conduct the largest experimental study to date on identifying beneficial loss functions for the sentence retrieval component. Our analysis indicates that sampling negative sentences is important for improving the performance and decreasing the computational complexity. Finally, we describe open issues and future challenges, and we motivate future research in the task.

Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future Challenges

Interaction design for Augmented Reality (AR) is gaining attention from both academia and industry. This survey discusses 260 articles (68.8% of articles published between 2015–2019) to review the field of human interaction in connected cities with emphasis on augmented reality-driven interaction. We provide an overview of Human-City Interaction and related technological approaches, followed by reviewing the latest trends of information visualization, constrained interfaces, and embodied interaction for AR headsets. We highlight under-explored issues in interface design and input techniques that warrant further research and conjecture that AR with complementary Conversational User Interfaces (CUIs) is a crucial enabler for ubiquitous interaction with immersive systems in smart cities. Our work helps researchers understand the current potential and future needs of AR in Human-City Interaction.