Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience

Artificial Intelligence and its sub-disciplines are becoming increasingly relevant in numerous areas of academia as well as industry and can now be considered a core area of Computer Science [ 84 ]. The Higher Education sector are offering more courses in Machine Learning and Artificial Intelligence than ever before. However, there is a lack of research pertaining to best practices for teaching in this complex domain that heavily relies on both computing and mathematical knowledge. We conducted a literature review and qualitative study with students and Higher Education lecturers from a range of educational institutions, with an aim to determine what might constitute best practices in this area in Higher Education. We hypothesised that confidence, mathematics anxiety, and differences in student educational background were key factors here. We then investigated the issues surrounding these and whether they inhibit the acquisition of knowledge and skills pertaining to the theoretical basis of artificial intelligence and machine learning. This article shares the insights from both students and lecturers with experience in the field of AI and machine learning education, with the aim to inform prospective pedagogies and studies within this domain and move toward a framework for best practice in teaching and learning of these topics.

Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs

Rust is an emerging programming language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and many projects start using the language. However, can Rust achieve the memory-safety promise? This article studies the question by surveying 186 real-world bug reports collected from several origins, which contain all existing Rust common vulnerability and exposures (CVEs) of memory-safety issues by 2020-12-31. We manually analyze each bug and extract their culprit patterns. Our analysis result shows that Rust can keep its promise that all memory-safety bugs require unsafe code, and many memory-safety bugs in our dataset are mild soundness issues that only leave a possibility to write memory-safety bugs without unsafe code. Furthermore, we summarize three typical categories of memory-safety bugs, including automatic memory reclaim, unsound function, and unsound generic or trait. While automatic memory claim bugs are related to the side effect of Rust newly-adopted ownership-based resource management scheme, unsound function reveals the essential challenge of Rust development for avoiding unsound code, and unsound generic or trait intensifies the risk of introducing unsoundness. Based on these findings, we propose two promising directions toward improving the security of Rust development, including several best practices of using specific APIs and methods to detect particular bugs involving unsafe code. Our work intends to raise more discussions regarding the memory-safety issues of Rust and facilitate the maturity of the language.

The multi-actor approach in thematic networks for agriculture and forestry innovation

The co-creation and sharing of knowledge among different types of actors with complementary expertise is known as the Multi-Actor Approach (MAA). This paper presents how Horizon2020 Thematic-Networks (TNs) deal with the MAA and put forward best practices during the different project phases, based on the results of a desktop study, interviews, surveys and expert workshops. The study shows that not all types of actors are equally involved in TN consortia and participatory activities, meaning TNs might be not sufficiently demand-driven and the uptake of the results is not optimal. Facilitators are key to contributing to the relationships and the mutual understanding between different actors. Moreover, a user-friendly digital knowledge platform linked to demonstration activities and peer-to-peer exchange can improve the sharing of knowledge, enhancing impact in agricultural and forestry innovation in the longer term.

Guidelines for volcano-observatory operations during crises: recommendations from the 2019 volcano observatory best practices meeting

In November 2019, the fourth Volcano Observatory Best Practices workshop was held in Mexico City as a series of talks, discussions, and panels. Volcanologists from around the world offered suggestions for ways to optimize volcano-observatory crisis operations. By crisis, we mean unrest that may or may not lead to eruption, the eruption itself, or its aftermath, all of which require analysis and communications by the observatory. During a crisis, the priority of the observatory should be to acquire, process, analyze, and interpret data in a timely manner. A primary goal is to communicate effectively with the authorities in charge of civil protection. Crisis operations should rely upon exhaustive planning in the years prior to any actual unrest or eruptions. Ideally, nearly everything that observatories do during a crisis should be envisioned, prepared, and practiced prior to the actual event. Pre-existing agreements and exercises with academic and government collaborators will minimize confusion about roles and responsibilities. In the situation where planning is unfinished, observatories should prioritize close ties and communications with the land and civil-defense authorities near the most threatening volcanoes.To a large extent, volcanic crises become social crises, and any volcano observatory should have a communication strategy, a lead communicator, regular status updates, and a network of colleagues outside the observatory who can provide similar messaging to a public that desires consistent and authoritative information. Checklists permit tired observatory staff to fulfill their duties without forgetting key communications, data streams, or protocols that need regular fulfilment (Bretton et al. Volcanic Unrest. Advances in Volcanology, 2018; Newhall et al. Bull Volcanol 64:3–20, 2020). Observatory leaders need to manage staff workload to prevent exhaustion and ensure that expertise is available as needed. Event trees and regular group discussions encourage multi-disciplinary thinking, consideration of disparate viewpoints, and documentation of all group decisions and consensus. Though regulations, roles and responsibilities differ around the world, scientists can justify their actions in the wake of an eruption if they document their work, are thoughtful and conscientious in their deliberations, and carry out protocols and procedures developed prior to volcanic unrest. This paper also contains six case studies of volcanic eruptions or observatory actions that illustrate some of the topics discussed herein. Specifically, we discuss Ambae (Vanuatu) in 2017–2018, Kīlauea (USA) in 2018, Etna (Italy) in 2018, Bárðarbunga (Iceland) in 2014, Cotopaxi (Ecuador) in 2015, and global data sharing to prepare for eruptions at Nyiragongo (Democratic Republic of Congo). A Spanish-language version of this manuscript is provided as Additional file 1.