This chapter focuses on the evolution of Human-Centered Design (HCD) in aerospace systems over the last forty years. Human Factors and Ergonomics first shifted from the study of physical and medical issues to cognitive issues circa the 1980s. The advent of computers brought with it the development of human-computer interaction (HCI), which then expanded into the field of digital interaction design and User Experience (UX). We ended up with the concept of interactive cockpits, not because pilots interacted with mechanical things, but because they interacted using pointing devices on computer displays. Since the early 2000s, complexity and organizational issues gained prominence to the point that complex systems design and management found itself center stage, with the spotlight on the role of the human element and organizational setups. Today, Human Systems Integration (HSI) is no longer only a single-agent problem, but a multi-agent research field. Systems are systems of systems, considered as representations of people and machines. They are made of statically and dynamically articulated structures and functions. When they are at work, they are living organisms that generate emerging functions and structures that need to be considered in evolution (i.e., in their constant redesign). This chapter will more specifically, focus on human factors such as human-centered systemic representations, life critical systems, organizational issues, complexity management, modeling and simulation, flexibility, tangibility and autonomy. The discussion will be based on several examples in civil aviation and air combat, as well as aerospace.
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IFIP TC13 Conference on Human-Computer Interaction是人機交互領域的研究者和實踐者展示其工作的重要平臺。多年來，這些會議吸引了來自幾個國家和文化的研究人員。官網鏈接： ·
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Interactive machine learning (IML) is a field of research that explores how to leverage both human and computational abilities in decision making systems. IML represents a collaboration between multiple complementary human and machine intelligent systems working as a team, each with their own unique abilities and limitations. This teamwork might mean that both systems take actions at the same time, or in sequence. Two major open research questions in the field of IML are: "How should we design systems that can learn to make better decisions over time with human interaction?" and "How should we evaluate the design and deployment of such systems?" A lack of appropriate consideration for the humans involved can lead to problematic system behaviour, and issues of fairness, accountability, and transparency. Thus, our goal with this work is to present a human-centred guide to designing and evaluating IML systems while mitigating risks. This guide is intended to be used by machine learning practitioners who are responsible for the health, safety, and well-being of interacting humans. An obligation of responsibility for public interaction means acting with integrity, honesty, fairness, and abiding by applicable legal statutes. With these values and principles in mind, we as a machine learning research community can better achieve goals of augmenting human skills and abilities. This practical guide therefore aims to support many of the responsible decisions necessary throughout the iterative design, development, and dissemination of IML systems.
In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding. However, the increase in the number and category of information data will also increase the cost of information acquisition. For technology-based enterprises or users, in addition to general papers, patents, etc., policies related to technology or the development of their industries should also belong to a type of scientific and technological resources. The cost and difficulty of acquiring users. Extracting valuable science and technology policy resources from a huge amount of data with mixed contents and providing accurate and fast retrieval will help to break down information barriers and reduce the cost of information acquisition, which has profound social significance and social utility. This article focuses on the difficulties and problems in the field of science and technology policy, and introduces related technologies and developments.
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements.
The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen's cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts.
While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to profit from their application, resulting in a need for explanations for both the decision making process and the model. For many systems, such as common black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. We propose a rule-based learning system specifically conceptualised and, thus, especially suited for these scenarios. Its models are inherently transparent and easily interpretable by design. One key innovation of our system is that the rules' conditions and which rules compose a problem's solution are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability.
Microcosm, Hyper-G, and the Web were developed and released after 1989. There were strengths and weaknesses associate with each of these hypertext systems. The architectures of these systems were relatively different from one another. Standing above its competitors, the Web became the largest and most popular information system. This paper analyses the reasons for which the Web became the first successful hypermedia system by looking and evaluating the architecture of the Web, Hyper-G, and Microcosm systems. Three reasons will be given beyond this success with some lessons to learn. Currently, Semantic Web is a recent development of the Web to provide conceptual hypermedia. More importantly, study of the Web with its impact on technical, socio-cultural, and economical agendas is introduced as web science.
This paper explores Null Island, a fictional place located at 0$^\circ$ latitude and 0$^\circ$ longitude in the WGS84 geographic coordinate system. Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. While it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness. The article summarizes error sources that lead to data being associated with Null Island. We identify four evolutionary phases which help explain how this fictional place evolved and established itself as an entity reaching beyond the geospatial profession to the point of being discovered by the visual arts and the general population. After providing an accurate account of data that can be found at (0, 0), geospatial, technological and social implications of Null Island are discussed. Guidelines to avoid misplacing data to Null Island are provided. Since data will likely continue to appear at this location, our contribution is aimed at both GIScientists and the general population to promote awareness of this error source.
Universal coding of integers~(UCI) is a class of variable-length code, such that the ratio of the expected codeword length to $\max\{1,H(P)\}$ is within a constant factor, where $H(P)$ is the Shannon entropy of the decreasing probability distribution $P$. However, if we consider the ratio of the expected codeword length to $H(P)$, the ratio tends to infinity by using UCI, when $H(P)$ tends to zero. To solve this issue, this paper introduces a class of codes, termed generalized universal coding of integers~(GUCI), such that the ratio of the expected codeword length to $H(P)$ is within a constant factor $K$. First, the definition of GUCI is proposed and the coding structure of GUCI is introduced. Next, we propose a class of GUCI $\mathcal{C}$ to achieve the expansion factor $K_{\mathcal{C}}=2$ and show that the optimal GUCI is in the range $1\leq K_{\mathcal{C}}^{*}\leq 2$. Then, by comparing UCI and GUCI, we show that when the entropy is very large or $P(0)$ is not large, there are also cases where the average codeword length of GUCI is shorter. Finally, the asymptotically optimal GUCI is presented.
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.
Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and non-task-oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier.
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