Organizing human factors knowledge for the evaluation and design of interfaces

This paper presents research conducted in the area of human factors knowledge collection and organization. Providing that a large amountof human factors engineering data is available now concerning user interface aspects, this research started from the following question: Can this knowledge be transferred to user interface designers, and by which means can this be done? An important part of human factors knowledge is included in recommendations and guidelines. It is the problems with that type of knowledge that this paper focuses on. A first attempt to tackle some of these problems is made by offering a generic model for deciphering recommendations into rules. This model stresses the importance of attributes such as human factors criteria, classes of premises, and conclusions, as well as interface objects. Definitions of criteria for organizing recommendations are offered, and a few examples of rule‐based reasoning are provided. The remaining problems with a rule approach are then discussed according to two dimensions: intrinsic problems and usage problems. In addition, it appears that an approach based on literature recommendations is not sufficient. Other areas that need further work are described, particularly concerning task and interface modelling, and human factors expertise modelling. The paper concludes on the limits and benefits of such an approach.     

On optimization of expertise matching with various constraints

This paper studies the problem of expertise matching with various constraints. Expertise matching, which aims to find the alignment between experts and queries, is a common problem in many applications such as conference paper-reviewer assignment, product-reviewer alignment, and product-endorser matching. Most existing methods formalize this problem as an information-retrieval problem and focus on finding a set of experts for each query independently. However, in real-world systems, various constraints are often needed to be considered. For example, in order to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint-based optimization framework. In the proposed framework, the problem of expertise matching is linked to a convex cost flow problem, which guarantees an optimal solution under various constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems, namely conference paper-reviewer assignment and teacher-course assignment. Experimental results validate the effectiveness of the proposed approach. Based on the proposed method, we have also developed an online system for paper-reviewer suggestions, which has been used for paper-reviewer assignment in a top conference and feedbacks from the conference organizers are very positive.     

A novel composite model approach to improve software quality prediction

Context:How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances.ObjectiveThe main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques.MethodIn this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a “composite” model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances.ResultsThe results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones.ConclusionOur approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.     

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future. To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs. However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously. This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently. More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp. A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations. We evaluate our proposed method on four benchmark datasets. Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.     

人体运动分析的实例学习方法

目的 面向实时、准确、鲁棒的人体运动分析应用需求,从运动分析的特征提取和运动建模问题出发,本文人体运动分析的实例学习方法。方法 在构建人体姿态实例库基础上,首先,采用运动检测方法得到视频每帧的人体轮廓;其次,基于形状上下文轮廓匹配方法,从实例库中检索得到每帧视频的候选姿态集;最后,通过统计建模和转移概率建模实现人体运动分析。结果 对步行、跑步、跳跃等测试视频进行实验,基于轮廓的形状上下文特征表示和匹配方法具有良好的表达能力;本文方法运动分析结果,关节夹角平均误差在5°左右,与其他算法相比,有效提高了运动分析的精度。结论 本文人体运动分析的实例学习方法,能有效分析单目视频中的人体运动,并克服了映射的深度歧义,对运动的视角变化鲁棒,具有良好的计算效率和精度。     

A question of our marketing or our preconceptions: Commentary on the paper ‘A strategy for human factors/ergonomics: developing the discipline and profession’

The present paper is a commentary on the recently published IEA strategy for human factors/ergonomics (Dul, J., et al. (2012), A strategy for human factors/ergonomics: developing the discipline and profession. Ergonomics, 55(4), 377–395). Two main issues that demand attention are: (i) the way others understand our profession and discipline, and (ii) the way we understand our profession and added value to industry. First, it is advocated that the discussion on the future of human factors/ergonomics (HFE) should be focused more on the quality of the delivered value of HFE and less on its visibility and marketing. Second, the three fundamental characteristics of HFE, as proposed in the report, are discussed and the consequences of this proposal are further developed. Arguments are put forward on the endemic epistemological vagueness within the discipline and on the optimistic definition of its aim. Finally, a proposal is made at the epistemological level, which challenges some established convictions of the discipline. It is advocated that such an epistemological evolution may be necessary if HFE is to make progress towards contributing to system performance.

Practitioner Summary: The paper is a commentary on the IEA strategy for human factors/ergonomics. Issues discussed are, the way others understand our profession and the way we understand our profession and added value to industry. Some of the established convictions of the discipline are challenged and proposals are made to overcome these.     

Expertise transfer,knowledge elicitation,and delayed recall in a design context

Abstract

This paper describes a new method for knowledge elicitation that may contribute to effective expertise transfer from human experts to knowledge-based systems. The method was applied to knowledge transfer in an aerospace design context. Knowledge was transferred directly from an expert designer to both expert and novice “receivers” of information. Transfer occurred in a natural way, without intervention from a knowledge engineer. To evaluate the process, the information receivers were required to recall the transmitted knowledge after a seven week delay. Results suggest that this method can be effective for expertise transfer and can indicate desirable characteristics for knowledge-based systems which aim to be adaptable to users’ differing levels of competence.     

Personalized teleoperation via intention recognition

ABSTRACT

One of the challenges of teleoperation is the recognition of a user’s intended commands, particularly in the manning of highly dynamic systems such as drones. In this paper, we present a solution to this problem by developing a generalized scheme relying on a Convolutional Neural Network (CNN) that is trained to recognize a user’s intended commands, directed through a haptic device. Our proposed method allows the interface to be personalized for each user, by pre-training the CNN differently according to the input data that is specific to the intended end user. Experiments were conducted using two haptic devices and classification results demonstrate that the proposed system outperforms geometric-based approaches by nearly 12%. Furthermore, our system also lends itself to other human–machine interfaces where intention recognition is required.     

THE SYSTEMIC FORMALIZATION OF KNOWLEDGE INVOLVING UNCERTAINTY

Abstract

We have developed generic models useful for modeling human expertise involving uncertainty. These generic models are based on fundamental work carried out in the field of systems science, in which various researchers have developed formalisms for modeling systems in general. In this paper, we present our Systemic U-Knowledge Framework, which utilizes our models and new mathematical constructs we have developed for modeling uncertainty. We have validated the resulting formalism by modeling the expertise of an education expert, and have used the model as a basis for automated problem-solving.     

Tackling the Password Problem: Image- and Gesture-Based Alternatives to Alphanumeric Passwords

Abstract

Alphanumeric passwords remain a ubiquitous means of user authentication, yet they are plagued by a fundamental problem: Secure passwords are difficult to create and remember. This paper suggests that image- or gesture-based passwords might strike a better balance between security and usability. It examines two such systems that are currently in widespread commercial use and examines alternative approaches that may offer insights for future improvements. Finally, it considers the possibility that touch-screen gesture passwords may become a viable biometric measure, which may allow them to provide multi-factor gesture-based authentication.     

The contributions of human factors and ergonomics to a sustainable minerals industry

This article describes examples of the application of human factors research and development work to a sustainable minerals industry. It begins by outlining human-related aspects of the minerals industry and the key human factors work previously undertaken in this domain. The focus then switches to sustainability in the minerals industry. Sustainability principles are introduced and illustrations provided of how human factors research and development work fits within such a framework. Three case studies of human factors in the minerals industry research are presented and the sustainability implications in each case study are highlighted. Finally, future trends related to human factors work in a sustainable minerals industry are addressed, in particular the opportunities and possible adverse consequences that increasing deployment of mining automation might bring.

Practitioner Summary: Minerals industries are a major global activity with significant sustainability implications. Aspects of sustainability in mining are examined using three case studies. These illustrate the contribution of human factors/ergonomics in reducing risks; developing emergency response management systems; and the value of participatory ergonomics in improving the design of mining equipment.     

Modelling Problem-solving for an Educational System

Abstract

When constructing a system that presents students with problem-solving, the essential problem to be considered is the definition of the system's conceptual model of the problem-solving expertise. We study two possible approaches of the definition of this model: refining generic problem-solving models and modelling by data-abstraction from an observed expertise. We emphasise that these two approaches have opposite advantages. The former facilitates the definition of rational systematic problem-solving models, the latter facilitates the respect of the problem-solving pedagogic specificities of a particular domain. In order to help in the refinement of the model constructed by either method we claim that a paper-based model is not sufficient and advocate the use of prototyping as a means to support modelling. To allow this prototyping we need a high-level language that (1) allows a quick operationalisation of the model while not enforcing predefined conceptual primitives, and (2) allows a control of what problem-solving material is used according to both problem-solving and pedagogical issues, these two aspects not being mixed. We present an example of such a language, the Zola language, and how it tackles these objectives, with examples from a system (under construction) that aims at training students to linear programming techniques.     

Interactive Management of Human Factors Knowledge for Human Systems Integration Using Systems Modeling Language

Abstract

The field of Human Factors in Knowledge Management is often seen as a problem of capturing, organizing, and retrieving information to build knowledge. This process is inextricably bound up with human cognition and, as such, the management of knowledge occurs within an intricately structured behavioral, cultural, and social context. This paper emphasizes the importance of interactive human factors in knowledge management and introduces a model-based human systems integration framework based on systems modeling language (SysML).     

Suitability assessment framework of agent-based software architectures

ContextA common distributed intelligent system architecture is Multi Agent Systems (MASs). Creating systems with this architecture has been recently supported by Agent Oriented Software Engineering (AOSE) methodologies. But two questions remain: how do we determine the suitability of a MAS implementation for a particular problem? And can this be determined without AOSE expertise?ObjectiveGiven the relatively small number of software engineers that are AOSE experts, many problems that could be better solved with a MAS system are solved using more commonly known but not necessarily as suitable development approaches (e.g. object-oriented). The paper aims to empower software engineers, who are not necessarily AOSE experts, in deciding whether or not they should advocate the use of an MAS technology for a given project.MethodThe paper will construct a systematic framework to identify key criteria in a problem requirement definition to assess the suitability of a MAS solution. The criteria are first identified using an iterative process. The features are initially identified from MAS implementations, and then validated against related work. This is followed by a statistical analysis of 25 problems that characterise agent-oriented solutions previously developed to group features into key criteria.ResultsKey criteria were sufficiently prominent using factor analysis to construct a framework which provides a process that identifies within the requirements the criteria discovered. This framework is then evaluated for assessing suitability of a MAS architecture, by non-AOSE experts, on two real world problems: an electricity market simulation and a financial accounting system.ConclusionSubstituting a software engineer’s personal inclination to (or not to) use a MAS, our framework provides an objective mechanism. It can supplant current practices where the decision to use a MAS architecture for a given problem remains an informal process. It was successfully illustrated on two real world problems to assess the suitability of a MAS implementation. This paper will potentially facilitate the take up of MAS technology.     

A socio-technical analysis of functional properties in a joint cognitive system: a case study in an aircraft cockpit

Abstract

In a socio-technical work domain, humans, device interfaces and artefacts all affect transformations of information flow. Such transformations, which may involve a change of auditory to visual information & vice versa or alter semantic approximations into spatial proximities from instruments readings, are generally not restricted to solely human cognition. This paper applies a joint cognitive system approach to explore a socio-technical system. A systems ergonomics perspective is achieved by applying a multi-layered division to transformations of information between, and within, human and technical agents. The approach uses the Functional Resonance Analysis Method (FRAM), but abandons the traditional boundary between medium and agent in favour of accepting aircraft systems and artefacts as agents, with their own functional properties and relationships. The joint cognitive system perspective in developing the FRAM model allows an understanding of the effects of task and information propagation, and eventual distributed criticalities, taking advantage of the functional properties of the system, as described in a case study related to the cockpit environment of a DC-9 aircraft.

Practitioner Summary: This research presents the application of one systemic method to understand work systems and performance variability in relation to the transformation of information within a flight deck for a specific phase of flight. By using a joint cognitive systems approach both retrospective and prospective investigation of cockpit challenges will be better understood.

Abbreviations: ATC: air traffic control; ATCO: air traffic controller; ATM: air traffic management; CSE: cognitive systems engineering; DSA: distributed situation awareness; FMS: flight management system; FMV: FRAM model visualize; FRAM: functional resonance analysis method; GF: generalised function; GW: gross weight; HFACS: human factors analysis and classification system; JCS: joint cognitive systems; PF: pilot flying; PNF: pilot not flying; SA: situation awareness; SME: subject matter expert; STAMP: systems theoretic accident model and processes; VBA: visual basic for applications; WAD: work-as-done; WAI: work-as-imagined; ZFW: zero fuel weight     

The role of expertise research and human factors in capturing, explaining, and producing superior performance

OBJECTIVES: The goal of this article is to identify some of the major trends and findings in expertise research and their connections to human factors. BACKGROUND: Progress in the study of superior human performance has come from improved methods of measuring expertise and the development of better tools for revealing the mechanisms that support expert performance, such as protocol analysis and eye tracking. METHODS: We review some of the challenges of capturing superior human performance in the laboratory and the means by which the expert performance approach may overcome such challenges. We then discuss applications of the expert performance approach to a handful of domains that have long been of interest to human factors researchers. RESULTS: Experts depend heavily on domain-specific knowledge for superior performance, and such knowledge enables the expert to anticipate and prepare for future actions more efficiently. Training programs designed to focus learners' attention on task-related knowledge and skills critical to expert performance have shown promise in facilitating skill acquisition among nonexperts and in reducing errors by experts on representative tasks. CONCLUSIONS: Although significant challenges remain, there is encouraging progress in domains such as sports, aviation, and medicine in understanding some of the mechanisms underlying human expertise and in structuring training and tools to improve skilled performance. APPLICATIONS: Knowledge engineering techniques can capture expert knowledge and preserve it for organizations and for the development of expert systems. Understanding the mechanisms that underlie expert performance may provide insights into the structuring of better training programs for improvingskill and in designing systems to support professional expertise.     

Feasibility of augmented reality technology for communication in the construction industry

Problem statementThe current shortage of skilled and experienced management personnel in the construction industry makes it necessary to devise solutions that fully leverage the available expertise. Augmented Reality (AR) is an emerging technology that has the potential to provide contextually relevant communication to field engineers and offer value through real-time communication of spatiotemporal details.PurposeThis paper identifies the communication scenarios in which AR may (or may not) be able to replace in-person meetings to address construction-related communication needs, based on data collected from current practitioners. Furthermore, the feedback from practitioners defines contextual factors that would impact their willingness to adopt AR for communication in the domain of construction engineering and management.MethodologySemi-structured interviews were conducted with practitioners and thematic analysis was performed on the interview data. This process identified trends in the practitioners’ responses to reveal their perceptions on use of AR for communication and also the factors that may impact the success of AR for communication in practice.FindingsPractitioners perceived AR to be of value in the following situations: improving the communication between the design team and construction team; effectively leveraging the expertise of experienced personnel; and to aid in inspections. The factors affecting the adoption of this technology were classified into technological factors (e.g. Cost of equipment) and non-technological factors (e.g. Relationship building).ContributionMany researchers have explored AR for construction, but the vast majority have explored the topic in controlled settings. Conversely, this study relates to real-life practical settings with direct involvement of industry practitioners. Therefore, the contribution of this work is in defining the factors that must be considered if researchers or practitioners intend to realize the theoretical value suggested for AR in practical settings, while taking into account existing challenges.     

应用于人体关节缺损修复的建模与可视化

目的 关节缺损疾病治疗目前存在的主要问题是缺乏精确的关节模型以及个体化的修复方案,为此提出量化关节骨缺损的精确建模与可视化方法。方法 利用骨骼图像增强、多模态影像融合、关节结构分割、关节病变结构建模与定量分析等核心关键技术,从关节CT或MRI影像中构建和恢复缺损关节的空间立体结构,为关节缺损信息提供量化参数和3维模型,从而帮助医生快速准确地对关节缺损疾病进行诊疗。结果 针对建模与可视化方法中的核心关键技术进行了深入研究,实验结果显示上述方法能够为关节缺损修复提供精确的3维量化模型。结论 基于CT、MRI影像的关节结构建模与可视化技术为评价骨缺损大小提供了精确有效的方法,在关节盂或肱骨结构等疾病的诊疗方面具有重要的临床意义,此项技术的发展对关节缺损疾病的修复发挥着重要作用。     

Some surprising differences between novice and expert errors in computerized office work

Abstract

This paper investigates the impact of different levels of expertise on errors in human-computer interaction. In a field study 174 clerical workers from 12 different companies were observed during their normal office work and were questioned on their expertise with computers. The level of expertise was determined by (a) the length of time an employee had worked with a computer (computer expertise); (b) the number of programs she knew (program expertise); and (c) the daily time s/he spent working with the computer (daily work-time expertise). These different operationalizations of novices and experts led to different results. In contrast to widespread assumptions, experts did not make fewer errors than novices (except in knowledge errors). On the other hand, experts spent less time handling the errors than novices. A cluster analysis produced four groups in the workforce: occasional users, frequent users, beginners, and general users.     

AI FOR SPECIAL NEEDS—AN ‘INTELLIGENT’ COMMUNICATION AID FOR BUSS USERS

Abstract

This paper describes the application of artificial intelligence ideas and techniques to the design and implementation of a computer system for nonvocal, physically handicapped users of the communication system Bliss. The research was performed to investigate the possibility of using an automated device to emulate and replace the human assistance required by Bliss users, thus enabling independent and yet unconstrained expression. The system described outperforms human assistance on small-scale test data, using relatively simple heuristic methods. It enables communication with one-third of the work required by a standard word processing package. Issues related to the practicability of AI theory and practice in an application area such as this are discussed.