Högskolan i Skövde

Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.

To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

Threat evaluation is concerned with estimating the level of threat posed by enemy units to one's own assets. This is an impact analysis problem which is important to address for supporting operators in achieving situation awareness. Due to the risky and complex nature of the threat evaluation tasks, it is imperative that the operators are supported by computerized systems as well as that they are an integral part of the threat evaluation process. To do so, the operators have to understand and be able to provide their input to the process, hence the need to make the threat evaluation process transparent to the operators. In order to implement a transparent threat evaluation support system, we argue that the process of visual analytics should provide valuable guidance. In this paper we suggest a model for using visual analytics in a threat evaluation context. We also investigate the potential of recognized threat evaluation models to be used within a visual analytics context. © 2013 IEEE.

In the military aviation domain, the decisionmaker, i.e. the pilot, often has to process huge amounts of information in order to make correct decisions. This is further aggravated by factors such as time-pressure, high workload and the presence of uncertain information. A support system that aids the pilot to achieve his/her goals has long been considered vital for performance progress in military aviation. Research programs within the domain have studied such support systems, though focus has not been on team collaboration. Based on identified challenges of assessing team situation awareness we suggest an approach to future military aviation support systems based on information fusion. In contrast to most previous work in this area, focus is on supporting team situation awareness, including team threat evaluation. To deal with these challenges, we propose the development of a situational adapting system, which presents information and recommendations based on the current situation.

The working situation of fighter pilots is often very challenging. The pilots are requested to perform their tasks and make decisions in situations characterised by time-pressure, huge amounts of data and high workload, knowing that wrong decisions might result in fatal consequences. To aid the pilots, several automatic support systems have been implemented in modern fighter aircraft and will continue to be implemented in pace with technological advancements and new demands posed on the pilots. For example, innovations within the information fusion (IF) domain have made it possible to fuse large amounts of data, stemming from different sensors, databases etc., to create a better foundation for making decisions and act than would have been possible if the information sources had been used separately. However, there are both positive and negative effects of automation, such as decreased workload and improved situation awareness on the one hand, but skill degradation and complacent behaviour on the other. To avoid the possible negative consequences of automation, while at the same time ameliorating the positive ones, a human-centred automation (HCA) approach to system design has been proposed as a way of optimizing the collaboration between the human and the machine. As a design approach, HCA stresses the importance of a cooperative human-machine relationship, where the operator is kept in the automation loop. However, how to introduce HCA within the fighter aircraft domain as well as its implications for the interface and automation design of support systems within the field has not been investigated.

This thesis investigates the implications of introducing HCA into the fighter aircraft domain. Through literature surveys and empirical investigations, general and domain specific HCA guidelines have been identified. These advocate, for example, that an indication of the reliability of the information and the recommendations provided by the different aircraft support systems must be given as well as that support for appropriate updates of the pilots’ individual and team awareness of the situation must be provided. A demonstrator, mirroring some of the identified guidelines, has been implemented and used to evaluate the guidelines together with system developers within the domain. The evaluation indicated that system developers of modern fighter aircraft implicitly incorporate many of the identified HCA guidelines when designing. However, the evaluation further revealed that to explicitly incorporate these guidelines into the development approach, preferably through the development of a domain specific style guide, would aid the system developers design automated support systems that provide appropriate support for the pilots. The results presented in this thesis are expected to aid developers of modern fighter aircraft support systems by incorporating HCA into the traditional simulator-based design (SBD) approach. This approach is frequently used within the field and stresses early and frequent user-involvement when designing, in which complementary HCA evaluations could be performed to further improve the support systems implemented from an automation perspective. Furthermore, it is expected that the results presented in this thesis will contribute to the research regarding how to incorporate the human operator in the information fusion processes, which has been recognised as a research gap within the IF field. Thus, a further contribution of this thesis is the suggestion of how the HCA development approach could be of aid when improving the interaction between the operator and the automated fusion system.

More and more complex semi-automated systems are being developed, aiding human operators to collect and analyze data and information and even to recommend decisions and act upon these. The goal of such development is often to support the operators make better decisions faster, while at the same time decrease their workload. However, these promises are not always fulfilled and several incidents have highlighted the fact that the introduction of automated technologies might instead increase the need for human involvement andexpertise in the tasks carried out.

The significance of communicating information regarding an automated system's performance and to explain its strengths and limitations to its operators is strongly highlighted within the system transparencyand operator-centered automation literature. However, it is not common that feedback containing system qualifiers is incorporated into the primary displays of the automated system, obscuring its transparency. In this thesis, we deal with the investigation of the effects of explaining and visualizing system reasoning and performance parameters in different domains on the operators' trust, workload and performance. Different proof-of-concept prototypes have been designed with transparency characteristics in mind, and quantitative and qualitative evaluations together with operators of these systems have been carried out.

Our results show that the effects of automation transparency can positively influence the performance and trust calibration of operators of complex systems, yet possibly at the costs of higher workload and longer decision-making times. Further, this thesis provides recommendations for designers and developers of automated systems in terms of general design concepts and guidelines for developing transparent automated systems for the future.

This report outlines trends and application areas within the research field of intelligent user interfaces(IUIs) from 2010-2018. The purpose of the report is to give an overview of the IUI research area andpoint out particular subfields that have been given attention in the recent years, indicating possible trendsfor future research. Our report indicates that the field of IUIs is very broad, resulting in rather diverseresearch trends within the area. However, general trends could be identified, such as an increasing interest inbetter human-machine decision-making, where strategies for explaining the automatic reasoning are beinginvestigated together with ways of improving the trustworthiness of the systems and their possible adaptationsto individuals’ needs. The report also outlines research on multimodal interactions, adaptivity and humanrobotcollaboration, addressing challenges such as increased human workload, unobtrusiveness, privacy andmultiparty communication.

Pilots of fighter aircraft are often exposed to the risk of being hit by enemy fire from ground-based threats. It has been argued that the pilots could be aided by a tactical support system that automatically assesses the danger posed by such threats and analyzes the survivability of the mission. It has also been argued that the automation design of such system must be properly adapted according to the pilots' needs. In this paper, empirical results are presented regarding the characteristics of an operator-centered survivability support system in the fighter aircraft domain, where both the development of the system and its automation design are discussed. The results indicate a strong potential for the survivability model and the automation guidelines within the fighter aircraft domain, but also a need for further refinements of the model and the guidelines to reflect the specific characteristics of the domain.

Fighter pilots must often make decisions fast, under time-pressure and based on uncertain or incomplete data. Thus, decision-making in this envi-ronment poses several challenges on the pilots such as how to fulfil the goal of the mission, while at the same time limit the potential costs and risks taken to fulfil this goal. Another challenge involves the dynamic coordination of actions within the team of pilots needed to succeed with the mission efficiently. This paper discusses challenges and opportunities of introducing a decision-support tool in the fighter aircraft, aiding the pilots determine the best course(s) of ac-tion with regard to the team’s resources, opportunities and the possible risks in-volved. To do so, we apply the concept of option awareness, guiding the future development of decision support in the fighter aircraft domain.

Military fighter pilots have to make suitable decisions fast in an environment where continuously increasing flows of information from sensors, team members and databases are provided. Not only do the huge amounts of data aggravate the pilots’ decision making process: time-pressure, presence of uncertain data and high workload are factors that can worsen the performance of pilot decision making. In this paper, initial ideas of how to support the pilots accomplishing their tasks are presented. Results from interviews with two fighter pilots are described as well as a discussion about how these results can guide the design of a military fighter pilot decision support system, with focus on team cooperation.

Advancements in technology and the need for improving the pilots' working situations have stimulated the growth of automated functions within the fighter aircraft domain. Functions that aid the pilots perform their tasks and to make decisions fast in an often rapidly changing environment have been introduced with the ultimate aim of easing the pilots' workloads and improving their situation awareness. However, both positive and negative effects of automating pilots' tasks have been documented, such as decreased workload and skill degradation, and it has been acknowledged that it is important to consider the needs of the human operator when designing automated functions. Human-Centred Automation (HCA) has been suggested as an approach for designing automated systems that brings forward the positive effects of automation. However, how to apply the concept of HCA in light of the specific prerequisites and demands of the modern fighter aircraft domain has not been fully investigated. This paper presents the results from interviews made together with fighter aircraft system developers, where the concept of HCA was investigated. Their opinions of the applicability of the concept of HCA within the fighter aircraft domain is represented as well as their thoughts about identified HCA guidelines in relation to the proposed support system. Conclusions drawn from the study are that HCA is indeed an important concept to consider within the domain as well as that the proposed guidelines could be of great use during the design process. Additionallly, insight into how to design automated support systems with the concept of HCA in mind is given.

It has long been considered crucial to develop decision support systems that aid fighter pilots achieve their goals. Such systems often require automation of tasks formerly performed manually by the pilots, in situations characterized by huge amounts of (possibly uncertain and incomplete) sensor data and contextual information, time-pressure and dynamically changing tasks. Thus, careful investigations must be performed so as to develop such systems that provide accurate support for their users. This paper reports on the findings concerning research within the field of human-centred automation as well as presents empirical results concerning the applicability of automation guidelines when designing information fusion based support systems in the fighter aircraft domain.

Motivation – To guide the development of human-centred automation within the fighter aircraft domain.

Research approach – Identified human-centred automation guidelines have been analysed in relation to existing fighter aircraft automated functions together with system developers at Saab Aeronautics.

Findings/Design – The results show that the human-centred automation guidelines have been considered during the development process. From these results, implications for the design of guidelines and for the design of automated systems in the aircraft domain are drawn.

Research limitations/Implications – Deeper analysis of how automated functions can support pilots in future fighter aircraft is needed since the proposed guidelines are too general for the military fighter aircraft domain. Thus, future work involves an evaluation of the guidelines together with fighter aircraft system developers and/or military strategists. Such analysis must be carried out with specific automated functions in mind.

Originality/Value – By comparing with existing automated functions, the research makes contributions to HCA guidelines to be used in the fighter aircraft domain. Suggestions of human-centred automation improvements within the fighter aircraft domain are presented. The analysis has also identified differences between the proposed guidelines and parts of the studied implementation examples.

Take away message – The HCA guidelines must be adapted according to the specific tasks that the automated functions are intended to assist the operators with. To adapt the automation according to the level of experience of the operators as well as to expand the cooperative automation functions between aircraft in a team have been identified as future directions for automation improvements within the fighter aircraft domain.

Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale. © 2013 Springer-Verlag Berlin Heidelberg.

To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

We present an empirical study where the effects of three levels of system transparency of an automated target classification aid on fighter pilots’ performance and initial trust in the system were evaluated. The levels of transparency consisted of (1) only presenting text–based information regarding the specific object (without any automated support), (2) accompanying the text-based information with an automatically generated object class suggestion and (3) adding the incorporated sensor values with associated (uncertain) historic values in graphical form. The results show that the pilots needed more time to make a classification decision when being provided with display condition 2 and 3 than display condition 1. However, the number of correct classifications and the operators’ trust ratings were the highest when using display condition 3. No difference in the pilots’ decision confidence was found, yet slightly higher workload was reported when using display condition 3. The questionnaire results report on the pilots’ general opinion that an automatic classification aid would help them make better and more confident decisions faster, having trained with the system for a longer period.

Early and accurate diagnosis of sepsis is critical for patientsafety. However, this is a challenging task due to the very general symptomsassociated with sepsis, the immaturity of the tools used by theclinicians as well as the time-delays associated with the diagnostic methodsused today. This paper explores current literature regarding guidelinesfor clinical decision support, and support for sepsis diagnosis inparticular, together with guidelines extracted from interviews with fourclinicians and one biomedical analyst working at a hospital and clinicallaboratory in Sweden. The results indicate the need for the developmentof visual and interactive aids for enabling early and accurate diagnosisof sepsis.

Surveillance systems analyze and present vast amounts of heterogeneous sensor data. In order to support operators while monitoring such systems, the identification of anomalous behavior or situations that might need further investigation may reduce operators’ cognitive load. Bayesian networks can be used in order to detect anomalies in data. In order to understand the outcome generated from an anomaly detection application based on Bayesian networks, proper explanations must be given to operators.

This paper presents the findings of a literature analysis regarding what constitutes an explanation, which properties an explanation may have and a review of different explanation methods for Bayesian networks. Moreover, we present the empirical tests conducted with two of these methods in a maritime scenario. Findings from the survey and the experiments show that explanation methods for Bayesian networks can be used in order to provide operators with more detailed information to base their decisions on.

In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area.

For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.

Aggregation functions are extensively used in decision making processes to combine available information. Arithmetic mean and weighted mean are some of the most used ones. In order to use a weighted mean, we need to define its weights. The Analytical Hierarchy Process (AHP) is a well known technique used to obtain weights based on interviews with experts. From the interviews we define a matrix of pairwise comparisons of the importance of the weights. We call these AHP-like matrices absolute preferences of weights. We propose another type of matrix that we call a relative preference matrix. We define this matrix with the same goal—to find the weights for weighted aggregators. We discuss how it can be used for eliciting the weights for the weighted mean and define a similar approach for the Choquet integral.

A team of fighter pilots in a distributed environment with limited access to information rely on technology to pursue teamwork. In order to design systems that support distributed teamwork, it is, therefore, necessary to understand how access to information affects the team members. Certain factors, such as mutual performance monitoring, shared mental models, adaptability, and backup behavior are considered essential for effective teamwork. We investigate these factors in this work, focusing on how visually communicated information affects fighter pilots’ perception of these factors. For that, a questionnaire including the teamwork factors in relation to certain defined scenarios that contain various levels of information was distributed to fighter pilots. We show that the studied factors are affected by the level of information available to the pilots. Especially, mutual performance monitoring increases with the degree of available information. © 2023 Human Factors and Ergonomics Society.

This paper discusses the results of four empirical evaluations that assess the effects that visualizing system metainformation have on decision-making, particularly on confidence, trust, workload, time and performance. These four case studies correspond to the analysis of (1) the effects that visualizing uncertainty associated with sensor values (position, speed, altitude, etc. and track quality) have on decision-making on a ground to air defense scenario; (2) the effects that the visualization of the car’s certainty on its own capability of driving autonomously have on drivers’ trust and performance; (3) the influence that the visualization of various qualifiers associated with the proposals given by the support system has on air traffic operators carrying out identification tasks and (4) the effects that the presentation of different abstraction levels of information have on classification tasks carried out by fighter pilots. We summarize the results of these four case studies and discuss lessons learned for the design of future computerized support systems regarding the visualization of meta-information.

This paper presents an empirical study that addresses the effects the visualization of uncertainty has on decision-making. We focus our investigations on an area where uncertainty plays an important role and the decision time is limited. For that, we selected an air defense scenario, where expert operators have a few minutes to make a well-informed decision based on uncertain sensor data regarding the identity of an object and where the consequences of a late or wrong decision are severe. An approach for uncertainty visualization is proposed and tested using a prototype that supports the interactive analysis of multivariate spatio-temporal sensor data. The uncertainty visualization embeds the accuracy of the sensor data values using the thickness of the lines in the graphical representation of the sensor values. Semi-transparent filled circles represent the uncertain position, while a track quality value between 0 and 1 accounts for the quality of the estimated track for each target. Twenty-two experienced air traffic operators were divided into two groups (with and without uncertainty visualization) and carried out identification and prioritization tasks using the prototype. The results show that the group aided by visualizations of uncertainty needed significantly fewer attempts to make a final identification, and a significant difference between the groups when considering the identities and priorities assigned was observed (participants with uncertainty visualization selected higher priority values and more hostile and suspect identities). These results may show that experts put themselves in the ``worst-case scenario" in the presence of uncertainty when safety is an issue. Additionally, the presentation of uncertainty neither increased the participants' expressed workload, nor the time needed to make a classification. However, the inclusion of the uncertainty information did not have a significant effect on the performance (true positives, false negatives and false positives) or the participants' expressed confidence in their decisions.

In the defense domain, to estimate if a targetis threatening and to which degree is a complex task, thatis typically carried out by human operators due to the highrisks and uncertainties associated. To their aid, different supportsystems have been implemented to analyze the data and providerecommendations for actions. Since the ultimate responsibilitylies in human operators, it is of utmost importance that theytrust and know how to use these systems, as well as have anunderstanding of their inner workings, strengths and limitations.This paper presents, first, a formative user study to char-acterize how air traffic operators carry out threat evaluationrelated tasks. Grounded in these findings and in guidelinesfound in the literature, we present a transparent and highlyinteractive prototype that aims at reducing operator’s cognitiveload and support threat assessment activities. The literaturereview provided on design guidelines, the outcomes of the userstudy, the design of the prototype as well as the results of aninitial evaluation can provide guidance for both researchers andprospective developers of future threat evaluation systems.

To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

To ensure that a wireless telecommunication system is reliably functioning at all times, root-causes of potential network failures need to be identified and remedied, ideally before a noticeable network performance degradation occurs. Network operators are today observing a multitude of key performance indicators (KPIs) and are notified of possible network problems through alarms issued by different parts of the network. However, the number of cascading alarms together with the number of observable KPIs are easily overwhelming the operator’s cognitive capacity. In this paper we show how exploratory data analysis and machine learning, in particular topic modelling, can assist the operator when monitoring network performance and identifying anomalous network behaviour as well as supporting the operator’s analysis of the anomaly and identification of its root-cause. 

We propose a taxonomy for classifying and describing papers which contribute to making Machine Learning (ML) techniques interactive and interpretable for users. The taxonomy is composed of six elements – Dataset, Optimizer, Model, Predictions, Evaluator and Goodness – where each can bemade available for user interpretation and interaction. We give definitions to the terms interpretable and interactive in the context of useroriented Machine Learning, describe the role of each of the elements in the taxonomy, and describe papers as seen through the lens of the proposed taxonomy.