
Göran FalkmanUniversity of Skövde · School of Informatics
Göran Falkman
PhD, Docent
About
113
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2,384
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Introduction
Additional affiliations
January 2004 - December 2012
September 2005 - December 2006
July 1999 - present
Publications
Publications (113)
Surveillance operators normally analyze vast amounts of sensor data in order to find conflict situations, and threatening or unusual activities while allowing the continuous flow of goods and people. Semi-automatic support may reduce the time needed for the detection of such situations, generating early warnings that can prevent accidents or provid...
Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more limitations: They are not designed for sequential analysis of incomplete trajectories or online learni...
Modern healthcare's need for knowledge sharing and bridging the research–practice gap requires new forms of collaboration, in which clinicians of varying clinical and research expertise work together over geographical and organisational borders. To support such distributed communities of practice (CoPs), an understanding of their collaboration proc...
Information technology (IT) support for remote collaboration of geographically distributed communities of practice (CoP) in health care must deal with a number of sociotechnical aspects of communication within the community. In the mid-1990s, participants of the Swedish Oral Medicine Network (SOMNet) began discussing patient cases in telephone conf...
In 1995, the MedView project, based on a co-operation between computing science and clinical medicine was initiated. The overall goal of the project was to develop models, methods and tools to support clinicians in their daily diagnostic work. As part of MedView, two information visualisation tools were developed and tested as solutions to the prob...
Driver intention recognition (DIR) methods mostly rely on deep neural networks (DNNs). To use DNNs in a safety-critical real-world environment it is essential to quantify how confident the model is about the produced predictions. Therefore, this study evaluates the performance and calibration of a temporal convolutional network (TCN) for multiple p...
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 men...
Artificial intelligence (AI) is nowadays included into an increasing number of critical systems. Inclusion of AI in such systems may, however, pose a risk, since it is, still, infeasible to build AI systems that know how to function well in situations that differ greatly from what the AI has seen before. Therefore, it is crucial that future AI syst...
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,...
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 hum...
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value...
The competitiveness in the manufacturing industry raises demands for using recent data analysis algorithms for manufacturing process development. Data-driven analysis enables extraction of novel knowledge from already existing sensors and data, which is necessary for advanced manufacturing process refinement involving aged machinery. Improved data...
The execution of teamwork varies widely depending on the domain and task in question. Despite the considerable diversity of teams and their operation, researchers tend to aim for unified theories and models regardless of field. However, we argue that there is a need for translation and adaptation of the theoretical models to each specific domain. T...
p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great val...
We describe a research process where fighter pilots’ behaviors were investigated from a teamwork perspective and the findings conveyed to the designers of cockpit interfaces in order to improve the fighter aircraft system. The teamwork perspective was selected because fighter aircraft are complex systems that require an advanced and trained pilot,...
We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the me...
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (s...
This paper investigates the nature of user participation in the process of designing fighter aircraft cockpits. The role of the users, i.e. pilots, in the design of cockpit interfaces is explored. We present the results of an on-line questionnaire with twelve designers of cockpit interfaces for fighter aircraft. The results show that the designers...
Fighter pilots depend on collaboration and teamwork to perform successful air missions. However, such collaboration is challenging due to limitations in communication and the amount of data that can be shared between aircraft. In order to design future support systems for fighter pilots, this paper aims at characterizing how pilots collaborate whil...
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” [23]. To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features,...
Fighter pilots performing air missions rely heavily on teamwork for successful outcomes. Designing systems that support such teamwork in highly dynamic missions is a challenging task, and to the best of our knowledge, current teamwork models are not specifically adapted for this domain. This paper presents a model of task performance for military f...
Effective team work is regarded as a key factor for success in missions performed by fighter aircraft in a Tactical Air Unit (TAU). Many factors contribute to how a team will succeed in their mission. From the existing literature on teamwork, Salas, Sims and Burke [1], suggested five main factors and three supporting mechanisms for effective team w...
This paper examines an icon set designed for displaying uncertainty surrounding threat levels of an approaching object in an aircraft cockpit. This is done through an experiment that compares an icon set designed for this experiment with two icon sets from existing research that were tested in static laboratory conditions. The experiment used a fli...
We describe ongoing research where the aim is to apply recent results from the research field of information fusion to bibliometric analysis and information retrieval. We highlight the importance of ‘uncertainty’ within information fusion and argue that this concept is crucial also for bibliometrics and information retrieval. More specifically, we...
We describe ongoing research where the aim is to apply recent results from the research field of information fusion to bibliometric analysis and information retrieval. We highlight the importance of ‘uncertainty’ within information fusion and argue that this concept is crucial also for bibliometrics and information retrieval. More specifically, we...
This paper presents 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 three levels of transparency consisted of 1) only presenting
text–based information regarding the specific object (without any
au...
This paper discusses the results of four empirical
evaluations that assess the effects that visualizing system meta-information
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 (...
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 61 Swedish drivers was carried out where a continuous representation of the uncertainty of the car’s ability to autonomously drive was displayed to one of the groups...
Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Generally, previous anomaly detection algorithms ofte...
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 sy...
In the defense domain, to estimate if a target is threatening and to which degree is a complex task, that is typically carried out by human operators due to the high risks and uncertainties associated. To their aid, different support systems have been implemented to analyze the data and provide recommendations for actions. Since the ultimate respon...
Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in da...
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 pilo...
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 h...
Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light...
The surveillance of large sea, air or land areas normally involves the analysis of large volumes of heterogeneous data from multiple sources. Timely detection and identification of anomalous behavior or any threat activity is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining application...
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 infor-mation, time-pressure and d...
Situation recognition is an important problem within the surveillance domain, which addresses the prob-lem of recognizing a priori defined patterns of interesting situations that may be of concurrent and temporal nature, and which possibly are occurring in the present flow of data and information. There may be many viable approaches, with different...
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...
Many approaches for anomaly detection use statistical based methods that build profiles of normality. In these cases, anomalies are defined as deviations from normal models build from representative data. Detection systems based solely on these approaches typically generate high false alarm rates due to the difficulty of creating flawless models. I...
The protection of defended assets such as military bases and population centers against ballistic weapons (e.g. rockets and mortars) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We prop...
In the military aviation domain, the decision maker, 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 co...
The allocation of firing units to hostile targets is an important process within the air defense domain. Many algorithms have been proposed for solving various weapon allocation problems, but evaluation of the performance of such algorithms is problematic, since it does not exist any standard scenarios on which to test the algorithms. It is to a la...
This paper presents a novel application of the theory of con-formal prediction for distribution-independent on-line learn-ing and anomaly detection. We exploit the fact that confor-mal predictors give valid prediction sets at specified confi-dence levels under the relatively weak assumption that the (normal) training data together with (normal) obs...
Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies include investigations on the analytical reasoning process that needs to be supported. In this paper, we present an approach to evaluate the ability of certain visual representations from an integrated visual-computational envir...
Situation recognition is an important problem to solve for introducing new capabilities in surveillance applications. It is concerned with recognizing a priori defined situations of interest, which are character-ized as being of temporal and concurrent nature. The purpose is to aid decision makers with focusing on information that is known to likel...
Situation recognition – the task of tracking states and identifying situations – is a problem that is important to look into
for aiding decision makers in achieving enhanced situation awareness. The purpose of situation recognition is, in contrast
to producing more data and information, to aid decision makers in focusing on information that is impo...
In air defense situations, decision makers have to protect defended assets through assigning available firing units to threatening
targets in real-time. To their help they have decision support systems known as threat evaluation and weapon allocation (TEWA)
systems. The problem of performance evaluation of such systems is of great importance, due t...
Maritime surveillance systems analyze vast amounts of heterogeneous sensor data from a large number of objects. In order to support the operator while monitoring such systems, the identification of anomalous vessels or situations that might need further investigation may reduce the operator's cognitive load. While it is worth acknowledging that man...
This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian mixture model (GMM) and the adaptive kernel density estimator (KDE). A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy mod...
Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon sys...
Monitoring the surveillance of large sea areas normally involves the analysis of huge quantities of heteroge-neous data from multiple sources (radars, cameras, automatic identification systems, reports, etc.). The rapid identification of anomalous behavior or any threat activity in the data is an important objective for enabling homeland security....
The goal of visual analytical tools is to support the analytical reasoning process, maximizing human perceptual, understanding and reasoning capabilities in complex and dynamic situations. Visual analytics software must be built upon an understanding of the reasoning process, since it must provide appropriate interactions that allow a true discours...
The process of tracking and identifying developing situations is an ability of importance within the surveillance domain. We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers. Situation recognition requires that many subproblems are solved. For instance, we need to establish which situati...
Research on information fusion and situation management within the military domain, is often focused on data-driven approaches for aiding decision makers in achieving situation awareness. We have in a companion paper identified situation recognition as an important topic for further studies on knowledge-driven approaches. When developing new algori...
The allocation of weapons to targets (such as missiles and hostile aircrafts) is a well-known resource allocation problem within the field of operations research. It has been proven that this problem, in general, is NP-complete. For this reason, optimal solutions to the static weapon-target allocation (WTA) problem can not be obtained in real-time...
Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the
foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed.
In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian...
Founded on the Semantic Web technologies OWL and RDF, SOMWeb is an online community of practice that is used for knowledge
sharing and dissemination within an oral medicine community in Sweden. It is shown how patterns for communication and collaboration
within SOMWeb can be identified and represented in OWL, in terms of knowledge components, such...
The surveillance of large sea areas often generates huge amounts of multidimensional data. Exploring, analyzing and finding anomalous behavior within this data is a complex task. Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator...
In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from t...
The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situa...
SOMWeb is an online collaboration system based on Semantic Web technologies, which is used for knowledge sharing and dissemination within an oral medicine community in Sweden. Based on a previous study of the use of SOMWeb, general patterns of interaction and communicative activities involved in community collaboration have been identified. The pat...
In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for huma...
Maritime situation awareness is of importance in a lot of areas - e.g. detection of weapon smuggling in military peacekeeping operations, and harbor traffic control missions for the coast guard. In this paper, we have combined the use of Self Organizing Maps with Gaussian Mixture Models, in order to enable situation awareness by detecting deviation...
Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert's actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to...