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This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, [Formula: see text], embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of [Formula: see text] as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with [Formula: see text] values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of [Formula: see text] that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.

Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on one integrated plan and with clear decision milestones. The alignment is cross-functional and connects different operations functions with each other to set an overall delivery ability. There are always challenges connecting different functions in a company which most S&OP practitioners agree with, still, that is one of the things that the S&OP-process should bridge. Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools have contributed to an improved cross functional communication over time. S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion as e.g., make-to-stock (MTS) and make-to-order (MTO) contexts, may experience even further challenges. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant ones is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge. The intent with this paper is to, based on S&OP-challenges, see if ML can be used to improve these challenges. In a brief literature review together with empiric data from a single industrial case (SIC), S&OP-challenges were defined and structured. Based on the challenges in several S&OP-sub-areas, classified into data quality, horizontal and vertical disconnects, specific tasks were specified and structured into anomaly detection, clustering and classification, and predictions. Which exact ML-method to use require further work and tests. Still, this is a good starting point to take the next step and the specified tasks could also be used for other practitioners that want to start using ML/AI in their daily activities. CC BY-NC 4.0Corresponding Author, Nils-Erik Ohlson, Jönköping University, School of Engineering, Gjuterigatan 5, SE 553 18 Jönköping, Sweden, E-mail: nilserik.ohlson@ju.seVF-KDO

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. To this end, a case study was carried out on fighter pilots and it was investigated how teamwork is performed in this specialised and challenging environment, with a specific focus on the dependence on technology for these teams. The collaboration between the fighter pilots is described and analysed using a generic theoretical model for effective teamwork from the literature. The results show that domain-specific application and modification is needed in order for the model to capture fighter pilot's teamwork. The study provides deeper understanding of the working conditions for teams of pilots and gives design implications for how tactical support systems can enhance teamwork in the domain. Practitioner summary: This article presents a qualitative interview study with fighter pilots based on a generic theoretical teamwork model applied to the fighter domain. The purpose is to understand the conditions under which teams of fighter pilots work and to provide guidance for the design of future technological aids.

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc). CC BY 4.0

We are largely used to hearing explanations. For example, if someone thinks you are sad today, they might reply to your "why?"with "because you were so Hmmmmm-mmm-mmm". Today's Artificial Intelligence (AI), however, is - if at all - largely providing explanations of decisions in a visual or textual manner. While such approaches are good for communication via visual media such as in research papers or screens of intelligent devices, they may not always be the best way to explain; especially when the end user is not an expert. In particular, when the AI's task is about Audio Intelligence, visual explanations appear less intuitive than audible, sonified ones. Sonification has also great potential for explainable AI (XAI) in systems that deal with non-audio data - for example, because it does not require visual contact or active attention of a user. Hence, sonified explanations of AI decisions face a challenging, yet highly promising and pioneering task. That involves incorporating innovative XAI algorithms to allow pointing back at the learning data responsible for decisions made by an AI, and to include decomposition of the data to identify salient aspects. It further aims to identify the components of the preprocessing, feature representation, and learnt attention patterns that are responsible for the decisions. Finally, it targets decision-making at the model-level, to provide a holistic explanation of the chain of processing in typical pattern recognition problems from end-to-end. Sonified AI explanations will need to unite methods for sonification of the identified aspects that benefit decisions, decomposition and recomposition of audio to sonify which parts in the audio were responsible for the decision, and rendering attention patterns and salient feature representations audible. Benchmarking sonified XAI is challenging, as it will require a comparison against a backdrop of existing, state-of-the-art visual and textual alternatives, as well as synergistic complementation of all modalities in user evaluations. Sonified AI explanations will need to target different user groups to allow personalisation of the sonification experience for different user needs, to lead to a major breakthrough in comprehensibility of AI via hearing how decisions are made, hence supporting tomorrow's humane AI's trustability. Here, we introduce and motivate the general idea, and provide accompanying considerations including milestones of realisation of sonifed XAI and foreseeable risks. acceptedVersion

The growing neural gas (GNG) is an unsupervised topology learning algorithm that models a data space through interconnected units that stand on the populated areas of that space. Its output is a graph that can be visually represented on a two-dimensional plane, and be used as means to disclose cluster patterns in datasets. GNG, however, creates highly connected graphs when trained on high dimensional data, which in turn leads to highly clutter representations that fail to disclose any meaningful patterns. Moreover, its sequential learning limits its potential for faster executions on local datasets, and, more importantly, its potential for training on distributed datasets while leveraging from the computational resources of the infrastructures in which they reside. This paper presents two methods that improve GNG for the visualization of cluster patterns in large and high-dimensional datasets. The first one focuses on providing more meaningful and accurate cluster pattern representations of high-dimensional datasets, by avoiding connections that lead to high-dimensional graphs in the modeled topology, which may, in turn, lead to visual cluttering in 2D representations. The second method presented in this paper enables the use of GNG on big and distributed datasets with faster execution times, by modeling and merging separate parts of a dataset using the MapReduce model. Quantitative and qualitative evaluations show that the first method leads to the creation of lower-dimensional graph structures, which in turn provide more accurate and meaningful cluster representations; and that the second method preserves the accuracy and meaning of the cluster representations while enabling its execution in distributed settings. CC BY 4.0