Augmenting process elicitation with visual priming: An empirical exploration of user behaviour and modelling outcomes

Business process models have become an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach to process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions similarly to how they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. An empirical investigation comparing both the modelling outputs and participant behaviour of this virtual world role-play elicitor with an S-BPM process modelling tool found that while the modelling approaches of the two groups varied greatly, the virtual world elicitor may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.     

The Effects of Task Interruption on Human Performance: A Study of the Systematic Classification of Human Behavior and Interruption Frequency

Interruptions are prevalent phenomena in modern working environments; yet, few interruption studies have been conducted on different types of human tasks. A study using computer‐based human behavior tasks was carried out to investigate the effects of interruptions with 4 different primary‐interrupting task sets. The tasks used in this study were determined by cognitive and motor skill processes based on human behavior classification theory. The results showed that interruption effects were most negative in cognitive/cognitive task sets, and skill/cognitive task sets showed the least amount of effects in task completion time, F(3, 332) = 77.88, p < .001. In error rate performance, skill/skill task sets showed the highest rate, and error rates for cognitive primary task sets (cognitive/cognitive and cognitive/skill task sets) were not significantly different, F(1, 42) = 41.18, p < .001; F(1, 42) = 3.56, p = 0.0661. Interruption frequency also negatively affected task performance, F(1, 412) = 89.88, p < .001, but skill tasks’ quantitative performance did not show significant effects at different interruption frequencies, F(1, 187) = 3.78, p = .0534. The results showed that interruptions increase more time to complete in cognitive tasks and produce more errors in skill tasks. Also, similar types of primary‐interrupting tasks were more susceptible from interruptions. Thus, based on task composition of work process, we can estimate different effects from the interruptions and memory load, and task similarity in primary‐interrupting task relationship were considered a main factor.     

Development of Human Posture Simulation Method for Assessing Posture Angles and Spinal Loads

Video‐based posture analysis employing a biomechanical model is gaining a growing popularity for ergonomic assessments. A human posture simulation method of estimating multiple body postural angles and spinal loads from a video record was developed to expedite ergonomic assessments. The method was evaluated by a repeated measures study design with three trunk flexion levels, two lift asymmetry levels, three viewing angles, and three trial repetitions as experimental factors. The study comprised two phases evaluating the accuracy of simulating self‐ and other people's lifting posture via a proxy of a computer‐generated humanoid. The mean values of the accuracy of simulating self‐ and humanoid postures were 12° and 15°, respectively. The repeatability of the method for the same lifting condition was excellent (～2°). The least simulation error was associated with side viewing angle. The estimated back compressive force and moment, calculated by a three‐dimensional biomechanical model, exhibited a range of 5% underestimation. The posture simulation method enables researchers to quantify simultaneously body posture angles and spinal loading variables with accuracy and precision comparable to on‐screen posture‐matching methods.     

A hybridization of genetic algorithms and fuzzy logic for the single-machine scheduling with flexible maintenance problem under human resource constraints

This work focuses on the problem of scheduling jobs on a single machine that requires flexible maintenance under human resource competence and availability constraints. To solve the problem we developed two fuzzy genetic algorithms that are based on respectively the sequential and total scheduling strategies. The one respecting the sequential strategy consists in two phases. In the first phase, the integrated production and maintenance schedules are generated. In the second one, the human resources are assigned to maintenance activities. The second algorithm respecting a total strategy consists in generating the integrated production and maintenance schedules that explicitly satisfy the human resource constraints. In regard to these two different strategies, we studied then two integrated fuzzy genetic algorithms that use the fuzzy logic framework to deal with the uncertain nature of both production and maintenance data. The proposed genetic algorithms have been implemented and applied to non-standard test problems which integrate production, maintenance and human resource data. The experimental results show that the consideration of human resource constraints and uncertainties allows to get more realistic and applicable solutions. Moreover, the comparison between the two proposed algorithms shows that the one based on the total strategy outperforms the one based on the sequential strategy regarding the objective functions’ optimization. However, this latter is better in terms of computational times.     

哈尼梯田遗产地人类活动强度定量化研究

人类活动强度是表征人类对环境影响和作用程度的指标,可通过土地利用类型来定量化。本研究运用地理信息系统 (GIS) 技术,以哈尼梯田遗产地所在的元阳县的土地利用数据为基础,结合研究区实际情况,建立了元阳县不同土地利用类型的人类影响强度系数,量化了研究区域的人类活动强度的大小和空间分异。结果表明元阳县人类活动强度整体较低,但从元阳县到遗产区再到遗产核心区,人类活动强度呈明显的递增趋势。空间上看,元阳县人类活动强度西高东低,高值一般出现在 1500 m-2000 m 的中高海拔地区。遗产区和遗产核心区的人类活动强度则以主要梯田片区为表征,呈块状分布,高值主要集中在牛角寨、坝达、多依树、老虎嘴4大梯田片区。     

Detangler: Visual Analytics for Multiplex Networks

A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.     

Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content‐based Guidance

Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human‐in‐the‐optimization method that allows users to directly explore and search the latent vector space of generative image modelling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In addition, the user can express their intention through image editing tools. The system samples latent vectors based on inputs and presents new candidates to the user iteratively. An advantage of our formulation is that one can apply our method to arbitrary pre‐trained model without developing specialized architecture or data. We demonstrate our method with various generative image modelling applications, and show superior performance in a comparative user study with prior art iGAN [ZKSE16].