We present a novel hierarchical model for human activity recognition. In contrast with approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a structured support vector machine. A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.
Business model innovations emerge over time and are influenced by managerial interaction with stakeholders. Especially with regard to business model innovation for sustainability, manager-stakeholder interaction can radically change a company’s business model and underlying logic. However, the majority of the literature shows how manager–stakeholder interaction may limit business model innovation when stakeholders reinforce existing managerial cognitions. In this chapter we study how stakeholders can also stimulate business model innovation by affecting managerial cognitive change. Through three case studies, we find that this can occur through three shaping processes: market approach shaping, product/service offering shaping, and credibility shaping. We also find that the impact of new or latent stakeholders is greater than that of existing stakeholders. We end the chapter by sketching a research agenda to further unravel the role of stakeholders affecting managerial cognition around business model innovation for sustainability.
Crime script analysis as a methodology to analyse criminal processes is underdeveloped. This is apparent from the various approaches in which scholars apply crime scripting and present their cybercrime scripts. The plethora of scripting methods raise significant concerns about the reliability and validity of these scripting studies. In this methodological paper, we demonstrate how object-oriented modelling (OOM) could address some of the currently identified methodological issues, thereby refining crime script analysis. More specifically, we suggest to visualise crime scripts using static and dynamic modelling with the Unified Modelling Language (UML) to harmonise cybercrime scripts without compromising their depth. Static models visualise objects in a system or process, their attributes and their relationships. Dynamic models visualise actions and interactions during a process. Creating these models in addition to the typical textual narrative could aid analysts to more systematically consider, organise and relate key aspects of crime scripts. In turn, this approach might, amongst others, facilitate alternative ways of identifying intervention measures, theorising about offender decision-making, and an improved shared understanding of the crime phenomenon analysed. We illustrate the application of these models with a phishing script.
MULTIFILE
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
