Current capabilities, requirements and a proposed strategy for interdependency analysis in the UK

The UK government recently commissioned a research study to identify the state-of-the-art in Critical Infrastructure modelling and analysis, and the government/industry requirements for such tools and services. This study (Cetifs) concluded with a strategy aiming to bridge the gaps between the capabilities and requirements, which would establish interdependency analysis as a commercially viable service in the near future. This paper presents the findings of this study that was carried out by CSR, City University London, Adelard LLP, a safety/security consultancy and Cranfield University, defense academy of the UK

Investigating styles in variability modeling: Hierarchical vs. constrained styles

Context: A common way to represent product lines is with variability modeling. Yet, there are different ways to extract and organize relevant characteristics of variability. Comprehensibility of these models and the ease of creating models are important for the efficiency of any variability management approach. Objective: The goal of this paper is to investigate the comprehensibility of two common styles to organize variability into models - hierarchical and constrained - where the dependencies between choices are specified either through the hierarchy of the model or as cross-cutting constraints, respectively. Method: We conducted a controlled experiment with a sample of 90 participants who were students with prior training in modeling. Each participant was provided with two variability models specified in Common Variability Language (CVL) and was asked to answer questions requiring interpretation of provided models. The models included 9 to 20 nodes and 8 to 19 edges and used the main variability elements. After answering the questions, the participants were asked to create a model based on a textual description. Results: The results indicate that the hierarchical modeling style was easier to comprehend from a subjective point of view, but there was also a significant interaction effect with the degree of dependency in the models, that influenced objective comprehension. With respect to model creation, we found that the use of a constrained modeling style resulted in higher correctness of variability models. Conclusions: Prior exposure to modeling style and the degree of dependency among elements in the model determine what modeling style a participant chose when creating the model from natural language descriptions. Participants tended to choose a hierarchical style for modeling situations with high dependency and a constrained style for situations with low dependency. Furthermore, the degree of dependency also influences the comprehension of the variability model

Revisiting port performance measurement: A hybrid multi-stakeholder framework for the modelling of port performance indicators

This study develops a new port performance measurement model by taking the perspectives from different port stakeholders. The novelty lies in the modelling of interdependencies among port performance measures, and the combination of weights of interdependent measures with both qualitative and quantitative evaluations of the measures from multiple stakeholders for quantitative port performance measurement. It represents an effective performance measurement tool and offers a diagnostic instrument for performance evaluation and/or monitoring of ports and terminals so as to satisfy different requirements of various port stakeholders in a flexible manner. © 201

Managing Epistemic Uncertainties in the Underlying Models of Safety Assessment for Safety-Critical Systems

When conducting safety assessment for safety-critical systems, epistemic uncertainty is an ever-present challenge when reasoning about the safety concerns and causal relationships related to hazards. Uncertainty around this causation thus needs to be managed well. Unfortunately, existing safety assessment tends to ignore unknown uncertainties, and stakeholders rarely track known uncertainties well through the system lifecycle. In this thesis, an approach is described for managing epistemic uncertainties about the system and safety causal models that are applied in a safety assessment. First, the principles that define the requirements for the approach are introduced. Next, these principles are used to construct three distinct steps that constitute an approach to manage such uncertainties. These three steps involve identifying, documenting and tracking the uncertainties throughout the system lifecycle so as to enable intervention to address the uncertainties. The approach is evaluated by integrating it with two existing safety assessment techniques, one using models from a system viewpoint and the other with models from a component viewpoint. This approach is also evaluated through peer reviews, semi-structured interviews with practitioners, and by review against requirements derived from the principles. Based on the evaluation results, it is plausible that our approach can provide a feasible and systematic way to manage epistemic uncertainties in safety assessment for safety-critical systems

Drivers to sustainable manufacturing practices and circular economy: A perspective of leather industries in Bangladesh

© 2017 Elsevier Ltd Sustainable manufacturing practices and the circular economy have recently received significant attention in academia and within industries to improve supply chain practices. Manufacturing industries have started adopting sustainable manufacturing practices and a circular economy in their supply chain to mitigate environmental concerns, as sustainable manufacturing practices and a circular economy result in the reduction of waste generation and energy and material usage. The leather industry, in spite of it contributing remarkably to a country's economic growth and stability, does not bear a good image because of its role in polluting the environment. Therefore, the leather industries of Bangladesh are trying to implement sustainable manufacturing practices as a part of undertaking green supply chain initiatives to remedy their image with the buyer and to comply with government rules and regulations. The main contribution of this study is to assess, prioritize and rank the drivers of sustainable manufacturing practices in the leather industries of Bangladesh. We have used graph theory and a matrix approach to examine the drivers. The results show that knowledge of the circular economy is paramount to implementing sustainable manufacturing practices in the leather industry of Bangladesh. This study will assist managers of leather companies to formulate strategies for the optimum utilization of available resources, as well as for the reduction of waste in the context of the circular economy

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment