Symbol Extraction Method and Symbolic Distance for Analysing Medical Time Series

The analysis of time series databases is very important in the area of medicine. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, index trees, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of the time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. This paper focuses on the process of transforming numerical time series into a symbolic domain and on the definition of both this domain and a distance for comparing symbolic temporal sequences. The work is applied to the isokinetics domain within an application called I4

Modelling Medical Time Series Using Grammar-Guided Genetic Programming

The analysis of time series is extremely important in the field of medicine, because this is the format of many medical data types. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. This paper describes the definition of the symbolic domain, the process of converting numerical into symbolic time series and a distance for comparing symbolic temporal sequences. Then, the paper focuses on a method to create the symbolic reference model for a certain population using grammar-guided genetic programming. The work is applied to the isokinetics domain within an application called I4

Analyzing time series from eye tracking using Symbolic Aggregate Approximation

This thesis explores the viability of transforming the data produced when tracking the eyes into a discrete symbolic representation. For this transformation, we utilize Symbolic Aggregate Approximation to investigate a new possibility for effectively categorizing data collected via eye tracking technologies. This categorization illustrates tendencies for, e.g., tracking problems, problems with the set-up, normal vision, or vision disturbances. Accordingly, this will contribute to evaluating the eyes' performance and allow professionals to develop a diagnosis based on evidence from objective measurements. The results are based on implementing a symbolic discretization method applied to experiments on a real-world dataset containing recordings of eye movements. In the future, the knowledge and transformation via the SAX method can be utilized to make sense of data and identify anomalies implemented in various domains and for multiple stakeholders.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

Understanding consumer needs and preferences in new product development: the case of functional food innovations

As the majority of new products fail it is important to focus on the needs and preferences of the consumers in new product development. Consumers are increasingly recognised as important co-developers of innovations, often developing new functions for technologies, solving unforeseen problems and demanding innovative solutions. The central research question of the paper is: How to understand consumer needs and preferences in the context of new product development in order to improve the success of emerging innovations, such as functional foods. Important variables appear to be domestication, trust and distance, intermediate agents, user representations and the consumer- and product specific characteristics. Using survey and focus group data, we find that consumers need and prefer easy-to-use new products, transparent and accessible information supply by the producer, independent control of efficacy and safety, and introduction of a quality symbol for functional foods. Intermediate agents are not important in information diffusion. Producers should concentrate on consumers with specific needs, like athletes, women, obese persons, and stressed people. This will support developing products in line with the needs and mode of living of the users.consumer needs, preferences, new product development, functional foods

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Youtube, Dr, Pimple Popper, and the Human Body

Pimple poppers around the world rejoice at the Youtube series, Dr. Pimple Popper, where they can experience a range of different ways to pop something out of the skin. A highly popular series that features videos of Dr. Sandra Lee, a certified medical dermatologist, who squeezes and cuts open cysts, lipomas, and any other forms of degenerative skin conditions. These videos are taken by her medical assistants, while she performs surgeries on her patients. Dr. Lee has gained a mass following as her views and subscriptions surpass a million. The popularity of her channel demonstrates validity of a research topic, as many speculations can be made about this channel’s popular appeal. This channel is a media and communications topic, while integrating other academic fields, as it transcends into symbolic constructions and perceptions of what is a clean human body. The perception of the human body has historically been abstractified and critiqued as a medium of interpretation or as a tool to execute systemic power. Now, we must ponder the current interpretation of the human body, as it becomes the focus of a popular Youtube series. This postulation can be explored through Michel Foucault’s medical gaze theory and Mary Douglas’s cultural theory on dirt. The utilization of anthropological and philosophical theories, applied to Dr. Pimple Popper, can give us answers about how Dr. Lee’s patients are affecting the way viewers make sense of their own bodies through visually graphic material

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

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