Dialog-based Automation of Decision Making in Processes

The use of chatbots has spread, generating great interest in the industry for the possibility of automating tasks within the execution of their processes. The implementation of chatbots, however simple, is a complex endeavor that involves many low-level details, which makes it a time-consuming and error-prone task. In this paper we aim at facilitating the development of decision-support chatbots that guide users or help knowledge workers to make decisions based on interactions between different process participants, aiming at decreasing the workload of human workers, for example, in healthcare to identify the first symptoms of a disease. Our work concerns a methodology to systematically build decision-support chatbots, semi-automatically, from existing DMN models. Chatbots are designed to leverage natural language understanding platforms, such as Dialogflow or LUIS. We implemented Dialogflow chatbot prototypes based on our methodology and performed a pilot test that revealed insights into the usability and appeal of the chatbots developed

Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot

Characterisation of genetic risk factors for mental illness in rodent models, impact of Map2k7+/- and Fxyd6-/- mice on neural systems and working memory

Even in wealthy and seemingly prosperous countries like the United Kingdom, the spectre of mental illness and psychiatric disorders remains highly prevalent. These disorders present a huge economic burden to societies, where in the UK alone, mental disorders cost the economy an estimated €134 billion a year; along with the unmeasurable societal and human costs. This has led to an intense debate over the past few decades just as to what factors contribute to these illnesses. It is now understood that a number of biological and non-biological factors contribute. These include socio-economic pressures, early-life trauma, gestational and peri-natal infections; genetic and familial factors, and molecular and cellular factors. However, while the definitions and diagnostic criteria of mental disorders remain based in the subjective realms of the DSM and ICD, treatment and understanding of psychiatric illness has had little chance to progress over the last fifty years. As a result, neuroscientists are starting to direct psychiatric disorder research from the bottom-up; where genetic, cognitive and neuroconnectivity factors are being investigated to serve as a future basis for diagnosis and treatment. One of the most complex and debilitating psychiatric disorders, schizophrenia, exhibits a complex array of genetic, cognitive and neuroconnectivity abnormalities. Current challenges in schizophrenia research is to understand how identified genetic abnormalities contribute to neuroconnectivity and cognitive impairments which are prominent in schizophrenia. Recently, genetic association studies have implicated two genes as risk factors for schizophrenia - FXYD6 and MAP2K7. Currently it is unclear exactly how these genes contribute to schizophrenia pathology, particularly cognitive symptoms and neural circuitry.;This thesis investigates these two genes by utilising two mouse models, first a heterozygous mouse line of Map2k7+/- and second, a gene knock-out line of Fxyd6-/-. MAP2K7 is a gene that expresses a kinase that is involved in the c-Jun N-terminal kinase (JNK) pathway, which is implicated in neuronal activity, receptor function, and cortical and hippocampal plasticity. Recent studies have found a decreased expression of MA2PK7 in the PFC, ACC and hippocampal regions in schizophrenia patients; regions associated with memory and decision making. A component of the cognitive profile of MAP2K7 was therefore investigated using Map2k7+/- mouse lines in a working memory paradigm in the radial arm maze. This test is known as the n-back test or the retention interval test. For the first time this investigation reveals that Map2k7+/- mice exhibit a subtle yet significant spatial working memory deficit compared to WT mice; as judged by their average performance over the whole experiment. WT mice exhibited an overall average performance of 70% and MAP2K7+/- mice 66% (p<0.001). This indicates that MAP2K7 may play a subtle role in working memory function in rodents, and may represent a component of the aberrations in the genetic architecture that gives rise to working memory impairments in psychiatric disorders, particularly schizophrenia. This experiment also backs up previous evidence for this radial arm maze paradigm as a robust behavioural test for testing rodent working memory.;FXYD6 belongs to a group of proteins that are known to be involved in modulating NaKATPase activity. Previously, NaKATPase has been associated with bipolar disorder and depression, but has now also been implicated in schizophrenia. Previous studies have found that FXYD6 is also abnormally expressed in the PFC of schizophrenia patients, and therefore may contribute to the cognate symptoms of the disorder. This experiment, therefore, investigated how Fxyd6 contributes to local brain activation, particularly in neural systems relevant to cognition, using gene knockout Fxyd6-/- mouse models and semi quantitative 2DG autoradiographic imaging. Three regions showed a significant deviation in activity in Fxyd6-/- mice compared to WT mice. The subiculum, medial septum and lateral septum all exhibited significant reductions in activity in Fxyd6-/- mice compared to WT mice. Notably the subiculum is heavily implicated with memory functions, particularly working memory and disambiguation of previously learned memory. Indicating a possible role for FXYD6 and NaKATPase in working memory processing and memory disambiguation in the subiculum. Finally, the role of glutamate in relation to FXYD6 function and brain activity was assessed by administering the NMDA receptor antagonist ketamine and analysing regional brain activity using semi quantitative 2DG autoradiographic imaging. Generally, regions which were affected by ketamine in WT mice including PFC, thalamic and septal regions, were not affected in Fxyd6-/- mice. It is hypothesized that this may be down to a compensatory effect that knocking-out Fxyd6 may have on glutamate reuptake. Because NaKATPase is involved in glutamate reuptake into glia and neurons, the blockage of NMDA receptors may have less effect due to a reduction in glutamate reuptake, and therefore higher than normal postsynaptic glutamate concentrations. In conclusion, this investigation highlights two genes which may have roles in working memory functioning and neural circuitry that contribute to cognitive processes. While the evidence from this investigation does not explicitly associate these genes with symptoms of schizophrenia and other psychiatric disorders; the evidence does provide indication that they are involved in cognitive processes in rodents, and possibly humans. This investigation provides an interesting path of investigation for the potential roles of these genes regardless of their relationship to psychiatric disorders and will inform future research into the genetic architecture of neural circuits and cognition.Even in wealthy and seemingly prosperous countries like the United Kingdom, the spectre of mental illness and psychiatric disorders remains highly prevalent. These disorders present a huge economic burden to societies, where in the UK alone, mental disorders cost the economy an estimated €134 billion a year; along with the unmeasurable societal and human costs. This has led to an intense debate over the past few decades just as to what factors contribute to these illnesses. It is now understood that a number of biological and non-biological factors contribute. These include socio-economic pressures, early-life trauma, gestational and peri-natal infections; genetic and familial factors, and molecular and cellular factors. However, while the definitions and diagnostic criteria of mental disorders remain based in the subjective realms of the DSM and ICD, treatment and understanding of psychiatric illness has had little chance to progress over the last fifty years. As a result, neuroscientists are starting to direct psychiatric disorder research from the bottom-up; where genetic, cognitive and neuroconnectivity factors are being investigated to serve as a future basis for diagnosis and treatment. One of the most complex and debilitating psychiatric disorders, schizophrenia, exhibits a complex array of genetic, cognitive and neuroconnectivity abnormalities. Current challenges in schizophrenia research is to understand how identified genetic abnormalities contribute to neuroconnectivity and cognitive impairments which are prominent in schizophrenia. Recently, genetic association studies have implicated two genes as risk factors for schizophrenia - FXYD6 and MAP2K7. Currently it is unclear exactly how these genes contribute to schizophrenia pathology, particularly cognitive symptoms and neural circuitry.;This thesis investigates these two genes by utilising two mouse models, first a heterozygous mouse line of Map2k7+/- and second, a gene knock-out line of Fxyd6-/-. MAP2K7 is a gene that expresses a kinase that is involved in the c-Jun N-terminal kinase (JNK) pathway, which is implicated in neuronal activity, receptor function, and cortical and hippocampal plasticity. Recent studies have found a decreased expression of MA2PK7 in the PFC, ACC and hippocampal regions in schizophrenia patients; regions associated with memory and decision making. A component of the cognitive profile of MAP2K7 was therefore investigated using Map2k7+/- mouse lines in a working memory paradigm in the radial arm maze. This test is known as the n-back test or the retention interval test. For the first time this investigation reveals that Map2k7+/- mice exhibit a subtle yet significant spatial working memory deficit compared to WT mice; as judged by their average performance over the whole experiment. WT mice exhibited an overall average performance of 70% and MAP2K7+/- mice 66% (p<0.001). This indicates that MAP2K7 may play a subtle role in working memory function in rodents, and may represent a component of the aberrations in the genetic architecture that gives rise to working memory impairments in psychiatric disorders, particularly schizophrenia. This experiment also backs up previous evidence for this radial arm maze paradigm as a robust behavioural test for testing rodent working memory.;FXYD6 belongs to a group of proteins that are known to be involved in modulating NaKATPase activity. Previously, NaKATPase has been associated with bipolar disorder and depression, but has now also been implicated in schizophrenia. Previous studies have found that FXYD6 is also abnormally expressed in the PFC of schizophrenia patients, and therefore may contribute to the cognate symptoms of the disorder. This experiment, therefore, investigated how Fxyd6 contributes to local brain activation, particularly in neural systems relevant to cognition, using gene knockout Fxyd6-/- mouse models and semi quantitative 2DG autoradiographic imaging. Three regions showed a significant deviation in activity in Fxyd6-/- mice compared to WT mice. The subiculum, medial septum and lateral septum all exhibited significant reductions in activity in Fxyd6-/- mice compared to WT mice. Notably the subiculum is heavily implicated with memory functions, particularly working memory and disambiguation of previously learned memory. Indicating a possible role for FXYD6 and NaKATPase in working memory processing and memory disambiguation in the subiculum. Finally, the role of glutamate in relation to FXYD6 function and brain activity was assessed by administering the NMDA receptor antagonist ketamine and analysing regional brain activity using semi quantitative 2DG autoradiographic imaging. Generally, regions which were affected by ketamine in WT mice including PFC, thalamic and septal regions, were not affected in Fxyd6-/- mice. It is hypothesized that this may be down to a compensatory effect that knocking-out Fxyd6 may have on glutamate reuptake. Because NaKATPase is involved in glutamate reuptake into glia and neurons, the blockage of NMDA receptors may have less effect due to a reduction in glutamate reuptake, and therefore higher than normal postsynaptic glutamate concentrations. In conclusion, this investigation highlights two genes which may have roles in working memory functioning and neural circuitry that contribute to cognitive processes. While the evidence from this investigation does not explicitly associate these genes with symptoms of schizophrenia and other psychiatric disorders; the evidence does provide indication that they are involved in cognitive processes in rodents, and possibly humans. This investigation provides an interesting path of investigation for the potential roles of these genes regardless of their relationship to psychiatric disorders and will inform future research into the genetic architecture of neural circuits and cognition

Combining Attentional Control and Semantic Memory Retrieval: A Sensitive Marker for Early Stage AD and AD-related Biomarkers in Healthy Older Adults

Past studies have shown that measures of attentional control and semantic memory retrieval are sensitive markers of Alzheimer disease: AD). The present study examined the utility of combining measures of attentional control and semantic retrieval within a single task to discriminate healthy aging from early stage AD and show sensitivity to AD biomarkers in healthy control individuals. On each trial of the present task, participants viewed a category: e.g. “a unit of time”) and verified whether a subsequent target item was an exemplar of the category: “hour”) or not: “clock”). Importantly, the nonmembers of the category were associatively related: e.g., a “clock” is not “a unit of time”, but is highly related), and hence, placed a premium on attentional control systems to reject. Results indicated that accuracy to the foil items was the strongest discriminator between healthy aging and very mild AD. Furthermore, accuracy correlated significantly with AD biomarkers, including tau, p-tau, Aβ42 and PIB, in healthy control participants who are at increased risk for developing Alzheimer disease. Discussion focuses on the combined influence of attentional control with explicit retrieval from semantic memory as a marker of early stage AD as well as a sensitive correlate of established biomarkers for AD risk in healthy control participants

Enhancing natural language understanding using meaning representation and deep learning

Natural Language Understanding (NLU) is one of the complex tasks in artificial intelligence. Machine learning was introduced to address the complex and dynamic nature of natural language. Deep learning gained popularity within the NLU community due to its capability of learning features directly from data, as well as learning from the dynamic nature of natural language. Furthermore, deep learning has shown to be able to learn the hidden feature(s) automatically and outperform most of the other machine learning approaches for NLU. Deep learning models require natural language inputs to be converted to vectors (word embedding). Word2Vec and GloVe are word embeddings which are designed to capture the analogy context-based statistics and provide lexical relations on words. Using the context-based statistical approach does not capture the prior knowledge required to understand language combined with words. Although a deep learning model receives word embedding, language understanding requires Reasoning, Attention and Memory (RAM). RAM are key factors in understanding language. Current deep learning models focus either on reasoning, attention or memory. In order to properly understand a language however, all three factors of RAM should be considered. Also, a language normally has a long sequence. This long sequence creates dependencies which are required in order to understand a language. However, current deep learning models, which are developed to hold longer sequences, either forget or get affected by the vanishing or exploding gradient descent. In this thesis, these three main areas are of focus. A word embedding technique, which integrates analogy context-based statistical and semantic relationships, as well as extracts from a knowledge base to hold enhanced meaning representation, is introduced. Also, a Long Short-Term Reinforced Memory (LSTRM) network is introduced. This addresses RAM and is validated by testing on question answering data sets which require RAM. Finally, a Long Term Memory Network (LTM) is introduced to address language modelling. Good language modelling requires learning from long sequences. Therefore, this thesis demonstrates that integrating semantic knowledge and a knowledge base generates enhanced meaning and deep learning models that are capable of achieving RAM and long-term dependencies so as to improve the capability of NLU

The Neural Correlates of Visual Hallucinations in Parkinson's Disease

Visual hallucinations are common in Parkinson’s disease (PD) and linked to worse outcomes: increased mortality, higher carer burden, cognitive decline, and worse quality of life. Recent functional studies have aided our understanding, showing large-scale brain network imbalance in PD hallucinations. Imbalance of different influences on visual perception also occurs, with impaired accumulation of feedforward signals from the eyes and visual parts of the brain. Whether feedback signals from higher brain control centres are also affected is unknown and the mechanisms driving network imbalance in PD hallucinations remain unclear. In this thesis I will clarify the computational and structural changes underlying PD hallucinations and link these to functional abnormalities and regional changes at the cellular level. To achieve this, I will employ behavioural testing, diffusion weighted imaging, structural and functional MRI in PD patients with and without hallucinations. I will quantify the use of prior knowledge during a visual learning task and show that PD with hallucinations over-rely on feedback signals. I will examine underlying structural connectivity changes at baseline and longitudinally and will show that posterior thalamic connections are affected early, with frontal connections remaining relatively preserved. I will show that PD hallucinations are associated with a subnetwork of reduced structural connectivity strength, affecting areas crucial for switching the brain between functional states. I will assess the role of the thalamus as a potential driver of network-level changes and show preferential medial thalamus involvement. I will utilise data from the Allen Institute transcription atlas and show how differences in regional gene expression in health contributes to the selective vulnerability of specific white matter connections in PD hallucinations. These findings reveal the structural correlates of visual hallucinations in PD, link these to functional and behavioural changes and provide insights into the cellular mechanisms that drive regional vulnerability, ultimately leading to hallucinations

Investigating Inconsistency Understanding to Support Interactive Inconsistency Resolution in Declarative Process Models

Handling inconsistencies in business rules is an important part of corporate compliance management. This includes the resolution of inconsistencies, which currently is a fully automated process that might not always be plausible in a real-world scenario. To include human experts and develop interactive resolution approaches, an understanding of inconsistencies is crucial. Thus, we focus on investigating inconsistency understanding in declarative process models by testing the applicability of insights from declarative process model understanding to different inconsistency characteristics. In the future, this will provide the basis for a series of cognitive experiments evaluating the effects of inconsistency characteristics and representation on inconsistency understanding in declarative process models

Automated Injection of Curated Knowledge Into Real-Time Clinical Systems: CDS Architecture for the 21st Century

abstract: Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs. This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards. Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment. Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Representational dynamics across multiple timescales in human cortical networks

Human cognition occurs at multiple timescales, including immediate processing of the ongoing experiences and slowly drifting higher-level thoughts. To understand how the brain selects and represents these various types of information to guide behavior, this thesis examined representational content within sensory regions, multiple demand (MD) network, and default mode network (DMN). Chapter 1 provides a background review of the current literature. It begins by reviewing experimental investigations of component visual processes that unfold over time. Next, the MD network is introduced as a collection of frontal and parietal regions involved in implementing cognitive control by assembling the required operations for task-relevant behavior. Finally, the DMN is introduced in the context of temporal processing hierarchies, with focus on its representation of situation models summarizing interactions among entities and the environment. The first experiment, presented in Chapter 2, used EEG/MEG to track multiple component processes of selective attention. Five distinct processing operations with different time-courses were quantified, including representation of visual display properties, target location, target identity, behavioral significance, and finally, possible reactivation of the attentional template. Chapter 3 used fMRI to examine neural representations of task episodes, which are temporally organized sequences of steps that occur within a given context. It was found that MD and visual regions showed sensitivity to the fine structure of the contents within a task. DMN regions showed gradual change throughout the entire task, with increased activation at the offset of the entire episode. Chapter 4 analyzed activation profiles of DMN regions using six diverse tasks to examine their functional convergence during social, episodic, and self-referential thought. Results supported proposals of separate subsystems, yet also suggest integration within the DMN. The final chapter, Chapter 5, provides an extended discussion of theoretical concepts related to the three experiments and proposes possible avenues for further research

Individual Differences in Human Brain Functional Network Organization

The human brain is organized at many spatial scales, including the level of areas and systems. Resting-state functional magnetic resonance imaging is a non-invasive technique that allows for the study of areal- and systems-level brain organization in vivo. Over two decades of research has sought to identify and characterize the functional communities that comprise the brain’s network architecture. Consequently, a convergent description of group-average functional network organization in healthy adults has emerged. Recent advances have allowed for the study of such organization in single individuals. Investigation of functional network organization in highly sampled individuals has revealed brain regions that deviate from the group-level description, i.e. individual differences in human brain functional network organization. This dissertation work characterizes individual differences in functional network organization, referred to as network variants, across a large sample of healthy adults. Network variants appear to be stable over time within an individual and organized systematically across individuals. They occur in characteristic cortical locations and associate with characteristic functional networks. Further, their task-evoked activity is consistent with their idiosyncratic functional network association. Finally, individuals may be sub-typed into one of two groups, where individuals in the same sub-group have a similar distribution of network variants. The sub-group phenomenon is heritable and relates to differences in neuropsychological measures of behavior. Network variants appear to be trait-like, functionally-relevant components of individual human brain functional network organization