Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

Editorial: Executive function(s): Conductor, Orchestra or Symphony? Towards a Trans-Disciplinary Unification of Theory and Practice Across Development, in Normal and Atypical Groups

There are several theories of executive function(s) that tend to share some theoretical overlap yet are also conceptually distinct, each bolstered by empirical data (Norman and Shallice, 1986; Shallice & Burgess, 1991; Stuss and Alexander, 2007; Burgess, Gilbert, & Dumentheil, 2007; Burgess & Shallice, 1996; Miyake et al., 2000). The notion that executive processes are supervisory, and most in demand in novel situations was an early conceptualization of executive function that has been adapted and refined over time (Norman & Shallice, 1986; Shallice, 2001; Burgess, Gilbert & Dumentheil, 2007). Presently there is general consensus that executive functions are multi-componential (Shallice, 2001), and are supervisory only in the sense that attention in one form or another is key to the co-ordination of other hierarchically organized ‘lower’ cognitive processes. Attention in this sense is defined as (i) independent but interrelated attentional control processes (Stuss & Alexander, 2007); (ii) automatic orientation towards stimuli in the environment or internally–driven thought (Burgess, Gilbert & Dumontheil, 2007); (iii) the automatically generated interface between tacit processes and strategic conscious thought (Barker, Andrade, Romanowski, Morton and Wasti, 2006; Morton and Barker, 2010); and (iv) distinct but interrelated executive processes that maintain, update and switch across different sources of information (Miyake et al., 2000). One problem is that executive dysfunction or dysexecutive syndrome (Baddeley & Wilson, 1988) after brain injury typically produces a constellation of deficits across social, cognate, emotional and motivational domains that rarely map neatly onto theoretical frameworks (Barker, Andrade & Romanowski, 2004). As a consequence there is debate that conceptual theories of executive function do not always correspond well to the clinical picture (Manchester, Priestley & Jackson, 2004). Several studies have reported cases of individuals with frontal lobe pathology and impaired daily functioning despite having little detectable impairment on traditional tests of executive function (Shallice & Burgess, 1991; Eslinger & Damasio, 1985; Barker, Andrade & Romanowski, 2004; Andrés & Van der Linden, 2002; Chevignard et al., 2000; Cripe, 1998; Fortin, Godbout & Braun, 2003). There is also some suggestion that weak ecological validity limits predictive and clinical utility of many traditional measures of executive function (Burgess et al, 2006; Lamberts, Evans & Spikman, 2010; Barker, Morton, Morrison, McGuire, 2011). Complete elimination of environmental confounds runs the risk of generating results that cannot be generalized beyond constrained circumstances of the test environment (Barker, Andrade & Romanowski, 2004). Several researchers have concluded that a new approach is needed that is mindful of the needs of the clinician yet also informed by the academic debate and progress within the discipline (McFarquhar & Barker, 2012; Burgess et al., 2006). Finally, translational issues also confound executive function research across different disciplines (psychiatry, cognitive science, and developmental psychology) and across typically developing and clinical populations (including Autism Spectrum Disorders, Head Injury and Schizophrenia – Blakemore & Choudhury, 2006; Taylor, Barker, Heavey & McHale, 2013). Consequently, there is a need for unification of executive function approaches across disciplines and populations and narrowing of the conceptual gap between theoretical positions, clinical symptoms and measurement

Early diagnosis of disorders based on behavioural shifts and biomedical signals

There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life. Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing. This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.Jendearen portaeran eragin zuzena duten gaixotasun ugari daude. Hala ere, askotan, gaixotasuna pairatzen duten pertsonak ez dira euren egoerataz ohartzen, eta familiarteko edo lankideek identifikatu ohi dute berau jokabide aldaketetaz ohartzean. Portaera aldaketa hauek nabarmentzean, ordea, beranduegi izan ohi da eta atzerazeinak diren kalteak eraginda egon ohi dira. Osasun kalte larriak eta gehiegizko kostuak ekiditeko eta gaixoen bizi kalitatea hobetzeko gakoa, gaixotasuna garaiz detektatzea da. Gaur egun, etengabe zabaltzen ari den Nonahiko Konputazioaren paradigmari esker, erabiltzaileen portaera ereduak era diskretu batean monitorizatu daitezke, gailu teknologikoekin izandako interakzioari esker. Eskuartean dugun arazoari konponbidea emateko teknologi hau erabiltzeak gaixotasunen sorrera eta aurrerapena nonahi eta era diskretu batean monitorizatzeko gai diren sistemak garatzea ekarriko luke, hauek garaiz hautematea ahalbidetuz. Gaixotasun konkretu batzuentzat soluzioak proposatu izan dira teknologi honetan oinarrituz, baina metodologia orokor bat, gaixotasun sorta zabal baten detekzio goiztiarrerako erabilgarria izango dena, oraindik ez da aurkeztu. Tesi honek hutsune hori betetzea du helburu, mota honetako gaixotasunak garaiz hautemateko, era diskretu batean atzitutako datu fisiologiko eta konportamentalen erabileran oinarritzen den behaketa sistema orokor bat proposatuz. Proposatutako metodologia bi balidazio egoera desberdinen arteko analisi gurutzatu baten emaitza da: estresa lantokian eta Alzheimerra etxean, balidazio egoera bakoitzari dagozkion ekarpenak ere ondorioztatu ahal izan direlarik. Bi kasuen arteko antzekotasun eta desberdintasunen analisiak, gaixotasun berri bat nonahiko konputazioan oinarrituta detektatzeko jarraitu beharreko pausoak bere osotasunean eta era orokor batean definitzea ahalbidetu du

2015-2016 Bulletin

After 2003 the University of Dayton Bulletin went exclusively online. This copy was downloaded from the University of Dayton\u27s website in March of 2018. Please note: Even though this copy says draft it is the final copy.https://ecommons.udayton.edu/bulletin/1072/thumbnail.jp

2012-2013 Bulletin

After 2003 the University of Dayton Bulletin went exclusively online. This copy was downloaded from the University of Dayton\u27s website.https://ecommons.udayton.edu/bulletin/1011/thumbnail.jp

High Frequency Physiological Data Quality Modelling in the Intensive Care Unit

Intensive care medicine is a resource intense environment in which technical and clinical decision making relies on rapidly assimilating a huge amount of categorical and timeseries physiologic data. These signals are being presented at variable frequencies and of variable quality. Intensive care clinicians rely on high frequency measurements of the patient's physiologic state to assess critical illness and the response to therapies. Physiological waveforms have the potential to reveal details about the patient state in very fine resolution, and can assist, augment, or even automate decision making in intensive care. However, these high frequency time-series physiologic signals pose many challenges for modelling. These signals contain noise, artefacts, and systematic timing errors, all of which can impact the quality and accuracy of models being developed and the reproducibility of results. In this context, the central theme of this thesis is to model the process of data collection in an intensive care environment from a statistical, metrological, and biosignals engineering perspective with the aim of identifying, quantifying, and, where possible, correcting errors introduced by the data collection systems. Three different aspects of physiological measurement were explored in detail, namely measurement of blood oxygenation, measurement of blood pressure, and measurement of time. A literature review of sources of errors and uncertainty in timing systems used in intensive care units was undertaken. A signal alignment algorithm was developed and applied to approximately 34,000 patient-hours of simultaneously collected electroencephalography and physiological waveforms collected at the bedside using two different medical devices

Neural foundations of cooperative social interactions

The embodied-embedded-enactive-extended (4E) approach to study cognition suggests that interaction with the world is a crucial component of our cognitive processes. Most of our time, we interact with other people. Therefore, studying cognition without interaction is incomplete. Until recently, social neuroscience has only focused on studying isolated human and animal brains, leaving interaction unexplored. To fill this gap, we studied interacting participants, focusing on both intra- and inter-brain (hyperscanning) neural activity. In the first study, we invited dyads to perform a visual task in both a cooperative and a competitive context while we measured EEG. We found that mid-frontal activity around 200-300 ms after receiving monetary rewards was sensitive to social context and differed between cooperative and competitive situations. In the second study, we asked participants to coordinate their movements with each other and with a robotic partner. We found significantly stronger EEG amplitudes at frontocentral electrodes when people interacted with a robotic partner. Lastly, we performed a comprehensive literature review and the first meta-analysis in the emerging field of hyperscanning that validated it as a method to study social interaction. Taken together, our results showed that adding a second participant (human or AI/robotic) fostered our understanding of human cognition. We learned that the activity at frontocentral electrodes is sensitive to social context and type of partner (human or robotic). In both studies, the participants’ interaction was required to show these novel neural processes involved in action monitoring. Similarly, studying inter-brain neural activity allows for the exploration of new aspects of cognition. Many cognitive functions involved in successful social interactions are accompanied by neural synchrony between brains, suggesting the extended form of our cognition