Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Cyborgs as Frontline Service Employees: A Research Agenda

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose This paper identifies and explores potential applications of cyborgian technologies within service contexts and how service providers may leverage the integration of cyborgian service actors into their service proposition. In doing so, the paper proposes a new category of ‘melded’ frontline service employees (FLEs), where advanced technologies become embodied within human actors. The paper presents potential opportunities and challenges that may arise through cyborg technological advancements and proposes a future research agenda related to these. Design/methodology This study draws on literature in the fields of services management, Artificial Intelligence [AI], robotics, Intelligence Augmentation [IA] and Human Intelligence [HIs] to conceptualise potential cyborgian applications. Findings The paper examines how cyborg bio- and psychophysical characteristics may significantly differentiate the nature of service interactions from traditional ‘unenhanced’ service interactions. In doing so, we propose ‘melding’ as a conceptual category of technological impact on FLEs. This category reflects the embodiment of emergent technologies not previously captured within existing literature on cyborgs. We examine how traditional roles of FLEs will be potentially impacted by the integration of emergent cyborg technologies, such as neural interfaces and implants, into service contexts before outlining future research directions related to these, specifically highlighting the range of ethical considerations. Originality/Value Service interactions with cyborg FLEs represent a new context for examining the potential impact of cyborgs. This paper explores how technological advancements will alter the individual capacities of humans to enable such employees to intuitively and empathetically create solutions to complex service challenges. In doing so, we augment the extant literature on cyborgs, such as the body hacking movement. The paper also outlines a research agenda to address the potential consequences of cyborgian integration

Über die Selbstorganisation einer hierarchischen Gedächtnisstruktur für kompositionelle Objektrepräsentation im visuellen Kortex

At present, there is a huge lag between the artificial and the biological information processing systems in terms of their capability to learn. This lag could be certainly reduced by gaining more insight into the higher functions of the brain like learning and memory. For instance, primate visual cortex is thought to provide the long-term memory for the visual objects acquired by experience. The visual cortex handles effortlessly arbitrary complex objects by decomposing them rapidly into constituent components of much lower complexity along hierarchically organized visual pathways. How this processing architecture self-organizes into a memory domain that employs such compositional object representation by learning from experience remains to a large extent a riddle. The study presented here approaches this question by proposing a functional model of a self-organizing hierarchical memory network. The model is based on hypothetical neuronal mechanisms involved in cortical processing and adaptation. The network architecture comprises two consecutive layers of distributed, recurrently interconnected modules. Each module is identified with a localized cortical cluster of fine-scale excitatory subnetworks. A single module performs competitive unsupervised learning on the incoming afferent signals to form a suitable representation of the locally accessible input space. The network employs an operating scheme where ongoing processing is made of discrete successive fragments termed decision cycles, presumably identifiable with the fast gamma rhythms observed in the cortex. The cycles are synchronized across the distributed modules that produce highly sparse activity within each cycle by instantiating a local winner-take-all-like operation. Equipped with adaptive mechanisms of bidirectional synaptic plasticity and homeostatic activity regulation, the network is exposed to natural face images of different persons. The images are presented incrementally one per cycle to the lower network layer as a set of Gabor filter responses extracted from local facial landmarks. The images are presented without any person identity labels. In the course of unsupervised learning, the network creates simultaneously vocabularies of reusable local face appearance elements, captures relations between the elements by linking associatively those parts that encode the same face identity, develops the higher-order identity symbols for the memorized compositions and projects this information back onto the vocabularies in generative manner. This learning corresponds to the simultaneous formation of bottom-up, lateral and top-down synaptic connectivity within and between the network layers. In the mature connectivity state, the network holds thus full compositional description of the experienced faces in form of sparse memory traces that reside in the feed-forward and recurrent connectivity. Due to the generative nature of the established representation, the network is able to recreate the full compositional description of a memorized face in terms of all its constituent parts given only its higher-order identity symbol or a subset of its parts. In the test phase, the network successfully proves its ability to recognize identity and gender of the persons from alternative face views not shown before. An intriguing feature of the emerging memory network is its ability to self-generate activity spontaneously in absence of the external stimuli. In this sleep-like off-line mode, the network shows a self-sustaining replay of the memory content formed during the previous learning. Remarkably, the recognition performance is tremendously boosted after this off-line memory reprocessing. The performance boost is articulated stronger on those face views that deviate more from the original view shown during the learning. This indicates that the off-line memory reprocessing during the sleep-like state specifically improves the generalization capability of the memory network. The positive effect turns out to be surprisingly independent of synapse-specific plasticity, relying completely on the synapse-unspecific, homeostatic activity regulation across the memory network. The developed network demonstrates thus functionality not shown by any previous neuronal modeling approach. It forms and maintains a memory domain for compositional, generative object representation in unsupervised manner through experience with natural visual images, using both on- ("wake") and off-line ("sleep") learning regimes. This functionality offers a promising departure point for further studies, aiming for deeper insight into the learning mechanisms employed by the brain and their consequent implementation in the artificial adaptive systems for solving complex tasks not tractable so far.Gegenwärtig besteht immer noch ein enormer Abstand zwischen der Lernfähigkeit von künstlichen und biologischen Informationsverarbeitungssystemen. Dieser Abstand ließe sich durch eine bessere Einsicht in die höheren Funktionen des Gehirns wie Lernen und Gedächtnis verringern. Im visuellen Kortex etwa werden die Objekte innerhalb kürzester Zeit entlang der hierarchischen Verarbeitungspfade in ihre Bestandteile zerlegt und so durch eine Komposition von Elementen niedrigerer Komplexität dargestellt. Bereits bekannte Objekte werden so aus dem Langzeitgedächtnis abgerufen und wiedererkannt. Wie eine derartige kompositionell-hierarchische Gedächtnisstruktur durch die visuelle Erfahrung zustande kommen kann, ist noch weitgehend ungeklärt. Um dieser Frage nachzugehen, wird hier ein funktionelles Modell eines lernfähigen rekurrenten neuronalen Netzwerkes vorgestellt. Im Netzwerk werden neuronale Mechanismen implementiert, die der kortikalen Verarbeitung und Plastizität zugrunde liegen. Die hierarchische Architektur des Netzwerkes besteht aus zwei nacheinander geschalteten Schichten, die jede eine Anzahl von verteilten, rekurrent vernetzten Modulen beherbergen. Ein Modul umfasst dabei mehrere funktionell separate Subnetzwerke. Jedes solches Modul ist imstande, aus den eintreffenden Signalen eine geeignete Repräsentation für den lokalen Eingaberaum unüberwacht zu lernen. Die fortlaufende Verarbeitung im Netzwerk setzt sich zusammen aus diskreten Fragmenten, genannt Entscheidungszyklen, die man mit den schnellen kortikalen Rhythmen im gamma-Frequenzbereich in Verbindung setzen kann. Die Zyklen sind synchronisiert zwischen den verteilten Modulen. Innerhalb eines Zyklus wird eine lokal umgrenzte winner-take-all-ähnliche Operation in Modulen durchgeführt. Die Kompetitionsstärke wächst im Laufe des Zyklus an. Diese Operation aktiviert in Abhängigkeit von den Eingabesignalen eine sehr kleine Anzahl von Einheiten und verstärkt sie auf Kosten der anderen, um den dargebotenen Reiz in der Netzwerkaktivität abzubilden. Ausgestattet mit adaptiven Mechanismen der bidirektionalen synaptischen Plastizität und der homöostatischen Aktivitätsregulierung, erhält das Netzwerk natürliche Gesichtsbilder von verschiedenen Personen dargeboten. Die Bilder werden der unteren Netzwerkschicht, je ein Bild pro Zyklus, als Ansammlung von Gaborfilterantworten aus lokalen Gesichtslandmarken zugeführt, ohne Information über die Personenidentität zur Verfügung zu stellen. Im Laufe der unüberwachten Lernprozedur formt das Netzwerk die Verbindungsstruktur derart, dass die Gesichter aller dargebotenen Personen im Netzwerk in Form von dünn besiedelten Gedächtnisspuren abgelegt werden. Hierzu werden gleichzeitig vorwärtsgerichtete (bottom-up) und rekurrente (lateral, top-down) synaptische Verbindungen innerhalb und zwischen den Schichten gelernt. Im reifen Verbindungszustand werden infolge dieses Lernens die einzelnen Gesichter als Komposition ihrer Bestandteile auf generative Art gespeichert. Dank der generativen Art der gelernten Struktur reichen schon allein das höhere Identitätssymbol oder eine kleine Teilmenge von zugehörigen Gesichtselementen, um alle Bestandteile der gespeicherten Gesichter aus dem Gedächtnis abzurufen. In der Testphase kann das Netzwerk erfolgreich sowohl die Identität als auch das Geschlecht von Personen aus vorher nicht gezeigten Gesichtsansichten erkennen. Eine bemerkenswerte Eigenschaft der entstandenen Gedächtnisarchitektur ist ihre Fähigkeit, ohne Darbietung von externen Stimuli spontan Aktivitätsmuster zu generieren und die im Gedächtnis abgelegten Inhalte in diesem schlafähnlichen "off-line" Regime wiederzugeben. Interessanterweise ergibt sich aus der Schlafphase ein direkter Vorteil für die Gedächtnisfunktion. Dieser Vorteil macht sich durch eine drastisch verbesserte Erkennungsrate nach der Schlafphase bemerkbar, wenn das Netwerk mit den zuvor nicht dargebotenen Ansichten von den bereits bekannten Personen konfrontiert wird. Die Leistungsverbesserung nach der Schlafphase ist umso deutlicher, je stärker die Alternativansichten vom Original abweichen. Dieser positive Effekt ist zudem komplett unabhängig von der synapsenspezifischen Plastizität und kann allein durch die synapsenunspezifische, homöostatische Regulation der Aktivität im Netzwerk erklärt werden. Das entwickelte Netzwerk demonstriert so eine im Bereich der neuronalen Modellierung bisher nicht gezeigte Funktionalität. Es kann unüberwacht eine Gedächtnisdomäne für kompositionelle, generative Objektrepräsentation durch die Erfahrung mit natürlichen Bildern sowohl im reizgetriebenen, wachähnlichen Zustand als auch im reizabgekoppelten, schlafähnlichen Zustand formen und verwalten. Diese Funktionalität bietet einen vielversprechenden Ausgangspunkt für weitere Studien, die die neuronalen Lernmechanismen des Gehirns ins Visier nehmen und letztendlich deren konsequente Umsetzung in technischen, adaptiven Systemen anstreben

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

An interactive 3-D application for pain management: Results from a pilot study in spinal cord injury rehabilitation

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ElevierResearch on pain following spinal cord injury (SCI) has revealed that patients not only experience several types of pain that could prove to be challenging to address, but also that each individual can interpret such pain in different subjective ways. In this paper we introduce a 3-D system for facilitating the efficient management of pain, and thus, supporting clinicians in overcoming the aforementioned challenges. This system was evaluated by a cohort of 15 SCI patients in a pilot study that took place between July and October 2010. Participants reported their experiences of using the 3-D system in an adapted version of the System Usability Scale (SUS) questionnaire. Statistically significant results were obtained with regards to the usability and efficiency of the 3-D system, with the majority of the patients finding it particularly useful to report their pain. Our findings suggest that the 3-D system can be an efficient tool in the efforts to better manage the pain experience of SCI patients

Machine Analysis of Facial Expressions

No abstract

Efficacy of Fixed Infrared Thermography for Identification of Subjects with Influenza-like Illness

Due to pandemic threats and the occurrence of biological terrorism, technological advancements are being vetted, developed, and implemented as part of surveillance systems and tools. A potential surveillance tool is infrared thermography (IRT), and its efficacy for screening was the focus of this dissertation. IRT-screened participants\u27 temperatures were compared to laboratory diagnostics to confirm the presence or absence of influenza-like illness (ILI). An archival dataset of personnel on United States Navy and Marine vessels that were identified as exceeding an ILI threshold limit provided the data for the 320 study participants. Using a guiding thermo-science framework, derived from past IRT studies, the primary research question concerned whether IRT could statistically differentiate between afebrile participants (without ILI) and febrile participants (with ILI) using receiver operating characteristics (ROC). Results showed that IRT could differentiate between febrile and afebrile participants 91% of the time (ROC = 0.91; χ 2 = 230.71, p = \u3c 0.01), indicating excellent efficacy in this study setting. In addition, the correlation between oral temperatures and IRT surface temperatures was analyzed by gender. A strong correlation between the two variables for males (r = 0.90, n = 226, p \u3c 0.01) and females (r = 0.87, n = 94, p \u3c 0.01) was shown with little variance between the genders (observed z = 1.12, SE= 0.26). These findings have significant positive social change implications as they could provide senior public health decision makers with informed knowledge of IRTs benefits and limitations for rapid screening of febrile individuals in public settings to impede the transmission of ILI

FACIAL EXPRESSIONS: CAN PARENTS RECOGNISE CHILDREN’S EMOTIONS

Communication and the ability to understand emotions are key factors in everyday relationships between children and their parents. The purpose of this research is to identify whether parents are able to recognize emotions in their children’s facial expressions and to explore if there is a difference between parents recognising the emotion in regard to their gender, age or the number of children they have. The sample of examinees consisted of N=273 parents of preschool-aged children attending kindergartens. The results of the survey demonstrate that emotions which are mostly recognized by parents are: fear, anger, surprise, disgust, happiness and sadness, whereas fear proved to be the most easily recognized emotion and sadness the least easily recognized emotion. It has also been established that parents’ answers do not show greater inconsistencies, and that no relevant correlation between gender, age and the number of children and the level of parents’ recognition of six basic emotions through children’s facial expressions has been found