9,806 research outputs found

    Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work

    Full text link
    The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines n = 236 articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of n = 48 articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users' information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user's cognitive resources. The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system's limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs

    DeepOnto: A Python Package for Ontology Engineering with Deep Learning

    Full text link
    Applying deep learning techniques, particularly language models (LMs), in ontology engineering has raised widespread attention. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present Deeponto, a Python package designed for ontology engineering. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to include other essential components including reasoning, verbalisation, normalisation, projection, and more. Building on this module, Deeponto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methodologies, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of Deeponto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).Comment: under review at Semantic Web Journa

    Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

    Full text link
    The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.Comment: 32 pages, 6 figures, 5 tables. Accepted for publication in the Proceedings of the ACM on Human-Computer Interaction (PACM HCI), CSCW 202

    Using Online Video Observations and Real Time, Peer Reflective Analysis of Culturally Responsive Teaching Pedagogy in a University Teacher Preparatory Program for Preservice Teachers

    Full text link
    This research aimed to understand the impacts of using online video observations and real-time peer reflection to teach and address culturally responsive teaching in a Pacific Northwest university’s teacher preparatory program. Six active university students enrolled in a university’s new teacher preparatory program (i.e., preservice, new teacher candidates) actively participated in all areas of this study (i.e., nonrandom sampling) and provided both quantitative and qualitative data. Study participants completed self-evaluative pre- and post-surveys in a research group session. Surveys were built using the ready 4 rigor framework (Hammond & Jackson, 2015) and the four areas of culturally responsive teaching as a foundation for a psychometric response scale (i.e., Likert scale 1–5) and peer reflection prompts. In group settings, study participants watched videos of their peers and themselves engaging in classroom instruction. After video observations, they participated in real-time, peer reflective analysis of teaching performance. Using a quantitative and qualitative approach to analyze the pre- and post-survey responses and reflective discussions, data revealed participants gained a deeper understanding of their ability to deliver culturally responsive teaching pedagogy. Overall, these data points suggested a change in participant awareness of culturally responsive teaching performance levels before and after engaging in video observations and real-time, peer reflective analysis involving culturally responsive teaching pedagogy

    Investigating neural differentiation capacity in Alzheimer’s disease iPSC-derived neural stem cells

    Get PDF
    Neurodegeneration in Alzheimer’s disease (AD) may be exacerbated by dysregulated hippocampal neurogenesis. Neural stem cells (NSC) maintain adult neurogenesis and depletion of the NSC niche has been associated with age-related cognitive decline and dementia. We hypothesise that familial AD (FAD) mutations bias NSC toward premature neural specification, reducing the stem cell niche over time and accelerating disease progression. Somatic cells derived from patients with FAD (PSEN1 A246E and PSEN1 M146L heterozygous mutations) and healthy controls were reprogrammed to generate induced pluripotent stem cells (iPSC). Pluripotency for patient and control iPSC lines was confirmed, then cells were amplified and cryopreserved as stores. iPSC were subjected to neural specification to rosette-forming SOX2+/nestin+ NSCs for comparative evaluations between FAD and age-matched controls. FAD patient and control NSC were passaged under defined steady state culture conditions to assess stem cell maintenance using quantitative molecular markers (SOX2, nestin, NeuN, MAP2 and βIII-tubulin). We observed trends towards downregulated expression of the nestin coding gene NES (p=0.051) and upregulated expression of MAP2 (p=0.16) in PSEN1 NSC compared with control NSC, indicative of a premature differentiation phenotype induced by presence of the PSEN1 mutation. Cell cycle analysis of PSEN1 NSC showed that compared with controls, a greater number of PSEN1 NSC were retained in G0/G1 phase of the cell cycle (p=0.39), fewer progressed to S-phase (p=0.11) and fewer still reached G2 phase (p=0.23), suggesting cell cycle progression may be impaired in PSEN1 NSC. Nuclear DNA fragmentation was increased (p=0.10) in FAD NSC compared with controls, indicative of elevated cell death/apoptosis. Flow cytometry-based analysis of live, nestin+ NSC and NPC indicated increased apoptosis (p=0.14) in FAD NSC compared with controls, as well as increasing levels of apoptosis (p=0.33) in FAD NSC as they specified to neural progenitor cells. Global RNA sequencing was used to identify transcriptomic changes occurring during both disease and control neural specification. GO analysis of DEGs between PSEN1 and control NSC at P3 revealed significant upregulation (FDR<0.0000259) of 5 biological processes related to transcription and gene expression as well as significant upregulation (FDR<0.000000725) of 12 molecular functions related to DNA binding and transcription factor activity. These data suggest significant changes in gene expression were occurring in PSEN1 NSC at P3 compared with control NSC at the same stage in neural specification. The number of DEGs (p<0.05) between PSEN1 and control NSC at P3 was 9.92-fold higher than the number of DEGs between PSEN1 and control NSC at P2, suggesting transcriptomic differences between PSEN1 and control NSC become more pronounced as cells specify further down the neural lineage. Gene ontology (GO) analysis of differentially expressed genes (DEGs) specific to AD neural differentiation revealed significant dysregulation (FDR p<0.05) of genes related to neurogenesis, apoptosis, cell cycle, transcriptional control, and cell growth/maintenance as PSEN1 NSC matured from P2 to P3. The number of DEGs (p<0.05) in PSEN1 neural differentiation was 4.7-fold higher than the number of DEGs seen in control neural differentiation, indicating more transcriptional changes occurred in PSEN1 NSC than in controls at the same time point in neural specification. Dysregulation of Notch signalling was specific to PSEN1 neural differentiation and Notch related DEGs significantly upregulated (p<0.05) in PSEN1 NSC at P3 compared with P2 included NCOR2, JAG2, CHAC1 and RFNG. qPCR based validation displayed significant upregulation of RFNG (p=0.04) in PSEN1 NSC at P3 compared with PSEN1 NSC at P2, and indicated a trend towards upregulation of JAG2 expression, correlating with RNA sequencing data. Data generated in this study indicate that presence of the PSEN1 mutation significantly increases the number of transcriptional changes occurring during neural differentiation. It is plausible that transcriptional changes to Notch signalling cause dysregulated neural specification and increased apoptosis in PSEN1 NSC, ultimately resulting in depletion of the NSC niche

    a systematic review

    Get PDF
    Funding Information: This study is part of an interdisciplinary research project, funded by the Special Research Fund (Bijzonder Onderzoeksfonds) of Ghent University.Introduction: Ontologies are a formal way to represent knowledge in a particular field and have the potential to transform the field of health promotion and digital interventions. However, few researchers in physical activity (PA) are familiar with ontologies, and the field can be difficult to navigate. This systematic review aims to (1) identify ontologies in the field of PA, (2) assess their content and (3) assess their quality. Methods: Databases were searched for ontologies on PA. Ontologies were included if they described PA or sedentary behavior, and were available in English language. We coded whether ontologies covered the user profile, activity, or context domain. For the assessment of quality, we used 12 criteria informed by the Open Biological and Biomedical Ontology (OBO) Foundry principles of good ontology practice. Results: Twenty-eight ontologies met the inclusion criteria. All ontologies covered PA, and 19 included information on the user profile. Context was covered by 17 ontologies (physical context, n = 12; temporal context, n = 14; social context: n = 5). Ontologies met an average of 4.3 out of 12 quality criteria. No ontology met all quality criteria. Discussion: This review did not identify a single comprehensive ontology of PA that allowed reuse. Nonetheless, several ontologies may serve as a good starting point for the promotion of PA. We provide several recommendations about the identification, evaluation, and adaptation of ontologies for their further development and use.publishersversionpublishe

    Complexity Science in Human Change

    Get PDF
    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    „Wild Democracy” – The figurative conceptualization of the Parliament in Hungarian editorial cartoons (1989 – 2019) [védés előtt]

    Get PDF
    The expression of the Parliament is often associated with abstract concepts such as politics, democracy, or nationhood (Kapitány & Kapitány, 2002; Szabó & Oross, 2018) when instead of the literal meaning of the ‘building’, we refer to its figurative meanings. It has already been confirmed that political cartoons are rich in figurative devices (e.g., conceptual metaphor) (i.a. El Refaie, 2009) and they serve as a suitable corpus for the investigation of the figurative meaning of the Parliament. In the case of a conceptual metaphor, for instance, the Parliament (considered as a target domain) is understood via the source domain conceptually different from the target (e.g., COLOSSEUM). In that way, certain characteristic features of the source domain are mapped onto the target domain, and we are able to interpret politics, specifically the Parliament itself as the site of real, dangerous, life-or-death physical battles. All these figurative meanings can influence how we think about politics, its processes, and actors, how we argue in the case of a political problem and how we would try to solve it. The current research aims to examine how the Hungarian Parliament is visually represented in editorial cartoons and how these visual representations – through figurative conceptual devices such as conceptual metaphors and conceptual metonymies – construct the concept of the parliament. Furthermore, the thesis discusses how these cognitive devices cooperate with ironies and cultural references (such as idioms, allusions, and national symbols) which are determinant in evaluation procedures and the creation of emotional bonds between the viewer and the cartoon. In doing so, the dissertation studies the caricaturistic representations of the Parliament in three various periods (Körösényi, 2015); thus, the investigation is longitudinal (describing thirty years since 1989) and comparative. What are the novelties of the research? First, it examines Hungarian editorial cartoons in a cognitive linguistic framework, unlike this, so far Hungarian political cartoons have been discussed by historians (e.g., Tamás, 2014). Second, although the Parliament is an important concept (Kapitány & Kapitány, 2002), its figurative meaning has not been studied so widely yet. Third, it is a multimodal investigation of conceptual processes that fits into the trend of cognitive linguistic research that focuses on the cooperation of different processes. Fourth, this research examines a large data set in context where the contextual factors are limited to three types, namely idioms, allusions, and national symbols (context types are usually not defined in such concrete ways, e.g., Charteris-Black, 2011). Fifth, the dissertation applies Extended Conceptual Metaphor Theory (ECMT) (Kövecses, 2020) in practice in a larger corpus. Sixth, it is a diachronic investigation which is rare in the field of cartoon research (e.g., Frantzich, 2013) also in cognitive research, especially in multimodal research. The main results show that 1) the representation of the Parliament is strongly linked to such conceptual procedures as conceptual metonymy and conceptual metaphor. These cognitive devices are likely to cooperate with ironies and cultural references. 2) a limited number of cognitive devices (e.g., the conceptual metonymy THE PARLIAMENT STANDS FOR THE GOVERNMENT, or the conceptual metaphor THE PARLIAMENT IS A PLACE FOR PHYSICAL CONFLICT) are recurring in the corpus during the period between 1989 and 2019. However, regarding the perspectivization, content and function of these cognitive devices, it is said that the compared periods of democracy (Körösényi, 2015) show significant differences based on the diverse preferences and distribution of the cognitive devices with specific cultural references in each era. 3) the increase of more aggressive scenes emerges from the metaphoric domain of PHYSICAL CONFLICT, which goes hand in hand with a change in the use of national symbols referring to the perceived extreme nationalist content, and political slogans which are dominated by the direct elements (literal citations, showing violence overtly). An unexpected result is the detection of a shift in communication acting in the opposite direction, according to which in linguistic changes indirect processes took place (e.g., increasing use of causal type ironies), in visual processes direct changes became predominant, so for instance, violence appeared literally. In sum, the Parliament seems a permanent phenomenon throughout the years, however, this research points to its different meanings and nuances of meaning variants. So even the stability of the meaning of such a strong national symbol can be questioned

    Deep Multimodality Image-Guided System for Assisting Neurosurgery

    Get PDF
    Intrakranielle Hirntumoren gehören zu den zehn häufigsten bösartigen Krebsarten und sind für eine erhebliche Morbidität und Mortalität verantwortlich. Die größte histologische Kategorie der primären Hirntumoren sind die Gliome, die ein äußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen Hirnläsionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung für neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden. Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen Kontrastverstärkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und Anästhesie, was den Nutzen präopera-tiver Bilddaten für die Steuerung des Eingriffs einschränkt. Bildgesteuerte Systeme bieten Ärzten einen unschätzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner Bildgebungsmodalitäten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsächlich um computergestützte Systeme, die mit Hilfe von Computer-Vision-Methoden die Durchführung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen müssen jedoch immer noch den Operationsplan aus präoperativen Bildern gedanklich mit Echtzeitinformationen zusammenführen, während sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung überwachen. Daher war die Notwendigkeit einer Bildführung während neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte. Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems für die peri-operative bildgeführte Neurochirurgie (IGN), nämlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die Gesamtüberle-bensrate maximiert und die postoperative neurologische Morbidität minimiert wird. Im Rahmen dieser Arbeit werden zunächst neuartige Methoden für die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen präope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jüngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem für den Menschen verständliche, erklärbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die für die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zuständig ist. Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. Für das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework für die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den Würfelkoeffizienten für das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-Ansätze wie 3D-Faltungen über alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet. Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, für die Registrierung präoperativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgeführt: BITE und RESECT. Zwei erfahrene Neurochirurgen führten eine zusätzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung überlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. Darüber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfähige Ergebnisse liefern, was seine Allgemeingültigkeit unter Beweis stellt und daher für die intraoperative neurochirurgische Führung von Nutzen sein kann. Für das Modul "Erklärbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben Erklärungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklärbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. Darüber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-Modalitäten zur end-gültigen Vorhersage zu verstehen. Der Erklärungsprozess könnte medizinischen Fachleu-ten zusätzliche Informationen über die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann. Außerdem wurde ein interaktives neurochirurgisches Display für die Eingriffsführung entwickelt, das die verfügbare kommerzielle Hardware wie iUS-Navigationsgeräte und Instrumentenverfolgungssysteme unterstützt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der Fähigkeit zur Integration von (1) präoperativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestätigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgeführt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt. In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jüngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen präzise zu führen und prä- und intraoperative Patientenbilddaten sowie interventionelle Geräte in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend für die Anwendung von Deep-Learning-Modellen zur Unterstützung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse

    Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning

    Full text link
    Visual-based defect detection is a crucial but challenging task in industrial quality control. Most mainstream methods rely on large amounts of existing or related domain data as auxiliary information. However, in actual industrial production, there are often multi-batch, low-volume manufacturing scenarios with rapidly changing task demands, making it difficult to obtain sufficient and diverse defect data. This paper proposes a parallel solution that uses a human-machine knowledge hybrid augmentation method to help the model extract unknown important features. Specifically, by incorporating experts' knowledge of abnormality to create data with rich features, positions, sizes, and backgrounds, we can quickly accumulate an amount of data from scratch and provide it to the model as prior knowledge for few-data learning. The proposed method was evaluated on the magnetic tile dataset and achieved F1-scores of 60.73%, 70.82%, 77.09%, and 82.81% when using 2, 5, 10, and 15 training images, respectively. Compared to the traditional augmentation method's F1-score of 64.59%, the proposed method achieved an 18.22% increase in the best result, demonstrating its feasibility and effectiveness in few-data industrial defect detection.Comment: 24 pages, 15 figure
    corecore