2,855 research outputs found

    NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation

    Get PDF
    Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's unique needs. Furthermore, the resource-intensive nature of these interventions, often confined to clinical settings, poses barriers to widespread, cost-effective, and sustained implementation, resulting in suboptimal outcomes in terms of intervention adaptability, intensity, and duration. In response to these challenges, this paper introduces NeuroAIreh@b, an innovative cognitive profiling and training methodology that uses an AI-driven framework to optimize neurorehabilitation prescription. NeuroAIreh@b effectively bridges the gap between neuropsychological assessment and computational modeling, thereby affording highly personalized and adaptive neurorehabilitation sessions. This approach also leverages virtual reality-based simulations of daily living activities to enhance ecological validity and efficacy. The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention. The NeuroAIreh@b methodology holds the potential for efficacy studies in large randomized controlled trials in the future

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Understanding wildlife exploitation and ways forward on different scales

    Get PDF
    Der Rückgang der Wildtiere kann Ökosysteme tiefgreifend verändern und das Risiko von Ernährungsunsicherheit und neu auftretenden Krankheiten erhöhen, die wiederum die globale Gesundheit, Gesellschaft und Wirtschaft bedrohen. Aufbauend auf dem theoretischen Überbau des Konzepts komplexer sozial-ökologischer Systeme untersuche ich in dieser Dissertation die Jagd und den Wildtierhandel in einem ganzheitlichen, differenzierten und skalensensitiven Ansatz. Dabei untersuche ich die Ursachen der Wildtiernutzung auf verschiedenen Ebenen (z.B. Nutzergruppen) und Skalen (lokal, global). Ich untersuchte ein lokales Umfeld durch eine Fallstudie um den Taï-Nationalpark in der Elfenbeinküste, indem ich 348 Jäger, 202 Buschfleischhändler, 190 Restaurantbesitzer und 985 Verbraucher in 47 städtischen und ländlichen Siedlungen befragte. Darüber hinaus untersuchte ich mithilfe von 114 persönlichen Interviews mit Nationalparkdirektoren in 25 afrikanischen und europäischen Ländern die Ausprägung der Jagd über den sozioökonomisch und ökologisch kontrastreichen globalen Süd-Nord-Gradienten. Die lokale Fallstudie zeigte die Heterogenität der Wildfleisch-Warenkette, in der mehrere Akteure Wildfleisch und verschiedene Taxa aus unterschiedlichen wirtschaftlichen, kulturellen oder ernährungsbedingten Beweggründen nutzen. Die globale Perspektive zeigte die sich verändernden Erscheinungsformen und Gründe für die Jagd entlang des globalen Süd-Nord-Gradienten. Im Süden überwog die illegale und kommerzielle Jagd auf Pflanzenfresser, während im Norden die legale, kulturell und sozial motivierte Jagd auf Huftiere und die illegale Jagd auf Raubtiere außerhalb von Parkgrenzen dominierte. Die Einbindung lokaler Gemeinschaften und die Berücksichtigung universeller Mechanismen menschlicher Kooperation könnte dem Naturschutz und der sozialen Gerechtigkeit zugutekommen. Nichtsdestotrotz verdeutlichen die Auswirkungen großräumiger Faktoren auf lokale Systeme die Notwendigkeit, gut umgesetzte lokale Maßnahmen mit einer angemessenen globalen Governance zu kombinieren, um den Raubbau an der Natur einzudämmen.Declining wildlife can profoundly alter ecosystems and increase the risks of food insecurity and emerging diseases that threaten global health, societies, and economies. Building on the theoretical superstructure of complex social-ecological systems, I examine wildlife trade in a holistic, differentiated, and scale-sensitive approach, exploring the causes of wildlife use at different levels (e.g. user groups) and scales (local, global). I examined a local setting through a case study around Taï National Park in Côte d'Ivoire by interviewing 348 hunters, 202 bushmeat traders, 190 restaurant owners, and 985 consumers in 47 urban and rural settlements. Furthermore, I investigated the manifestation of hunting across the Global South-North gradient through 114 face-to-face interviews with national park directors in 25 African and European countries. The local case study revealed the heterogeneity of the wild meat commodity chain, in which multiple actors use wild meat and different taxa for varying economic, cultural, or nutritional motivations. The global perspective revealed the shifting manifestations and reasons for hunting along the Global South-North gradient. Illegal, commercial hunting of herbivores prevailed in the South, while legal, culturally-, and socially-motivated hunting of ungulates and the illegal pursuit of predators outside park boundaries were common in the North. Engaging local communities and incorporating universal mechanisms of human cooperation into conservation could benefit conservation and social justice. The impacts of large-scale drivers on local systems highlight the need for combining well-implemented local action and appropriate global governance to curb wildlife overexploitatio

    Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

    Get PDF
    Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

    Developmental Bootstrapping of AIs

    Full text link
    Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure
    • …
    corecore