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  • 2014-2023
  • Open Access
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Blanco González-Tejero, Cristina; Lucas Ancillo, Antonio De; Gavrila Gavrila, Sorin; García Blanco, Antonio;

    Intellectual property (IP) management has posed continuous problems in the digital world, so understanding its associated concepts and the particularities they present is crucial. Within artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have enabled the intelligent processing and analysis of large volumes of data, making them widely used tools. In order to help fill the research gap that exists due to the novelty of the concepts, a bibliometric analysis is proposed of 404 scientific documents linked to AI, ML, NLP and IP, extracted from the Web of Science (WoS) core collection repository. The results demonstrate a current trend in research on the management of IP, related to digital tools and highlight the issues that arise from the management of IP stemming from their use. This research also identifies how these tools have been used to facilitate the management and identification of IP. In this sense, this study brings originality to the field of intellectual property management by examining previous studies and proposing new avenues for future research, thus broadening the current understanding of the subject. Entrepreneurs and business leaders can benefit from this study as it uncovers the complexities of IP management and thus enhances understanding of the opportunities and challenges in the AI era

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Biblioteca Digital d...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Biblioteca Digital d...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    The healthcare sector relies on Artificial Intelligence (AI) to automate tasks and assist in health care as patient data is dynamic and voluminous. Using Machine Learning methods, healthcare providers attempt to improve service by using algorithms that help provide an individualized treatment plan mitigating risk factors. Palliative Care (PC) is a form of health care provided to patients with life limiting illness. This requires regular monitoring of symptoms, the performance status of the individual and maintaining the Quality of Life (QoL) of the patient and provide support to the caregiver. To maintain the QoL, it is necessary to provide relief from the symptom burden due to the serious illness. Palliative care addresses to provide relief from the symptoms that are commonly observed such as pain, nausea, fatigue, depression, dyspnoea, lack of appetite and sleep. This paper highlights the various contexts in which Machine Learning (ML), Natural Language Processing (NLP) and Multi-Agent Systems (MAS) serve as useful tools in assisting Palliative Care. AI models implementing machine learning and deep learning algorithms have been developed for predicting mortality in PC patients. Using hierarchical clustering of biomarkers and NLP techniques, predictions on the survival curve of patients from the time of visit have been observed. Machine Learning algorithms have also been employed to identify the pauses in clinical conversations and classify them accordingly. PC is a multidisciplinary approach and Multi-Agent Systems have been suggested to analyse patients, manage symptoms and plan proactive care using a multi-layer network of Intelligent software agents.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Part of book or chapter of book . 2023
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    Part of book or chapter of book . 2023
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    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Part of book or chapter of book . 2023
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      Part of book or chapter of book . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lydia Zvyagintseva; Joel Blechinger;

    In this paper, we argue that the crisis of teaching can be understood as a crisis of labour that continues to impact academic librarians because it is a historical process grounded in larger socio-political shifts precipitated by capitalism. We demonstrate that the emergence and development of teaching—and specifically teaching information literacy (IL) as a kind of librarian curriculum—in academic libraries in North America corresponds to the emergence of neoliberalism. The shocks created by neoliberal fiscal austerity along with anxiety about de-professionalization and de-skilling provoked by cheaper and more widely available information technology created a mounting crisis of legitimacy in librarianship throughout the late 1970s and into the 1980s. Librarians ostensibly remedied this crisis through the positioning of IL as a central contribution of the profession to the academy and society. The COVID-19 pandemic and economic recessions have only intensified the proletarianization processes that have been ongoing since the 1970s. As teaching, learning, and assessment technologies proliferate in the academy, librarians cannot teach more efficiently to meet the needs of growing university populations. Instead, they must rethink the purpose and goals of librarian teaching in the context of the academy. The question of teaching will not be solved until material conditions of librarian labour in the academy are solved.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Canadian Journal of ...arrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tommaso Indelli;

    Law 8 October 2010 n. 170, containing “New legal norms about Specific Learning Disabilities at school”, introduced obligation of a specific didactic for students with Specific Learning Disabilities (DSA). School teaching of History had to adapt to new legislation, for an increasingly inclusive teaching.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Didattica della stor...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Didattica della storia
    Article . 2023
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Didattica della stor...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Didattica della storia
      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ahmet Özcan; Seyat Polat;

    The aim of our study is to discuss the use of artificial intelligence-supported platforms, which have become increasingly popular in recent months, in the context of ethics, opportunities, challenges, and the role of the researcher. In this context, we analysed platforms such as ChatGPT, ChatPDF, Consensus, SciSpace, and Scite Assistant. Within the scope of our analyses, we concluded that various regulations regarding the use of AI-supported platforms in scientific research should be enacted as soon as possible. Although such platforms offer opportunities for researchers, they also bring challenges such as referencing and reproducibility of scientific work. Besides, the use of AI-supported platforms in scientific research also puts the role of researchers into question.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Research ...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Research ...arrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    The Ghost in the Machine - AI's Impact on Cultural Heritage (Research) Over the past decade, deep learning methods have made remarkable advancements. This progress can be attributed to various factors such as massive parallelization through the utilization of Graphics Processing Units (GPUs) for massive parallelization. This shift in hardware has significantly accelerated the training of deep neural networks, allowing researchers to tackle increasingly complex problems. Another critical factor contributing to the success of deep learning is the acquisition of vast training datasets sourced from the World Wide Web, which has become a treasure trove of information. As a result, these models have become adept at capturing intricate patterns and representations in various domains. Furthermore, the development of efficient and reusable neural network architectures has also played a crucial role in the advancement of deep learning. Putting everything together, these evolutions have paved the way for the achievement of human-like or even superhuman performance in specific domains. Notably, the emergence of pre-trained large language models has demonstrated the capability to grasp the intricate semantics of natural languages, yielding exceptional outcomes in classification, prediction, and generation tasks. Similarly, in the realm of image generation, models such as Stable Diffusion and Dall-E have showcased their prowess. Tasks that once demanded human expertise for their execution are now on the brink of being supported or entirely taken over by machine intelligence. In the subsequent sections, we will illuminate some recent breakthroughs in AI-assisted search and retrieval systems within the domain of cultural heritage. One such example is the development of a multimodal search system for Iconclass, incorporating vision-language pre-trained machine learning models. However, it is paramount to approach the application of these cutting-edge generative AI models in scientific and research contexts with due diligence. One must remain mindful of potential inaccuracies and hallucinations that these systems can inadvertently produce. It's worth noting that deep learning and large language models constitute only a specific subset of artificial intelligence, falling under the broader category of machine learning. Symbolic knowledge representation represents another distinct subdomain of AI, distinguished by its mathematical rigor and formalism. In this realm, any inaccuracies or inconsistencies in underlying assumptions can be readily identified and rectified. Knowledge graphs built upon ontologies present a viable avenue for enhancing the explainability of black-box statistical deep learning systems. Furthermore, they possess the capacity to flag false or counterfeit information. As a result, future information systems are poised to embrace hybrid solutions that amalgamate symbolic and subsymbolic AI approaches to combine the strengths of both paradigms, offering not only reliable but also trustworthy results. 

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    Other literature type . 2023
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    Presentation . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other literature type . 2023
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      Presentation . 2023
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      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mentec, François;

    Le recrutement a toujours été une tâche cruciale pour la réussite des entreprises, notamment pour les entreprises de services pour lesquelles l’embauche est un élément central de leur modèle commercial. La croissance du marché du travail ainsi que l’augmentation du nombre de compétences spécialisées requises par les entreprises ont motivé l’exploration de techniques pour optimiser et même automatiser certaines parties du processus de recrutement. Les nombreux progrès réalisés dans les domaines de l’intelligence artificielle et du traitement automatique du langage naturel au cours des dernières décennies ont offert la possibilité de traiter efficacement les données utilisées lors du recrutement. Nous examinons l’utilisation d’un système de recommandation d’emploi dans une entreprise de conseil, en mettant l’accent sur l’explication de la recommandation et sa perception par les utilisateurs. Tout d’abord, nous expérimentons avec des recommandations basées sur la connaissance en utilisant l’ontologie européenne des compétences et des professions ESCO qui présente des résultats prometteurs, mais en raison des limites actuelles, nous utilisons finalement un système de recommandation sémantique qui fait désormais partie des processus de l’entreprise et offre la possibilité d’études qualitatives et quantitatives sur l’impact des recommandations et de leurs explications. Nous relions la disponibilité des explications à des gains majeurs d’efficacité pour les recruteurs. L’explication offre également un moyen précieux d’affiner les recommandations grâce à des retours utilisateurs contextuels. Un tel retour d’information est non seulement utile pour générer des recommandations en temps réel, mais aussi pour fournir des données précieuses pour évaluer les modèles et améliorer davantage le système. À l’avenir, nous préconisons que la disponibilité des recommandations devienne la norme pour tous les systèmes de recommandation d’emploi. Recruitment has always been a crucial task for the success of companies, and especially consulting companies for which hiring is a centerpiece of their business model. The growth of the labor market along the increasing number of specialized skills that are required by companies has motivated the exploration of techniques to optimize and even automate parts of the recruitment process. The numerous progress made in the fields of Artificial Intelligence and Natural Language Processing during the past few decades offered the opportunity to efficiently process the data used during the recruitment. We examine the use of a job recommender system in a consulting company, with a focus on the explanation of the recommendation and its perception by users. First, we experiment with knowledge-based recommendations using the European ontology of skills and occupation ESCO which showcases promising results, but because of current limitations, we finally use a semantic-based recommender system that has since become part of the company processes and offers the opportunity for qualitative and quantitative studies on the impact of the recommendations and its explanations. We link the explanation availability to major gains in efficiency for recruiters. It also offers them a valuable way to fine-tune recommendations through contextual feedback. Such a feedback is not only useful for generating recommendations at run-time, but also for providing valuable data to evaluate models and further improve the system. Going forward we advocate that the availability of recommendations should be the standard for every job recommender systems.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Mémoires en Sciences...arrow_drop_down
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    Authors: Karvinen, Lasse;
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    UEF eRepository
    Master thesis . 2023
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      Master thesis . 2023
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    Authors: Yang, Antoine;

    L’objectif de cette thèse est de construire et de former des modèles d’apprentissage automatique combinant la puissance du traitement du langage naturel avec la compréhension visuelle, permettant une compréhension complète et détaillée du contenu des vidéos. Premièrement, nous proposons deux nouvelles méthodes pour développer des modèles de réponses aux questions sur des vidéos sans avoir recours à une annotation manuelle coûteuse. Nous générons automatiquement des données de réponses aux questions sur des vidéos à partir de vidéos commentées à l’aide de modèles de génération de questions utilisant uniquement du texte. Nous montrons ensuite qu’un transformateur multi-modal entraîné de manière contrastée sur les données générées peut répondre aux questions visuelles sans entraînement supplémentaire. Afin de contourner la procédure de génération de données, nous présentons une approche alternative, nommée FrozenBiLM, qui exploite directement des modèles de langage masqué bidirectionnels. Deuxièmement, nous développons TubeDETR, un modèle de transformateur capable de localiser spatialement et temporellement une requête en langage naturel dans une vidéo non découpée. Contrairement aux approches spatio-temporelles antérieures, TubeDETR peut être efficacement entraîné de bout en bout sur des vidéos non rognées. Troisièmement, nous présentons un nouveau modèle et un nouvel ensemble de données pour la compréhension de multiple évènements dans les vidéos non découpées. Nous introduisons le modèle Vid2Seq qui génère des descriptions denses en langage naturel et les limites temporelles correspondantes pour tous les événements dans une vidéo non découpée en prédisant une seule séquence de jetons. De plus, Vid2Seq peut être efficacement pré-entraîné sur des vidéos commentées à grande échelle en utilisant les transcriptions de paroles comme pseudo-supervision. Enfin, nous présentons VidChapters-7M, un ensemble de données à grande échelle de vidéos chapitrées par les utilisateurs. Sur la base de cet ensemble de données, nous évaluons des modèles de pointe sur trois tâches, dont la génération de chapitres vidéo. Nous montrons également que les modèles de génération de chapitres vidéo se transfèrent bien au sous-titrage vidéo dense. The goal of this thesis is to build and train machine learning models that combine the power of natural language processing with visual understanding, enabling a comprehensive and detailed comprehension of the content within videos. First, we propose two scalable approaches to develop video question answering models without the need for costly manual annotation. We automatically generate video question answering data from narrated videos using text-only question-generation models. We then show that a multi-modal transformer trained contrastively on the generated data can answer visual questions in a zero-shot manner. In order to bypass the data generation procedure, we present an alternative approach, dubbed FrozenBiLM, that directly leverages bidirectional masked language models. Second, we develop TubeDETR, a transformer model that can spatially and temporally localize a natural language query in an untrimmed video. Unlike prior spatio-temporal grounding approaches, TubeDETR can be effectively trained end-to-end on untrimmed videos. Third, we present a new model and a new dataset for multi-event understanding in untrimmed videos. We introduce the Vid2Seq model which generates dense natural language descriptions and corresponding temporal boundaries for all events in an untrimmed video by predicting a single sequence of tokens. Moreover, Vid2Seq can be effectively pretrained on narrated videos at scale using transcribed speech as pseudo-supervision. Finally, we introduce VidChapters-7M, a large-scale dataset of user-chaptered videos. Based on this dataset, we evaluate state-of-the-art models on three tasks including video chapter generation. We also show that video chapter generation models transfer well to dense video captioning in both zero-shot and finetuning settings.

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    This paper delves into the transformative intersection of emerging technologies and digital libraries, illuminating a path toward an enriched and accessible knowledge landscape. Focusing on Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Augmented Reality (AR), and Virtual Reality (VR), the study explores how these technologies redefine digital library experiences. AI and ML algorithms empower intuitive content curation and recommendation, reshaping the way users interact with digital resources. NLP bridges the gap between human language intricacies and digital systems, enhancing search functionalities and making information retrieval seamless. AR overlays digital information onto the physical world, expanding interactive learning possibilities, while VR immerses users in virtual realms, revolutionizing educational paradigms. The paper critically examines the practical integration of these technologies, ensuring digital libraries not only preserve vast knowledge repositories but also present information in engaging and accessible formats. Through AI-driven metadata generation and content tagging, digital libraries are systematically organized and enriched, amplifying search accuracy. These innovations not only preserve the past but also illuminate a future where knowledge is universally accessible, fostering curiosity, learning, and exploration. The study not only theoretically explores the potential of these technologies but also delves into the perceptions of practical library users, ensuring a user-centric approach in shaping the digital libraries of tomorrow. This research contributes significantly to the evolving landscape of digital libraries, paving the way for inclusive, immersive, and engaging knowledge experiences for diverse users worldwide.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Blanco González-Tejero, Cristina; Lucas Ancillo, Antonio De; Gavrila Gavrila, Sorin; García Blanco, Antonio;

    Intellectual property (IP) management has posed continuous problems in the digital world, so understanding its associated concepts and the particularities they present is crucial. Within artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have enabled the intelligent processing and analysis of large volumes of data, making them widely used tools. In order to help fill the research gap that exists due to the novelty of the concepts, a bibliometric analysis is proposed of 404 scientific documents linked to AI, ML, NLP and IP, extracted from the Web of Science (WoS) core collection repository. The results demonstrate a current trend in research on the management of IP, related to digital tools and highlight the issues that arise from the management of IP stemming from their use. This research also identifies how these tools have been used to facilitate the management and identification of IP. In this sense, this study brings originality to the field of intellectual property management by examining previous studies and proposing new avenues for future research, thus broadening the current understanding of the subject. Entrepreneurs and business leaders can benefit from this study as it uncovers the complexities of IP management and thus enhances understanding of the opportunities and challenges in the AI era

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    The healthcare sector relies on Artificial Intelligence (AI) to automate tasks and assist in health care as patient data is dynamic and voluminous. Using Machine Learning methods, healthcare providers attempt to improve service by using algorithms that help provide an individualized treatment plan mitigating risk factors. Palliative Care (PC) is a form of health care provided to patients with life limiting illness. This requires regular monitoring of symptoms, the performance status of the individual and maintaining the Quality of Life (QoL) of the patient and provide support to the caregiver. To maintain the QoL, it is necessary to provide relief from the symptom burden due to the serious illness. Palliative care addresses to provide relief from the symptoms that are commonly observed such as pain, nausea, fatigue, depression, dyspnoea, lack of appetite and sleep. This paper highlights the various contexts in which Machine Learning (ML), Natural Language Processing (NLP) and Multi-Agent Systems (MAS) serve as useful tools in assisting Palliative Care. AI models implementing machine learning and deep learning algorithms have been developed for predicting mortality in PC patients. Using hierarchical clustering of biomarkers and NLP techniques, predictions on the survival curve of patients from the time of visit have been observed. Machine Learning algorithms have also been employed to identify the pauses in clinical conversations and classify them accordingly. PC is a multidisciplinary approach and Multi-Agent Systems have been suggested to analyse patients, manage symptoms and plan proactive care using a multi-layer network of Intelligent software agents.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lydia Zvyagintseva; Joel Blechinger;

    In this paper, we argue that the crisis of teaching can be understood as a crisis of labour that continues to impact academic librarians because it is a historical process grounded in larger socio-political shifts precipitated by capitalism. We demonstrate that the emergence and development of teaching—and specifically teaching information literacy (IL) as a kind of librarian curriculum—in academic libraries in North America corresponds to the emergence of neoliberalism. The shocks created by neoliberal fiscal austerity along with anxiety about de-professionalization and de-skilling provoked by cheaper and more widely available information technology created a mounting crisis of legitimacy in librarianship throughout the late 1970s and into the 1980s. Librarians ostensibly remedied this crisis through the positioning of IL as a central contribution of the profession to the academy and society. The COVID-19 pandemic and economic recessions have only intensified the proletarianization processes that have been ongoing since the 1970s. As teaching, learning, and assessment technologies proliferate in the academy, librarians cannot teach more efficiently to meet the needs of growing university populations. Instead, they must rethink the purpose and goals of librarian teaching in the context of the academy. The question of teaching will not be solved until material conditions of librarian labour in the academy are solved.

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    Authors: Tommaso Indelli;

    Law 8 October 2010 n. 170, containing “New legal norms about Specific Learning Disabilities at school”, introduced obligation of a specific didactic for students with Specific Learning Disabilities (DSA). School teaching of History had to adapt to new legislation, for an increasingly inclusive teaching.

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    Didattica della storia
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    Authors: Ahmet Özcan; Seyat Polat;

    The aim of our study is to discuss the use of artificial intelligence-supported platforms, which have become increasingly popular in recent months, in the context of ethics, opportunities, challenges, and the role of the researcher. In this context, we analysed platforms such as ChatGPT, ChatPDF, Consensus, SciSpace, and Scite Assistant. Within the scope of our analyses, we concluded that various regulations regarding the use of AI-supported platforms in scientific research should be enacted as soon as possible. Although such platforms offer opportunities for researchers, they also bring challenges such as referencing and reproducibility of scientific work. Besides, the use of AI-supported platforms in scientific research also puts the role of researchers into question.

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    The Ghost in the Machine - AI's Impact on Cultural Heritage (Research) Over the past decade, deep learning methods have made remarkable advancements. This progress can be attributed to various factors such as massive parallelization through the utilization of Graphics Processing Units (GPUs) for massive parallelization. This shift in hardware has significantly accelerated the training of deep neural networks, allowing researchers to tackle increasingly complex problems. Another critical factor contributing to the success of deep learning is the acquisition of vast training datasets sourced from the World Wide Web, which has become a treasure trove of information. As a result, these models have become adept at capturing intricate patterns and representations in various domains. Furthermore, the development of efficient and reusable neural network architectures has also played a crucial role in the advancement of deep learning. Putting everything together, these evolutions have paved the way for the achievement of human-like or even superhuman performance in specific domains. Notably, the emergence of pre-trained large language models has demonstrated the capability to grasp the intricate semantics of natural languages, yielding exceptional outcomes in classification, prediction, and generation tasks. Similarly, in the realm of image generation, models such as Stable Diffusion and Dall-E have showcased their prowess. Tasks that once demanded human expertise for their execution are now on the brink of being supported or entirely taken over by machine intelligence. In the subsequent sections, we will illuminate some recent breakthroughs in AI-assisted search and retrieval systems within the domain of cultural heritage. One such example is the development of a multimodal search system for Iconclass, incorporating vision-language pre-trained machine learning models. However, it is paramount to approach the application of these cutting-edge generative AI models in scientific and research contexts with due diligence. One must remain mindful of potential inaccuracies and hallucinations that these systems can inadvertently produce. It's worth noting that deep learning and large language models constitute only a specific subset of artificial intelligence, falling under the broader category of machine learning. Symbolic knowledge representation represents another distinct subdomain of AI, distinguished by its mathematical rigor and formalism. In this realm, any inaccuracies or inconsistencies in underlying assumptions can be readily identified and rectified. Knowledge graphs built upon ontologies present a viable avenue for enhancing the explainability of black-box statistical deep learning systems. Furthermore, they possess the capacity to flag false or counterfeit information. As a result, future information systems are poised to embrace hybrid solutions that amalgamate symbolic and subsymbolic AI approaches to combine the strengths of both paradigms, offering not only reliable but also trustworthy results. 

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    Authors: Mentec, François;

    Le recrutement a toujours été une tâche cruciale pour la réussite des entreprises, notamment pour les entreprises de services pour lesquelles l’embauche est un élément central de leur modèle commercial. La croissance du marché du travail ainsi que l’augmentation du nombre de compétences spécialisées requises par les entreprises ont motivé l’exploration de techniques pour optimiser et même automatiser certaines parties du processus de recrutement. Les nombreux progrès réalisés dans les domaines de l’intelligence artificielle et du traitement automatique du langage naturel au cours des dernières décennies ont offert la possibilité de traiter efficacement les données utilisées lors du recrutement. Nous examinons l’utilisation d’un système de recommandation d’emploi dans une entreprise de conseil, en mettant l’accent sur l’explication de la recommandation et sa perception par les utilisateurs. Tout d’abord, nous expérimentons avec des recommandations basées sur la connaissance en utilisant l’ontologie européenne des compétences et des professions ESCO qui présente des résultats prometteurs, mais en raison des limites actuelles, nous utilisons finalement un système de recommandation sémantique qui fait désormais partie des processus de l’entreprise et offre la possibilité d’études qualitatives et quantitatives sur l’impact des recommandations et de leurs explications. Nous relions la disponibilité des explications à des gains majeurs d’efficacité pour les recruteurs. L’explication offre également un moyen précieux d’affiner les recommandations grâce à des retours utilisateurs contextuels. Un tel retour d’information est non seulement utile pour générer des recommandations en temps réel, mais aussi pour fournir des données précieuses pour évaluer les modèles et améliorer davantage le système. À l’avenir, nous préconisons que la disponibilité des recommandations devienne la norme pour tous les systèmes de recommandation d’emploi. Recruitment has always been a crucial task for the success of companies, and especially consulting companies for which hiring is a centerpiece of their business model. The growth of the labor market along the increasing number of specialized skills that are required by companies has motivated the exploration of techniques to optimize and even automate parts of the recruitment process. The numerous progress made in the fields of Artificial Intelligence and Natural Language Processing during the past few decades offered the opportunity to efficiently process the data used during the recruitment. We examine the use of a job recommender system in a consulting company, with a focus on the explanation of the recommendation and its perception by users. First, we experiment with knowledge-based recommendations using the European ontology of skills and occupation ESCO which showcases promising results, but because of current limitations, we finally use a semantic-based recommender system that has since become part of the company processes and offers the opportunity for qualitative and quantitative studies on the impact of the recommendations and its explanations. We link the explanation availability to major gains in efficiency for recruiters. It also offers them a valuable way to fine-tune recommendations through contextual feedback. Such a feedback is not only useful for generating recommendations at run-time, but also for providing valuable data to evaluate models and further improve the system. Going forward we advocate that the availability of recommendations should be the standard for every job recommender systems.

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    Authors: Karvinen, Lasse;
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    Master thesis . 2023
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      Master thesis . 2023
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