776 research outputs found

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    Advances in automatic terminology processing: methodology and applications in focus

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The information and knowledge era, in which we are living, creates challenges in many fields, and terminology is not an exception. The challenges include an exponential growth in the number of specialised documents that are available, in which terms are presented, and the number of newly introduced concepts and terms, which are already beyond our (manual) capacity. A promising solution to this ‘information overload’ would be to employ automatic or semi-automatic procedures to enable individuals and/or small groups to efficiently build high quality terminologies from their own resources which closely reflect their individual objectives and viewpoints. Automatic terminology processing (ATP) techniques have already proved to be quite reliable, and can save human time in terminology processing. However, they are not without weaknesses, one of which is that these techniques often consider terms to be independent lexical units satisfying some criteria, when terms are, in fact, integral parts of a coherent system (a terminology). This observation is supported by the discussion of the notion of terms and terminology and the review of existing approaches in ATP presented in this thesis. In order to overcome the aforementioned weakness, we propose a novel methodology in ATP which is able to extract a terminology as a whole. The proposed methodology is based on knowledge patterns automatically extracted from glossaries, which we considered to be valuable, but overlooked resources. These automatically identified knowledge patterns are used to extract terms, their relations and descriptions from corpora. The extracted information can facilitate the construction of a terminology as a coherent system. The study also aims to discuss applications of ATP, and describes an experiment in which ATP is integrated into a new NLP application: multiplechoice test item generation. The successful integration of the system shows that ATP is a viable technology, and should be exploited more by other NLP applications

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    Insect neuroethology of reinforcement learning

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    Historically, reinforcement learning is a branch of machine learning founded on observations of how animals learn. This involved collaboration between the fields of biology and artificial intelligence that was beneficial to both fields, creating smarter artificial agents and improving the understanding of how biological systems function. The evolution of reinforcement learning during the past few years was rapid but substantially diverged from providing insights into how biological systems work, opening a gap between reinforcement learning and biology. In an attempt to close this gap, this thesis studied the insect neuroethology of reinforcement learning, that is, the neural circuits that underlie reinforcement-learning-related behaviours in insects. The goal was to extract a biologically plausible plasticity function from insect-neuronal data, use this to explain biological findings and compare it to more standard reinforcement learning models. Consequently, a novel dopaminergic plasticity rule was developed to approximate the function of dopamine as the plasticity mechanism between neurons in the insect brain. This allowed a range of observed learning phenomena to happen in parallel, like memory depression, potentiation, recovery, and saturation. In addition, by using anatomical data of connections between neurons in the mushroom body neuropils of the insect brain, the neural incentive circuit of dopaminergic and output neurons was also explored. This, together with the dopaminergic plasticity rule, allowed for dynamic collaboration amongst parallel memory functions, such as acquisition, transfer, and forgetting. When tested on olfactory conditioning paradigms, the model reproduced the observed changes in the activity of the identified neurons in fruit flies. It also replicated the observed behaviour of the animals and it allowed for flexible behavioural control. Inspired by the visual navigation system of desert ants, the model was further challenged in the visual place recognition task. Although a relatively simple encoding of the olfactory information was sufficient to explain odour learning, a more sophisticated encoding of the visual input was required to increase the separability among the visual inputs and enable visual place recognition. Signal whitening and sparse combinatorial encoding were sufficient to boost the performance of the system in this task. The incentive circuit enabled the encoding of increasing familiarity along a known route, which dropped proportionally to the distance of the animal from that route. Finally, the proposed model was challenged in delayed reinforcement tasks, suggesting that it might take the role of an adaptive critic in the context of reinforcement learning

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    A Behavioural Decision-Making Framework For Agent-Based Models

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    In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making

    Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

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    This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea 2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068

    Computational and human-based methods for knowledge discovery over knowledge graphs

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    The modern world has evolved, accompanied by the huge exploitation of data and information. Daily, increasing volumes of data from various sources and formats are stored, resulting in a challenging strategy to manage and integrate them to discover new knowledge. The appropriate use of data in various sectors of society, such as education, healthcare, e-commerce, and industry, provides advantages for decision support in these areas. However, knowledge discovery becomes challenging since data may come from heterogeneous sources with important information hidden. Thus, new approaches that adapt to the new challenges of knowledge discovery in such heterogeneous data environments are required. The semantic web and knowledge graphs (KGs) are becoming increasingly relevant on the road to knowledge discovery. This thesis tackles the problem of knowledge discovery over KGs built from heterogeneous data sources. We provide a neuro-symbolic artificial intelligence system that integrates symbolic and sub-symbolic frameworks to exploit the semantics encoded in a KG and its structure. The symbolic system relies on existing approaches of deductive databases to make explicit, implicit knowledge encoded in a KG. The proposed deductive database DSDS can derive new statements to ego networks given an abstract target prediction. Thus, DSDS minimizes data sparsity in KGs. In addition, a sub-symbolic system relies on knowledge graph embedding (KGE) models. KGE models are commonly applied in the KG completion task to represent entities in a KG in a low-dimensional vector space. However, KGE models are known to suffer from data sparsity, and a symbolic system assists in overcoming this fact. The proposed approach discovers knowledge given a target prediction in a KG and extracts unknown implicit information related to the target prediction. As a proof of concept, we have implemented the neuro-symbolic system on top of a KG for lung cancer to predict polypharmacy treatment effectiveness. The symbolic system implements a deductive system to deduce pharmacokinetic drug-drug interactions encoded in a set of rules through the Datalog program. Additionally, the sub-symbolic system predicts treatment effectiveness using a KGE model, which preserves the KG structure. An ablation study on the components of our approach is conducted, considering state-of-the-art KGE methods. The observed results provide evidence for the benefits of the neuro-symbolic integration of our approach, where the neuro-symbolic system for an abstract target prediction exhibits improved results. The enhancement of the results occurs because the symbolic system increases the prediction capacity of the sub-symbolic system. Moreover, the proposed neuro-symbolic artificial intelligence system in Industry 4.0 (I4.0) is evaluated, demonstrating its effectiveness in determining relatedness among standards and analyzing their properties to detect unknown relations in the I4.0KG. The results achieved allow us to conclude that the proposed neuro-symbolic approach for an abstract target prediction improves the prediction capability of KGE models by minimizing data sparsity in KGs

    Towards Video Transformers for Automatic Human Analysis

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    [eng] With the aim of creating artificial systems capable of mirroring the nuanced understanding and interpretative powers inherent to human cognition, this thesis embarks on an exploration of the intersection between human analysis and Video Transformers. The objective is to harness the potential of Transformers, a promising architectural paradigm, to comprehend the intricacies of human interaction, thus paving the way for the development of empathetic and context-aware intelligent systems. In order to do so, we explore the whole Computer Vision pipeline, from data gathering, to deeply analyzing recent developments, through model design and experimentation. Central to this study is the creation of UDIVA, an expansive multi-modal, multi-view dataset capturing dyadic face-to-face human interactions. Comprising 147 participants across 188 sessions, UDIVA integrates audio-visual recordings, heart-rate measurements, personality assessments, socio- demographic metadata, and conversational transcripts, establishing itself as the largest dataset for dyadic human interaction analysis up to this date. This dataset provides a rich context for probing the capabilities of Transformers within complex environments. In order to validate its utility, as well as to elucidate Transformers' ability to assimilate diverse contextual cues, we focus on addressing the challenge of personality regression within interaction scenarios. We first adapt an existing Video Transformer to handle multiple contextual sources and conduct rigorous experimentation. We empirically observe a progressive enhancement in model performance as more context is added, reinforcing the potential of Transformers to decode intricate human dynamics. Building upon these findings, the Dyadformer emerges as a novel architecture, adept at long-range modeling of dyadic interactions. By jointly modeling both participants in the interaction, as well as embedding multi- modal integration into the model itself, the Dyadformer surpasses the baseline and other concurrent approaches, underscoring Transformers' aptitude in deciphering multifaceted, noisy, and challenging tasks such as the analysis of human personality in interaction. Nonetheless, these experiments unveil the ubiquitous challenges when training Transformers, particularly in managing overfitting due to their demand for extensive datasets. Consequently, we conclude this thesis with a comprehensive investigation into Video Transformers, analyzing topics ranging from architectural designs and training strategies, to input embedding and tokenization, traversing through multi-modality and specific applications. Across these, we highlight trends which optimally harness spatio-temporal representations that handle video redundancy and high dimensionality. A culminating performance comparison is conducted in the realm of video action classification, spotlighting strategies that exhibit superior efficacy, even compared to traditional CNN-based methods.[cat] Aquesta tesi busca crear sistemes artificials que reflecteixin les habilitats de comprensió i interpretació humanes a través de l'ús de Transformers per a vídeo. L'objectiu és utilitzar aquestes arquitectures per comprendre millor la interacció humana i desenvolupar sistemes intel·ligents i conscients de l'entorn. Això implica explorar àmplies àrees de la Visió per Computador, des de la recopilació de dades fins a l'anàlisi de l'estat de l'art i la prova experimental d'aquests models. Una part essencial d'aquest estudi és la creació d'UDIVA, un ampli conjunt de dades multimodal i multivista que enregistra interaccions humanes cara a cara. Amb 147 participants i 188 sessions, UDIVA inclou contingut audiovisual, freqüència cardíaca, perfils de personalitat, dades sociodemogràfiques i transcripcions de les converses. És el conjunt de dades més gran conegut per a l'anàlisi de la interacció humana diàdica i proporciona un context ric per a l'estudi de les capacitats dels Transformers en entorns complexos. Per tal de validar la seva utilitat i les habilitats dels Transformers, ens centrem en la regressió de la personalitat. Inicialment, adaptem un Transformer de vídeo per integrar diverses fonts de context. Mitjançant experiments exhaustius, observem millores progressives en els resultats amb la inclusió de més context, confirmant la capacitat dels Transformers. Motivats per aquests resultats, desenvolupem el Dyadformer, una arquitectura per interaccions diàdiques de llarga duració. Aquesta nova arquitectura considera simultàniament els dos participants en la interacció i incorpora la multimodalitat en un sol model. El Dyadformer supera la nostra proposta inicial i altres treballs similars, destacant la capacitat dels Transformers per abordar tasques complexes. No obstant això, aquestos experiments revelen reptes d'entrenament dels Transformers, com el sobreajustament, per la seva necessitat de grans conjunts de dades. La tesi conclou amb una anàlisi profunda dels Transformers per a vídeo, incloent dissenys arquitectònics, estratègies d'entrenament, preprocessament de vídeos, tokenització i multimodalitat. S'identifiquen tendències per gestionar la redundància i alta dimensionalitat de vídeos i es realitza una comparació de rendiment en la classificació d'accions a vídeo, destacant estratègies d'eficàcia superior als mètodes tradicionals basats en convolucions
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