8,492 research outputs found

    Cyber Conflict and Just War Theory

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    Intelligent architecture to support second generation general accounting

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThis study aimed to innovate the world of accounting software. After so many years, accountants are faced with an unbelievable amount of work, which is not always productive, effective and efficient for both the accountant and the company that provided him with the data required to carry out the accounting. There is already accounting software with various automation processes, from ornamentation to profitability analysis and management reporting. There is also software that is updated in accordance with the accounting laws, i.e., the platform changes its mechanisms according to the changes in the law. Despite the existence of this software, manual work remains, and the amount of information accountants are faced with is still very large. It is difficult for accountants to do a 100% reliable job with so much information and data they have. One of the most common situations in the accounting world is undoubtedly the miscalculation or forgetting of some financial or non-financial data found in accounting operations (income statements, balance sheets, etc.). To render accounting operations efficient, effective and productive, errorfree and 100% reliable, an intelligent architecture has been developed to support second generation general accounting. This architectural design was developed with a view to make the existing software smarter with the help of artificial intelligence. A study was carried out on accounting keys and concepts, on AI and main process automation techniques to build the model. With these studies it was intended to acquire all possible requirements for the creation of the architecture. Towards the end of the thesis the model was validated

    ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

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    Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT

    Intelligent computing : the latest advances, challenges and future

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    Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

    Calibrating trust between humans and artificial intelligence systems

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    As machines become increasingly more intelligent, they become more capable of operating with greater degrees of independence from their users. However, appropriate use of these autonomous systems is dependent on appropriate trust from their users. A lack of trust towards an autonomous system will likely lead to the user doubting the capabilities of the system, potentially to the point of disuse. Conversely, too much trust in a system may lead to the user overestimating the capabilities of the system, and potentially result in errors which could have been avoided with appropriate supervision. Thus, appropriate trust is trust which is calibrated to reflect the true performance capabilities of the system. The calibration of trust towards autonomous systems is an area of research of increasing popularity, as more and more intelligent machines are introduced to modern workplaces. This thesis contains three studies which examine trust towards autonomous technologies. In our first study, in Chapter 2, we used qualitative research methods to explore how participants characterise their trust towards different online technologies. In focus groups, participants discussed a variety of factors which they believed were important when using digital services. We had a particular interest in how they perceived social media platforms, as these services rely upon users continued sharing of their personal information. In our second study, in Chapter 3, using our initial findings we created a human-computer interaction experiment, where participants collaborated with an Autonomous Image Classifier System. In this experiment, we were able to examine the ways that participants placed trust in the classifier during different types of system performance. We also investigated whether users’ trust could be better calibrated by providing different displays of System Confidence Information, to help convey the system’s decision making. In our final study, in Chapter 4, we built directly upon the findings of Chapter 3, by creating an updated version of our human-computer interaction experiment. We provided participants with another cue of system decision making, Gradient-weighted Class Activation Mapping, and investigated whether this cue could promote greater trust towards the classifier. Additionally, we examined whether these cues can improve participants’ subjective understanding of the system’s decision making, as a way of exploring how to improve the interpretability of these systems. This research contributes to our current understanding of calibrating users’ trust towards autonomous systems, and may be particularly useful when designing Autonomous Image Classifier Systems. While our results were inconclusive, we did find some support for users preferring the more complicated interfaces we provided. Users also reported greater understanding of the classifier’s decision making when provided with the Gradient-weighted Class Activation Mapping cue. Further research may clarify whether this cue is an appropriate method of visualising the decision-making of Autonomous Image Classifier Systems in real-world settings

    Deep learning for speech to text transcription for the portuguese language

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    Automatic speech recognition (ASR) is the process of transcribing audio recordings into text, i.e. to transform speech into the respective sequence of words. This process is also commonly known as speechto- text. Machine learning (ML), the ability of machines to learn from examples, is one of the most relevant areas of artificial intelligence in today’s world. Deep learning is a subset of ML which makes use of Deep Neural Networks, a particular type of Artificial Neural Networks (ANNs), which are intended to mimic human neurons, that possess a large number of layers. This dissertation reviews the state-of-the-art on automatic speech recognition throughout time, from early systems which used Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) to the most up-to-date end-to-end (E2E) deep neural models. Considering the context of the present work, some deep learning algorithms used in state-of-the-art approaches are explained in additional detail. The current work aims to develop an ASR system for the European Portuguese language using deep learning. This is achieved by implementing a pipeline composed of stages responsible for data acquisition, data analysis, data pre-processing, model creation and evaluation of results. With the NVIDIA NeMo framework was possible to implement the QuartzNet15x5 architecture based on 1D time-channel separable convolutions. Following a data-centric methodology, the model developed yielded state-of-the-art Word Error Rate (WER) results of WER = 0.0503; Sumário: Aprendizagem profunda para transcrição de fala para texto para a Língua Portuguesa - O reconhecimento automático de fala (ASR) é o processo de transcrever gravações de áudio em texto, i.e., transformar a fala na respectiva sequência de palavras. Esse processo também é comumente conhecido como speech-to-text. A aprendizagem de máquina (ML), a capacidade das máquinas de aprenderem através de exemplos, é um dos campos mais relevantes da inteligência artificial no mundo atual. Deep learning é um subconjunto de ML que faz uso de Redes Neurais Profundas, um tipo particular de Redes Neurais Artificiais (ANNs), que se destinam a imitar neurónios humanos, que possuem um grande número de camadas Esta dissertação faz uma revisão ao estado da arte do reconhecimento automático de fala ao longo do tempo, desde os primeiros sistemas que usavam Hidden Markov Models (HMMs) e Gaussian Mixture Models (GMMs até sistemas end-to-end (E2E) mais recentes que usam modelos neuronais profundos. Considerando o contexto do presente trabalho, alguns algoritmos de aprendizagem profunda usados em abordagens de ponta são explicados mais detalhadamente. O presente trabalho tem como objetivo desenvolver um sistema ASR para a língua portuguesa europeia utilizando deep learning. Isso é conseguido por meio da implementação de um pipeline composto por etapas responsáveis pela aquisição de dados, análise dos dados, pré-processamento dos dados, criação do modelo e avaliação dos resultados. Com o framework NVIDIA NeMo foi possível implementar a arquitetura QuartzNet15x5 baseada em convoluções 1D separáveis por canal de tempo. Seguindo uma metodologia centrada em dados, o modelo desenvolvido produziu resultados de taxa de erro de palavra (WER) semelhantes aos de estado da arte de WER = 0.0503

    Recent progress in piezotronic sensors based on one-dimensional zinc oxide nanostructures and its regularly ordered arrays: from design to application

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    Piezotronic sensors and self-powered gadgets are highly sought-after for flexible, wearable, and intelligent electronics for their applications in cutting-edge healthcare and human-machine interfaces. With the advantages of a well-known piezoelectric effect, excellent mechanical properties, and emerging nanotechnology applications, one-dimensional (1D) ZnO nanostructures organized in the form of a regular array have been regarded as one of the most promising inorganic active materials for piezotronics. This report intends to review the recent developments of 1D ZnO nanostructure arrays for multifunctional piezotronic sensors. Prior to discussing rational design and fabrication approaches for piezotronic devices in precisely controlled dimensions, well-established synthesis methods for high-quality and well-controlled 1D ZnO nanostructures are addressed. The challenges associated with the well-aligned, site-specific synthesis of 1D ZnO nanostructures, development trends of piezotronic devices, advantages of an ordered array of 1D ZnO in device performances, exploring new sensing mechanisms, incorporating new functionalities by constructing heterostructures, the development of novel flexible device integration technology, the deployment of novel synergistic strategies in piezotronic device performances, and potential multifunctional applications are covered. A brief evaluation of the end products, such as small-scale miniaturized unconventional power sources in sensors, high-resolution image sensors, and personalized healthcare medical devices, is also included. The paper is summarized towards the conclusion by outlining the present difficulties and promising future directions. This study will provide guidance for future research directions in 1D ZnO nanostructure-based piezotronics, which will hasten the development of multifunctional devices, sensors, chips for human-machine interfaces, displays, and self-powered systems

    Using mixed methods to explore test anxiety in young people with learning difficulties

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    There is growing evidence to suggest that children and young people are experiencing more mental health difficulties (NHS Digital, 2020) and this is especially the case for those with learning difficulties (NHS Digital, 2021). More specifically, there has been a focus on the impact of testing on children and young people’s mental health (McCaldin et al., 2019). Much of the current research on test anxiety focuses on children and young people in general and uses quantitative approaches (Putwain, 2007). This research explores test anxiety in young people (Year 7 and 8) with learning difficulties in cognition and learning. A mixed methods approach was used to measure levels of trait test anxiety and to explore their experiences of test anxiety, specifically their views on tests and what they perceive contributes towards and alleviates test anxiety. The young people in this study experience a range of feelings associated with tests and exams across different levels of trait test anxiety. They reported that difficulties with understanding and writing and self-concept contributed towards test anxiety. Importantly, they perceive that practical support, emotional support and distraction and relaxation activities are ways to alleviate test anxiety. These findings link with Zeidner’s (1998) Integrative Transactional Model of Test Anxiety. The implications of this research are that whilst some students with learning difficulties do not experiences high trait test anxiety, they can experience some degree of worry and anxiety. Therefore, it is important to be able to provide universal support to all students in order to provide proactive support to help with their worries regarding exams. Additionally, for some students who do experience high trait test anxiety, a more bespoke approach may be beneficial as it is clear there are many factors which can be associated with higher levels of anxiety

    Towards a Burning Method: how might the contemporary performer build on the legacy of Grotowski's Total Act?

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    Towards a Burning Method is a practical-theoretical investigation in the context of the contemporary theatre practice of Jerzy Grotowski's ideas, particularly the Total Act from the Theatre of Productions period. The study employs a practice of confrontation with Grotowski's ideas rather than identification, and text and practice are inseparable, interpenetrating each other. The method of confrontation (which is one of Grotowski's ideas) means, in the context of my research, a dialogue which through its dynamics creates a performative situation, stimulates the deepening of knowledge, and seeks its own answers. The resulting performance Burning Method - Four Lectures on Conditional Love is also integral to the theoretical reflections, both representing and containing Grotowski's ideas and my response to them. Other practical activities include video notes of process, classes with students and workshops. Oneof the important results of the research is to broaden the understanding of what performance is, what kind of forms it takes in relation to the Total Act. The thesis consists of four chapters. Chapter One introduces the cultural-historical context of the emergence of the Total Act, while the following chapters are a guide to understanding the Act in practice. The final chapter is entirely devoted to my practical activities through a dialogue on contemporary performance, its lineage of origin and projective reflections for the future. A reference point or dialogue partner on contemporary theatre is mainly Grotowski's book Towards a Poor Theatre but also Artaud's The Theatre and its Double. The research has helped me understand what performance work might result from thecontinuation of a distinctly masculine Polish Romantic tradition, and what new possibilities emerge from my perspective as a female performer and theatre-maker. In reading Grotowski (and confronting it in practice) I discover the potential for creative freedom, with the rigour of attention to the execution of ideas. At the same time, working with video projections I verify my thinking about the performer's body and space by finding another dimension of expression in the image in relation to the audience. I also believe that actor training from this tradition has no temporal, cultural or other limitations as its essence is the search for the living impulse in the performer's body. Summing up the research is an opening for further explorations revealing the potential and currency of Grotowski's ideas for today
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