8,558 research outputs found

    Measuring the Impact of Public Display Advertising in Smart Cities: An Advertising Effectiveness Test

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    The average age of the participants in this research, which evaluated the effects of public display advertising in smart cities, was found to be 31.2 years, with a gender distribution that is balanced. When compared to a prior review, exposure and memory rates showed a 5% improvement in recall rates and a 12% increase in exposure length, suggesting increased advertising effectiveness and reach. Purchase intent increased by 11.8% and interaction levels improved by 10%, according to consumer engagement ratings. In addition, post-exposure attitudes demonstrated a 2.7% improvement in relevance and a 5.4% rise in likeability, highlighting a favorable opinion of public display advertising. These results contribute to the disciplines of urban informatics and advertising effectiveness by providing insightful information on the changing role of public display advertising in the setting of smart cities

    Applications of Deep Learning Models in Financial Forecasting

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    In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting. The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data. The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided

    A reinforcement learning recommender system using bi-clustering and Markov Decision Process

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    Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

    Technology and Contemporary Classical Music: Methodologies in Practice-Based Research

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    This position paper provides a distillation of the NCRM Innovation Forum, ‘Technology and Contemporary Classical Music: Methodologies in Creative Practice Research’, hosted by Cyborg Soloists in June 2023. It features contributions from a variety of creative practitioner-researchers to debate the current state and future of technologically focused, practice-based research in contemporary classical music. The position paper is purposefully polyphonic and pluralistic. By collating a range of perspectives, experiences and expertise, the paper seeks to provoke and delineate a space for further questioning, inquiry, and response. The paper will be of interest to those working within creative practice research, particularly in relation to music, music technologists and those interested in research methodologies more broadly

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Learning analytics for enhanced professional capital development: a systematic review

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    Background/MotivationThis article presents a systematic review aimed at examining the utilization of learning analytics (LA) to enhance teachers’ professional capital.AimThe study focuses on three primary research questions: (1) exploring the characteristics and approaches of LA in professional capital, (2) investigating suggestions from LA for assessing and improving professional capital, and (3) examining variables studied in enhancing the most intricate dimension of professional capital using LA.MethodologyTo address the research objectives, a systematic review was conducted focusing on the key concepts “learning analytics” and “professional capital.” Following the procedures outlined encompassed in four stages: identification, screening, inclusion, and adequacy. The PRISMA 2009 protocol guided the systematic review process.Principal findingsThe findings of the study underscore the efficacy of LA as a catalyst for improving professional capital, particularly through collaborative learning and the utilization of tools like forums and online learning platforms. Social capital emerges as a pivotal component in integrating diverse types of professional capital, fostering opportunities for knowledge creation and social networking.Conclusion/SignificanceIn conclusion, the study highlights the paramount significance of addressing teachers’ professional capital development through collaborative approaches and leveraging technology, particularly in primary education. The article concludes by emphasizing the imperative for more research and knowledge dissemination in this field, aiming to ensure equity in learning and address the challenges posed by the COVID−19 pandemic

    Explainable Artificial Intelligence for Interpretable Data Minimization

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    Black box models such as deep neural networks are increasingly being deployed in high-stakes fields, including justice, health, and finance. Furthermore, they require a huge amount of data, and such data often contains personal information. However, the principle of data minimization in the European Union’s General Data Protection Regulation requires collecting only the data that is essential to fulfilling a particular purpose. Implementing data minimization for black box models can be difficult because it involves identifying the minimum set of variables that are relevant to the model’s prediction, which may not be apparent without access to the model’s inner workings. In addition, users are often reluctant to share all their personal information. We propose an interactive system to reduce the amount of personal data by determining the minimal set of features required for a correct prediction using explainable artificial intelligence techniques. Our proposed method can inform the user whether the provided variables contain enough information for the model to make accurate predictions or if additional variables are necessary. This humancentered approach can enable providers to minimize the amount of personal data collected for analysis and may increase the user’s trust and acceptance of the system

    Implementing precision methods in personalizing psychological therapies: barriers and possible ways forward

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    This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: No data was used for the research described in the article.Highlights: • Personalizing psychological treatments means to customize treatment for individuals to enhance outcomes. • The application of precision methods to clinical psychology has led to data-driven psychological therapies. • Applying data-informed psychological therapies involves clinical, technical, statistical, and contextual aspects

    Differentiated instruction based on multiple intelligences as promising joyful and meaningful learning

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    Boredom has previously been linked to negative academic outcomes such as academic motivation, strategies, and achievement. The understanding of multiple intelligence approaches creates opportunities for all learners to develop their potential to optimize learning through differentiated instructions. This research aims: i) to design and to develop differentiated instruction based on learners’ multiple intelligences for elementary schools; and ii) to measure the effectivity of students’ learning attitude and mastery of concepts. Using design and development research (DDR) anchored on analysis, development, design, implementation, and evaluation (ADDIE) model and quasi-experimental research, the differentiated instruction based on multiple intelligence was conducted in a private Islamic Elementary School in collaboration with 3 class teachers. Based on the results of observations conducted at the school, the findings revealed that the learners can maximize their logical-mathematical, language, kinesthetic, interpersonal, and intrapersonal intelligences through a differentiated instruction based on multiple intelligence approach. Students experienced a joyful and meaningful learning atmosphere; hence it was expected that their intelligences can be developed naturally. In addition. this instruction was found to be effective to enhance science concept mastery especially in the aspects of remembering, understanding, and applying. The differentiated instruction based on multiple intelligences should be developed further to examine the effectiveness of the model in thematic learning for students both with low and high achievement
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