3,264 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    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

    Fairness-aware Machine Learning in Educational Data Mining

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    Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E

    "It's not a career": Platform work among young people aged 16-19

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    In the online gig economy, or platform work as it is sometimes known, work can be organised through websites and smartphone apps. People can drive for Uber or Deliveroo, sell items on eBay or Etsy, or rent their properties on Airbnb. This research examines the views of young people between the ages of 16 and 19 in the United Kingdom to see whether they knew about the online gig economy, whether they were using it already to earn money, and whether they expected to use it for their careers. It discovers careers professionals’ levels of knowledge, and their ability (and desire) to include the gig economy in their professional practice. This research contributes to discussions about what constitutes decent work, and whether it can be found within the online gig economy. The results point to ways in which careers practice could include platform work as a means of extending young people’s knowledge about alternative forms of work. This study also makes a theoretical contribution to literature, bringing together elements of careership, cognitive schema theory, and motivational theory and psychology of working theory, in a novel combination, to explain how young people were thinking about platform work in the context of their careers

    Low- and high-resource opinion summarization

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    Customer reviews play a vital role in the online purchasing decisions we make. The reviews express user opinions that are useful for setting realistic expectations and uncovering important details about products. However, some products receive hundreds or even thousands of reviews, making them time-consuming to read. Moreover, many reviews contain uninformative content, such as irrelevant personal experiences. Automatic summarization offers an alternative – short text summaries capturing the essential information expressed in reviews. Automatically produced summaries can reflect overall or particular opinions and be tailored to user preferences. Besides being presented on major e-commerce platforms, home assistants can also vocalize them. This approach can improve user satisfaction by assisting in making faster and better decisions. Modern summarization approaches are based on neural networks, often requiring thousands of annotated samples for training. However, human-written summaries for products are expensive to produce because annotators need to read many reviews. This has led to annotated data scarcity where only a few datasets are available. Data scarcity is the central theme of our works, and we propose a number of approaches to alleviate the problem. The thesis consists of two parts where we discuss low- and high-resource data settings. In the first part, we propose self-supervised learning methods applied to customer reviews and few-shot methods for learning from small annotated datasets. Customer reviews without summaries are available in large quantities, contain a breadth of in-domain specifics, and provide a powerful training signal. We show that reviews can be used for learning summarizers via a self-supervised objective. Further, we address two main challenges associated with learning from small annotated datasets. First, large models rapidly overfit on small datasets leading to poor generalization. Second, it is not possible to learn a wide range of in-domain specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We address the first challenge by explicitly modeling summary properties (e.g., content coverage and sentiment alignment). Furthermore, we leverage small modules – adapters – that are more robust to overfitting. As we show, despite their size, these modules can be used to store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and ‘resolution.’ This task is harder to learn, and we present a few-shot method for training a query-based summarizer on small annotated datasets. In the second part, we focus on the high-resource setting and present a large dataset with summaries collected from various online resources. The dataset has more than 33,000 humanwritten summaries, where each is linked up to thousands of reviews. This, however, makes it challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To address this problem, we propose selecting small subsets of informative reviews. Only these subsets are encoded by the deep encoder and subsequently summarized. We show that the selector and summarizer can be trained end-to-end via amortized inference and policy gradient methods

    A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

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    Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations

    Mapping the Focal Points of WordPress: A Software and Critical Code Analysis

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    Programming languages or code can be examined through numerous analytical lenses. This project is a critical analysis of WordPress, a prevalent web content management system, applying four modes of inquiry. The project draws on theoretical perspectives and areas of study in media, software, platforms, code, language, and power structures. The applied research is based on Critical Code Studies, an interdisciplinary field of study that holds the potential as a theoretical lens and methodological toolkit to understand computational code beyond its function. The project begins with a critical code analysis of WordPress, examining its origins and source code and mapping selected vulnerabilities. An examination of the influence of digital and computational thinking follows this. The work also explores the intersection of code patching and vulnerability management and how code shapes our sense of control, trust, and empathy, ultimately arguing that a rhetorical-cultural lens can be used to better understand code\u27s controlling influence. Recurring themes throughout these analyses and observations are the connections to power and vulnerability in WordPress\u27 code and how cultural, processual, rhetorical, and ethical implications can be expressed through its code, creating a particular worldview. Code\u27s emergent properties help illustrate how human values and practices (e.g., empathy, aesthetics, language, and trust) become encoded in software design and how people perceive the software through its worldview. These connected analyses reveal cultural, processual, and vulnerability focal points and the influence these entanglements have concerning WordPress as code, software, and platform. WordPress is a complex sociotechnical platform worthy of further study, as is the interdisciplinary merging of theoretical perspectives and disciplines to critically examine code. Ultimately, this project helps further enrich the field by introducing focal points in code, examining sociocultural phenomena within the code, and offering techniques to apply critical code methods

    A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

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    In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure
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