4,172 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Multidisciplinary perspectives on Artificial Intelligence and the law
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
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution
Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners
Neural Machine Translation with Dynamic Graph Convolutional Decoder
Existing wisdom demonstrates the significance of syntactic knowledge for the
improvement of neural machine translation models. However, most previous works
merely focus on leveraging the source syntax in the well-known encoder-decoder
framework. In sharp contrast, this paper proposes an end-to-end translation
architecture from the (graph \& sequence) structural inputs to the (graph \&
sequence) outputs, where the target translation and its corresponding syntactic
graph are jointly modeled and generated. We propose a customized Dynamic
Spatial-Temporal Graph Convolutional Decoder (Dyn-STGCD), which is designed for
consuming source feature representations and their syntactic graph, and
auto-regressively generating the target syntactic graph and tokens
simultaneously. We conduct extensive experiments on five widely acknowledged
translation benchmarks, verifying that our proposal achieves consistent
improvements over baselines and other syntax-aware variants
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting
Encoder-decoder deep neural networks have been increasingly studied for
multi-horizon time series forecasting, especially in real-world applications.
However, to forecast accurately, these sophisticated models typically rely on a
large number of time series examples with substantial history. A rapidly
growing topic of interest is forecasting time series which lack sufficient
historical data -- often referred to as the ``cold start'' problem. In this
paper, we introduce a novel yet simple method to address this problem by
leveraging graph neural networks (GNNs) as a data augmentation for enhancing
the encoder used by such forecasters. These GNN-based features can capture
complex inter-series relationships, and their generation process can be
optimized end-to-end with the forecasting task. We show that our architecture
can use either data-driven or domain knowledge-defined graphs, scaling to
incorporate information from multiple very large graphs with millions of nodes.
In our target application of demand forecasting for a large e-commerce
retailer, we demonstrate on both a small dataset of 100K products and a large
dataset with over 2 million products that our method improves overall
performance over competitive baseline models. More importantly, we show that it
brings substantially more gains to ``cold start'' products such as those newly
launched or recently out-of-stock
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