1,689 research outputs found
Using Microcomputers and P/G% to Predict court Cases
The purpose of this article is to analyze a microcomputer program that can process a set of (1) prior cases, (2) predictive criteria for distinguishing among the cases, and (3) the relations between each prior case and each criterion in order to arrive at an accurate decision rule. Such a rule will enable all the prior cases to be predicted without inconsistencies, and thereby maximize the likelihood of accurately predicting future cases. To illustrate the program, this article uses five substantive fields, including the predicting of cases dealing with religion in the public schools, legislative redistricting, housing discrimination, international law, and criminal law
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
Consistency in a Partitioned Network: A Survey
Recently, several strategies for transaction processing in partitioned distributed database systems with replicated data have been proposed. We survey these strategies in light of the competing goals of maintaining correctness and achieving high availability. Extensions and combinations are then discussed, and guidelines for the selection of a strategy for a particular application are presented
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings
In this paper, we describe the approach that we employed to address the task
of Entity Recognition over Wet Lab Protocols -- a shared task in EMNLP
WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase,
we experiment with various contextualised word embeddings (like Flair,
BERT-based) and a BiLSTM-CRF model to arrive at the best-performing
architecture. In the second phase, we create an ensemble composed of eleven
BiLSTM-CRF models. The individual models are trained on random train-validation
splits of the complete dataset. Here, we also experiment with different output
merging schemes, including Majority Voting and Structured Learning Ensembling
(SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for
the partial and exact match of the entity spans, respectively. We were ranked
first and second, in terms of partial and exact match, respectively
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