The objective of this study was to use interval-level metrics to code a random sample of body worn camera footage from a large (N similar to 700) municipal police department in 2019. Just over 1,100 videos were coded ...
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The objective of this study was to use interval-level metrics to code a random sample of body worn camera footage from a large (N similar to 700) municipal police department in 2019. Just over 1,100 videos were coded for (1) community member factors;(2) officer behaviors-including an overall "performance" score;and (3) encounter outcomes. Our goal was to answer the following: Do police receive higher overall performance scores when interacting with some types of community members compared to others? Which community member factors significantly predict specific officer behaviors? Which community member factors significantly predict encounter outcomes? We found that officers received higher performance scores when interacting with women, and with community members with mental illness. We found that socio-economic-status and gender were the most common predictors of officer behaviors, while race and ethnicity, socio-economic-status, gender, and armed status predicted encounter outcomes. The policy implications of these findings are discussed.
While data science education has gained increased recognition in both academic institutions and industry, there has been a lack of research on automated coding assessment for novice students. Our work presents a first...
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ISBN:
(纸本)9781450389358
While data science education has gained increased recognition in both academic institutions and industry, there has been a lack of research on automated coding assessment for novice students. Our work presents a first step in this direction, by leveraging the coding metrics from traditional software engineering (Halstead Volume and Cyclomatic Complexity) in combination with those that reflect a data science project's learning objectives (number of library calls and number of common library calls with the solution code). Through these metrics, we examined the code submissions of 97 students across two semesters of an introductory data science course. Our results indicated that the metrics can identify cases where students had overly complicated codes and would benefit from scaffolding feedback. The number of library calls, in particular, was also a significant predictor of changes in submission score and submission runtime, which highlights the distinctive nature of data science programming. We conclude with suggestions for extending our analyses towards more actionable intervention strategies, for example by tracking the fine-grained submission grading outputs throughout a student's submission history, to better model and support them in their data science learning process.
Network coding consists of intelligently aggregating data packets by means of binary or linear combinations. Recently, network coding has been proposed as a complementary solution for energy efficient multi-hop routin...
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Network coding consists of intelligently aggregating data packets by means of binary or linear combinations. Recently, network coding has been proposed as a complementary solution for energy efficient multi-hop routing in Wireless Sensor Networks (WSNs). This is because network coding, through the aggregation of packets, considerably reduces the number of transmissions throughout the network. Although numerous network coding techniques for energy efficient routing have been developed in the literature, not much is known about a single survey article reporting on such energy efficient network coding within multi-hop WSNs. As a result, this paper addresses this gap by first classifying and discussing the recent developed energy efficient network coding techniques. The paper then identifies and explains open research opportunities based on analysis of merits of such techniques. This survey aims at providing the reader with a brief and concise idea on the current state-of-art research on network coding mainly focusing on its applications for energy efficient WSNs. (C) 2016 The Authors. Published by Elsevier B.V.
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