1,777 research outputs found
TED Talk Recommender Using Speech Transcripts
Nowadays, online video platforms mostly recommend related videos by analyzing
user-driven data such as viewing patterns, rather than the content of the
videos. However, content is more important than any other element when videos
aim to deliver knowledge. Therefore, we have developed a web application which
recommends related TED lecture videos to the users, considering the content of
the videos from the transcripts. TED Talk Recommender constructs a network for
recommending videos that are similar content-wise and providing a user
interface.Comment: 3 page
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Incentives for Plug-in Electric Vehicles Are Becoming More Important Over Time for Consumers
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Factors Affecting Demand for Plug-in Charging Infrastructure: An Analysis of Plug-in Electric Vehicle Commuters
The public sector and the private sector, which includes automakers and charging network companies, are increasingly investing in building charging infrastructure to encourage the adoption and use of plug-in electric vehicles (PEVs) and to ensure that current facilities are not congested. However, building infrastructure is costly and, as with road congestion, when there is significant uptake of PEVs, we may not be able to “build out of congestion.” We modelled the choice of charging location that more than 3000 PEV drivers make when given the options of home, work, and public locations. Our study focused on understanding the importance of factors driving demand such as: the cost of charging, driver characteristics, access to charging infrastructure, and vehicle characteristics. We found that differences in the cost of charging play an important role in the demand for charging location. PEV drivers tend to substitute workplace charging for home charging when they pay a higher electricity rate at home, more so when the former is free. Additionally, socio-demographic factors like dwelling type and gender, as well as vehicle technology factors like electric range, influence the choice of charging location
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
Data-centric AI is at the center of a fundamental shift in software
engineering where machine learning becomes the new software, powered by big
data and computing infrastructure. Here software engineering needs to be
re-thought where data becomes a first-class citizen on par with code. One
striking observation is that a significant portion of the machine learning
process is spent on data preparation. Without good data, even the best machine
learning algorithms cannot perform well. As a result, data-centric AI practices
are now becoming mainstream. Unfortunately, many datasets in the real world are
small, dirty, biased, and even poisoned. In this survey, we study the research
landscape for data collection and data quality primarily for deep learning
applications. Data collection is important because there is lesser need for
feature engineering for recent deep learning approaches, but instead more need
for large amounts of data. For data quality, we study data validation,
cleaning, and integration techniques. Even if the data cannot be fully cleaned,
we can still cope with imperfect data during model training using robust model
training techniques. In addition, while bias and fairness have been less
studied in traditional data management research, these issues become essential
topics in modern machine learning applications. We thus study fairness measures
and unfairness mitigation techniques that can be applied before, during, or
after model training. We believe that the data management community is well
poised to solve these problems
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