2,900 research outputs found
Movies Tags Extraction Using Deep Learning
Retrieving information from movies is becoming increasingly
demanding due to the enormous amount of multimedia
data generated each day. Not only it helps in efficient
search, archiving and classification of movies, but is also instrumental
in content censorship and recommendation systems.
Extracting key information from a movie and summarizing
it in a few tags which best describe the movie presents
a dedicated challenge and requires an intelligent approach
to automatically analyze the movie. In this paper, we formulate
movies tags extraction problem as a machine learning
classification problem and train a Convolution Neural Network
(CNN) on a carefully constructed tag vocabulary. Our
proposed technique first extracts key frames from a movie
and applies the trained classifier on the key frames. The
predictions from the classifier are assigned scores and are
filtered based on their relative strengths to generate a compact
set of most relevant key tags. We performed a rigorous
subjective evaluation of our proposed technique for a
wide variety of movies with different experiments. The evaluation
results presented in this paper demonstrate that our
proposed approach can efficiently extract the key tags of a
movie with a good accuracy
"Tap it again, Sam": Harmonizing the frontiers between digital and real worlds in education
Lifelong leaners are intrinsically motivated to embed learning activities into daily life activities. Finding a suitable combination of the two is not trivial since lifelong learners have to face conflicts of time and location. Hence, lifelong learners normally build personal learning ecologies in those moments they set aside to learn making use of their available resources. On the other hand, the advent of Near Field Communication (NFC) technology facilitates the harmonization in the interactions between the digital world and daily physical spaces. Likewise, NFC enabled phones are becoming more and more popular. The contribution of this manuscript is threefold: first, scientific literature where NFC has been used with a direct or indirect purpose to learn is reviewed, and potential uses for lifelong learners are identified; based on these findings the Ecology of Resources for Lifelong Learning is presented as suitable setup for the scaffolding of learning activities with NFC augmented physical spaces; finally, this ecology is piloted and different learning scenarios are proposed for further extension
Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTube
With the 2022 US midterm elections approaching, conspiratorial claims about
the 2020 presidential elections continue to threaten users' trust in the
electoral process. To regulate election misinformation, YouTube introduced
policies to remove such content from its searches and recommendations. In this
paper, we conduct a 9-day crowd-sourced audit on YouTube to assess the extent
of enactment of such policies. We recruited 99 users who installed a browser
extension that enabled us to collect up-next recommendation trails and search
results for 45 videos and 88 search queries about the 2020 elections. We find
that YouTube's search results, irrespective of search query bias, contain more
videos that oppose rather than support election misinformation. However,
watching misinformative election videos still lead users to a small number of
misinformative videos in the up-next trails. Our results imply that while
YouTube largely seems successful in regulating election misinformation, there
is still room for improvement.Comment: 22 page
Big Data for Traffic Monitoring and Management
The last two decades witnessed tremendous advances in the Information and
Communications Technologies. Beside improvements in computational power and
storage capacity, communication networks carry nowadays an amount of data which
was not envisaged only few years ago. Together with their pervasiveness,
network complexity increased at the same pace, leaving operators and
researchers with few instruments to understand what happens in the networks,
and, on the global scale, on the Internet. Fortunately, recent advances in data
science and machine learning come to the rescue of network analysts, and allow
analyses with a level of complexity and spatial/temporal scope not possible
only 10 years ago. In my thesis, I take the perspective of an Internet Service
Provider (ISP), and illustrate challenges and possibilities of analyzing the
traffic coming from modern operational networks. I make use of big data and
machine learning algorithms, and apply them to datasets coming from passive
measurements of ISP and University Campus networks. The marriage between data
science and network measurements is complicated by the complexity of machine
learning algorithms, and by the intrinsic multi-dimensionality and variability
of this kind of data. As such, my work proposes and evaluates novel techniques,
inspired from popular machine learning approaches, but carefully tailored to
operate with network traffic
Movie Tags Prediction and Segmentation Using Deep Learning
The sheer volume of movies generated these days requires an automated analytics for ef cient
classi cation, query-based search, and extraction of desired information. These tasks can only be ef ciently
performed by a machine learning based algorithm. We address the same issue in this paper by proposing a
deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with
respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to
train a deep learning model. Subsequently, we propose an ef cient shot detection algorithm to nd the key
frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top
three tags for each frame. The tags are then assigned weighted scores and are ltered to generate a compact
set of most relevant tags. This process also generates a corpus which is further used to segment a movie based
on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of
tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not
only ef cacious in predicting the most relevant tags for a movie, but also in segmenting the movie with
respect to the selected tags with a high accuracy
- …