192 research outputs found
IceBreaker: Solving Cold Start Problem for Video Recommendation Engines
Internet has brought about a tremendous increase in content of all forms and,
in that, video content constitutes the major backbone of the total content
being published as well as watched. Thus it becomes imperative for video
recommendation engines such as Hulu to look for novel and innovative ways to
recommend the newly added videos to their users. However, the problem with new
videos is that they lack any sort of metadata and user interaction so as to be
able to rate the videos for the consumers. To this effect, this paper
introduces the several techniques we develop for the Content Based Video
Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM
Multimedia Conference 2018. We employ different architectures on the CBVRP
dataset to make use of the provided frame and video level features and generate
predictions of videos that are similar to the other videos. We also implement
several ensemble strategies to explore complementarity between both the types
of provided features. The obtained results are encouraging and will impel the
boundaries of research for multimedia based video recommendation systems
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
In multilingual societies like the Indian subcontinent, use of code-switched
languages is much popular and convenient for the users. In this paper, we study
offense and abuse detection in the code-switched pair of Hindi and English
(i.e. Hinglish), the pair that is the most spoken. The task is made difficult
due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish
language. We apply transfer learning and make a LSTM based model for hate
speech classification. This model surpasses the performance shown by the
current best models to establish itself as the state-of-the-art in the
unexplored domain of Hinglish offensive text classification.We also release our
model and the embeddings trained for research purpose
Touchless Typing using Head Movement-based Gestures
Physical contact-based typing interfaces are not suitable for people with
upper limb disabilities such as Quadriplegia. This paper, thus, proposes a
touch-less typing interface that makes use of an on-screen QWERTY keyboard and
a front-facing smartphone camera mounted on a stand. The keys of the keyboard
are grouped into nine color-coded clusters. Users pointed to the letters that
they wanted to type just by moving their head. The head movements of the users
are recorded by the camera. The recorded gestures are then translated into a
cluster sequence. The translation module is implemented using CNN-RNN, Conv3D,
and a modified GRU based model that uses pre-trained embedding rich in head
pose features. The performances of these models were evaluated under four
different scenarios on a dataset of 2234 video sequences collected from 22
users. The modified GRU-based model outperforms the standard CNN-RNN and Conv3D
models for three of the four scenarios. The results are encouraging and suggest
promising directions for future research.Comment: *The two lead authors contributed equally. The dataset and code are
available upon request. Please contact the last autho
Exploring Graph Neural Networks for Indian Legal Judgment Prediction
The burdensome impact of a skewed judges-to-cases ratio on the judicial
system manifests in an overwhelming backlog of pending cases alongside an
ongoing influx of new ones. To tackle this issue and expedite the judicial
process, the proposition of an automated system capable of suggesting case
outcomes based on factual evidence and precedent from past cases gains
significance. This research paper centres on developing a graph neural
network-based model to address the Legal Judgment Prediction (LJP) problem,
recognizing the intrinsic graph structure of judicial cases and making it a
binary node classification problem. We explored various embeddings as model
features, while nodes such as time nodes and judicial acts were added and
pruned to evaluate the model's performance. The study is done while considering
the ethical dimension of fairness in these predictions, considering gender and
name biases. A link prediction task is also conducted to assess the model's
proficiency in anticipating connections between two specified nodes. By
harnessing the capabilities of graph neural networks and incorporating fairness
analyses, this research aims to contribute insights towards streamlining the
adjudication process, enhancing judicial efficiency, and fostering a more
equitable legal landscape, ultimately alleviating the strain imposed by
mounting case backlogs. Our best-performing model with XLNet pre-trained
embeddings as its features gives the macro F1 score of 75% for the LJP task.
For link prediction, the same set of features is the best performing giving ROC
of more than 80
Characterization of polyphenols and mineral contents in three medicinal weeds
Aims: Common weeds Rorippa palustris (L.) Besser, Euphorbia rothiana Spreng. and Schoenoplectiella articulata (L.) Lye are used for food, medicinal, green biofertilizer and biosorbent applications. In this work, their polyphenol and mineral contents have been characterized. Methodology: Samples from aforementioned three plants were manually collected in Raipur city (CG, India) and processed for the analyses. Folin-Ciocalteu and aluminum chloride were used for the spectrophotometric determination of polyphenols. The mineral contents were quantified by X-ray fluorescence. Results: The total concentration of 20 elements (viz. P, S, Cl, As, Se, K, Rb, Mg, Ca, Sr, Ba, Al, Ti, Cr, Mn, Fe, Co, Zn, Mo and Pb), total polyphenol and flavonoid contents in the leaves ranged from 46372 to 71501, from 47877 to 73791 and from 1950 to 9400 mg/kg, respectively. Remarkable concentrations of several nutrients (P, S, Cl, K, Mg, Ca and Fe) were observed. Conclusion: The biomass from medicinal weeds R. palustris, E. rothiana and S. articulata featured very high K, Ca and Fe contents. Other nutrients (polyphenols, flavonoids, P, S, Cl and Mg) were identified at moderate levels. These species may hold promise as bioindicators
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