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Found 10 papers, 2 papers with code
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling
Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks.
StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic
In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic.
GCCN: Global Context Convolutional Network
These global contextual features are defined as local maxima pixels with high visual sharpness in each patch.
Signature-Graph Networks
SGN constructs unique undirected graphs for each image based on the CNN feature maps.
MARL: Multimodal Attentional Representation Learning for Disease Prediction
MARL improves the accuracy of state-of-the-art CNN models with a range of 19% to 57%.
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems
Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics.
Drone-as-a-Service Composition Under Uncertainty
Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts.
flexgrid2vec: Learning Efficient Visual Representations Vectors
We compare the performance of flexgrid2vec with a set of state-of-the-art visual representation learning models on binary and multi-class image classification tasks.
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to $9\%$ and decreased the centre location error by $162$ pixels on average.
OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model
While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language.
