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"Sarcasm Detection using Deep Learning with Contextual Features" by Md Saifullah Razali, Alfian Abdul Halin et al.

Abstract
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the ....

Convolutional Neural Network , Deep Learning , Deep Learning , Feature Extraction , Natural Language Processing , Sarcasm Detection , Ocial Networking Online , Task Analysis , ஆழமான கற்றல் , கேளுங்கள் பகுப்பாய்வு ,

"Learning to Charge RF-Energy Harvesting Devices in WiFi Networks" by Yizhou Luo and Kwan Wu Chin

Future WiFi networks will be powered by renewable sources. They will also have radio frequency (RF)-energy harvesting devices. In these networks, a solar-powered access point (AP) will be tasked with supporting both nonenergy harvesting or legacy data users such as laptops, and RF-energy harvesting sensor devices. A key issue is ensuring the AP uses its harvested energy efficiently. To this end, this article contributes two novel solutions that allow the AP to control its transmit power to meet the data rate requirement of legacy users and also to ensure RF-energy devices harvest sufficient energy to transmit their sensed data. Advantageously, these solutions can be deployed in current wireless networks, and they do not require perfect channel gain information to sensor devices or noncausal energy arrivals at an AP. The first solution uses a deep Q-network (DQN) whilst the second solution uses model predictive control (MPC) to manage the AP’s transmit power subject to its available e ....

Receding Horizon Control , Einforcement Learning Rl , Signal To Noise Ratio , Task Analysis , Wireless Fidelity , கேளுங்கள் பகுப்பாய்வு ,

"SA-LuT-Nets: Learning Sample-adaptive Intensity Lookup Tables for Brai" by Biting Yu, Luping Zhou et al.

Abstract
In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity lookup table (LuT) that dynamically transforms the intensity contrast of each input MR image to adapt to the following segmentation task. Specifically, the proposed deep SA-LuT-Net framework consists of a LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific nonlinear intensity mapping function through communication with the segmentat ....

Image Segmentation , Magnetic Resonance Imaging Mri , Neural Network , Solid Modeling , Able Lookup , Task Analysis , Hree Dimensional Displays , கேளுங்கள் பகுப்பாய்வு ,

"Learning Graph Convolutional Networks for Multi-Label Recognition and " by Zhaomin Chen, Xiu Shen Wei et al.

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label- ....

Graph Convolutional Networks , Computational Modeling , Convolutional Neural Networks , Face Recognition , Graph Convolutional Networks , Image Recognition , Abel Dependency , Ulti Label Recognition , Task Analysis , கணக்கீட்டு மாடலிங் , கேளுங்கள் பகுப்பாய்வு ,

"Relation Regularized Scene Graph Generation" by Yuyu Guo, Lianli Gao et al.

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph convolution networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on ....

Feature Extraction , Raph Convolution Networks Gcns , Cene Graph Generation Sgg , Task Analysis , Isual Relationship , கேளுங்கள் பகுப்பாய்வு ,