65 research outputs found
SYSTEM FOR RECOMMENDING CHAPTER SUMMARIES AND RELATED CONTENTS BASED ON USER INTEREST LEVEL
This application discloses a system and method for recommending chapter summaries and related content based on user\u27s interest level while reading electronic books. The system includes an application residing on an electronic tablet, mobile telephone or any other device used for reading. The system automatically determines if a chapter of the book is uninteresting to a user and saves time by showing summaries and additional content relevant to the chapter of the book
Advanced Dialer Based On Target Contact\u27s Activity
An advanced dialer system prioritizes modes of communication based on a target contact’s activity. The system, upon authorization by the user or target contact to access the user’s contacts, receives a selection of a target contact and checks the target contact’s activity status on all possible communication platforms. These communication platforms may include messaging applications using internet connectivity, traditional voice calling applications, video chat applications, etc. The system may further check the target contact’s network connectivity and feasibility of connection. The advanced dialer system calculates feasibility score for different communication platforms to reach the target contact. The system also ranks these communication platforms based on the calculated feasibility scores. The user may select the communication platform with the highest score/highest rank and communicate with the target contact. If the target contact is not reachable, the user may select the next highest ranked communication platform. The system automatically displays more likely dialing modes to the user, enabling easier communication
Robust Few-shot Learning Without Using any Adversarial Samples
The high cost of acquiring and annotating samples has made the `few-shot'
learning problem of prime importance. Existing works mainly focus on improving
performance on clean data and overlook robustness concerns on the data
perturbed with adversarial noise. Recently, a few efforts have been made to
combine the few-shot problem with the robustness objective using sophisticated
Meta-Learning techniques. These methods rely on the generation of adversarial
samples in every episode of training, which further adds a computational
burden. To avoid such time-consuming and complicated procedures, we propose a
simple but effective alternative that does not require any adversarial samples.
Inspired by the cognitive decision-making process in humans, we enforce
high-level feature matching between the base class data and their corresponding
low-frequency samples in the pretraining stage via self distillation. The model
is then fine-tuned on the samples of novel classes where we additionally
improve the discriminability of low-frequency query set features via cosine
similarity. On a 1-shot setting of the CIFAR-FS dataset, our method yields a
massive improvement of & in adversarial accuracy on the PGD
and state-of-the-art Auto Attack, respectively, with a minor drop in clean
accuracy compared to the baseline. Moreover, our method only takes
of the standard training time while being faster than
state-of-the-art adversarial meta-learning methods. The code is available at
https://github.com/vcl-iisc/robust-few-shot-learning.Comment: TNNLS Submission (Under Review
Artificial Neural Network Modeling for Adsorption Efficiency of Cr(VI) Ion from Aqueous Solution Using Waste Tire Activated Carbon
In this study, waste tires were used to develop activated carbon for the adsorption of Cr(VI) from aqueous solutions, and an artificial neural network (ANN) model was applied to predict the adsorption efficiency of waste-tire activated carbon (WTAC). SEM and FTIR were used to characterize the developed WTAC. A three-layer ANN with different training algorithms and hidden layers with different numbers of neurons was developed using 79 data sets gathered from batch adsorption experiments with different initial Cr(VI) ion concentrations, contact periods, temperatures, and doses. Conjugate gradient backpropagation of Powell-Beale restarts (traincgb) was found to be the best training algorithm among all the training algorithms, with an RMSE of 5.894 and an R2 of 0.985. The ANN topology had 4, 8, and 4 neurons in the input, hidden, and output layers. The correlation coefficient of the ANN models of Cr(VI) ion adsorption efficiency is 0.977
DAD++: Improved Data-free Test Time Adversarial Defense
With the increasing deployment of deep neural networks in safety-critical
applications such as self-driving cars, medical imaging, anomaly detection,
etc., adversarial robustness has become a crucial concern in the reliability of
these networks in real-world scenarios. A plethora of works based on
adversarial training and regularization-based techniques have been proposed to
make these deep networks robust against adversarial attacks. However, these
methods require either retraining models or training them from scratch, making
them infeasible to defend pre-trained models when access to training data is
restricted. To address this problem, we propose a test time Data-free
Adversarial Defense (DAD) containing detection and correction frameworks.
Moreover, to further improve the efficacy of the correction framework in cases
when the detector is under-confident, we propose a soft-detection scheme
(dubbed as "DAD++"). We conduct a wide range of experiments and ablations on
several datasets and network architectures to show the efficacy of our proposed
approach. Furthermore, we demonstrate the applicability of our approach in
imparting adversarial defense at test time under data-free (or data-efficient)
applications/setups, such as Data-free Knowledge Distillation and Source-free
Unsupervised Domain Adaptation, as well as Semi-supervised classification
frameworks. We observe that in all the experiments and applications, our DAD++
gives an impressive performance against various adversarial attacks with a
minimal drop in clean accuracy. The source code is available at:
https://github.com/vcl-iisc/Improved-Data-free-Test-Time-Adversarial-DefenseComment: IJCV Journal (Under Review
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