Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Building a chatbot with an iterative development process poses certain challenges for the chatbot developer. The developer is expected to produce a deployable version of the chatbot at the end of a short development cycle. Every iteration should incrementally increase the capability of the chatbot and implement a subset of overall user stories based upon a priority list, similar to any other project developed using iterative development. In this regard, commercial chatbot-building platforms offer multiple advantages to the chatbot developer, provided that the developer can map these user stories in a particular form. To do so, for every query the chatbot is expected to answer, the developer must evaluate the intention of the user. Based on the intention, the query must be processed differently, which may involve execution of some business logic. In addition, the processing of the query may require specific data items which the user must supply as part of the conversation with the chatbot. Thus, the chatbot is defined by supplying a "context"that it may encounter, and the "reaction"that must take place when the context is observed. In this work, we discuss the effects of using a platform to build a chatbot and discuss the Contextual Reactive pattern used for chatbot definition. © 2020 ACM.

World University root node

ACM International Conference Proceeding Series

Contextual Reactive Pattern on Chatbot building Platforms

Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination. © 2020 ACM.

COMPASS 2020 - Proceedings of the 2020 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

RoadCare: A Deep-learning Based Approach to Quantifying Road Surface Quality

Dropout prevents overfitting in deep neural networks. Typical strategy of dropout involves random termination of connections irrespective of their importance. Termination blocks the propagation of class discriminative information across the network. As a result, dropout may lead to inferior performance. We propose a deterministic dropout where only unimportant connections are dropped ensuring propagation of class discriminative information. We identify the unimportant connections using a novel composite random forest, integrated into the network. We prove that better generalization is achieved by terminating these unimportant connections. The proposed algorithm is useful in preventing overfitting in noisy datasets. The proposal is equally good for datasets with smaller number of training examples. Experiments on several benchmark datasets show up to 8% improvement in classification accuracy. © 2020 Elsevier B.V.

Pattern Recognition Letters

Deterministic dropout for deep neural networks using composite random forest

In the standard model of committee selection we are given a set of ordinal votes over a set of candidates and a desired committee size, and the task is to select a committee that relates to the given votes. Motivated by possible interactions and dependencies between candidates, we study a generalization of committee selection in which the candidates are connected via a network and the task is to select a committee that also satisfies certain properties with respect to this candidate network. To accommodate few possibilities of taking voter preferences into account, we consider three standard and diverse voting rules (namely, k-Borda, Chamberlin-Courant, and Gehrlein stability); to model different aspects of interactions and dependencies between candidates, we consider two graph properties (namely, Independent Set and Connectivity). We study the parameterized complexity of the corresponding combinatorial problems and discuss certain implications of our algorithmic results. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

IJCAI International Joint Conference on Artificial Intelligence

Well-structured committees

Skeletal Bone age assessment is one of the routine radiological procedures performed by paediatricians and endocrinologists for investigating genetic disorders, developmental abnormalities and metabolic complications. In this process skeletal age is compared against child's chronological age to uncover discrepancies if any. Hand radiographs being the cheapest, reliable and widely used modality, are used to predict the bone age in children from 1-18 years of age. Conventional methods make use of atlases to predict the age which are time consuming, tedious and have problems of inter-observer variability. We propose an end to end approach which uses inception trained from scratch, achieves 80% accuracy in predicting age within 1 year from the ground truth. Further, attention maps are generated to explain what regions of the image, the model is focusing on while assessing the bone age and the heat maps thus generated match the features used by the radiologists while predicting manually. © 2020 IEEE.

2020 IEEE 17th India Council International Conference, INDICON 2020

Unboxing the blackbox - Visualizing the model on hand radiographs in skeletal bone age assessment

Labelled data are not only time consuming but often expensive and difficult to procure as it involves skilful inputs by humans to tag and annotate. Contrary to this unlabelled data is comparatively easier to procure but fewer methods exist to optimally use them. Semi-Supervised Learning overcomes this problem and assists to build better classifiers by using unlabelled data along with sufficient labelled data and may actually yield higher accuracy with considerably less human input effort. But if the labelled data set is inadequate in size then the Semi-Supervised techniques are also stuck. We propose a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model. The model retrains with the unlabelled data to fortify the learning mechanism. We show that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment. © 2020, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Integrated Semi-Supervised Model for Learning and Classification

The spread of fake news poses a critical problem in today's world, where most individuals consume information from online platforms. Fake news detection is an arduous task, marred by the lack of a robust ground truth database for training classification models. Fake news articles manipulate multimedia content (text and images) to disseminate false information. Existing fake news datasets are either small in size or predominantly contain unimodal data. We propose two novel benchmark multimodal datasets, consisting of text and images, to enhance the quality of fake news detection. The first dataset includes manually collected real and fake news data from multiple online sources. In the second dataset, we study the effect of data augmentation by using a Bag of Words approach to increase the quantity of fake news data. These datasets are significantly larger in size in comparison to the existing datasets. We conducted extensive experiments by training state of the art unimodal and multimodal fake news detection algorithms on our dataset and comparing it with the results on existing datasets, showing the effectiveness of our proposed datasets. The experimental results show that data augmentation to increase the quantity of fake news does not hamper the accuracy of fake news detection. The results also conclude that the utilization of multimodal data for fake news detection substantially outperforms the unimodal algorithms. © 2020 for this paper by its authors.

CEUR Workshop Proceedings

NewsBag: A multimodal benchmark dataset for fake news detection

Research in face recognition has evolved over the past few decades. With initial research focusing heavily on constrained images, recent research has focused more on unconstrained images captured in-the-wild settings. Faces captured in unconstrained settings with disguise accessories persist to be a challenge for automated face verification. To this effect, this research proposes a novel deep learning framework for disguised face verification. A novel Inverse Disguise Quality metric is proposed for evaluating amount of disguise in the input image, which is utilized in likelihood ratio as a quality score for enhanced verification performance. The proposed framework is model-agnostic and can be applied in conjunction with existing state-of-the-art face verification models for obtaining improved performance. Experiments have been performed on the Disguised Faces in Wild (DFW) 2018 and DFW 2019 datasets, with three state-of-the-art deep learning models, where it demonstrates substantial improvement compared to the base model. © 2020, Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Disguised Face Verification Using Inverse Disguise Quality

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/ tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models. © 2020, Association for the Advancement of Artificial Intelligence.

AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

On the robustness of face recognition algorithms against attacks and bias

Satellite communication applications use turbo codes as the standard error correcting code due to its Shannon's capacity approaching performance. However, short block length turbo codes exhibit significant performance degradation. This is a limiting factor in applications like transmissions over the telecommand links for satellite communications that involve the use of short length turbo codes. A novel reliability-based turbo decoding algorithm that addresses the performance improvement of short block length turbo codes, is being proposed in this paper. Simulation results show a coding gain of 2.45 dB at BER of 10-3 for short length codewords. The proposed decoding algorithm has low computational complexity compared to the conventional iterative decoding algorithm. The relatively lower computational complexity and the conspicuous improvement in BER performance make the method quite attractive. © 2020 Inderscience Enterprises Ltd.