Abstract

To perform network forensic analysis on an incident in software defined networking (SDN), logs are of the utmost importance. Without any logs, an investigator is not be able to complete and justify the forensic analysis. With the advancement of network and communication technologies, the volume of logs that needs to be collected and retained is growing exponentially big. The network administrators face the problems in maintaining such huge logs for longer period of time for forensic analysis. Network Providers rely on purging data based on a certain stipulated duration expected by the local rules for forensics evidence. SDN providers have limitations in managing such big data since the expense involved is commensurate with the duration of data retention. Here, in this work we propose a novel idea to reduce and summarize large forensic data sets for faster querying as well as reducing its space complexity by using bloom filters. Through this work we aim to propose a system which can deliver more optimized forensic data availability in SDN platform compared to existing systems.

Abstract

Deep Learning on face recognition problems has shown extremely high accuracy owing to their ability in finding strongly discriminating features. However, face images in the wild show variations in pose, lighting, expressions, and the presence of facial attributes (for example eyeglasses). We ask, why then are these vari- ations not detected and used during the matching process? We demonstrate that this is indeed possible while restricting ourselves to facial attribute variation, to prove the case in point. We show two ways of doing so. a) By using the face attribute labels as a form of prior, we bin the matching template pairs into three bins depend- ing on whether each template of the matching pair possesses a given facial attribute or not. By operating on each bin and averaging the result, we better the EER of SOTA by over 1 % over a large set of matching pairs. b) We use the attribute labels and correlate them with each neuron of an embedding generated by a SOTA architecture pre-trained DNN on a large Face dataset and fine-tuned on face-attribute labels. We then suppress a set of maximally correlating neurons and perform matching after doing so. We demonstrate this improves the EER by over 2 %.

Abstract

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal vali-dation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% - 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% - 19%). The traditional ma-chine

Abstract

Learning an efficient label embedding framework for word images enables effective word spotting of handwritten documents. In this work, we propose different schemes of label embedding for word images using deep neural architectures and their representations. We refer to our first scheme as the two-stage label embedding technique which projects both word images and their corresponding textual strings into a common subspace. We further introduce an end-to-end label embedding scheme using deep neural architecture which simplifies the embedding process and reports state-of-the-art performance for the task of word spotting and recognition. We also validate the role of synthetic data as a complementary modality to further enhance the embedding process. On the challenging IAM handwritten dataset, we report an mAP of 0.9753 for query-by-string-based word spotting, while under lexicon-based word recognition, our proposed method reports 1.67 and 3.62 character and word error rates, respectively. We also present the detailed ablation study on various variants of our end-to-end embedding architecture and perform analysis under varying embedding sizes. We further validate the embedding scheme on degraded printed document datasets from both Latin and Indic scripts.

Abstract

In our work, we study the age of information (AoI) in a multi-source system where K sources transmit updates of their time-varying processes via a common-aggregator node to a destination node through a channel with packet delivery errors. We analyze AoI for an (α, β, 0, 1)-Gilbert-Elliot (GE) packet erasure channel with a round-robin scheduling policy. We employ maximum distance separable (MDS) scheme at aggregator for encoding the multi-source updates. We characterize the mean AoI for the MDS coded system for the case of large blocklengths. We further show that the optimal coding rate that achieves maximum coding gain over the uncoded system is n(1 − P) − O(n), where P , β α+β 0 + α α+β 1, and this maximum coding gain is (1 + P)/(1 + O(1)). In our work, we study the age of information (AoI) in a multi-source system where K sources transmit updates of their time-varying processes via a common-aggregator node to a destination node through a channel with packet delivery errors. We analyze AoI for an (α, β, 0, 1)-Gilbert-Elliot (GE) packet erasure channel with a round-robin scheduling policy. We employ maximum distance separable (MDS) scheme at aggregator for encoding the multi-source updates. We characterize the mean AoI for the MDS coded system for the case of large blocklengths. We further show that the optimal coding rate that achieves maximum coding gain over the uncoded system is n(1 − P) − O(n), where P , β α+β 0 + α α+β 1, and this maximum coding gain is (1 + P)/(1 + O(1)).

Abstract

This paper considers a communication system where a source sends time-sensitive information to its destination. We assume that both arrival and service processes of the messages are memoryless and the source has a single server with no buffer. Besides, we consider that the service is interrupted by an independent random process, which we model using the OnOff process. For this setup, we study the age of information for two queueing disciplines: 1) non-preemptive, where the messages arriving while the server is occupied are discarded, and 2) preemptive, where the in-service messages are replaced with newly arriving messages in the Off states. For these disciplines, we derive closed-form expressions for the mean peak age and mean age. Index Terms—Age of information, Peak age, On-Off process, preemptive discipline, non-preemptive discipline

Abstract

In this paper, we study the min-max optimization problems on Riemannian manifolds. We introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for solving the original min-max problems. Under the Riemannian Polyak–Łojasiewicz (PL) condition on the Hamiltonian function, its minimizer corresponds to the desired min-max saddle point. We also provide cases where this condition is satisfied. To minimize the Hamiltonian function, we propose Riemannian Hamiltonian methods (RHM) and present their convergence analysis. We extend RHM to include consensus regularization and to the stochastic setting. We illustrate the efficacy of the proposed RHM in applications such as subspace robust Wasserstein distance, robust training of neural networks, and generative adversarial networks.

Abstract

This work proposes a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We improve the quality of lip sync using audio as an additional input, helping the network to attend to the mouth region. We use additional priors using face segmentation and face mesh to improve the structure of the reconstructed faces. Finally, we improve the visual quality of the generations by incorporating a carefully designed identity-aware generator module. The identityaware generator takes the source image and the warped motion features as input to generate a high-quality output with fine-grained details. Our method produces state-ofthe-art results and generalizes well to unseen faces, languages, and voices. We comprehensively evaluate our approach using multiple metrics and outperforming the current techniques both qualitative and quantitatively. Our work opens up several applications, including enabling low bandwidth video calls. We release a demo video and additional information at http://cvit.iiit.ac.in/

Abstract

Video Question Answering methods focus on commonsense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential contextualisation cues to the reasoning process. To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. To explore this direction, we focus on news videos and require QA systems to comprehend and answer questions about the topics presented by combining visual and textual cues in the video. We introduce the “NewsVideoQA” dataset that comprises more than 8, 600 QA pairs on 3, 000+ news videos obtained from diverse news channels from around the world. We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods.

Abstract

Talking-face video generation works have achieved state-of-the-art results in synthesizing videos with lip synchronization. However, most of the previous works deal with low-resolution talking-face videos (up to 256×256 pixels), thus, generating extremely high-resolution videos still remains a challenge. We take a giant leap in this work and propose a novel method to synthesize talking-face videos at resolutions as high as 4K! Our task presents several key challenges: (i) Scaling the existing methods to such high resolutions is resource-constrained, both in terms of compute and the availability of very high-resolution datasets, (ii) The synthesized videos need to be spatially and temporally coherent. The sheer number of pixels that the model needs to generate while maintaining the temporal consistency at the video level makes this task non-trivial and has never been attempted before in literature. To address these issues, we propose to train the lip-sync generator in a compact Vector Quantized (VQ) space for the first time. Our