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Sumit Kumar

Publications and source records attributed to Sumit Kumar.

At least 109 records · Page 6Linked to original sources
arXiv2022

PrivPAS: A real time Privacy-Preserving AI System and applied ethics

With 3.78 billion social media users worldwide in 2021 (48% of the human population), almost 3 billion images are shared daily. At the same time, a consistent evolution of smartphone cameras has led to a photography explosion with 85% of all new pictures being captured using smartphones. However, lately, there has been an increased discussion of privacy concerns when a person being photographed is unaware of the picture being taken or has reservations about the same being shared. These privacy violations are amplified for people with disabilities, who may find it challenging to raise dissent even if they are aware. Such unauthorized image captures may also be misused to gain sympathy by third-party organizations, leading to a privacy breach. Privacy for people with disabilities has so far received comparatively less attention from the AI community. This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A real time Privacy-Preserving AI System) a novel framework to identify sensitive content. Additionally, we curate and annotate a dataset to identify and localize accessibility markers and classify whether an image is sensitive to a featured subject with a disability. We demonstrate that the proposed lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline, trained on face anonymized data, achieves an F1-score of 73.1%.

cs.CV↗
arXiv2022

Microwave Optomechanically Induced Transparency and Absorption Between 250 and 450 mK

High-quality microwave amplifiers and notch-filters can be made from microwave optomechanical systems in which a mechanical resonator is coupled to a microwave cavity by radiation pressure. These amplifiers and filters rely on optomechanically induced transparency (OMIT) and absorption (OMIA), respectively. Such devices can amplify microwave signals with large, controllable gain, high dynamic range and very low noise. Furthermore, extremely narrowband filters can be constructed with this technique. We briefly review previous measurements of microwave OMIT and OMIA before reporting our own measurements of these phenomena, which cover a larger parameter space than has been explored in previous works. In particular, we vary probe frequency, pump frequency, pumping scheme (red or blue), probe power, pump power and temperature. We find excellent agreement between our measurements and the predictions of input/output theory, thereby guiding further development of microwave devices based on nanomechanics.

quant-ph↗
arXiv2021

VoiceMoji: A Novel On-Device Pipeline for Seamless Emoji Insertion in Dictation

Most of the speech recognition systems recover only words in the speech and fail to capture emotions. Users have to manually add emoji(s) in text for adding tone and making communication fun. Though there is much work done on punctuation addition on transcribed speech, the area of emotion addition is untouched. In this paper, we propose a novel on-device pipeline to enrich the voice input experience. It involves, given a blob of transcribed text, intelligently processing and identifying structure where emoji insertion makes sense. Moreover, it includes semantic text analysis to predict emoji for each of the sub-parts for which we propose a novel architecture Attention-based Char Aware (ACA) LSTM which handles Out-Of-Vocabulary (OOV) words as well. All these tasks are executed completely on-device and hence can aid on-device dictation systems. To the best of our knowledge, this is the first work that shows how to add emoji(s) in the transcribed text. We demonstrate that our components achieve comparable results to previous neural approaches for punctuation addition and emoji prediction with 80% fewer parameters. Overall, our proposed model has a very small memory footprint of a mere 4MB to suit on-device deployment.

cs.CL↗
arXiv2021

Adaptive Beam Search to Enhance On-device Abstractive Summarization

We receive several essential updates on our smartphones in the form of SMS, documents, voice messages, etc. that get buried beneath the clutter of content. We often do not realize the key information without going through the full content. SMS notifications sometimes help by giving an idea of what the message is about, however, they merely offer a preview of the beginning content. One way to solve this is to have a single efficient model that can adapt and summarize data from varied sources. In this paper, we tackle this issue and for the first time, propose a novel Adaptive Beam Search to improve the quality of on-device abstractive summarization that can be applied to SMS, voice messages and can be extended to documents. To the best of our knowledge, this is the first on-device abstractive summarization pipeline to be proposed that can adapt to multiple data sources addressing privacy concerns of users as compared to the majority of existing summarization systems that send data to a server. We reduce the model size by 30.9% using knowledge distillation and show that this model with a 97.6% lesser memory footprint extracts the same or more key information as compared to BERT.

cs.CL↗
arXiv2021

Non-linear additive twist of Fourier coefficients of $GL(3) \times GL(2)$ and $GL(3)$ Maass forms

Let $λ_π(m,n)$ be the Fourier coefficients of a Hecke-Maass cusp form $π$ for $SL(3,\mathbb{Z})$ and $λ_{f}(n)$ be the Fourier coefficients of Hecke-eigen form $f$ for $SL(2,\mathbb{Z})$. The aim of this article is to get a non-trivial bound on the sum which is non-linear additive twist of the coefficients $λ_π(m,n)$ and $λ_{f}(n)$. More precisely, for any $0 < β< 1$ and $ε>0$, we have $$\sum_{n=1}^{\infty} λ_π(r,n) \, e\left(αn^β\right) V\left(\frac{n}{X}\right) \ll_{π,ε} α\sqrtβr^{\frac{7}{6}}X^{\frac{3}{4}+\frac{9β}{28}+ ε}.$$ and $$\sum_{n=1}^{\infty} λ_π(r,n) \, λ_{f}(n) \, e\left(αn^β\right) V\left(\frac{n}{X}\right) \ll_{π, f,ε} (αβ)^{\frac{3}{2}} rX^{\frac{3}{4}+\frac{29β}{44}+ε},$$ where $V(x)$ is a smooth function supported in $[1,2]$ and satisfying $V^{(j)}(x) \ll_{j} 1$.

math.NT↗
arXiv2021

Microwave optomechanical measurement of non-metallized SiN strings at mK temperatures

The mechanical properties of amorphous materials (glasses) at low temperatures are dominated by effects of low energy excitations that are thought to be atomic-scale tunneling two level systems (TTLS). In nanometer-scale glass samples, the temperature dependence of the sound speed and dissipation is modified relative to that of bulk glass samples. In addition to this size effect, the usual presence of a polycrystalline metal in nanomechanical resonators leads to a further departure from the well-studied behavior of insulating bulk glass. We report a dual chip optomechanical measurement technique used to characterize non-metallized amorphous SiN strings at low temperatures. A harp consisting of SiN strings of width 350 nm and lengths 40 to 80 $μ$m is coupled to an Al superconducting microwave cavity on a separate chip. The strings are driven dielectrically and their motion is detected via its modulation of the microwave resonance frequency.

cond-mat.mes-hall↗
arXiv2021

TweetBLM: A Hate Speech Dataset and Analysis of Black Lives Matter-related Microblogs on Twitter

In the past few years, there has been a significant rise in toxic and hateful content on various social media platforms. Recently Black Lives Matter movement came into the picture, causing an avalanche of user generated responses on the internet. In this paper, we have proposed a Black Lives Matter related tweet hate speech dataset TweetBLM. Our dataset comprises 9165 manually annotated tweets that target the Black Lives Matter movement. We annotated the tweets into two classes, i.e., HATE and NONHATE based on their content related to racism erupted from the movement for the black community. In this work, we also generated useful statistical insights on our dataset and performed a systematic analysis of various machine learning models such as Random Forest, CNN, LSTM, BiLSTM, Fasttext, BERTbase, and BERTlarge for the classification task on our dataset. Through our work, we aim at contributing to the substantial efforts of the research community for the identification and mitigation of hate speech on the internet. The dataset is publicly available.

cs.CL↗
arXiv2021

3-OGC: Catalog of gravitational waves from compact-binary mergers

We present the third Open Gravitational-wave Catalog (3-OGC) of compact-binary coalescences, based on the analysis of the public LIGO and Virgo data from 2015 through 2019 (O1, O2, O3a). Our updated catalog includes a population of 57 observations, including four binary black hole mergers that had not previously been reported. This consists of 55 binary black hole mergers and the two binary neutron star mergers GW170817 and GW190425. We find no additional significant binary neutron star or neutron star--black hole merger events. The most confident new detection is the binary black hole merger GW190925\_232845 which was observed by the LIGO Hanford and Virgo observatories with $\mathcal{P}_{\textrm{astro}} > 0.99$; its primary and secondary component masses are $20.2^{+3.9}_{-2.5} M_{\odot}$ and $15.6^{+2.1}_{-2.6} M_{\odot}$, respectively. We estimate the parameters of all binary black hole events using an up-to-date waveform model that includes both sub-dominant harmonics and precession effects. To enable deep follow-up as our understanding of the underlying populations evolves, we make available our comprehensive catalog of events, including the sub-threshold population of candidates, and the posterior samples of our source parameter estimates.

astro-ph.HE↗
arXiv2021

SoftNER: Mining Knowledge Graphs From Cloud Incidents

The move from boxed products to services and the widespread adoption of cloud computing has had a huge impact on the software development life cycle and DevOps processes. Particularly, incident management has become critical for developing and operating large-scale services. Prior work on incident management has heavily focused on the challenges with incident triaging and de-duplication. In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for mining Knowledge Graphs from incident reports. First, we build a novel multi-task learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for extracting factual information in the form of named entities. Next, we present an approach to mine relations between the named entities for automatically constructing knowledge graphs. We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents. We show that the unsupervised machine learning pipeline has a high precision of 0.96. Our multi-task learning based deep learning model also outperforms the state-of-the-art NER models. Lastly, using the knowledge extracted by SoftNER, we are able to build accurate models for applications such as incident triaging and recommending entities based on their relevance to incident titles.

cs.SE↗
arXiv2021

Microwave single-tone optomechanics in the classical regime

We report on the quantitative experimental illustration of elementary optomechanics within the classical regime. All measurements are performed in a commercial dilution refrigerator on a mesoscopic drumhead aluminium resonator strongly coupled to a microwave cavity, using only strict single-tone schemes. Sideband asymmetry is reported using in-cavity microwave pumping, along with noise squashing and back-action effects. Results presented in this paper are analysed within the simple classical electric circuit theory, emphasizing the analogous nature of classical features with respect to their usual quantum description. The agreement with theory is obtained with no fitting parameters. Besides, based on those results a simple method is proposed for the accurate measurement of the ratio between microwave internal losses and external coupling.

cond-mat.mes-hall↗
arXiv2021

Coexistence of Communications and Cognitive MIMO Radar: Waveform Design and Prototype

New generation of radar systems will need to coexist with other radio frequency (RF) systems, anticipating their behavior and reacting appropriately to avoid interference. In light of this requirement, this paper designs, implements, and evaluates the performance of phase-only sequences (with constant power) for intelligent spectrum utilization using the custom built cognitive Multiple Input Multiple Output (MIMO) radar prototype. The proposed transmit waveforms avoid the frequency bands occupied by narrowband interferers or communication links, while simultaneously have a small cross-correlation among each other to enable their separability at the MIMO radar receiver. The performance of the optimized set of sequences obtained through solving a non-convex bi-objective optimization problem, is compared with the state-of-the-art counterparts, and its applicability is illustrated by the developed prototype. A realistic Long Term Evolution (LTE) downlink is used for the communications, and the real-time system implementation is validated and evaluated through the throughput calculations for communications and the detection performance measurement for the radar system.

eess.SP↗
arXiv2021

Blockchain Technology: Introduction, Integration and Security Issues with IoT

Blockchain was mainly introduced for secure transactions in connection with the mining of cryptocurrency Bitcoin. This article discusses the fundamental concepts of blockchain technology and its components, such as block header, transaction, smart contracts, etc. Blockchain uses the distributed databases, so this article also explains the advantages of distributed Blockchain over a centrally located database. Depending on the application, Blockchain is broadly categorized into two categories; Permissionless and Permissioned. This article elaborates on these two categories as well. Further, it covers the consensus mechanism and its working along with an overview of the Ethereum platform. Blockchain technology has been proved to be one of the remarkable techniques to provide security to IoT devices. An illustration of how Blockchain will be useful for IoT devices has been given. A few applications are also illustrated to explain the working of Blockchain with IoT.

cs.CR↗
arXiv2021

Neural Knowledge Extraction From Cloud Service Incidents

In the last decade, two paradigm shifts have reshaped the software industry - the move from boxed products to services and the widespread adoption of cloud computing. This has had a huge impact on the software development life cycle and the DevOps processes. Particularly, incident management has become critical for developing and operating large-scale services. Incidents are created to ensure timely communication of service issues and, also, their resolution. Prior work on incident management has been heavily focused on the challenges with incident triaging and de-duplication. In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for unsupervised knowledge extraction from service incidents. We frame the knowledge extraction problem as a Named-entity Recognition task for extracting factual information. SoftNER leverages structural patterns like key,value pairs and tables for bootstrapping the training data. Further, we build a novel multi-task learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for named-entity extraction. We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents. We show that the unsupervised machine learning based approach has a high precision of 0.96. Our multi-task learning based deep learning model also outperforms the state of the art NER models. Lastly, using the knowledge extracted by SoftNER we are able to build significantly more accurate models for important downstream tasks like incident triaging.

cs.SE↗
arXiv2021

On-Device Document Classification using multimodal features

From small screenshots to large videos, documents take up a bulk of space in a modern smartphone. Documents in a phone can accumulate from various sources, and with the high storage capacity of mobiles, hundreds of documents are accumulated in a short period. However, searching or managing documents remains an onerous task, since most search methods depend on meta-information or only text in a document. In this paper, we showcase that a single modality is insufficient for classification and present a novel pipeline to classify documents on-device, thus preventing any private user data transfer to server. For this task, we integrate an open-source library for Optical Character Recognition (OCR) and our novel model architecture in the pipeline. We optimise the model for size, a necessary metric for on-device inference. We benchmark our classification model with a standard multimodal dataset FOOD-101 and showcase competitive results with the previous State of the Art with 30% model compression.

cs.CV↗
arXiv2020

A Remote Carrier Synchronization Technique for Coherent Distributed Remote Sensing Systems

Phase, frequency, and time synchronization are crucial requirements for many applications, such as multi-static remote sensing and communication systems. Moreover, the synchronization solution becomes even more challenging when the nodes are orbiting or flying on airborne or spaceborne platforms. This paper compares the available technologies used for the synchronization and coordination of nodes in distributed remote sensing applications. Additionally, this paper proposes a general system model and identifies preliminary guidelines and critical elements for implementing the synchronization mechanisms exploiting the inter-satellite communication link. The distributed phase synchronization loop introduced in this work deals with the self-interference in a full-duplex point to point scenario by transmitting two carriers at each node. All carriers appear with different frequency offsets around a central frequency, called the application central-frequency or the beamforming frequency. This work includes a detailed analysis of the proposed algorithm and the required simulations to verify its performance for different phase noise, AWGN, and Doppler shift scenarios.

eess.SP↗
arXiv2020

A Conglomerate of Multiple OCR Table Detection and Extraction

Information representation as tables are compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used, however industry still faces challenge in detecting and extracting tables from OCR documents or images. This paper proposes an algorithm that detects and extracts multiple tables from OCR document. The algorithm uses a combination of image processing techniques, text recognition and procedural coding to identify distinct tables in same image and map the text to appropriate corresponding cell in dataframe which can be stored as Comma-separated values, Database, Excel and multiple other usable formats.

cs.IR↗