arXiv subjects

Abhishek Singh

Publications and source records attributed to Abhishek Singh.

At least 73 records · Page 4Linked to original sources

arXiv2020

Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing

As we await a vaccine, social-distancing via efficient contact tracing has emerged as the primary health strategy to dampen the spread of COVID-19. To enable efficient digital contact tracing, we present a novel system to estimate pair-wise individual proximity, via a joint model of Bluetooth Low Energy (BLE) signals with other on-device sensors (accelerometer, magnetometer, gyroscope). We explore multiple ways of interpreting the sensor data stream (time-series, histogram, etc) and use several statistical and deep learning methods to learn representations for sensing proximity. We report the normalized Decision Cost Function (nDCF) metric and analyze the differential impact of the various input signals, as well as discuss various challenges associated with this task.

eess.SP↗

arXiv2020

Verifiable Proof of Health using Public Key Cryptography

In the current pandemic, testing continues to be the most important tool for monitoring and curbing the disease spread and early identification of the disease to perform health-related interventions like quarantine, contact tracing and etc. Therefore, the ability to verify the testing status is pertinent as public places prepare to safely open. Recent advances in cryptographic tools have made it possible to build a secure and resilient digital-id system. In this work, we propose to build an end to end COVID-19 results verification protocol that takes privacy, computation, and other practical concerns into account for designing an inter-operable layer of testing results verification system that could potentially enable less stringent and more selective lockdowns. We also discuss various concerns encompassing the security, privacy, ethics and equity aspect of the proposed system.

cs.CR↗

arXiv2020

Digital Landscape of COVID-19 Testing: Challenges and Opportunities

The COVID-19 Pandemic has left a devastating trail all over the world, in terms of loss of lives, economic decline, travel restrictions, trade deficit, and collapsing economy including real-estate, job loss, loss of health benefits, the decline in quality of access to care and services and overall quality of life. Immunization from the anticipated vaccines will not be the stand-alone guideline that will help surpass the pandemic and return to normalcy. Four pillars of effective public health intervention include diagnostic testing for both asymptomatic and symptomatic individuals, contact tracing, quarantine of individuals with symptoms or who are exposed to COVID-19, and maintaining strict hygiene standards at the individual and community level. Digital technology, currently being used for COVID-19 testing include certain mobile apps, web dashboards, and online self-assessment tools. Herein, we look into various digital solutions adapted by communities across universities, businesses, and other organizations. We summarize the challenges experienced using these tools in terms of quality of information, privacy, and user-centric issues. Despite numerous digital solutions available and being developed, many vary in terms of information being shared in terms of both quality and quantity, which can be overwhelming to the users. Understanding the testing landscape through a digital lens will give a clear insight into the multiple challenges that we face including data privacy, cost, and miscommunication. It is the destiny of digitalization to navigate testing for COVID-19. Block-chain based systems can be used for privacy preservation and ensuring ownership of the data to remain with the user. Another solution involves having digital health passports with relevant and correct information. In this early draft, we summarize the challenges and propose possible solutions to address the same.

cs.CY↗

arXiv2020

Clinical Landscape of COVID-19 Testing: Difficult Choices

The coronavirus disease 2019 (COVID-19) pandemic has spread rapidly across the world, leading to enormous amounts of human death and economic loss. Until definitive preventive or curative measures are developed, policies regarding testing, contact tracing, and quarantine remain the best public health tools for curbing viral spread. Testing is a crucial component of these efforts, enabling the identification and isolation of infected individuals. Differences in testing methodologies, time frames, and outcomes can have an impact on their overall efficiency, usability and efficacy. In this early draft, we draw a comparison between the various types of diagnostic tests including PCR, antigen, and home tests in relation to their relative advantages, disadvantages, and use cases. We also look into alternative and unconventional methods. Further, we analyze the short-term and long-term impacts of the virus and its testing on various verticals such as business, government laws, policies, and healthcare.

q-bio.OT↗

arXiv2020

FedML: A Research Library and Benchmark for Federated Machine Learning

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.

cs.LG↗

arXiv2020

Privacy in Deep Learning: A Survey

The ever-growing advances of deep learning in many areas including vision, recommendation systems, natural language processing, etc., have led to the adoption of Deep Neural Networks (DNNs) in production systems. The availability of large datasets and high computational power are the main contributors to these advances. The datasets are usually crowdsourced and may contain sensitive information. This poses serious privacy concerns as this data can be misused or leaked through various vulnerabilities. Even if the cloud provider and the communication link is trusted, there are still threats of inference attacks where an attacker could speculate properties of the data used for training, or find the underlying model architecture and parameters. In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues. We also show that there is a gap in the literature regarding test-time inference privacy, and propose possible future research directions.

cs.LG↗

arXiv2020

Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic

In this work we propose a WiFi colocation methodology for digital contact tracing. The approach works by having a device scan and store nearby access point information to perform proximity inference. We make our approach resilient to different practical scenarios by configuring a device to turn into a hotspot if access points are unavailable, which makes the approach feasible in both dense urban areas and sparse rural places. We compare various shortcomings and advantages of this work over other conventional ways of doing digital contact tracing. Preliminary results indicate the feasibility of our approach for determining proximity between users, which is relevant for improving existing digital contact tracing and exposure notification implementations.

cs.CY↗

arXiv2020

PoinT-5: Pointer Network and T-5 based Financial NarrativeSummarisation

Companies provide annual reports to their shareholders at the end of the financial year that describes their operations and financial conditions. The average length of these reports is 80, and it may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (the combination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that we used in the Financial Narrative Summarisation (FNS) 2020 task. The proposed method uses pointer networks to extract important narrative sentences from the report, and then T-5 is used to paraphrase extracted sentences into a concise yet informative sentence. We evaluate our method using ROUGE-N (1,2), L, and SU4. The proposed method achieves the highest precision scores in all the metrics and highest F1 scores in ROUGE1, and LCS and the only solution to cross the MUSE solution baseline in ROUGE-LCS metrics.

cs.CL↗

arXiv2020

Voice@SRIB at SemEval-2020 Task 9 and 12: Stacked Ensembling method for Sentiment and Offensiveness detection in Social Media

In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we haveused our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on macro F1-score, precision, accuracy, and recall on the datasets. We intend to show that hyper-parameter tuning and data pre-processing steps help a lot in improving the scores. In our experiments, we are able to achieve 0.886 F1-Macro on OffenEval Greek language subtask post-evaluation, whereas the highest is 0.852 during the Evaluation Period. We stood third in Spanglish competition with our best F1-score of 0.756. Codalab username is asking28.

cs.CL↗

arXiv2020

Target Privacy Threat Modeling for COVID-19 Exposure Notification Systems

The adoption of digital contact tracing (DCT) technology during the COVID-19pandemic has shown multiple benefits, including helping to slow the spread of infectious disease and to improve the dissemination of accurate information. However, to support both ethical technology deployment and user adoption, privacy must be at the forefront. With the loss of privacy being a critical threat, thorough threat modeling will help us to strategize and protect privacy as digital contact tracing technologies advance. Various threat modeling frameworks exist today, such as LINDDUN, STRIDE, PASTA, and NIST, which focus on software system privacy, system security, application security, and data-centric risk, respectively. When applied to the exposure notification system (ENS) context, these models provide a thorough view of the software side but fall short in addressing the integrated nature of hardware, humans, regulations, and software involved in such systems. Our approach addresses ENSsas a whole and provides a model that addresses the privacy complexities of a multi-faceted solution. We define privacy principles, privacy threats, attacker capabilities, and a comprehensive threat model. Finally, we outline threat mitigation strategies that address the various threats defined in our model

cs.CR↗

arXiv2020

NoPeek: Information leakage reduction to share activations in distributed deep learning

For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the invertibility of raw data from intermediary representations. This can prevent client entities that hold sensitive data from using distributed deep learning services. We demonstrate that our method is resilient to such reconstruction attacks and is based on reduction of distance correlation between raw data and learned representations during training and inference with image datasets. We prevent such reconstruction of raw data while maintaining information required to sustain good classification accuracies.

cs.LG↗

arXiv2020

PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic

Several contact tracing solutions have been proposed and implemented all around the globe to combat the spread of COVID-19 pandemic. But, most of these solutions endanger the privacy rights of the individuals and hinder their widespread adoption. We propose a privacy-preserving contact tracing protocol for the efficient tracing of the spread of the global pandemic. It is based on the private set intersection (PSI) protocol and utilizes the homomorphic properties to preserve the privacy at the individual level. A hierarchical model for the representation of landscapes and rate-limiting factor on the number of queries have been adopted to maintain the efficiency of the protocol.

cs.CR↗

arXiv2020

Comparing manual contact tracing and digital contact advice

Manual contact tracing is a top-down solution that starts with contact tracers at the public health level, who identify the contacts of infected individuals, interview them to get additional context about the exposure, and also monitor their symptoms and support them until the incubation period is passed. On the other hand, digital contact tracing is a bottom-up solution that starts with citizens who on obtaining a notification about possible exposure to an infected individual may choose to ignore the notification, get tested to determine if they were actually exposed or self-isolate and monitor their symptoms over the next two weeks. Most experts recommend a combination of manual contact tracing and digital contact advice but they are not based on a scientific basis. For example, a possible hybrid solution could involve a smartphone based alert that requests the possible contact of an infected individual to call the Public Health (PH) number for next steps, or in some cases, suggest ways to self-assess in order to reduce the burden on PH so only most critical cases require a phone conversation. In this paper, we aim to compare the manual and digital approaches to contact tracing and provide suggestions for potential hybrid solutions.

cs.CY↗

arXiv2020

SplitNN-driven Vertical Partitioning

In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features. SplitNN does not share raw data or model details with collaborating institutions. The proposed configuration allows training among institutions holding diverse sources of data without the need of complex encryption algorithms or secure computation protocols. We evaluate several configurations to merge the outputs of the split models, and compare performance and resource efficiency. The method is flexible and allows many different configurations to tackle the specific challenges posed by vertically split datasets.

cs.LG↗

arXiv2020

Adding Location and Global Context to the Google/Apple Exposure Notification Bluetooth API

Contact tracing requires a strong understanding of the context of a user, and location with other sensory data could provide a context for any infection encounter. Although Bluetooth technology gives a good insight into the proximity aspect of an encounter, it does not provide any location context related to it which helps to make better decisions. Using the ideas presented in this paper, one shall be able to obtain this valuable information that could address the problem of false-positive and false-negative to a certain extent. All of this within the purview of Google/Apple Exposure Notification (GAEN) specification, while preserving complete user privacy. There are four ways of propagating context between any two users. Two such methods allow private location logging, without revealing the location history within an app. The other two are encryption-based methods. The first encryption method is a variant of Apple's FindMy protocol, that allows nearby Apple devices to capture the GPS location of a lost Apple device. The second encryption is a minor modification of the existing GAEN protocol so that global context is available to a healthy phone only when it is exposed - this is a better option comparatively. It will still be the role of Public Health smartphone app to decide, on how to use the location-time context, to build a full-fledged contact tracing and public health solution. Lastly, we highlight the benefits and potential privacy issues with each of these context propagation methods proposed here.

cs.CR↗

arXiv2020

A single-cell RNA expression map of coronavirus receptors and associated factors in developing human embryos

To predict if developing human embryos are permissive to coronaviruses, we analyzed publicly available single cell RNA-seq datasets of zygotes, 4-cell, 8-cell, morula, inner cell mass, epiblast, primitive endoderm and trophectoderm for the coronavirus receptors (ACE2, BSG, DPP4 and ANPEP), the Spike protein cleavage enzymes (TMPRSS2, CTSL). We also analyzed the presence of host genes involved in viral replication, the endosomal sorting complexes required for transport (ESCRT) and SARS-Cov-2 interactions. The results reveal that ACE2, BSG, DPP4 and ANPEP are expressed in the cells of the zygote, to blastocyst including the trophectodermal lineage. ACE2, TMPRSS, BSG and CTSL are co-transcribed in a proportion of epiblast cells and most cells of the trophectoderm. The embryonic and trophectodermal cells also express genes for proteins ESCRT, viral replication and those that interact with SARS-CoV-2. We identified 1985 genes in epiblast and 1452 genes in the trophectoderm that are enriched in the ACE2 and TMPRSS2 co-expressing cells; 216 genes of these are common in both the cell types. These genes are associated with lipid metabolism, lysosome, peroxisome and oxidative phosphorylation pathways. Together our results suggest that developing human embryos could be permissive to coronavirus entry by both canonical and non-canonical mechanisms and they also express the genes for proteins involved in viral endocytosis and replication. This knowledge will be useful for evidence-based patient management for IVF during the COVID-19 pandemic.

q-bio.TO↗

arXiv2020

Preparation, characterization and X-ray photoemission spectroscopy study of a correlated semimetal, SmBi

We report high quality single crystalline growth of SmBi using flux method. The compound crystallizes in the simple rock salt structure with space group Fm3m. The cubic structure of the single crystal was confirmed by Laue diffraction pattern. The magnetic susceptibility measurements reveal sharp antiferromagnetic order with Neel temperature of 9 K. The core level photoemission spectroscopy study of Sm 3d has been performed using monochromatic Al Kalpha source. We observe multiple features in the experimental spectra due to fonal state effect - a signature of hybridization between Sm 4f - Bi 6p states. Intense satellite features are also observed presumably due to mixed valency arising from Kondo coupling. No signature of surface-bulk difference is observed in the Sm 3d core level spectra.

cond-mat.str-el↗

arXiv2020

Up to 70 THz bandwidth from implanted Ge photoconductive antenna excited by a fibre laser

Phase-stable electromagnetic pulses in the THz frequency range offer several unique capabilities in time-resolved spectroscopy. However, the diversity of their application is limited by the covered spectral bandwidth. In particular, the upper frequency limit of photoconductive emitters - the most widespread technique in THz spectroscopy - reaches only up to 7 THz in regular transmission mode due to the absorption by infrared-active optical phonons. Here, we present ultra-broadband (extending up to 70 THz) THz emission from Au implanted Ge emitter which is compatible with a fibre laser operating at 1.1 and 1.55 μm wavelengths at a repetition rates of 10 and 20 MHz, respectively. This opens a perspective for the development of compact THz photonic devices operating up to multi-THz frequencies and compatible with Si CMOS technology.

physics.optics↗