Abstract

Due to the lack of a large annotated speech corpus, many low-resource Indian languages struggle to utilize recent advancements in deep neural network architectures for Automatic Speech Recognition (ASR) tasks. Collecting large-scale databases is an expensive and time-consuming task. Current approaches lack extensive traditional expert-based data acquisition guidelines, as they are tedious and complex. In this work, we present the International Institute of Information Technology Hyderabad-Crowd Sourced Telugu Database (IIITH-CSTD), a Telugu corpus collected through crowdsourcing

Abstract

This work introduces an ultra low power gm-boosted Colpitts voltage controlled oscillator (VCO) with a transformer based tank operating near 20 GHz. The proposed VCO is implemented in TSMC 65 nm CMOS technology. Through postlayout simulations, it is demonstrated that the proposed VCO exhibits a remarkable figure-of-merit of 196.26 dBc/Hz and achieves a phase noise of -111.21 dBc/Hz at a 1 MHz offset while operating at 19.6 GHz. Additionally, the VCO provides a tuning range of 1.7 GHz (18 - 19.7 GHz) and consumes only 1.2 mW of power from a 1 V supply.

Abstract

User authentication is a promising security solution in which an external user hav- ing his/her mobile device can securely access the real-time information directly from the deployed smart devices in an Internet of Things-enabled intelligent precision agri- cultural environment. To achieve this goal, we present a new signature-based three factor user authentication scheme. The established session key between a user and the accessed smart device is then used to make secure communication among them to fetch the real-time data of the device. A detailed security analysis including the random-oracle based formal security, formal security verification using the broadly recognized automated validation of internet security protocols and applications tool and nonmathematical informal security analysis show the robustness of the proposed scheme against a number of potential attacks. In addition, testbed experiments are performed for measuring computational time of various cryptographic primitives that are used for comparative study among the proposed scheme and other related exist- ing competing schemes. The detailed comparative analysis shows that the proposed scheme has a better trade-off among its offered security and functionality features, and communication and computational overheads as compared with those for other competing schemes.

Abstract

Recent developments in advanced driver assistance systems (ADAS) have raised the demands of mmWave radars in 24 GHz and 77 GHz band. For higher accuracy and precision, frequency modulated continuous wave (FMCW) technique has become very popular for mmWave radars, which requires low phase noise and high bandwidth chirp frequency synthesizers. In this work, we present analysis and design of a low phase noise, transformer tank based mmWave voltage controlled oscillator (VCO) near 20 GHz for multiplier based 77 GHz FMCW chirp synthesizer. The proposed VCO design is implemented in 65 nm CMOS technology. Post layout simulations show that the proposed VCO exhibits a figure-of-merit is 192 dBc/Hz and phase noise of -117.98 dBc/Hz at 1 MHz offset while operating at 18.94 GHz and achieves a tuning range of 1.42 GHz (18.94-20.36 GHz), while consuming a power of 13.78 mW from 1.1 V supply.

Abstract

The paper presents a 0.5V supply, gate leakage-based current/voltage reference for ultra-low power IoT and biomedical applications. The references are generated by the proper addition of PTAT and CTAT curves, which are obtained by exploiting the traditional architecture of the beta multiplier and using the body biasing effect. Gate leakage transistors replace the resistors to ensure low power and low area. The circuit doesn't involve any Op-Amps avoiding the issues of offset that are prominent in these circuits. Implemented in CMOS 90nm technology, the proposed current (voltage) reference achieves a typical accuracy of 34.6ppm/∘C(29.68ppm/∘C) over a wide temperature range of −55∘C to 75∘C with typical value 63.32pA(0.35V). Excellent line sensitivity of 0.0318%N and 0.0576%N are observed for voltage and current reference, respectively, in a supply range of 0.5V - 2.3V. The area occupied by the total circuit is 0.0096mm2, while the power consumption is 415pW at the typical corner of 27∘C and 0.5V supply.

Abstract

Password managers (PMs) are becoming common and popular on mobile devices. The convenience of automatically filling user credentials into login forms, especially on small-screen devices, has further helped in increasing the adoption of PMs. Modern mobile OSes (such as Android; the focus of our work) facilitate system-wide autofill frameworks to enable autofilling on both browsers and apps. On the other side, mobile OSes enable apps to directly render web content via WebView controls, which: (1) prevents redirecting the user to the main browser; and (2) improves seamless user experience. We will focus on a common scenario, where a webpage is loaded into a mobile app using WebView controls. Some common examples include in-app opening of hyperlinks in Skype or Gmail mobile apps. Another key use of such in-app functionality is the "Login with Apple/Facebook/Google" button for user authentication within a third-party mobile app. Upon choosing such an option, the third-party app loads the corresponding login page in WebView. We will present a novel attack - that we call AutoSpill - to steal users' saved credentials from PMs during an autofill operation on a login page loaded inside an app. AutoSpill violates Android's secure autofill process. We found that the majority of top Android PMs were vulnerable to AutoSpill; even without JavaScript injections. With JavaScript injections enabled, all of them were found vulnerable. We discovered the fundamental reasons for AutoSpill and will propose systematic countermeasures to fix AutoSpill properly. We responsibly disclosed our findings to the affected PMs and Android security team. Different PMs and Google accepted our work as a valid issue.

Abstract

Abstract—Initially designed to represent ownership of various assets, Non-Fungible Tokens (NFTs) have emerged as a new tool in the blockchain domain for investment and trading. The NFT markets are rapidly budding with significant growth in trading volumes over the last few years. While the NFT ecosystem is continuously evolving, users are exploring astute trading practices to gain financial profits. In this paper, we uncover shill looping, a novel NFT trade practice that NFT owners can exploit to artificially inflate the price of an NFT token. We investigate shill looping and its primary effects in a multi-billion dollar NFT collection called BAYC, showing that approximately 50% of these NFTs exhibit shill looping. Our empirical analysis shows that shill looping significantly boosts the average NFT values by over 45% in the best case. Our initial results highlight the severeness of the shill looping phenomenon

Abstract

This paper aims to explore the challenges faced by start-ups during COVID-19 and highlight solutions for catering to the new-normal consumer behaviour. The study accounts for 15 deep-tech start-ups sailing through the pandemic and their responsiveness. This study brings forth insights and experiences from the Indian start-up founders and CEOs during COVID-19.

Abstract

The sporadic large fluctuations seen in the stock market are due to different factors. These large fluctuations are termed extreme events (EE). We have identified fundamental, technical, and external factors and categorized positive or negative EE depending on the impact of these factors. During such events, the stock price time series is found to be nonstationary. Hence, the Hilbert–Huang transformation is used to identify EEs based on high instantaneous energy () concentration. The analysis shows that concentration in the stock price is very high during both positive and negative EE, surpassing a threshold of where and are the mean energy and standard deviation of energy, respectively. Further, support vector regression is used to predict the stock price during an EE, with the close price being found to be the most useful input than the open-high-low-close (OHLC) inputs. The maximum prediction accuracy for one step using close price and OHLC prices are 95.98% and 95.64%, respectively. Whereas, for the two step prediction, the accuracies are 94.09% and 93.58%, respectively. These results highlight that the accuracy of one-step predictions surpasses that of two-step predictions. Also, accuracy decreases when predicting stock prices closer to an EE. The EEs identified from predicted time series exhibit statistical characteristics similar to those obtained from the original data.

Abstract

It is generally known that in a mixture of coupled active and inactive nonlinear oscillators, the entire system may stop oscillating and become inactive if the fraction of active oscillators is reduced to a critical value. This emerging phenomenon, called the “aging transition,” can be analytically predicted from the view point of cluster synchronization. One can question whether a model-free, data-driven framework based on neural networks could be used to foretell when such a system will cease oscillation. Here, we demonstrate how a straightforward ESN with trained output weights can accurately forecast both the temporal evaluation and the onset of collapse in coupled paradigmatic limit-cycle oscillators. In particular, we have demonstrated that an ESN can identify the critical fraction of inactive oscillators in a large all-to-all, small-world, and scale-free network when it is trained only with two nodes (one active and the other inactive) selected from three different pre-collapse regimes. We further demonstrate that ESN can anticipate aging transition of the network when trained with the mean-field dynamics of active and inactive oscillators.