Abstract

During the past decade promising methods for computational prediction of electron ionization mass spectra have been developed. The most prominent ones are based on quantum chemistry (QCEIMS) and machine learning (CFM-EI, NEIMS). Here we provide a threefold comparison of these methods with respect to spectral prediction and compound identification. We found that there is no unambiguous way to determine the best of these three methods. Among other factors, we find that the choice of spectral distance functions play an important role regarding the performance for compound identification.

Abstract

How do higher-order interactions influence the dynamical landscape of a network of the second-order phase oscillators? We address this question using three coupled Kuramoto phase oscillators with inertia under pairwise and higher-order interactions, in search of various collective states, and new states, if any, that show marginal presence or absence under pairwise interactions. We explore this small network for varying phase lag in the coupling and over a range of negative to positive coupling strength of pairwise as well as higher-order or group interactions. In the extended coupling parameter plane of the network we record several well-known states such as synchronization, frequency chimera states, and rotating waves that appear with distinct boundaries. In the parameter space, we also find states generated by the influence of higher-order interactions: The 2+1 antipodal point and the 2+1 phase-locked states. Our results demonstrate the importantance of the choices of the phase lag and the sign of the higher-order coupling strength for the emergent dynamics of the network. We provide analytical support to our numerical results.

Abstract

One of the most significant recent advances in technology is the advent of unmanned aerial vehicles (UAVs), i.e., drones. They have widened the scope of possible applications and provided a platform for a wide range of creative responses to a variety of challenges. The Internet of Drones (IoD) is a relatively new concept that has arisen as a consequence of the combination of drones and the Internet. The fifth-generation (5G) and beyond cellular networks (i.e., drones in networks beyond 5G) are promising solutions for achieving safe drone operations and applications. They may have many applications, like surveillance or urban areas, security, surveillance, retaliation, delivering items, smart farming, film production, capturing nature videos, and many more. Due to the fact that it is susceptible to a wide variety of cyber-attacks, there are certain concerns regarding the privacy and security of IoD communications. In this paper, a secure blockchain-enabled authentication key management framework with the big data analytics feature for drones in networks beyond 5G applications is proposed (in short, SBBDA-IoD). The security of SBBDA-IoD against multiple attacks is demonstrated through a detailed security analysis. The Scyther tool is used to perform a formal security verification test on the SBBDA-IoD’s security, confirming the system’s resistance to various potential attacks. A detailed comparative analysis has identified that SBBDA-IoD outperforms the other schemes by a significant margin. Finally, a real-world implementation of SBBDA-IoD is shown to evaluate its effect on several measures of performance.

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.

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

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

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

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

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

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.