Abstract

Blockchain-based Distributed Ledgers (DLs) promise to transform the existing financial system by making it truly democratic. In the past decade, blockchain technology has seen many novel applications ranging from the banking industry to real estate. However, in order to be adopted universally, blockchain systems must be scalable to support a high volume of transactions. As we increase the throughput of the DL system, the underlying peer-to-peer network might face multiple levels of challenges to keep up with the requirements. Due to varying network capacities, the slower nodes would be at a relative disadvantage compared to the faster ones, which could negatively impact their revenue. In order to quantify their relative advantage or disadvantage, we introduce two measures of network fairness, 푝푓 , the probability of frontrunning and 훼푓 , the publishing fairness. We show that as we scale the blockchain, both these measures deteriorate, implying that the slower nodes face a disadvantage at higher throughputs. It results in the faster nodes getting more than their fair share of the reward while the slower nodes (slow in terms of network quality) get less. Thus, fairness and scalability in blockchain systems do not go hand in hand. In a setting with rational miners, lack of fairness causes miners to deviate from the “longest chain rule” or undercut, which would reduce the blockchain’s resilience against byzantine adversaries. Hence, fairness is not only a desirable property for a blockchain system but also essential for the security of the blockchain and any scalable blockchain protocol proposed must ensure fairness.

Abstract

—Music has the power to evoke intense emotional experiences and regulate the mood of an individual. With the advent of online streaming services, research in music recommendation services has seen tremendous progress. Modern methods leveraging the listening histories of users for session-based song recommendations have overlooked the significance of features extracted from lyrics and acoustic content. We address the task of song prediction through multiple modalities, including tags, lyrics, and acoustic content. In this paper, we propose a novel deep learning approach by refining Attentive Neural Networks using representations derived via a Transformer model for lyrics and Variational Autoencoder for acoustic features. Our model achieves significant improvement in performance over existing state-of-the-art models using lyrical and acoustic features alone. Furthermore, we conduct a study to investigate the impact of users’ psychological health on our model’s performance. Index Terms—Music Recommend

Abstract

The use of in situ hyperspectral reflectance and bio-physical measurements has been increasing in forestry. Due to limited physical accessibility in a forest environment, most of the reflectance measurements of trees are acquired at a leaf or bunch of leaves level. A few radiative transfer models are available for upscaling leaf spectra to canopy level. While these models are sophisticated, they retrieve canopy spectra based on certain assumptions. We propose ‘semi-empirical model for upscaling leaf spectra (SEMULS)’ which upscales in situ leaf spectra to canopy level based on the relationship between leaf spectra and its bio-physical parameters. The performance of the model has been quantitatively validated by comparing the upscaled canopy spectra with spectra from – CHRIS hyperspectral imagery acquired concurrently and from the PROSAIL model. Results indicate that the SEMULS retrievals are …

Abstract

Generation of spatial profiles of airborne volcanic ash that is spread at synoptic scales is a problem that directly impacts lives and properties. Robust algorithms are needed to model the distribution using sparse data sampled in the neighborhood of an erupting volcano. Existing Numerical Weather Prediction (NWP) algorithms model the dispersion at coarser spatial resolutions. In this study, we evaluate a geospatial interpolation technique called Kriging [1] to generate prediction and error surfaces. Location and temperature values of ash from 2010 Icelandic eruption were spatially autocorrelated using a stochastic kriging method, known as Empirical Bayesian Kriging (EBK) [7]. The EBK estimates were rigorously validated against NWP for regions with varying sample densities. Subsequently, a method to generate an accurate overlay map using EBK estimates to augment NWP outputs is proposed to aid in …

Abstract

This paper presents the application of computer vision and machine learning to autonomous approach and landing and taxiing for an air vehicle. Recently, there has been growing interest in developing unmanned aircraft systems (UAS). We present a system and method that uses pattern recognition which aids the landing of a UAS and enhances the human-crewed air vehicle landing. Auto-landing systems based on the Instrument Landing System (ILS) have already proven their importance through decades. The auto-land systems work in conjunction with a radio altimeter, ILS, MLS, or GNSS. Closer to the runway, both under VFR and IFR, pilots are expected to rely on visual references for landing. Modern systems like HUD or CVS allow a trained pilot to manually fly the aircraft using guidance cues from the flight guidance system. Notwithstanding the type of landing and instruments used, typically, Pilots are expected to have the runway threshold markings, aiming point, displacement arrows, and touch down markings/lights insight before Minimum Decision Altitude (MDA). Imaging sensors are the essential standard equipment in crewed and crewless aerial vehicles that are widely used during the landing maneuver. In this method, a dataset of visual objects from satellite images is subjected to pattern recognition training. This trained system learns and

Abstract

We, the people’ is the most popular phrase from the constitutions. In spite of the fact that the number of countries including preamble as part of their constitution has been on the rise, preambles have received scant attention in academia. The importance of preambles has been established in multiple studies yet preambles have been looked at in isolation from socio-economic-environmental contexts. Our article attempts to present a unique insight by correlating preambles with the socio-economicenvironmental and infrastructural context within which they exist. It explores whether these correlations exist and if they do with which features and to what extent and the possibility of a causal link. We pursue a statistical study between various indicators that reflect the growth of a country and the presence or absence of various elements in preambles across the world. Our study finds that correlations exist between the economic-social-environmental and infrastructural context of a nation-state and different elements in their preambles. Our study rigorously engages with patterns in development indicators across years to provide correlational insights into the role of preambles not just as a dormant reference but as active fragments of the socio-political-economic reality of a nation-state. We hope our article establishes grounds for a further study of the manner in which preambles and the nonpolitical aspects of a nation-state can engage with each other.

Abstract

Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual devices without intrusive sensors on each device. Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem which requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this paper, we propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We used CNNs to extract features from main readings in a given time frame and then used those features to classify if a given appliance is active at that time period or not. Following that, the extracted features are used to model a generation problem using LSTM. We train the LSTM to generate the disaggregated energy consumption of a particular appliance. Our neural network is capable of generating detailed feedback of demandside, providing vital insights to the end-user about their electricity consumption. The algorithm was designed for low power offline devices such as ESP32. Empirical calculations show that our model outperforms the state-of-the-art on the Reference Energy Disaggregation Dataset (REDD)

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the conventional system, but the main drawback of radiant technology is inability to cater the latent load and condensation. This problem has engaged researchers to push their emphasis on Personalized Conditioning System (PCS). It forms a micro-climate region near the occupant. This study is aimed at PCS combined with a radiant conditioning system, which evaluates the performance of the PCS and compares it with different radiant cooling systems. A radiant cubicle was used to investigate the performance of the PCS for both heating & cooling modes. For heating mode, the Standard Effective Temperature (SET) was calculated with the help of thermal comfort surveys which were in a range of 23.4°C to 27.3°C. For cooling mode, four different cases compared the performance of the system which are: Conventional Air Conditioning System (CACS), Radiant Cooled Ceiling System (RCCS), Personalized Radiant Conditioning System (PRCS), and both PRCS-RCCS coupled. It was observed that PRCS alone reaches the thermal comfort criteria with low energy consumption. The energy-saving of RCCS-PRCS was achieved 38% when compared with CACS. It can be concluded that PRCS is a new step in the field of thermal comfort.

Abstract

Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. However, the extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear. In this paper, we generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers. We propose two methods Question Reordering and Sentence Paraphrasing to generate adversarial attacks. We conduct experiments across three neural MWP solvers over two benchmark datasets. On average, our attack method is able to reduce the accuracy of MWP solvers by over 40 percentage points on these datasets. Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text. We verify the validity and quality of generated adversarial examples through human evaluation.

Abstract

Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks. Also, prior attacks do not maintain a consistent search space while comparing different search methods. In this paper, we propose a query efficient attack strategy to generate plausible adversarial examples on text classification and entailment tasks. Our attack jointly leverages attention mechanism and locality sensitive hashing (LSH) to reduce the query count. We demonstrate the efficacy of our approach by comparing our attack with four baselines across three different search spaces. Further, we benchmark our results across the same search space used in prior attacks. In comparison to attacks proposed, on an average, we are able to reduce the query count by 75% across all datasets and target models. We also demonstrate that our attack achieves a higher success rate when compared to prior attacks in a limited query setting.