Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme

Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler models fail to produce sufficient Value-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across markets. This indicates that, despite a regional proximity and homogeneity of the markets, index volatilities are driven by different factors

MIMO Beamforming for Secure and Energy-Efficient Wireless Communication

Considering a multiple-user multiple-input multiple-output (MIMO) channel with an eavesdropper, this letter develops a beamformer design to optimize the energy efficiency in terms of secrecy bits per Joule under secrecy quality-of-service constraints. This is a very difficult design problem with no available exact solution techniques. A path-following procedure, which iteratively improves its feasible points by using a simple quadratic program of moderate dimension, is proposed. Under any fixed computational tolerance the procedure terminates after finitely many iterations, yielding at least a locally optimal solution. Simulation results show the superior performance of the obtained algorithm over other existing methods.Comment: 12 pages, 2 figure

sFuzz: An efficient adaptive fuzzer for solidity smart contracts

Ministry of Education, Singapore under its Academic Research Funding Tier

Synthesis and Photocatalytic Activity for Toluene Removal of CDs/TiO2 - Zeolite Y

Hydrothermally synthesized carbon nanodots (CDs) were impregnated on TiO2. The product (CDs/TiO2) was mechanically mixed with zeolite Y for application in toluene photocatalytic oxidation reaction under UV radiation. Material properties of the samples were investigated by different methods. Toluene vapor was chosen as a typical volatile organic compound to investigate the performance of CDs/TiO2 – zeolite Y photocatalyst when these technological parameters were changed: toluene concentration, gas flow rate, humidity and UV light intensity. In each reaction, only one parameter was changed and the remaining conditions were fixed. The toluene concentrations at the beginning and the end of each reaction were analyzed with the use of gas chromatography (GC). The results of different reaction conditions show the trends for toluene treatment of the CDs/TiO2 – zeolite Y catalyst, thereby providing specific explanations for these trends. The experiments also show that toluene removal is highest when the toluene concentration in the inlet gas is 314 ppmv, the flow rate is 3 L/h, the humidity is 60%, and the catalyst (CDs/TiO2 – zeolite Y composite with 70% zeolite in weight) is illuminated by 4 UV lamps. Copyright © 2022 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Omega–3 long-chain fatty acids in the heart, kidney, liver and plasma metabolite profiles of Australian prime lambs supplemented with pelleted canola and flaxseed Oils

The objective of the study was to ascertain whether human health beneficial omega–3 long-chain (≥C20) polyunsaturated fatty acid (n-3 LC-PUFA) content in heart, kidney and liver can be enhanced by supplementing prime lambs with graded levels of canola and flaxseed oil. Health status of the lambs, as a consequence of the supplementation, was also investigated by examining their plasma metabolites. Sixty purebred and first-cross lambs were allocated to one of five treatments of lucerne hay basal diet supplemented with isocaloric and isonitrogenous wheat-based pellets without oil inclusion (Control) or graded levels of canola oil at 2.5% (2.5C), 5% (5C), flaxseed oil at 2.5% (2.5F) and 5% (5F) in a completely randomised design. Pre-slaughter blood, post-slaughter kidney, liver and heart samples were analysed for plasma metabolite and fatty acid profiles. Summations of docosapentaenoic acid and docosahexaenoic acid, and total n-3 LC-PUFA were enhanced in the liver and kidney of 5F supplemented lambs with a marked decrease in n-6/n-3 ratio and significant breed differences detected. There were generally no deleterious impacts on animal health status. A combination of 5% oil supplementation and lamb genetics is an effective and strategic management tool for enhancing n-3 LC-PUFA contents of heart, kidney and liver without compromising lamb health