Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation

The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.Comment: Accepted into ICCBS 202

Sentiment Analysis Across Multiple African Languages: A Current Benchmark

Sentiment analysis is a fundamental and valuable task in NLP. However, due to limitations in data and technological availability, research into sentiment analysis of African languages has been fragmented and lacking. With the recent release of the AfriSenti-SemEval Shared Task 12, hosted as a part of The 17th International Workshop on Semantic Evaluation, an annotated sentiment analysis of 14 African languages was made available. We benchmarked and compared current state-of-art transformer models across 12 languages and compared the performance of training one-model-per-language versus single-model-all-languages. We also evaluated the performance of standard multilingual models and their ability to learn and transfer cross-lingual representation from non-African to African languages. Our results show that despite work in low resource modeling, more data still produces better models on a per-language basis. Models explicitly developed for African languages outperform other models on all tasks. Additionally, no one-model-fits-all solution exists for a per-language evaluation of the models evaluated. Moreover, for some languages with a smaller sample size, a larger multilingual model may perform better than a dedicated per-language model for sentiment classification.Comment: Accepted to be published as part of SIAIA @ AAAI 202

An Expert System Approach to Audit Planning and Evaluation in the Belief-Function Framework

This is the author's final draft. The publisher's version is available from:

Beyond Bird Feed: Proso Millet for Human Health and Environment

Domesticated in 8000–10,000 BP in northern China, proso millet (Panicum miliaceum L.) is the best adaptive rotational crop for semiarid central High Plains of the USA, where average annual precipitation is 356–407 mm. Proso millet has multiple benefits when consumed as human food. Proso millet is rich in minerals, dietary fiber, polyphenols, vitamins and proteins. It is gluten-free and therefore, ideal for the gluten intolerant people. Proso millet contains high lecithin which supports the neural health system. It is rich in vitamins (niacin, B-complex vitamins, folic acid), minerals (P, Ca, Zn, Fe) and essential amino acids (methionine and cysteine). It has a low glycemic index and reduces the risk of type-2 diabetes. Unfortunately, in the USA, it is mostly considered as bird feed, whereas it is mainly used as human food in many other countries. Besides human health benefits, proso millet has an impeccable environmental benefit. Proso millet possesses many unique characteristics (e.g., drought tolerance, short-growing season) which makes it a promising rotational crop for winter wheat-based dryland farming systems. Proso millet provides the most economical production system when used in a two years wheat/summer fallow cropping system in semiarid High Plains of the USA. It helps in controlling winter annual grass weeds, managing disease and insect pressure and preserving deep soil moisture for wheat. Proso millet can also be used as a rotational crop with corn or sorghum owing to its tolerance for atrazine, the primary herbicide used in corn and sorghum production systems. Proso millet certainly is a climate-smart, gluten-free, ancient, and small grain cereal, which is healthy to humans and the environment. The main challenge is to expand the proso millet market beyond bird feed into the human food industry. To overcome the challenge, unique proso millet varieties for human food and ready-to-use multiple food products must be developed. This requires successful collaboration among experts from diverse disciplines such as breeders, geneticists, food chemists and food industry partners

Periodic Accretion From A Circumbinary Disk In The Young Binary UZ Tau E

Close pre-main-sequence binary stars are expected to clear central holes in their protoplanetary disks, but the extent to which material can flow from the circumbinary disk across the gap onto the individual circumstellar disks has been unclear. In binaries with eccentric orbits, periodic perturbation of the outer disk is predicted to induce mass flow across the gap, resulting in accretion that varies with the binary period. This accretion may manifest itself observationally as periodic changes in luminosity. Here we present a search for such periodic accretion in the pre-main-sequence spectroscopic binary UZ Tau E. We present BVRI photometry spanning 3 years; we find that the brightness of UZ Tau E is clearly periodic, with a best-fit period of 19.16 +/- 0.04 days. This is consistent with the spectroscopic binary period of 19.13 days, refined here from analysis of new and existing radial velocity data. The brightness of UZ Tau E shows significant random variability, but the overall periodic pattern is a broad peak in enhanced brightness, spanning more than half the binary orbital period. The variability of the H alpha line is not as clearly periodic, but given the sparseness of the data, some periodic component is not ruled out. The photometric variations are in good agreement with predictions from simulations of binaries with orbital parameters similar to those of UZ Tau E, suggesting that periodic accretion does occur from circumbinary disks, replenishing the inner circumstellar disks and possibly extending the timescale over which they might form planets

Predicting Blast-Induced Ground Vibrations in Some Indian Tunnels: a Comparison of Decision Tree, Artificial Neural Network and Multivariate Regression Methods

The present study compares three different techniques (decision tree, artificial neural network and multivariate regression analysis) for predicting blast-induced ground vibrations in some Indian tunnelling projects. The performance of these models was also compared to site-specific conventional predictor equations. A database consisting of 137 vibration records was randomly divided into training and testing sets for model generation. Eight input parameters (total charge, tunnel cross-section, maximum charge per delay, number of holes, hole diameter, distance from blasting face, hole depth and charge per hole) were selected for model development using bivariate correlation analysis. Results indicated that the decision tree is best suited for predicting vibrations. The decision tree further suggested that the intensity of near-field ground vibrations is mainly affected by total charge fired in a round, whereas the intensity of far-field vibrations is governed by maximum charge per delay and charge per hole. Conventional ground vibration predictors and machine learning techniques such as neural networks do not depict the relationship between input and output parameters. However, the present study substantiates that the decision tree can be a good tool for precise prediction of ground vibrations. Further, the decision tree can classify and relate different blast design parametersfor refining blast designs to control ground vibrations on site