I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

Maintenance and Restriping Strategies for Pavement Markings on Asphalt Pavements in Louisiana

In Louisiana, most districts restripe their roadways using waterborne paints every other year; this strategy is questionable in terms of efficiency and economy. Meanwhile, previous studies showed substantial variability in the paint service life throughout the United States ranging between 0.25 and 6.2 years. Shortcomings in modeling the retroreflectivity of waterborne paints appear to significantly contribute to these variations as several studies predicted these values using degradation curves with a coefficient of determination (R2) as low as 0.1. Therefore, the objective of this study was to (i) develop new cost-effective restriping strategies using 4-inch (15-mil thickness) and 6-inch (25-mil thickness) wide waterborne paints when applied on asphalt pavements in hot and humid climates, and (ii) employ an advanced machinelearning algorithm to develop performance prediction models for waterborne paints considering the variables that are believed to affect their performance. To achieve these objectives, National Transportation Product Evaluation Program (NTPEP) data were collected and analyzed to evaluate the field performance of waterborne paints commonly used in Southern United States. Results indicated that 4-inch wide standard paints exhibited service life up to four years depending on the line color, traffic and initial retroreflectivity, while 4-inch wide high-build paints had a service life of at least three years. Based on a life-cycle cost analysis, it was concluded that LaDOTD could restripe their district roads every three years instead of the current two-year period using the same product (4-inch or 6-inch wide) saving about $20 or$2 million, respectively, every year when restriping a 5,000-mile network. Additionally two machine-learning models were developed with an acceptable level of accuracy, and that can predict the skip and wheel retroreflectivity of waterborne paints for up to three years using only the initial measured retroreflectivity and the anticipated project conditions over the intended prediction horizon, such as line color, traffic, air temperature, etc. These models could be used by transportation agencies throughout the United States to (1) compare between different products and select the best product for a specific project, and (2) determine the expected service life of a specific product based on a specified threshold retroreflectivity to plan for future restriping activities

On Pseudo Fuzzy Length Space and Quotient of Fuzzy Length Space

In this paper we recall the definition of fuzzy length space on a fuzzy set after that we recall basic definitions and properties of this space. Then we introduce the notion pseudo fuzzy length space on a fuzzy set to prove that the fuzzy completion of pseudo fuzzy length is a fuzzy length space. Finally we defined the quotient of a fuzzy length space then we defined the fuzzy length to the quotient space

Development of Cost-Effective High-Modulus Asphalt Concrete (HMAC) Mixtures Using Crumb Rubber and Local Construction Materials in Louisiana

One of the emerging solutions to enhance the durability of asphalt pavements is the use of a French asphalt mix known as “High-Modulus Asphalt Concrete (HMAC).” This mix uses a hard asphalt binder, high binder content (about 6%), and low air voids content as compared to Superpave mixtures. The key objective of this study was to develop a cost-effective HMAC mixture using crumb rubber and local materials in Louisiana. To achieve this objective, four HMAC mixtures were prepared using two asphalt binders (PG 82-22 and PG 76-22 plus 10% crumb rubber) and two Reclaimed Asphalt Pavement (RAP) contents (20% and 40%); additionally, a conventional Superpave mixture in Louisiana was prepared as a control mixture. The laboratory performance of these five mixtures was evaluated in terms of workability, dynamic modulus, rutting resistance, and cracking resistance. The AASHTOWare Pavement ME Design software was also used to estimate the long-term field performance of these mixtures. Results indicated that the HMAC mixture prepared with 10% crumb rubber and 20% RAP successfully met the French mix design specifications for HMAC and LaDOTD specifications. This HMAC mix outperformed the control Superpave mix in terms of dynamic modulus, rutting resistance, and cracking resistance. Additionally, this HMAC mixture can reduce the required asphalt thickness by 1.5 or 2 inches based on traffic level. The cost-effectiveness analysis indicated that this HMAC mixture was more costeffective than conventional Superpave mixtures in Louisiana. In addition, this mixture is environmentallyfriendly since it can reduce the disposal of scrap tires in landfills

Not All Children with Cystic Fibrosis Have Abnormal Esophageal Neutralization during Chemical Clearance of Acid Reflux.

PurposeAcid neutralization during chemical clearance is significantly prolonged in children with cystic fibrosis, compared to symptomatic children without cystic fibrosis. The absence of available reference values impeded identification of abnormal findings within individual patients with and without cystic fibrosis. The present study aimed to test the hypothesis that significantly more children with cystic fibrosis have acid neutralization durations during chemical clearance that fall outside the physiological range.MethodsPublished reference value for acid neutralization duration during chemical clearance (determined using combined impedance/pH monitoring) was used to assess esophageal acid neutralization efficiency during chemical clearance in 16 children with cystic fibrosis (3 to <18 years) and 16 age-matched children without cystic fibrosis.ResultsDuration of acid neutralization during chemical clearance exceeded the upper end of the physiological range in 9 of 16 (56.3%) children with and in 3 of 16 (18.8%) children without cystic fibrosis (p=0.0412). The likelihood ratio for duration indicated that children with cystic fibrosis are 2.1-times more likely to have abnormal acid neutralization during chemical clearance, and children with abnormal acid neutralization during chemical clearance are 1.5-times more likely to have cystic fibrosis.ConclusionSignificantly more (but not all) children with cystic fibrosis have abnormally prolonged esophageal clearance of acid. Children with cystic fibrosis are more likely to have abnormal acid neutralization during chemical clearance. Additional studies involving larger sample sizes are needed to address the importance of genotype, esophageal motility, composition and volume of saliva, and gastric acidity on acid neutralization efficiency in cystic fibrosis children

Numerical Analysis of Friction Stir Welding on an Alumunium Butt Joint

In this paper, we present a three-dimensional numerical analysis of friction stir welding on an alumunium butt joint. A thin sheet of aluminum marking material was embedded into the 6061-aluminum alloy panel and its rear weld path. The positions after friction stir welding were investigated by metallographic techniques. Looking at the visualized material flow pattern, a three-dimensional model was developed to numerically simulate the temperature profile and plastic effects. The calculated velocity profile for plastic flow in the immediate vicinity of the tool generally agrees with the visualized results. Increasing the tool speed while maintaining a constant tool feed rate increases the material flow near the pin. The shape and size of the predicted weld zone match the experimentally measured ones

Website Phishing Detection Using Machine Learning Techniques

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods