Miracle-dz/UniFAR-FTUs huggingface dataset:
Propuesta de un sistema de control, monitoreo y asistencia para optimización de recursos energéticos en el hogar

This article presents the process carried out to generate the proposal of a system which will have a multiplatform mobile application for the control and monitoring of household resources, such as drinking water, electricity, LP gas and temperature with humidity from air in conjunction with a neural network for the generation of tips for optimizing the consumption of these energy resources in the home. This algorithm is present on an online platform accompanied by the database, which contains all the records of the home sensors, and a REST API
Reconstrucción autónoma de un mapa métrico de una pista de competencias usando visión artificial

This article describes the development of an algorithm for the reconstruction of a metric map from the images captured by the Intel Real Sense camera on board the AutoNOMOS mini robot. The stages that make up the algorithm consist of the perspective correction of the images, detection of lane lines, association of key points and the reconstruction of the map. The results obtained are presented on a real track, carrying out the processing on board and
UvA-DARE (Digital Academic Recognizing and Linking Entities in Old Dutch Text: A Case Study on VOC Notary Records

The increased availability of digitised historical archives allows researchers to discover detailed information about people and com-panies from the past. However, the unconnected nature of these datasets presents a non-trivial challenge. In this paper, we present an approach and experiments to recognise person names in digitised notary records and link them to their job registration in the Dutch East India com-pany’s records. Our approach shows that standard state-of-the-art language models have diﬃculties dealing with 18th century texts. However a small amount of domain adaption can improve the connection of information on sailors from diﬀerent archives.
Automatic Error Detection for Image to Text Tools

The focus of the work discussed in this paper is to present a breakdown of the most prevalent errors that are present in automatically generated text and present the results of diﬀerent methods of automatically identifying and correcting these errors. Inspired by analysing the errors present in text generated by image to text generating tools this work is also beneﬁcial to other ﬁelds which use automatic text generating, for example bots in help centres and automatic tweeting tools. While bots and tweeting tools will not reproduce the exact errors as the image to text generation tools they will both beneﬁt from being able to identify the errors as this will make them appear more human like in their interaction with clients and tweeters. This work centres on a dataset of captions which were automatically generated by NeuralTalk2 from data provided by NIST. This dataset was manually classiﬁed and then compared to other datasets of captions generated by other image to text tools to ensure that the errors identiﬁed as being signiﬁcant were in fact signiﬁcant across other datasets of automatically generated text
Analysis of protein-ligand interactions of SARS-CoV-2 against selective drug using deep neural networks

In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak In this paper, we design a Deep Neural Network (DNN) model that accurately finds the protein-ligand interactions with the drug used The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug © 2018 Tsinghua University Press
Explicación visual de la predicción de un clasificador de imágenes

El aprendizaje automático se ha convertido en una herramienta necesaria en la industria actual. La mayoría de los métodos comúnmente usados, más allá de las métricas y pruebas realizadas en realidad no se comprenden las razones detrás de sus predicciones. En el trabajo se propone un nuevo método para entender de manera visual el trasfondo de una predicción realizada. Abstract. Machine learning has become a necessary tool in today's industry. Most of the commonly used methods, beyond the metrics and tests performed, don't actually understand the reasons behind their predictions. In the work, a new method is proposed to understand visually the background of a prediction made.
Antioxidant and antimicrobial activities of essential oil and extracts of fennel (Foeniculum vulgare Mill.) seeds from Pakistan

The present study was conducted to examine the antioxidant and antimicrobial activities of essential oil, methanol and ethanol extracts of fennel (Foeniculum vulgare Mill.) seeds native to Pakistan. The seed essential oil and extract yields from fennel seeds were found to be 2.81 and 6.21–15.63% w/w, respectively. GC and GC–MS analysis of the fennel essential oil revealed the presence of 23 compounds, with trans-anethol (69.87%), fenchone (10.23%), estragole (5.45%) and limonene (5.10%) as the major components. The fennel seed extracts contained appreciable levels of total phenolic contents (627.21–967.50 GAE, mg/100 g) and total flavonoid contents (374.88–681.96 CE, mg/100 g). Fennel essential oil and extracts also exhibited good DPPH radical scavenging activity, showing IC50 32.32 and 23.61–26.75 µg/ml, and inhibition of peroxidation 45.05 and 48.80–70.35%, respectively. Of the fennel essential oil and solvent extracts tested, 80% ethanol extract exhibited the maximum antioxidant activity, whereas the essential oil showed appreciable antimicrobial activity against selected strains of bacteria and pathogenic fungi. The results of the present investigation demonstrated significant (p < 0.05) variations in the antioxidant and antimicrobial activities of fennel essential oil and extracts. Copyright © 2009 John Wiley & Sons, Ltd.
Efficient adsorption of Ag(І) and Au(ІІІ) on modified magnetic chitosan with amine functionalities

AbstractSodium tripolyphosphate (TPP) crosslinked magnetic chitosan adsorbent was prepared and immobilized with pentamine moieties. The adsorption behavior of the prepared adsorbent toward Ag(І) and Au(ІІІ) was studied at different experimental condition using batch and column methods. The maximum adsorption capacity was found to be 1.7 and 2.3 mmol g−1 for Ag(І) and Au(ІІІ), respectively. The mechanism of interaction between metal ion and adsorbent was found to proceed via complex formation and/or ion exchange. Kinetics and thermodynamic studies showed that the adsorption followed pseudo-second order with endothermic nature. The regeneration of the loaded resin was performed using 1.0 M acidified thiourea and was found to be 95–97%. The flow rate of 1.0 mL min−1 in column experiments was found to be the most preferable rate.
Spectrum of Non-Motor Symptoms in Parkinson's Disease

Parkinson's disease is predominantly classified as a movement disorder. Beyond the textbook definition of rigidity, tremors, and bradykinesia, Parkinson's disease encompasses an entire entity of non-motor symptom complexes that can precede the motor features by many years. Despite their significant clinical importance, the awareness of non-motor symptoms is quite negligible. Sleep disorders, gastrointestinal dysfunction, olfactory disturbances, anxiety, and depressive episodes are some of the most common non-motor presentations. The wide-spread occurrence of olfactory symptoms and the low cost of the assessment, is favoring olfactory dysfunction as a potential biomarker in Parkinson's. Sleep disorders may manifest before the motor and autonomic symptoms and might be linked to concomitant sleeping disorders like insomnia, REM sleep disorders, restless leg syndrome, narcolepsy, or obstructive sleep apnea. Non-motor symptoms can deteriorate the quality of life in Parkinson’s patients. Early detection of non-motor symptoms can help in the diagnosis of Parkinson’s disease and can fairly improve the survival and prognosis of these patients.
Fuzzy Adaptive Control for a Class of Nonlinear System with Prescribed Performance and Unknown Dead-Zone Inputs

Based on fuzzy logic systems and prescribed performance technology, this paper investigated the stable tracking problem of a class of nonlinear systems with unknown dead-zone inputs. Firstly, the original system is transformed into an equivalent system. Then, a Nussbaum-type function is added in the predefined performance controller to estimate the unknown control direction, and the boundedness of all signals in the closed-loop system is guaranteed. In addition, tracking errors will always remain within the prescribed performance boundaries. Finally, a comparative simulation example shows the effectiveness of the method proposed in this paper.
Estimation of emissions of nonmethane organic compounds from a closed landfill site using a landfill gas emission model

Nonmethane organic compounds (NMOC) emissions from landfills often constitute significant risks both to human health and the general environment. To date very little work has been done on tracking the emissions of NMOC from landfills. To this end, a concerted effort was made to investigate the total annual mass emission rate of NMOC from a closed landfill site in South Manchester, United Kingdom. This was done by using field estimates of NMOC concentration and the landfill parameters into the Landfill Gas Emission Model embedded in ACTS and RISK software. Two results were obtained: (i) a deterministic outcome of 1.7218 x 10 kg/year, which was calculated from mean values of the field estimates of NMOC concentration and the landfill parameters, and (ii) a probabilistic outcome of 1.66 x 10 1.78 x 10 kg/year, which is a range of value obtained after Monte Carlo simulation of the uncertain parameters of the landfill including NMOC concentration. A comparison between these two results suggests that the probabilistic outcome is a more representative and reliable estimate of the total annual mass emission of NMOC especially given the variability of the parameters of the model. Moreover, a comparison of the model result and the safety standard of 5.0 x 10 kg/year indicate that the mass emission of NMOC from the studied landfill is significantly less than previously thought. However, given that this can accumulate to a dangerous level over a long period of time (such as the age of this landfill site); it may have started affecting the health of the people living within the vicinity of the landfill. A case is therefore made for more studies to be carried out on the emissions of other gases such as CH4 and CO2 from the studied landfill site, as this would help to understand the synergistic effect of the various gases being emitted from the landfill Copyright © 2013 International Energy and Environment Foundation All rights reserved.
Flood Vulnerability Analysis in Urban Context: A Socioeconomic Sub-Indicators Overview

Despite indicators-based assessment models for flood vulnerability being a well-established methodology, a specific set of indicators that are universally or widely accepted has not been recognized yet. This work aims to review previous studies in the field of vulnerability analysis in order to overcome this knowledge gap identifying the most accepted sub-indicators of exposure, sensitivity and adaptive capacity. Moreover, this review aims to clarify the use of the terms of vulnerability and risk in vulnerability assessment. Throughout a three-phase process, a matrix containing all the sub-indicators encountered during the review process was constructed. Then, based on an adaptation of the Pareto diagram, a set of the most relevant sub-indicators was identified. According to the citation count of each sub-indicator, indeed, 33 sub-indicators were chosen to represent the most universally or widely accepted sub-indicators.
AutoML for Video Analytics with Edge Computing

Video analytics constitute a core component of many wireless services that require processing of voluminous data streams emanating from handheld devices. Multi-Access Edge Computing (MEC) is a promising solution for supporting such resource-hungry services, but there is a plethora of configuration parameters affecting their performance in an unknown and possibly time-varying fashion. To overcome this obstacle, we propose an Automated Machine Learning (AutoML) framework for jointly configuring the service and wireless network parameters, towards maximizing the analytics’ accuracy subject to minimum frame rate constraints. Our experiments with a bespoke prototype reveal the volatile and system/data-dependent performance of the service, and motivate the development of a Bayesian online learning algorithm which optimizes on-the-fly the service performance. We prove that our solution is guaranteed to find a near-optimal configuration using safe exploration, i.e., without ever violating the set frame rate thresholds. We use our testbed to further evaluate this AutoML framework in a variety of scenarios, using real datasets.
Interpretable Epilepsy Detection in Routine, Interictal EEG Data using Deep Learning

Epilepsy, a common serious neurological disorder, is characterized by its frequently occurring seizures that cause its patients to be three times as likely to die prematurely. While the application of machine learning to EEG recordings has enabled the successful prediction of whether and when such seizures will occur, the reliable detection of epilepsy during seizure-free periods is lacking. As far as the authors are aware, this work proposes the ﬁrst deep learning approach for the latter task – and the second machine learning approach altogether. Additionally, it does so in an interpretable fashion to validate the proposed method for a more wide-spread adoption in health-care and to potentially unveil unknown epileptic biomarkers. The performance of the Tiny Visual Geometry Group (t-VGG) convolutional neural network is evaluated against Temple University Hospital’s EEG Epilepsy Corpus , a data set of variable-length EEG recordings gathered during routine checkups. The t-VGG network predicted individual 10 second EEG windows with an Area Under the Precision-Recall Curve (AUPR) of 93 . 02% for epileptic predictions and 55 . 85% for healthy ones – a signiﬁcant improvement of respectively 7 . 24pp and 18 . 6pp ( p < . 001) over the current state-of-the-art. Averaging window predictions per recording improved the t-VGG’s respective AUPR performances further to 95 . 52% and 77 . 27%. The Gradient-weighted Class Activation Mapping method for interpretability conﬁrmed that the model was able to learn sensible features with connections to well-known epilepsy markers.
Usage of Business Analytics and Supply Chain Performance – An Empirical Study of Sri Lankan Apparel Sector

Advances in technology and innovation require companies to embrace these new trends to compete and stay ahead in the business world. In particular, there is a need for companies to incorporate Business Analytics practices within their organizations. Business Analytics consists of two components: Information Systems and Business Process Orientation. This study aims to investigate the impact of the use of Business Analytics on the Supply Chain Performance in apparel companies in Sri Lanka. This research focuses on discussing the objectives developed to achieve the purpose of the study. To achieve this objective, this current study investigates the relationship between the Information System, Supply Chain Performance and the effect of the use of the Information System in the supply chains of Sri Lankan apparel companies. The study uses a quantitative approach. In this study, for quantitative analysis study performs regression analysis and decision tree analysis. This study identifies a positive relationship between the Information System and the Supply Chain Performance. For further future studies, it is advisable to extend this study by examining the performance of medium-and large-scale companies in the country.
Supramolecular assemblies based on amphiphilic cyclodextrins

Abstract The capacity of two types of amphiphilic β-cyclodextrin (CD) derivatives, heptakis-C6-alkylamido β-CD (I) and heptakis-C6-ammonium tetradecakis-O2,O3-alkyl β-CD (II), to form organized assemblies with l α-phosphatidylcholine-dipalmitoyl (dppc) and iotacarrageenan is described. These systems are studied using a Langmuir balance. The formation of stable mixed layers between I and II and dppc is observed. The interaction between II and iota-carrageenan leads to a dimunition in the fluidity of monomolecular layers. The physicochemical properties of I are described via variable temperature 1H-NMR studies.
Quality of Sentiment Analysis Tools: The Reasons of Inconsistency

In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment analysis tools on five public datasets, based on the quality of predictive results in the presence of semantically equivalent documents, i.e., how consistent existing tools are in predicting the polarity of documents based on paraphrased text. We observe that sentiment analysis tools exhibit intra-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents by the same tool, and inter-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents across different tools. We introduce a heuristic to assess the data quality of an augmented dataset and a new set of metrics to evaluate tool inconsistencies. Our results indicate that tool inconsistencies is still an open problem, and they point towards promising research directions and accuracy improvements that can be obtained if such inconsistencies are resolved.
Digital Reconfiguration of a Single Arm 3-D Bowtie Antenna

This communication proposes the reconfiguration of a coplanar-fed single arm bowtie antenna structure by relying on the integration of a digital tunable capacitor (DTC). The coplanar-fed single arm bowtie antenna is presented in two configurations. The first configuration is a 2-D topology with a modified ground plane arrangement. The second configuration employs vertical folding for the ground plane as well as the radiating arm. In addition, vertical copper inserts are embedded within the bowtie single arm to further enhance the compactness of the structure and transform it into a 3-D topology. Both antennas are initially designed to operate at 868 MHz for Internet of Things (IoT) applications, with a digitally reconfigurable frequency operation for wireless communication. The 2-D antenna structure exhibits a 77% size reduction at the IoT operational frequency of 868 MHz in comparison with a similar rectangular patch antenna operating at the same frequency. On the other hand, the 3-D antenna structure reaches 84% of size reduction at the same frequency. A figure of merit (FOM) is derived to prove the superior performance of the presented antenna structure.
POSITIVE PSYCHOLOGY AND ITS FRIENDS: REVEALED

such as this from “a space for legally practicing psychologists”, under the pretext that “it should watch out for the good name of Positive psychology is the subject of debate. It has received significant criticism, to which its defenders have naturally reacted. Above all, this article, attempts to establish reasons for criticizing the bases of Positive Psychology itself: positivity and scientism. Against the strong emphasis on the benefits of positive affects, studies show that negative affects can be positive, just as positive affects can be negative. Positivity is not the positive side (because there is no such side). What Positive Psychology says it has found out scientifically about happiness does not seem to be anything that was not already known. Even its best theories, such as the Positive Activity Model and the Broaden-And-Build Theory are impressively trivial and plagued by tautologies. The clear fallacy of the positivity ratio is also shown. Finally, it is suggested that happiness is not sustainable as a principle of life (its pursuit is not universal nor the best thing to do in life) or as a scientific goal, unless at the cost of reifying the subjective experience.
DYNAMIC BEHAVIOR BASED FIRE RECOGNITION USING TRANSFER LEARNING

: Artificial intelligence has its demand in almost every possible field, such as, medical, agriculture, military and so on. In depth, it is used to detect cancerous cells, lane detection, natural language processing, number plate recognition, and also in identification of fire. Fire has its own set of behavioral traits. Its static behavior includes color, texture, brightness and edge characteristics for identification. The movement characteristics and shape will fluctuates incessantly constitute the dynamic behavior of fire. The proposed approach presents an effective way of recognizing fire. It consists of two stages namely, the dynamic behavior extraction and static behavior extraction followed by classification. The pre-trained neural network is used for classification, which is termed as transfer learning. The first stage aids in ruling out all the unnecessary characteristics. The output of the first stage goes through the second stage, where all its static features are studied to put it under the correct category of
A Convenient Synthesis of 3-Alkyl-4-aminobutanoic Acids

A series of 3-alkyl-4-aminobutanoic acids were prepared via the Michael addition of nitromethane to 2-alkenoic esters, followed by catalytic hydrogenation of the resultant 3-(nitromethyl)alkanoic esters using 10% palladium on carbon in acetic acid, and acid hydrolysis of the reduction products
Perceptual quality of BRDF approximations: dataset and metrics

Bidirectional Reflectance Distribution Functions (BRDFs) are pivotal to the perceived realism in image synthesis. While measured BRDF datasets are available, reflectance functions are most of the time approximated by analytical formulas for storage efficiency reasons. These approximations are often obtained by minimizing metrics such as L2—or weighted quadratic—distances, but these metrics do not usually correlate well with perceptual quality when the BRDF is used in a rendering context, which motivates a perceptual study. The contributions of this paper are threefold. First, we perform a large‐scale user study to assess the perceptual quality of 2026 BRDF approximations, resulting in 84138 judgments across 1005 unique participants. We explore this dataset and analyze perceptual scores based on material type and illumination. Second, we assess nine analytical BRDF models in their ability to approximate tabulated BRDFs. Third, we assess several image‐based and BRDF‐based (Lp, optimal transport and kernel distance) metrics in their ability to approximate perceptual similarity judgments.
Lithium Recovery Techniques from Solid and Liquid Mineral Resources

Lithium and its compounds are important materials in industry. In this paper, lithium recovery methods both from solid ores and brines were summarized, and the key problems existed were also pointed out. More details for lithium recovery from brines by solvent extraction, ion-exchange, precipitation and calcination leaching methods were discussed, and the new trend for lithium recovery in the future was also carried out.