Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating urban socioeconomic inequalities through airtime top-up transactions data

Eradicating poverty in all its forms everywhere remains as the number one Sustainable Development Goal of the 2030 Agenda for Sustainable Development. Developing countries face challenges in measuring the progress of poverty rates at the intra-urban level because they use traditional data collection methods such as censuses that are costly in time and resources. Therefore, local and central governments need ways of producing reliable, accurate, and up-to-date indicators to design effective policies about resource allocation for poverty alleviation programs that prioritize the most vulnerable citizens. For this purpose, we propose to exploit patterns observed in developing countries, where mobile phone usage is pervasive even among the poorest, and the dominant mobile subscription modality is prepaid to purchase airtime credit in advance. Our study analyzes a novel digital source with more than 9M mobile airtime top-up transactions to calculate meaningful indicators of customer economic activity. We aggregate it at the neighborhood spatial resolution to build a regression model to predict the neighborhood socioeconomic status (per capita income). Using a Linear Regression with Regularization L2 (Ridge), we can explain the neighborhood socioeconomic status with a prediction rate of up to 74% for urban neighborhoods of Guayaquil and Quito, Ecuador.
Real World Data for Clinical Trials Designs using Machine Learning

A common problem in the pharmaceutical industry is tracking drug usage in the post approval phase. This paper proposes a solution to analyzing the efficacy of "off label" antiretroviral therapies (ART) using observational data. A drug which been approved by FDA for men, is prescribed to women to evaluate its efficacy although women were not included in the initial clinical trials. The drug prescribed to women off label is compared to an antecedent drug for efficacy. The study gives rise to challenges with RWD comprising both arms subject to lack of randomization. The principal issue is that the groups may not be comparable due to the inherent nature of observational data. This is a significant departure from randomized controlled trials (RCT) which are a time tested gold standard. In this setting, we propose approaches that use patient-level pre-treatment covariates to adjust for lack of randomization and facilitate a viable comparison of treatment outcomes.
HRV and GSR as Viable Physiological Markers for Mental Health Recognition

Mental stress has become a standard part of day-to-day life. However, experiencing long-term and high-level stress affects the daily life and wellness of the person. Consequently, an individual's performance and management ability degrade significantly in critical situations. Electrocardiogram (ECG), Galvanic Skin Response (GSR), Electromyogram (EMG), Skin Temperature (ST), and Respiration are essential physiological biomarkers to quantify stress effectively. This paper aims to classify the stress level with improved performance based on GSR and ECG-derived Heart Rate Variability (HRV) features using machine and deep learning algorithms. It uses the Stress Recognition in Automobile Drivers (SRAD) dataset, which contains a collection of multiparameter recordings from 17 healthy participants who drive on a prescribed route under various pressure conditions. The work uses Pearson's Correlation, RFECV, and LightGBM feature selection methods with different classifiers to reduce redundancy between features and enhance performance. The accuracy and F1-score for stress level classifications are computed and compared using machine and deep learning algorithms. For binary classification (stress vs. non-stress), Random Forest achieves the best classification accuracy of 93.96 % which is higher than previous works. It also provides an accuracy of 81.41 % for three-class (baseline vs. medium stress vs. high stress) stress level classification.
MMBDE: A Two-stage Hybrid Feature Selection Method From Microarray Data

The discovery of diagnostically significant genes from microarray data is essential for disease diagnosis and drug research. However, the difficulty of analyzing microarray data comes from its high dimensionality and small sample size. Feature selection can effectively remove irrelevant and redundant features, reduce data dimensionality, and improve the accuracy of classifiers. This paper proposes a two-stage hybrid feature selection method MMBDE based on the improved min-Redundancy and Max-Relevance (mRMR) and the improved Binary Differential Evolution (BDE) algorithm. The improved mRMR is used to reduce the feature dimensionality at a coarse-scale significantly. In contrast, the improved BDE is used to refine the feature dimensionality at fine-scale further and select the best features. The experimental results show that MMBDE successfully reduces the dimensionality of microarray gene expression data, obtains high classification accuracy, and extracts effective features closely related to diseases from microarray gene expression data. The relevant datasets and codes can be obtained from https://github.com/xwdshiwo/MMBDE.
Learning Domain-Specific Word Embeddings from COVID-19 Tweets

The COVID-19 global pandemic has been a major catastrophic event that impacted the world’s economy. During the pandemic there was a rise in the use of social media such as Twitter by people to express their reactions and responses to the global pandemic. This drove researchers to analyze these micro-blogging texts, using natural language processing (NLP) methods, to understand information inherent in those texts. Most of these NLP tasks employ the use of word embeddings in training neural network models. These word embeddings are mainly trained on general text corpus which produce sub-optimal performance when used in domain-specific NLP tasks such as in COVID-19 related tweets. In this paper, we present a learned COVID-19 tweets domain-specific word embeddings for use in COVID-19 related tweets NLP tasks. Our evaluation results show that our domain-specific COVID-19 tweets word embeddings perform better than pretrained general word embeddings in a downstream domain-specific NLP task. Our COVID-19 tweets word embeddings are available for use by researchers who wish to perform downstream NLP tasks with pretrained domain-specific COVID-19 tweets word embeddings.
Cash flow prediction of a bank deposit using scalable graph analysis and machine learning

Cash flow prediction of a bank is an important task as it is not only related to liquidity risk but is also regulated by financial authorities. To improve the prediction, a graph analysis of bank transaction data is promising, while its size, scale-free nature, and various attributes make the task challenging.In this paper, we propose a graph-based machine learning method for the cash flow prediction t ask. Our contributions are as follows. (i) We introduce an extensible and scalable shared-memory parallel graph analysis platform that supports the vertex-centric, bulk synchronous parallel programming paradigm. (ii) We introduce two novel graph features upon the platform: (ii-a) an internal money flow feature based on the Markov process approximation, and (ii-b) an anomaly score feature derived from other graph features.The proposed method is examined with real bank transaction data. The proposed graph features reduce the error of a long-term (31-day) cash flow prediction by 56 % from that of a non-graph-based time-series prediction model. The graph analysis platform can compute graph features from a graph with 10 × 106 nodes and 593 × 106 edges in 2 hours 20 minutes.
A study on customer preference towards online shopping with special reference to Tiruchirappall district

Electronic commerce becomes one of the essential characteristics in the internet area. According to UCLA Center for communication policy (2001), online shopping has become the third most popular internet activity, immediately following e-mail Using/instant messaging and web browsing. It is even more popular than seeking out entertainment information and news, two commonly thought of activities when considering what internet users do when online. Of internet users, 48.9 percent made online purchase in 2001, with three-quarters of purchases indicating that they make 1–110 purchase per year. Hence this study made to know the customer preference towards online shopping in Tiruchirappalli District with sample respondents of 60. This research also aims to find out the key factors that influence online buying behaviour of consumers and to identify the determinants of online purchase intention among customers.
COVID-19 Fake News Detection via Graph Neural Networks in Social Media

Recently it is convenient for people to seek out and consume news from social media, but misinformation including fake news and low-quality information also spreads which may have extremely negative impacts on individuals and society especially in the pandemics e.g., Covid-19. Previous fake news detectors view articles or tweets as i.i. d data and ignore the relation between them. In this paper we propose a novel fake news detection framework by exploring the similarity relation between tweets and mapping this problem into a semi-supervised classification task on a graph. We evaluate our proposed framework on a real-world social media dataset and the experimental results demonstrate the effectiveness of our proposed method comparing to different baselines.
Automated Detection of Cells with Static Interference for Nominal Power Parameter Recommendation

For coverage and capacity optimization, Uplink Power Control is one of the key steps, in addition to antenna tilting and Downlink Power Control. For self-organizing networks, automated algorithms for Uplink Power Control are a necessity. As Uplink Power Control affects the noise in the neighboring cells, we propose automated detection of static interference of cells based on a Random Forest model. On customer data obtained from LTE networks our model performs with 94% accuracy, on unseen cells. Subsequently, a teacher-student model proposed corrected Nominal Power Parameter based on the primary cell and its neighbors. Our recommendation beats the state-of-the-art baseline methods of recommending Nominal Power Parameter.
PHOTONIC CRYSTAL AND PHOTONIC CRYSTAL FIBERS COMMUNICATIONS

The development of all optical communications could benefit from the index guiding photonic crystal fibers. In communication the photonic crystal fibers could provide many new solutions. Conventional optical fibers have within the last decades revolutionized the communications industry and it is today a mature technology being pushed to its limit with respect to properties such as losses, single mode operation and dispersion. The spectra have been used by others to develop optical frequency standards. The process can potentially be used for frequency conversion in fiber optic network. In this system the dispersive properties can be controlled by the optical lattice making it possible to achieve phase-matched four wave mixing, like look the process taking place in the photonic crystal fibers. In this paper we will discuss the use of photonic crystal fibers in communications.
Designing a cellular manufacturing system considering decision style, skill and job security by NSGA-II and response surface methodology

Cell formation is a traditional problem in cellular manufacturing systems that concerns the allocation of parts, operators and machines to the cells. This paper presents a new mathematical programming model for cell formation in which operators’ personality and decision-making styles, skill in working with machines, and also job security are incorporated simultaneously. The model involves the following five objectives: (1) minimising costs of adding new machines to and removing machines from the cells at the beginning of each period, (2) minimising total cost of material handling, (3) maximising job security, (4) minimising inconsistency of operators’ decision styles in cells and (5) minimising cost of suitable skill. On account of the NP-hard nature of the proposed model, NSGA-II as a powerful meta-heuristic approach is used for solving large-sized problems. Furthermore, response surface methodology (RSM) is used for tuning the parameters. Lastly, MOPSO and two scalarization methods are employed for validation of the results obtained. To the best of our knowledge, this is the first study that presents a multi-objective mathematical model for cell formation problem considering operators’ personality and skill, addition and removal of machines and job security.
Electrocatalytic Activity of L-Cystine towards the Sensitive and Simultaneous Measurement of Nitrobenzene and 4-Nitrophenol: A Voltammetric Study

A new simple, sensitive and selective electrochemical method for the determination of Nitrobenzene (NB) and 4-Nitrophenol (4-NP) at L-Cystine modified glassy carbon electrode was developed with the help of cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques. Based on the effect of pH, a detailed electrochemical redox mechanism was proposed and the number of electrons involved in the R-NO2→ RNO2 ̄● step was determined. The kinetic parameters such as heterogeneous rate constants (ks) and charge transfer coefficient (α) values were calculated and was found to be as 0.711×10-6 s-1, 1.486×10-6 s-1 and 0.521, 0.501 for both NB and 4-NP respectively. The NB and 4-NP systems were controlled by diffusion process and the diffusion coefficients values were determined as 3.24×10-7 cm2/s and 7.04×10-7 cm2/s for NB and 4-NP respectively. The limit of detection (LOD) and limit of quantification (LOQ) values were evaluated as 7.46×10-6 M, 24.9×10-6 M with linear dynamic range from 30 μM to 105 μM for NB and 0.1049×10-6 M, 0.349×10-6 M with linear dynamic range from 0.5 μM to 6 μM for 4-NP respectively. The developed method was effectively applied for the simultaneous determination of NB and 4NP. Moreover the surface nature of the electrode was investigated by the electrochemical impedance spectroscopy. Finally the developed method was successfully applied for the real sample analysis with satisfactory recovery values having low RSD values. KeywordsNitrobenzene, 4-Nitrophenol, L-Cystine, Glassy carbon electrode, Electrochemical impedance spectroscopy Analytical & Bioanalytical Electrochemistry 2015 by CEE www.abechem. com Anal. Bioanal. Electrochem. , Vol. 7, No. 6, 2015, 739-751 740
Texture Analysis and Classification Using Wavelet Extension and Gray Level Co-occurrence Matrix for Defect Detection in Small Dimension Images

: Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.
QMLEx: Data Driven Digital Transformation in Marketing Analytics

This paper presents a data driven approach to replace expert driven business processes. The QMLEx methodology combines NLP algorithms to improve data quality, ML techniques to perform the task and exploits external data sources to eliminate the need for the expert input. The methodology is applied to our internal process devoted to creating groups of products with similar features, one of the most relevant use case in marketing analytics.
Extension de KLM au Pointage du Regard et Validation Gestuelle dans HoloLens: Extending KLM to Model Spatial Search, Gaze Pointing and Mid-Air Validation for Hololens

The advances in mixed reality head-mounted displays led researchers to investigate such systems but the design of mixed reality app could be laborious. Evaluate the effects of the arrangement of these interfaces in the physical world on user performance is complex. In this context, the use of a predictive model allows researchers to estimate, prior to the implementation, the usability issues of such applications. To this end, we propose a new KLM extension to model HMD-based interaction focusing on searching, pointing and validation tasks. First, we define new KLM operators and estimate through two user studies the estimated time for each operator. Then, we validate the model for an ecological task. This final study confirms that the model could be applied for predicting the interaction time on mixed reality interaction. Les progrès réalisés dans le domaine des casques de réalité mixte (HMD) font entrer ce domaine dans une nouvelle ère où les prototypes conçus et développés en laboratoire peuvent techniquement devenir des produits commerciaux. Toutefois, le problème réside désormais dans la conception de ces systèmes : il est difficile d'anticiper les effets de la disposition spatiale de l'interface et le développement ou l’évaluation d'applications de réalité mixte sont fastidieux. Dans ce contexte, l'utilisation de modèles de prédiction du temps de réalisation d'une tâche est une approche prometteuse pour aider les concepteurs à anticiper les problèmes d'utilisabilité dès les premières étapes du processus de conception (i.e. avant le développement de telles solutions interactives). Dans ce but, nous présentons une nouvelle extension de KLM pour modéliser l'interaction basée sur les HMDs en mettant l'accent sur la recherche spatiale d'une cible, le pointage du regard et la validation gestuelle. Nous définissons d'abord de nouveaux opérateurs KLM requis pour modéliser ces interactions, et déterminons expérimentalement l'unité de temps pour chaque opérateur. Ensuite, nous validons le modèle en comparant le temps prédit avec le temps mesuré pour une tâche écologique combinant plusieurs de ces nouveaux opérateurs. Cette étude confirme la validité de nos nouveaux opérateurs et leur conformité avec le modèle KLM.
Anchoring effect in visual information processing during financial decisions: An eye-tracking study.

When reading a ﬁ nancial disclosure document, subjects are faced with multiple information cues and might simplify decisional complexity by relying on heuristics. This study explores whether, in an attempt to ﬁ lter information from the Payment Account Fees Information Document (FID), subjects anchor their evaluation to a speci ﬁ c item, leading to biased ﬁ nancial choices. By detecting the visual search strategy in 70 subjects through eye tracking, we observed that people exhibited systematic visual anchoring to the top of the document, which corresponds to the Liquidity section that displays the Annual Fee. Moreover, data revealed that subjects sometimes fail to recognize the most advantageous products. This mainly occurs when the Annual Fee is high, even if the other charges compensate for that amount, clarifying the link between visual search strategy and ﬁ nancial decisions. Data also showed the role of ﬁ nancial literacy in modulating attention, as poorly ﬁ nancially literate subjects are more prone to anchoring bias. The ﬁ ndings contribute to the neuroeconomics literature on anchoring effect and highlight practical implications for ﬁ nancial regulators and managers involved in the ergonomics of documents.
Predicting mortality in elderly trauma patients: a review of the current literature

Purpose of review Advances in medical care allow patients to live longer, translating into a larger geriatric patient population. Adverse outcomes increase with older age, regardless of injury severity. Age, comorbidities, and physiologic deterioration have been associated with the increased mortality seen in geriatric trauma patients. As such, outcome prediction models are critical to guide clinical decision making and goals of care discussions for this population. The purpose of this review was to evaluate the various outcome prediction models for geriatric trauma patients. Recent findings There are several prediction models used for predicting mortality in elderly trauma patients. The Geriatric Trauma Outcome Score (GTOS) is a validated and accurate predictor of mortality in geriatric trauma patients and performs equally if not better to traditional scores such as the Trauma and Injury Severity Score. However, studies recommend medical comorbidities be included in outcome prediction models for geriatric patients to further improve performance. Summary The ideal outcome prediction model for geriatric trauma patients has not been identified. The GTOS demonstrates accurate predictive ability in elderly trauma patients. The addition of medical comorbidities as a variable in outcome prediction tools may result in superior performance; however, additional research is warranted.
Aging of Bitumen and Asphalt Concrete : Comparing State of the Practice and Ongoing Developments in the United States and Europe

Aging is a crucial factor in pavement performance and being able to determine its effect on a mixture is necessary to link its initial properties to the properties over time in order to ensure the intended service life. This is becoming more important now that climate change leads to increased variation in weather conditions, while environmental considerations cause changes in the constituent materials that are used. As a result, past experience is becoming less reliable. In this paper, the USA and European approaches to aging are compared, showing that those contain the same test equipment and almost identical conditions for aging. This allows the exchange of data and experience. The current tests are suitable for binders and give an indication of the sensitivity to aging. For short term aging, Rolling Thin Film Oven Test (RTFOT) conditioning gives a reasonable indication of bitumen aging during asphalt concrete production and construction. This only holds for penetration grade binders during hot mix production and construction. For long term aging, because of the many variables involved, developing a single test method to characterize aging sensitivity seems impossible. However, using more elaborate protocols in existing, practical tests can provide more information and the necessary input for kinetic aging expressions. A pressure aging vessel (PAV) protocol for testing at two temperatures and time intervals, specifically at 90 and 100 degrees Celsius and for 20 and 40 hours, respectively, is suggested. Using the same conditioning in characterizing materials for pavement construction and research will facilitate the exchange of data and enable faster developments.
Manipulating harvest maturity and ethylene to extend storage life of feijoa : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Palmerston North, New Zealand

The following articles were removed due to copyright restrictions: Rupavatharam, S. , East, A.R. , Heyes, J.A. (2015), "Combined effects of pre-storage 1-methylcyclopropene application and controlled atmosphere storage on 'Unique' feijoa quality", New Zealand Journal of Crop and Horticultural Science, 43:2, 134-143 Rupavatharam, S. , East, A.R. , Heyes, J.A. (2015), "Re-evaluation of harvest timing in 'Unique' feijoa using 1-MCP and exogenous ethylene treatments", Postharvest Biology and Technology, 99, 132-159 Rupavatharm, S. , East A.R. , Heyes, J.A. , (2013), "Opportunities to manipulate harvest maturity of New Zealand feijoa (Acca sellowiana) to enable sea freight export", Conference paper, Acta Horticulturae (under publication).