Miracle-dz/UniFAR-FTUs huggingface dataset:
Empowering Nonprofit Organization to Reduce Donation Attrition with Machine Learning

Many Nonprofit organizations (NPOs) have a mission to empower vulnerable populations by providing safety and support services to build a healthier social community. The critical success factor for these organizations is generous and consistent donations from individuals, organizations, businesses, and governments. To remain financially viable and effective in mission, NPOs must achieve donation objectives. This demands a better understanding of donation activities and more specifically propensity/churn of existing donors. An Artificial Intelligence (AI) technique, Machine Learning can play a vital role in gaining insight into patterns of donors' response over the time and for various campaigns. Such data driven insights can help organizations design effective and personalized campaigns that result in reduced donor churn, attract new donors, and increase per donor donation amount. In this paper, we present an innovative application of unsupervised machine learning technique (K-Means) used with a Recency, Frequency, and Monetary (RFM) model to help improve outcomes of a US-based NPO with a mission to help families in need.
Different Indoor Localisation Techniques using Smartphones

Indoor localisation is an active area of research with a wide range of potential applications and opportunities. With a focus on smartphone-based implementation, in this paper, a survey on the state of the art techniques used for indoor localisation is performed in detail. In particular, fingerprinting, triangulation, trilateration, hybrid Angle of Arrival (AoA)/ranging, and pedestrian dead reckoning techniques will be discussed for Indoor Positioning Systems (IPS) using smartphone-based technologies. Finally, we will conclude our paper by highlighting the key advantages and disadvantages of each technique followed by possible future directions for IPS in 6G wireless networks.
Personality Prediction with Natural Language Processing using Questionnaire Responses

As the modern IT revolution is booming at a rapid growth speed, organizations and recruiters are finding it increasingly challenging to select the ideal applicant from a large number of applicants with diverse skill sets and personalities. Hence, selecting a candidate with a suitable personality for respective job profiles is a very important and great challenge for the HR department nowadays. Out of various personality prediction methods available out there, Myers-Briggs Type Indicator or MBTI is famous and accurate for our purpose of creating a personality prediction system for selecting candidates based on their personality. This study took into account all sixteen MB-Model coordinates. A comparative study of Random Forest, Logistic Regression, SVM, XGBoost has been done to perform personality prediction, and accuracy and confusion matrix for performance measurement of the models. While using TF-IDF, for the personality categories like Introversion/Extroversion the accuracy is 80.46%, for Sensing/Intuition it is 88.70%, for Thinking/Feeling it is 81.21% and for Perceiving vs Judging it is 72.97% with the Logistic Regression algorithm. Using Count vectorization for tokenizing, the accuracy is 80.97% for Introversion/Extroversion, for Sensing/Intuition it is 88.93%, for Thinking/Feeling it is 77.92% and for Perceiving vs Judging it is 73.48% with XGBoost algorithm, which gave the best performance.
A Low Complexity Estimation Method of Entropy for Real-Time Seizure Detection

In recent years, many studies have proposed seizure detection algorithms, but most of them require high computing resources and a large amount of memory, which are difficult to implement in wearable devices. This paper proposes a seizure detection algorithm that uses a small number of features to reduce the memory requirements of the algorithm. During feature extraction, this paper proposes an entropy estimation method that uses bitwise operations instead of logarithmic operations to reduce the algorithm’s demand for computing resources. The experimental results show that the computing time can be reduced by about 81.58%. The seizure detection algorithm in this paper is implemented in an ultra-low power embedded system and performs 7 classification tasks in the Bonn data set to verify the performance of the algorithm. The average classification performance is: Accuracy (97.13%), Specificity (97.57%) and Sensitivity (98.42%). Compared with previous studies, the algorithm of this paper has comparable classification performance, but the proposed algorithm only needs 0.23 seconds from feature extraction to classification result, to the best of our knowledge, which is the seizure detection algorithm with the least computing time currently applied to wearable devices.
Investigating the Need for Pediatric-Specific Automatic Seizure Detection

Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life [1]. These children experience seizures, which disrupt their lives and directly harm the developing brain. Electroencephalography (EEG) is the main tool used clinically to diagnose seizures and epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis [2]. Automated detection systems are a powerful tool that can help address the issue by reducing expert annotation time. Research on the automatic detection of seizures in pediatric EEG has been limited. Most seizure detection methods have been developed and tested using larger numbers of adult EEG [3], [4]. However, research has shown that brain events in EEG change with ageing [5], [6]. Therefore, model trained on EEGs from adults may not be be suitable for children. To test this hypothesis, we trained a seizure detection model on adult EEG and tested on adult and pediatric EEG recordings.
A Study of How Abnormalities of the CREB Protein Affect a Neuronal System and Its Signals: Modeling and Analysis Using Experimental Data

It is well understood that the CREB protein is highly involved in neuronal mechanisms underlying memory and learning in mammalian brain, and deficiencies in CREB activity can result in transition to certain pathological conditions. In this paper, we use some published experimental data, along with a neuronal system composed of the Izhikevich neuron model, to characterize how CREB abnormalities can alter neuronal signals and the system behavior. The abnormal data are extracted from intracellular recordings collected from the neurons of transgenic mice expressing VP16-CREB - a constitutively active form of CREB - whereas the normal data are obtained from the wild-type mice neurons. Upon estimating the neuron model parameters from the experimental data, we observe that the model exhibits good fit to both normal and abnormal data, for various synaptic input currents. To study the effect of CREB abnormalities on the considered neuronal system, we use the information theoretic redundancy parameter. It basically measures - for the system output neuron - the amount of spike count information overlap that exists between the states of the stimulus currents injected to the input neurons. Our analysis reveals a noticeable increase in the information redundancy, when CREB behaves abnormally. This finding motivates further exploration of the biological implications of the information redundancy in neuronal systems, and its use as a parameter to model abnormalities in CREB and perhaps other important transcription factors involved in learning and memory.
A Generic Interface for x-in-the-Loop Simulations Based on Distributed Co-Simulation Protocol

Co-simulation is a key step in the development of today’s complex cyber-physical systems (CPS), specially in the integration and validation activities. However, performing a co-simulation involving models developed in different environments and possibly deployed in different platforms with mixed real-time and non real-time constraints is a challenging engineering task. A promising technology that could help overcome communication and synchronisation difficulties is the non-proprietary standard Distributed Co-simulation Protocol (DCP). This standard defines an application-level communication protocol, independent of the platform and the communication medium, that regulates the exchange of information between the co-simulation entities. This paper presents a co-simulation interface based on the DCP standard. It offers a novel approach to apply the DCP standard. Instead of using it as a model encapsulation mechanism, having to develop an specific DCP slave for each application, it is proposed to use it as a generic co-simulation interface. To this end, a Simulink library has been developed, allowing to connect models developed in Simulink with the outside world in an standardised way. Moreover, by exploiting the code generation potential of Simulink, a wide variety of devices become accessible, thus enabling x-in-the-loop simulations, which are commonly used tests in the verification and validation process of CPSs. This library has been tested in a soft real-time co-simulation application between a Simulink instance and an application running on a Xilinx Zynq Ultrascale+ System-on-Chip. As an additional contribution, an analysis of DCP synchronisation problems when simulating closed-loop systems composed of two slaves is performed. Finding that the main causes are the occurrence of random delays and that the simulations of the two slaves start at an arbitrary time. A possible solution to this problem is also presented.
Decoupling Observer for Contact Force Estimation of Robot Manipulators Based on Enhanced Gaussian Process Model

This paper aims at addressing the challenge of contact force estimation of robot manipulators with incomplete model information. A novel decoupling observer is proposed based on an enhanced Gaussian process (EGP) model. Specifically, the dynamic model of the robot manipulator is decomposed into the nominal part established using Euler-Lagrange theory and a residual dynamics term with no priori information. To improve the model accuracy, the Gaussian process regression (GPR) technique is adopted to develop a data-driven compensation term for the residual dynamics. Compared to the purely data-driven models, the advantage of the proposed EGP model lies in its computational efficiency as the information contained in the nominal model has been exploited. Based on the statistical information of the residual dynamics learned via GPR, a novel decoupling observer is presented for real-time force estimation. Due to its capability of decoupling the contact force from residual dynamics and system noises, the proposed observer approach is no longer dependent on an accurate dynamic model of the robot manipulator. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods.
An Algorithm for Auto-threshold for Mouth ROI

Lip reading technology is best possible solution of speech recognition in noisy environments. Lip reading is a methodology to interpret by lip movement without the involvement of audio. The accuracy of lip-reading technology is based on accurate mouth region of interest (ROI). Viola Jones algorithm is used for mouth region extraction. The accuracy by viola jones is affected by merge threshold parameter of cascade object detector. Due to incorrect threshold multiple bounding boxes appears for mouth ROI. The correct selection of merge threshold leads to single bounding box on mouth region. The technique to find appropriate threshold to extract mouth ROI is presented in this paper. The algorithm is applied on GRID and LRW dataset. Experiment is tested on both frontal and profile face video frames. The accuracy obtained on frontal face frames from GRID dataset is 100 % while 86.20% accuracy achieved with profile video frames from LRW dataset.
Smart Residential Water Leak and Overuse Detection System Using Machine Learning

This paper proposes a water leak detection framework for residential properties using machine learning. Water is an essential natural element, and water conservation is at the core of the global sustainable development goals set by the United Nations in 2015. While several research studies investigated water conservation at a larger scale, this study focus on residential water leakage and consumption. Several studies have reported that residential leakage can go unnoticed for a very long time, resulting in significant water waste. Sensor technologies and machine learning can help in the early detection of these leaks to minimize the wasted water at residential properties. The proposed leakage detection model was tested using a physical prototype, and experimental results show that the proposed model detects leakage and overuse with an overall accuracy of 87%. In addition, the proposed system proved efficacy in detecting almost all cases of the leak with a recall of 83%.
Employee Happiness Measurement System on Assigned Task in Agile Process

In organizations driven by technology, amidst the digital age, an agile working policy is the most utilized policy as it meets the demand of the current day's work style and is suitable for organizations that are ready for changes. However, it requires standardized and efficient internal management. A project management tool is one of the tools that help employees in an organization support their agile work styles by allowing for smooth collaboration and progress tracking. Furthermore, such organizations prioritize happiness at work. However, project management tools have yet to be suited for managing the happiness of work and workload systematically. According to the study, certain departments in the organization found themselves to have many daily tasks. Department heads or staff were incapable of seeing the overview or plan or distributing workload evenly. This study introduced a combination of employee workload and happiness management tools and project management tools to create appropriate workload planning and focus more on workload flexibility or happiness at work in the future strategically. Additionally, this tool helps decrease excessive daily workload and time spent on project management. Furthermore, happiness from each individual in the organization; executive, human resources, and head of the department; and executive will reduce work stress, which increases the happiness of staff within departments and allows better adaptation to agile working. and also help gain employee happiness levels in the future.
Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With Tidal Structure Based on LSM-Tree

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes.
Design of Efficient Speech Emotion Recognition Based on Multi Task Learning

Speech emotion recognition technology includes feature extraction and classifier construction. However, the recognition efficiency is reduced due to noise interference and gender differences. To solve this problem, this paper used two multi-task learning models based on adversarial multi-task learning(ASP-MTL). The first model took emotion recognition as the main task and noise recognition as the auxiliary task, and removed the noise part identified by the auxiliary task. After identifying the non-noise part, the second model was constructed. The second model took emotion recognition as the main task and gender classification as the auxiliary task. These two multi-task learning models can not only can use shared information to learn the relationship between different tasks, but also can identify specific tasks. This paper used Audio/Visual Emotion Challenge (AVEC) database and AFEW6.0 database,which were recorded in the field environment. Considering the problem of data imbalance between datasets, the data balance operation was carried out on the data sets in the process of data preprocessing. The paper shows an increase of around 10% in terms of accuracy and F1 score with the recent works using AVEC database and AFEW6.0 datasets, which proved that this paper has made a great progress in SER.
Numerical Stability Analysis of Solutions of PDEs

Abstract In this paper we investigate two iterative methods for solving one problem of nonlinear optics. The main goal is not only to find a stationary solution but also to investigate its stability. It is shown that both methods have very different stability properties and the less stable algorithm is close to the approximation of the physically important non-stationary problem. We also propose a new iterative algorithm for solving a more complicated problem which describes the optical conjugation in stimulated Brillouin backscattering with pump depletion. This algorithm is based on a symmetrical splitting scheme and the nonlinear interaction is approximated by using the special mass conservation property of the discrete problem. Thus, we obtain a conservative iterative algorithm. The results of the numerical experiments are presented and they confirm our theoretical conclusions.
Predicting COVID-19 Related Tweets Using Ensemble of Transformers Models

In the past few years, COVID-19 has been consid-ered one of the most dangerous pandemics in several countries. There is a lot of information circulating on social media platforms about COVID-19, some of it is reliable, while others may be exag-gerated or unfounded. Using machine learning-driven sentiment analysis is considered a valuable tool that helps understand the community's feelings regarding many issues like the COVID-19 outbreak. Developing an accurate model that can assess if a tweet is about COVID-19 is a challenging task. This study aims to classify the tweets whether it is about COVID-19 or not using deep learning and transformers models. The developed model improves the gathering of tweets data about the COVID-19 epidemic without relying only on keywords such as ‘covid’ or ‘coronavirus'. In this work, we proposed the best model based on an ensemble method that effectively combines three models which are: BERTweet-covid19-base-cased, BERTweet, and RoBERTa. We applied the models to the data set provided by the Zindi community. The best results were achieved over the tested dataset in terms of Log-Loss with a minimum value of 0.154,0.174,0.170, and 0.191 for the proposed ensemble model, BERTweet-covid19-base-cased, BERTweet, and RoBERTa respectively. Our proposed model is ranked first among all the participant teams.
Throughput Evaluation of UL-NOMA Systems with Successive Interference Cancellation and Repetition

Recently, as the number of Internet of Things (IoT) devices has been increasing, there is a growing demand for communications utilizing radio frequency efficiently. Nonorthogonal multiple access (NOMA) is a promising scheme for realizing it. In this paper, major attention has been focused on the combination of power domain NOMA in uplink channel (UL-PD-NOMA), configured grant (CG) transmission scheme, and successive interference cancellation (SIC). We show results of computer simulation to evaluate the UL-PD-NOMA+CG+SIC system. In the simulation, we assumed a simple scheme where communications take place under some simple assumptions. Many devices (~ 106 /km2) of user equipment (UE) in a single circular service area transmit packets sporadically to one base station (BS) located at the center of the service area. The UEs share the same radio frequency band and the BS performs SIC to separate superimposed signals. All packets are replicated and transmitted repeatedly. This “repetition-SIC” method is aimed at improving throughput. The packets transmitted with repetition enter successive packet time slots. The simulation results have shown that normalized throughput value of the packet increases from 2.70 to 3.59 when the whole system is not so busy, compared to the case which lacks packet repetition. Therefore, we confirm the improvement in performance of the UL-PD-NOMA+CG+SIC system.
Subcortico-Cortical Interactions Of Edge Functional Connectivity In Parkinson’s Disease

Functional changes in both subcortical and cortical brain regions influence the clinical phenotype of parkinson’s disease. Although these changes has been studied on node-centrical network analysis, the precise mechanism of the brain subcortico-cortical functional interactions resulting in the described clinical symptoms is unknown in parkinson’s disease. Here, we quantified regional functional interactions for each participant (patients, n=73, matched healthy controls, n = 76) by adopting an edge-centric network approach. After clustering the edges into communities by k-means clustering, we calculated community entropy within and between networks. Compared with healthy controls, we found that the entropy decreased across the subcortex but peaked in striatum (putamen) and cortex peaked in dorsal attention network (anterior cingulate cortex) in parkinson’s disease. Across spatial scales, the regional functional interactions between putamen and primary, heteromodal systems was decreased, which is systematically associated with the Hoehn and Yahr scales. Together, our findings reveal that the putamen may play a pivotal role in modulating subcortico-cortical interactions at the functional level, which expand the previous investigations on the pathologic mechanisms of parkinson’s disease.
Apollo XXI - an Astronomy Portal

Astronomers have to deal with complex data to gain important insights which is a time-consuming task. Machine Learning techniques can help astronomers to analyze astronomical data in a simplified manner. The proposed web application consists of three different models. The first model can predict whether some meteor shower can be seen from a particular location along with the date and the name of the meteors. The proposed model is found to be 100% reliable in the experiments carried out so far. The second model can predict whether a celestial body is a candidate, confirmed or false positive instance of an exoplanet, based on the Kepler telescope data. This model uses random forest classifier and the accuracy achieved is 90.1%. The third model can predict whether there will be a delay in rocket launch according to different weather conditions. This model is based on decision tree classifier and has an accuracy of 98.3%.