Miracle-dz/UniFAR-FTUs huggingface dataset:
Data analytics on performance of computing students

This study was conducted to determine the relationship between computing students' initial and final academic performance to support decision making in higher education institutions. The data used in this research contains the initial GPA and CGPA of 710 computing students for a period of 5 years. Test of normality for the final graduating results and linear regression models were fitted to the data to predict the overall performance based on the initial result. This study revealed that there are strong linear relationships between the GPAs and CGPAs of computing students over the period of this study. The contribution of this work is that the result enables the student and the teacher to understand the trend of students' academic performance for the purpose of decisionmaking. Keeping track of the performance of the students help provide support whenever is needed. This results aimed to decrease student dropout by means of facilitating student to predict their probability of success in computing courses after enrollment. In addition, teachers will be able to boost student performance in their courses as a result of enhanced determination of student's abilities to learn the course and finetuning teaching approaches and techniques.
Diagnosing PTSD using electronic medical records from canadian primary care data

Post-traumatic stress disorder (PTSD) can be a debilitating condition and early intervention can be instrumental in preventing patients' suffering. Identifying patients at risk for PTSD is challenging because of the limitations of the available data set, variations in the symptoms of PTSD for different patients, and misdiagnosis due to symptoms being shared with other conditions. In this preliminary study, we explore a small set of structured primary care data extracted from patients' electronic medical records (EMR) from Manitoba, Canada. This data has a small subset of PTSD positive cases, and is used to assess the feasibility of applying machine learning algorithms to diagnose PTSD. We developed three supervised machine learning models, a multi-layered perceptron artificial neural network (ANN) model, a support vector machine (SVM), and a random forest classifier (RF) to identify PTSD patients using 890 patients' records. These methods obtained 0.79, 0.78, and 0.83 AUC respectively, which are better than all of the previous work that used EMR data having comparable size as our data. This study is geared towards understanding the primary care standard for PTSD patients in Canada in general and military-veteran population and developing a case definition for PTSD. This initial result demonstrates that an automated PTSD screening tool can be developed based on historical medical data for further study. In our ongoing work, we are exploring the providers' chart notes from the EMR data, which is unstructured text data, to improve the model accuracy and understand the progression of PTSD.
Statistical and Machine Learning-based E-commerce Sales Forecasting

Market share analysis and sales forecasting have always been an important research area. It is of great significance to predict sales through existing information and provide guidance to merchants and markets to obtain higher profits. However, most of the traditional research focuses on brick-and-mortar retail stores, while few works studied E-commerce markets. In this paper, we use the historical data in the e-commerce market to establish the model to predict the sales. According to the characteristics of different data, three types of prediction models are: Incentive Auto Regressive Integrated Moving Average(I-ARIMA), Long Short-Term Memory(LSTM) and Artificial Neural Network(ANN). These three methods can deal with the problem with different accuracy requirements and different data types. This paper studies the advantages and disadvantages of the three types of models on different data sets, and provide important guidelines to merchants on their marketing strategies.
An International Comparison of K-12 Computer Science Education Intended and Enacted Curricula

This paper presents an international study of K-12 Computer Science implementation across Australia, England, Ireland, Italy, Malta, Scotland and the United States. We present findings from a pilot study, comparing CS curriculum requirements (intended curriculum) captured through country reports, with what surveyed teachers (n=244) identify as enacting in their classroom (the enacted curriculum). We address the extent that teachers are implementing the intended curriculum as enacted curriculum, exploring specifically country differences in terms of programming languages and CS topics implemented. Our findings highlight the similarities and differences of intended and enacted CS curriculum within and across countries and the value of such comparisons.
Segmentation of Very Sparse and Noisy Point Clouds

This paper summarizes an approach to segment 3D point clouds into drivable ground, obstacles, and overhangs. It was developed for outdoor Time-of-Flight cameras which only provide very sparse measurements. The proposed methodology takes advantage of the matrix-like data structure of the CMOS sensor for segmentation in order to increase efficiency. Furthermore, it was tailored to handle typical offhighway characteristics with different slopes and missing measurements and can be adapted to various mounting positions and vehicle properties. First, the algorithm processes the data column-wise using geometric relations. Afterward, the neighborhood of a measurement is considered to improve the initial classification. Finally, overhangs are separated.
Black Citric Acid Polymer (PN) Pozzolana Activated - Na-PN-Pozzolana-CE Material Synthesis Tested As Cationic Exchanger

It was showed that black citric acid polymer (PN) have capacities to be a potential raw materials for realizing a cationic exchanger seeing that the treated water solution through PN kept a brown color of brown citric acid polymers from PN during its activations with NaOH. So, the objective of this publication was to realize not only materials and process which could clarify the brown color of the PN treated water but also to realize immediately a cationic exchanger materials with at the end a clarified transparent water. Consequently, two raw materials have been selected to do these realizations: pozzolana and black citric acid polymer (PN). The first step was to elaborate a procedure for synthesizing PN-pozzolana material founded upon water as PN’s liquid vector, pozzolana/PN weigh relationship, used pozzolana’s oxygen total atoms contents, water/pn molar relationship and water/pozzolana molar relationship. Then, PN-pozzolana was characterized by NaOH-0.05N titration enabled to value the on surface PN, the total PN dispersed, the on vacuole PN with maybe pozzolana vacuole porosity recovery rate. Also, the PN-pozzolana material was used to clarify by PN a treated water cationic exchanger according to a procedure using a PN-tubular filter with well-defined experimental conditions leading to good results. The second step was to elaborate a procedure for synthesizing Na-PN-pozzolana-CE cationic exchanger founded upon PN-pozzolana activation with NaOH as described on bibliography followed by thermic treatment. Then, Na-PN-pozzolana-CE cationic exchanger was characterized by HCl-0.049N enabled to value the total Na+ dispersed on PN-pozzolana and Na+ on surface per total PN moles ratio. Results and discussions of the thermic treatment were done seeing that there were all the time weight diminution of activated Na-PN-pozzolana thermic treated. Finally, exchange cationic tests with CaCO3 solution were carried-out with different contact time on these Na-PN-pozzolana-CE cationic exchanger and the Ca2+ contents of treated water solution was followed by EDTA-complexometric titration. Results showed clearly that not only PN-pozzolana material could clarify the brown treated water but also Na-PN-pozzolana-CE is a good cationic exchanger with a maximal Ca2+ retained rate capacity around 93.27 [%].
Factors influencing the crowd participation in knowledge-intensive crowdsourcing

The crowd participation is critical to the sustained development of knowledge-intensive crowdsourcing (KI-C) and its supportive online platforms. Prior studies suggested and investigated a variety of factors that influence the crowd participation in KI-C. However, to our knowledge, a structured account and integration of the important factors influencing participation is still lacking. So this study aims to fill the gap by conducting a literature review. This review classifies factors into four categories, i.e., task properties, crowdsourcing platform characteristics, motivations of the crowd, and requesters' feedbacks, according to the process of crowdsourcing and its components. Furthermore, we present eighteen factors under these categories. The findings from this study may offer guidelines for developing comprehensive models and approaches to sustain the crowd participation in KI-C.
Discovering resilience in the brain

Background Neurobiological research has traditionally focused on vulnerability rather than on resilience to severe stress. So far, only a few neuroimaging studies examining resilience have used designs that allow disentangling of the neural correlates of resilience from those related to psychopathology or trauma-exposure alone. The aim of the present study was to identify structural brain correlates of resilience, their specific functional resting-state connectivity patterns and correlations with behavioral measures. Methods Multimodal MRI scanning was performed in three groups of police officers: (1) a resilient group (N = 29; trauma-exposed, no psychopathology), (2) a vulnerable group (N = 33; trauma-exposed, psychopathology), and (3) a control group (N = 19; no trauma, no psychopathology). Using whole brain and region-of-interest approaches, we examined gray matter volume, volume and shape of the hippocampus, white matter integrity, and resting-state functional connectivity using software tools from the FSL-library. Results In resilient police officers we found an increase in structural connectivity in a part of the corticopontine tract. We did not find specific patterns for gray matter volumes or volume and shape of the hippocampus. Areas directly adjacent to the structural connectivity results showed increased resting-state functional connectivity with the precuneus/posterior cingulate cortex region. Both structural and functional connectivity results correlated with scores for specific emotion regulation strategies. Conclusions Resilient police officers show a specific pattern of increased structural and functional connectivity that implies regions involved in self-processing and is correlated with the use of higher order emotion regulation strategies. Chapter 8
Revealing malicious remote engineering attempts on Android apps with magic numbers

Malicious reverse engineering is a prominent activity conducted by attackers to plan their code tampering attacks. Android apps are particularly exposed to malicious reverse engineering, because their code can be easily analyzed and decompiled, or monitored using debugging tools, that were originally meant to be used by developers. In this paper, we propose a solution to identify attempts of malicious reverse engineering on Android apps. Our approach is based on a series of periodic checks on the execution environment (i.e., Android components) and on the app itself. The check outcome is encoded into a Magic Number and send to a sever for validation. The owner of the app is then supposed to take countermeasures and react, by disconnecting or banning the apps under attack. Our empirical validation suggests that the execution overhead caused by our periodic checks is acceptable, because its resource consumption is compatible with the resources commonly available in smartphones.
Optimizing Information Sharing in Crowd Networks based on Reinforcement Learning

With the progress of human society and the development of machine learning technology in recent years, crowd intelligence has become more and more widespread. Specifically, to optimize the finishing quality of complicated tasks, various homogeneous and heterogeneous intelligent agents (including both human and machines) are interconnected to form more and more complex and diverse intelligent networks named crowd networks, where agents can interact and collaborate with each other. In the process of
Sleep-disordered breathing in epilepsy: epidemiology, mechanisms, and treatment Shobi Sivathamboo1,2, Piero Perucca1,2,3, Dennis Velakoulis4,

Epilepsy is a group of neurological conditions in which there is a pathological and enduring predisposition to generate recurrent seizures. Evidence over the last few decades suggests that epilepsy may be associated with increased sleep-disordered breathing, which may contribute towards sleep fragmentation, daytime somnolence, reduced seizure control, and cardiovascular-related morbidity and mortality. Chronic sleep–disordered breathing can result in loss of gray matter and cause deficits to memory and global cognitive function. Sleep-disordered breathing is a novel and independent predictor of sudden cardiac death and, as such, may be involved in the mechanisms leading to sudden unexpected death in epilepsy. Despite this, the long-term consequences of sleep-disordered breathing in epilepsy remain unknown, and there are no guidelines for screening or treating this population. There is currently insufficient evidence to indicate continuous positive airway pressure (CPAP) for the primary or secondary prevention of cardiovascular disease, and recent evidence has failed to show any reduction of fatal or nonfatal cardiovascular endpoints. Treatment of sleep-disordered breathing may potentially improve seizure control, daytime somnolence, and neurocognitive outcomes, but few studies have examined this relationship. In this review, we examine sleep-disordered breathing in epilepsy, and discuss the potential effect of epilepsy treatments. We consider the role of CPAP and other interventions for sleep-disordered breathing and discuss their implications for epilepsy management.
Systematic review of intra-abdominal and intrathoracic pressures initiated by the Valsalva manoeuvre during high-intensity resistance exercises

The Valsalva manoeuvre, intra-abdominal pressure (IAP) and intrathoracic pressure (ITP) play important roles in resistance training and common daily activities. The purpose of this review is to summarize the ITP and IAP responses to resistance exercises and to determine which exercises elicit the highest or lowest body pressure values under high-intensity resistance exercise. The PubMed, Scopus and Web of Science databases were searched until November 1, 2018. A combination of the following search terms was used: Valsalva manoeuvre, hold breath, controlled breathing, controlled breath, abdominal pressure, intrathoracic pressure AND weight training, resistance exercise, power lifting. The search process yielded 1125 studies, of which 16 were accepted according to the selection criteria and methodological quality. The highest IAP was recorded during squats (over 200 mmHg) followed by deadlift, slide row and leg press (161–176 mmHg), and the lowest IAP was found during bench press (79±44 mmHg). The highest ITP was elicited by the leg press, deadlift and box lift (105–130 mmHg), which were higher than during the bench press (95±37 mmHg) and slide row (88±32 mmHg). We recommend the bench press and slide row as exercises useful for beginners and individuals with hypertension. Untrained individuals should not use heavy squats, deadlift, box lift and clean exercises until they have undergone progressive adaptation for lifting high loads resulting in high IAP and ITP. The values of IAP and ITP during high-intensity exercise seem to be determined mutually by the position of the human body and the external load.
Scheduling in Real-time Systems

Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.
Identification of prognostic biomarkers of cortical stroke in mouse model

According to the World Health Organization (WHO), worldwide, 15 million people suffer stroke each year. Stroke, i.e. the sudden and severe reduction of blood flow to a brain region, is the second leading cause of death and the third leading cause of disability. Stroke is also a leading cause of dementia and depression. A focal brain damage inevitably causes a drastic alteration of the whole complex neural network that characterizes the affected area. Although stroke damage can be devastating, many patients survive the initial event and display a spontaneous recovery, which can be further increased by rehabilitation therapy. Recovery is possible due to a reorganization of spared areas and connections, i.e. neuroplasticity. Functional recovery is highly variable in stroke patients and strongly depends on many factors (lesion location and volume, etc.). Currently, there are no ways of predicting either the degree or time course of recovery in individual subjects. For these reasons, the identification of biomarkers is crucial in the design and interpretation of stroke rehabilitation trials. Therefore, the aim of this work is the development of new prognostic and therapeutic tools in preclinical models. In this study I exploit a mouse model of stroke, the Middle cerebral artery occlusion (MCAO), that shows a higher variability and is thus closer to the human condition. I conducted experiments to evaluate the occurrence of motor deficits using a battery of behavioral tasks: gridwalk test, skilled reaching test, and retraction task in the M-platform (a robotic device that permits to quantitatively evaluate several kinetic/kinematic parameters related to forelimb movement). Moreover, the ischaemic lesion and electrophysiological alterations was analysed by means of histology and electroencephalographic signals (EEG) respectively. I studied how these mechanisms are altered by stroke, combining the data all these parameters, in order to define possible biomarkers that predict long-term motor recovery. The results obtained, permit new opportunities for therapeutic approaches after stroke allowing the definition of more effective rehabilitation paradigms that can be translated into clinical practice.
Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

: Brain-computer interface is a technology which creates a new way of communication between a person’s brain and the external world. To achieve this objective, the brainwaves of a person must be gathered by using specialized devices and then classified in different categories that are associated with specific commands. In the process of brainwave gathering, brain activities of a person can be influenced by different types of stimuli to get the desired results and one of the most important and popular stimuli used in this field is steady state visually evoked potential. Based on this background, this review seeks to show and analyze a series of articles that have been executed around the world related to brain-computer interface applications using steady state visually evoked potential. This review has been executed with the objective of identifying the advantages and limitations of utilizing steady state visually evoked potentials, its main areas of application and the future challenges. Additionally, this review analyzes the different technologies involved to the implementation of state visually evoked potential systems such as signal classification techniques, electroencephalography devices, channels, verification metrics and experimental environments used in the research projects. In summary, this review intends to guide the scientific community about the different aspects involved in conducting research on the development of brain-computer interface applications using electroencephalography devices and steady state visually evoked potential.
Feature points-based image registration between satellite imagery and aerial images of agricultural land

Rapid advancement in remote sensing sensors has resulted in an enormous increase in the use of satellite imagery SI and images taken from unmanned aerial vehicles UAVs in a wide range of remote sensing applications. These applications include urban planning, environment monitoring, map updating, change detection, and precision agriculture. This paper focuses on an agricultural application of SI and UAV images. SI-UAV images possess high temporal, textural, and intensity differences due to rapid changes in agricultural crops with the passage of time. Feature points such as scale invariant feature transform SIFT , oriented FAST and rotated BRIEF ORB , and speeded-up robust features SURF are not invariant to such differences and underperform in SI-UAV image registration. To deal with this problem, we propose a new method that combines the strength of nearest neighbor NN and brute force BF descriptor matching strategies to register SI? UAV images. The proposed method is named NN-BF. For NN-BF first corresponding feature point descriptor matches are identified between SI-UAV images of the training set with overlap error. Then the corresponding descriptors are matched with the descriptors of SI images of the test set with NN strategy. The resulting descriptor matches are then further matched with the descriptors of UAV images of the test set using BF strategy. Finally, the descriptor matches obtained are processed with RANSAC to remove outliers and estimate a homography for image registration. Experiments are performed on an agricultural land image dataset. The experimental results show that the NN-BF method improves SIFT, SURF, and ORB feature point performances and also outperforms recently proposed feature matching strategies for remote sensing images. SIFT on average obtains 6.1% and 18.9% better precision scores than SURF and ORB with NN-BF, respectively. SIFT also obtains lower root mean square error than SURF and ORB with NN-BF.
An Efficient Statistical Strategy to Monitor a Robot Swarm

Detecting anomalies in a robot swarm play a core role in keeping the desired performance, and meeting requirements and specifications. This paper deals with the problem of detecting anomalies in a robot swarm. In this regards, an unsupervised monitoring approach based on principal component analysis and k-nearest neighbor is proposed. The principal component analysis model is employed to generate residuals for anomaly detection. Then, the residuals are examined by computing the proposed exponentially smoothed k-nearest neighbor statistic for the purpose of anomaly detection. Here, instead of using parametric thresholds derived based on the Gaussian distribution, a nonparametric decision threshold is computed using the kernel density estimation method. This provides more flexibility to the proposed detector by relaxing assumption on the distribution underlying the data. Tests on data from ARGoS simulator show efficient performance of the proposed mechanism in monitoring a robot swarm.