Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or even millions of students to learn according to their interests without spatial and temporal constraints. Besides many advantages, online learning platforms face several challenges such as students’ lack of interest, high dropouts, low engagement, students’ self-regulated behavior, and compelling students to take responsibility for settings their own goals. In this study, we propose a predictive model that analyzes the problems faced by at-risk students, subsequently, facilitating instructors for timely intervention to persuade students to increase their study engagements and improve their study performance. The predictive model is trained and tested using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior of students according to their study variables. The performance of various ML algorithms is compared by using accuracy, precision, support, and f-score. The ML algorithm that gives the best result in terms of accuracy, precision, recall, support, and f-score metric is ultimately selected for creating the predictive model at different percentages of course length. The predictive model can help instructors in identifying at-risk students early in the course for timely intervention thus avoiding student dropouts. Our results showed that students’ assessment scores, engagement intensity i.e. clickstream data, and time-dependent variables are important factors in online learning. The experimental results revealed that the predictive model trained using Random Forest (RF) gives the best results with averaged precision =0.60%, 0.79%, 0.84%, 0.88%, 0.90%, 0.92%, averaged recall =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, averaged F-score =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, and average accuracy =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91% at 0%, 20%, 40%, 60%, 80% and 100% of course length.
Binarized Encoder-Decoder Network and Binarized Deconvolution Engine for Semantic Segmentation

Recently, semantic segmentation based on deep neural network (DNN) has attracted attention as it exhibits high accuracy, and many studies have been conducted on this. However, DNN-based segmentation studies focused mainly on improving accuracy, thus greatly increasing the computational demand and memory footprint of the segmentation network. For this reason, the segmentation network requires a lot of hardware resources and power consumption, and it is difficult to be applied to an environment where they are limited, such as an embedded system. In this paper, we propose a binarized encoder-decoder network (<italic>BEDN</italic>) and a binarized deconvolution engine (<italic>BiDE</italic>) accelerating the network to realize low-power, real-time semantic segmentation. <italic>BiDE</italic> implements a binarized segmentation network with custom hardware, greatly reducing the hardware resource usage and greatly increasing the throughput of network implementation. The deconvolution used for upsampling in a segmentation network includes zero padding. In order to enable deconvolution in a binarized segmentation network that cannot express zero, we introduce <italic>zero-aware binarized deconvolution</italic> which skips padded zero activations and <italic>zero-aware batch normalization embedded binary activation</italic> considering zero-skipped convolution. The <italic>BEDN</italic>, which is a binarized segmentation network proposed to be accelerated on <italic>BiDE</italic>, has acceptable accuracy while greatly reducing the computational and memory demands of the segmentation network through full-binarization and simple structure. <italic>BEDN</italic> has a network size of 0.21 MB, and its maximum memory usage is 1.38 MB. <italic>BiDE</italic> was implemented on Xilinx ZU7EV field-programmable gate array (FPGA) to operate at 187.5 MHz. <italic>BiDE</italic> accelerated the proposed <italic>BEDN</italic> within CamVid11 images of <inline-formula> <tex-math notation="LaTeX">$480\times {360}$ </tex-math></inline-formula> size at 25.89 frames per second (FPS) achieving a performance of 1.682 Tera operations per second (TOPS) and 824 Giga operations per second per watt (GOPS/W).
A Sequential Graph Convolutional Network with Frequency-domain Complex Network of EEG Signals for Epilepsy Detection

Automatic epilepsy seizure detection based on electroencephalography (EEG) signals has been a hot topic in the bioinformatics community. Recently, graph representations named complex networks have been increasingly utilized to characterize EEG signals. However, existing time-domain complex networks often suffer from undesired intra-class variance due to phase shift. Addressing this problem, we propose to obtain complex network representations in frequency domain where perfect data alignment can be achieved. The transformation to frequency domain highlights the urgency to retain sequential information in the signals. To this end, we propose to further extract features from the complex network representation using a novel deep model called Sequential Graph Convolutional Network (SGCN). Specifically, we incorporate state-of-the-art graph neural network (GNN) architecture with a novel sequential convolution operation which is key to preserving sequential information. Extensive experiments demonstrate the effectiveness and interpretability of our method. Our source code is available at https://github.com/JL-Wang-source-code/SGCN-for-epilepsy-detection.
Determination of Appropriate Conditions for Volatile Fatty Acids from Rubber Industrial Wastewater by GC-FID: Headspace Technique

The appropriate conditions of gas chromatography were developed and applied to the volatile fatty acids (VFAs) in from of acetic acid, propionic acid, and isobutyric acid analysis of wastewater from the rubber industry. The headspace technique was used for sample preparation. The purpose of this study is to find the appropiate preliminary conditions, and to identify an appropriate internal standard. The results showed that the linearity, instrumental precision and accuracy are preferable when using the commercially available mixed VFA standard solution to those obtained using the manually mixed standard solution. The chromatograph of VFA mixtures showed good separation by the HP-INNOVAX column. Quantification of VFAs in wastewater from the rubber industry was determined using an internal standard curve with crotonic acid as an internal standard. Crotonic acid performed better than butyric acid as an internal standard. Thus, these results provided evidence that this analytical method could be useful for routine monitoring of VFAs.
In vitro and in vivo antifungal activity of natural inhibitors against Penicillium expansum

Penicillium expansum is the causative agent of apple blue mold. The inhibitory effects of the capsaicin derived from Capsicum spp. fruits and yeast Hansenula wingei against P. expansum were evaluated in an in vitro and in in vivo assay using Fuji apples. The minimum inhibitory concentration of capsaicin determined using the broth micro-dilution method was 122.16 µg mL-1. Capsaicin did not reduce blue mold incidence in apples. However, it was able to delay fungal growth in the first 14 days of the in vivo assay. The in vivo effect of the yeast Hansenula wingei AM2(-2), alone and combined with thiabendazole at low dosage (40 µg mL-1), on the incidence of apple diseases caused by P. expansum was also described. H. wingei AM2(-2) combined with a low fungicide dosage (10% of the dosage recommended by the manufacturer) showed the best efficacy (100%) up to 7 days of storage at 21 oC, later showing a non-statistically different decrease (p > 0.05) after 14 (80.45%) and 21 days (72.13%), respectively. These results contribute providing new options for using antifungal agents against Penicillium expansum.
Impact of Sharing Point of Sales Data and Inventory Information on Bullwhip Effect

This paper reports the effect of sharing point of sales data and inventory information of downstream partners to mitigate the bullwhip effect in the fast moving consumer goods supply chain. The paper uses the "beer distribution game" in a laboratory setting using four treatments. The game was developed using Excel Macro and Visual Basic for Application Programing. In the control group, existence of the bullwhip effect is found due to order batching, shortage gaming and price fluctuation. The treatments investigate the existence of the bullwhip effect when either point of sales or inventory level information of downstream members are shared alone or the combination of both among supply chain partners. The results of these experiments conclude that, sharing point of sales data or inventory information individually will not reduce the bullwhip effect. However sharing point of sales data and inventory information together, will reduce the bullwhip effect in the focal industry to some degree.
Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming

Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of every agent, the ego agent can incorporate future interactions with human-driven vehicles into its planning. The problem is often formulated as a multi-agent game and solved using iterative algorithms operating on a discretized action or state space. Even if converging to a Nash equilibrium, the result will often be only sub-optimal. In this paper, we define a linear differential game for a set of interacting agents and solve it to optimality using mixed-integer programming. A disjunctive formulation of the orientation allows us to formulate linear constraints to prevent agent-to-agent collision while preserving the non-holonomic motion properties of the vehicle model. Soft constraints account for prediction errors. We then define a joint cost function, where a cooperation factor can adapt between altruistic, cooperative, and egoistic behavior. We study the influence of the cooperation factor to solve scenarios, where interaction between the agents is necessary to solve them successfully. The approach is then evaluated in a racing scenario, where we show the applicability of the formulation in a closed-loop receding horizon replanning fashion. By accounting for inaccuracies in the cooperative assumption and the actual behavior, we can indeed successfully plan an optimal control strategy interacting closely with other agents.
Fast numerical solution of optimal control problems for switched systems: An application to path planning

We consider a finite element approximation of the Hamilton-Jacobi-Bellman equation for the optimal control of switched systems. In particular, we show that the obtained finite dimensional problem belongs to a special class of problems that we already studied in a previous work. In that work, we also presented a simple and efficient solution algorithm.As an application, we present the problem of generating parking maneuvers for self-driving vehicles. The vehicle is described by a switched system. Each change between forward and backward motion is associated to a switching cost. This added cost penalizes the maneuvers with a larger number of direction changes and allows obtaining simpler and more human-like trajectories.
Diabetic Sensorimotor Polyneuropathy Severity Classification Using Adaptive Neuro Fuzzy Inference System

Diabetic sensorimotor polyneuropathy (DSPN) is an early indicator for non-healing diabetic wounds and diabetic foot ulcers, which account for one of the most common complications of diabetes, leading to increased healthcare cost, decreased quality of life, infections, amputations, and death. Early detection and intelligent classification tools for DSPN can allow correct diagnosis and treatment of painful diabetic neuropathy as well as a timely intervention to prevent foot ulceration, amputation, and other diabetic complications. Hence, to successfully mitigate the prevalence of DSPN, this study aims to depict an intelligent DSPN severity classifier using Adaptive Neuro Fuzzy Inference System (ANFIS). Michigan Neuropathy Screening Instrumentation (MNSI) was considered as the input for identification and stratification of DSPN. Patients have been classified into four classes: Absent, Mild, Moderate, and Severe. The model accuracy was validated with the results from different machine learning algorithms. The Accuracy, sensitivity, and specificity of the ANFIS model are 91.17±1.18%, 92±2.26%, 96.72±0.93%, respectively. The proposed classifier was used to classify the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trial patients and observed that in the first, eighth, and nineteenth EDIC years 18.31%, 39.45%, and 59.14% patients had different levels of DSPN. This study also investigates the changes in muscle activity during gait from three different lower limb muscles (vastus lateralis (VL), tibialis anterior (TA), and gastrocnemius medialis (GM)) electromyography (EMG) of DSPN patients with different severity levels classified by the proposed classifier and observed that VL and GM muscles show an increase in delay for activation peak and decrease in peak magnitude during gait with the progression of DSPN severity. Based on this observation, the ANFIS model was trained using the extracted EMG features for DSPN severity stratification and showed promising results. Our proposed ANFIS based severity classifier using both MNSI variables and EMG features will help health professionals to diagnose and stratify DSPN severity based on both signs and symptoms and electrophysiological changes due to DSPN.
Profiling purchasing behavior of Generation Z

Generation Z has emerged as one of the most mystifying consumers as they are tech-savvy, digitally connected and educated users of technologies in the marketplace. These digital natives are predicted to account for around 40% of all consumer shopping by 2020. Their cognitive power and social media networking have made them the market mavens who possess a wide range of information and consumer knowledge about many dimensions of the markets. To be the leader in the marketplace where so many options are available due to the free trade economy, marketers have to escalate their knowhow about their customers if they want to capture the attention of this segment of the market. The chief objective of the study is to find the purchasing habits of Generation Z, specifically from the angle of social influence and technology. A sample of 42 respondents was approached for a survey using a structured questionnaire. The results of this initial study indicate that Generation Z shoppers are coming up with unprecedented shopping habits and preferences. Using cross-tabulation analysis, results showed that some of the most classical influential factors such as product features, price consciousness and family recommendations have a significant effect on the purchasing habits of this generation. However, the results indicate the influence of computer literacy, peer and social influence and social media identity on the purchasing decision of Generation Z. The results of this study can be utilized to assist in predicting potential consumer adoption behavior and in designing favourable shopping environments that are compatible with these specific consumer traits.
EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.
Analysis of SVT-AV1 format for 4k video delivery

There has been considerable effort in designing new video coding and compression standards. The AV1 is one of the most recent formats valuable for OTT video services and internet delivery. In this paper SVT-AV1 is analyzed according to standard statistical techniques. Moreover, it is compared to VP9 and HEVC video formats in the case of 4k video delivery. The comparison is made for three video sequences from PSNR and time coding standpoint. The results show advantages of SVT-AV1 format.
Controllability over Graphs for Bilinear Systems over Lie Groups

This paper presents graph theoretic conditions for the controllability and accessibility of bilinear systems over the special orthogonal group and the general linear group, respectively, in the presence of drift terms. Such bilinear systems naturally induce two interaction graphs: one graph from the drift, and another from the controlled dynamics. As a result, the system controllability or accessibility becomes a property of the two graphs in view of the classical Lie algebra rank condition. We establish a systemic way of transforming the Lie bracket operations in the underlying Lie algebra, into specific operations of removing or creating links over the drift and controlled interaction graphs. As a result, we establish a series of graphical conditions for the controllability and accessibility of such bilinear systems, which rely only on the connectivity of the union of the drift and controlled interaction graphs. We present examples to illustrate the validity of the established results, and show that the proposed conditions are in fact considerably tight.
A Semantic Model for Context-Based Fake News Detection on Social Media

Context-based fake news detection provides means to define and describe a social context for news objects on social media, thereby facilitating detection of fake news through data analysis and patterns recognition. However, while content-based fake news detection has gained popularity with machine learning and NLP techniques, the context-based approach has seen very little exploitation. Therefore, it has become pertinent to significantly explore and integrate other technologies for context-based detection of fake news on social media. With semantic technologies capabilities to provide context-awareness for data, this paper analyses social media context and develops a taxonomy for entities classification. Furthermore, a semantic model is developed to describe classes extracted from the taxonomy towards fully semantically describing concepts, relations, instances, and axioms. The model would enhance fake news detection through semantic annotation for contextual features of news objects and datasets, providing a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not.
Reinforcement Learning Based Approach for Flip Attack Detection

This paper addresses the detection problem of flip attacks to sensor network systems where the attacker flips the distribution of manipulated sensor measurements of a binary state. The detector decides to continue taking observations or to stop based on the sensor measurements, and the goal is to have the flip attack recognized as fast as possible while trying to avoid terminating the measurements when no attack is present. The detection problem can be modeled as a partially observable Markov decision process (POMDP) by assuming an attack probability, with the dynamics of the hidden states of the POMDP characterized by a stochastic shortest path (SSP) problem. The optimal policy of the SSP solely depends on the transition costs and is independent of the assumed attack possibility. By using a fixed-length window and suitable feature function of the measurements, a Markov decision process (MDP) is used to approximate the behavior of the POMDP. The optimal solution of the approximated MDP can then be solved by any standard reinforcement learning methods. Numerical evaluations demonstrates the effectiveness of the method.
Improvement of a WSN for Quality Monitoring in Carasau Bread Industry: Hardware and Software Testing

The digitalization challenges and high quality standards offered by Industry 4.0 to the traditional bread manufacturing sector calls for engineering solutions, such as wireless sensors network (WSN). In this work it is presented the long-term test and statistical analysis of the improvement of the hardware and software modules of an existing WSN for Carasau bread industry.
Multi-camera Object Localization in Intelligent Transportation Systems

With growing human population and rapid urbanization the traffic infrastructure is becoming less efficient, causing decrease in road safety. Intelligent Transportation Systems (ITS) are increasingly evolving with the aim of relieving traffic congestion, reducing journey times and improving travel safety. In this paper we are presenting a multi-camera system, which can be used as an integral part of an ITS, as well as the method for localizing both vehicles and vulnerable road users (VRU) on a map projection, using this infrastructure. Furthermore, we evaluate localization results and discuss possible future improvements.