Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensuring Education Quality in E-Learning using the 4th Industrial Revolution Framework ROFSET

The impact that elearning has created over the last two decades has been revolutionary. It provides both an asynchronous and synchronous teaching environment that can be accessed by students around the world from urban to rural regions. There is no need for the student to physically travel to faraway places to seek quality education. With eLearning, students can learn from the best teachers while being in their homes. This provides tremendous opportunities to students in terms of affordability, and convenience. It is envisaged that elearning will continue to grow rapidly as the platform of choice. Unfortunately, elearning education is not widely accepted as a proper degree by many institutions and companies worldwide. There are good reasons for this unacceptability as mentioned below.
Interface stress transfer in an extruded ABS-rGO composite filament

Thermoplastic composite materials are typically used as feedstock filaments in material extrusion-based 3D printing. Understanding the mechanism of stress transfer at the filler/matrix interface in a composite filament helps in improving its properties. In this work, ABS/reduced graphene oxide (rGO) composite has been prepared by the solution mixing method, and 3D printable filaments are made by single screw extrusion. At filler loading of 0.2 wt. %, tensile strength of the composite filament is increased by ~40 % compared to the neat ABS filament. To understand the mechanism of stress transfer , a multi-scale mathematical model is presented. At the microscale, interface stress transfer is studied by using two-dimensional finite element (FE) analysis. The filler dimensions required for FE analysis are estimated using XRD and Raman analysis of rGO. In the present case, the filament making process yields filler dimensions comparable to the critical length, hence providing better stress transfer. The macroscopic strength of nanocomposite is estimated by using rule of mixtures. The better interface stress transfer and extrusion-induced filler orientation increases the filament strength. T.
Rule-based Text Extraction for Multimodal Knowledge Graph

—Textual information is widely integrated in visual tasks such as object/scene detection and image annotation. However, the textual information is not fully exploited, overlooking the wide background knowledge available for Web images. This work proposes a multimodal knowledge graph (KG) to represent the knowledge extracted from unstructured Web image surrounding text and to integrate the relationship between image and text entities. Existing multimodal KG works have mainly focused on advanced visual processes for extracting entities and relations from images, and only employed standard text processing techniques such as tokenization, stop word removal, and part-of-speech (POS) tagging to capture nouns only or basic subject-verb-object from text in the semantic enrichment process. Adversely, neglecting other rich information in the text. Thus, the proposed approach attempts to address this as an automatic relation extraction (RE) problem to extract all possible triples from the text information from simple to complex sentences, in constructing the multimodal KG which eventually can be used as a training seed for visual tasks. A linguistic analysis is performed on a set of Web news articles consisting of news images and their related text. The dependency relations and POS information obtained are used to formulate a set of domain-agnostic entity-relation extraction rules. A triple extractor incorporating these rules, is developed to extract the triples from a news articles dataset and construct the proposed MKG. The Precision and Recall metrics are used to evaluate the extractor’s performance. The evaluation results show that the proposed approach can extract entities and relations in the dataset with the precision score of 0.90 and recall score of 0.60. While the results are promising, the extraction rules can still be improved to capture all the knowledge.
Comparative analysis of modulation and demodulation methods of the control signals of rail line

Introduction: One of the ways of increasing the noise stability rail circuit is the use more informative waveform of control signal. Use in rail circuit modulated signals gives several informative selective traits in relatively narrow spectrum. The study of features of various modulation and demodulation methods with considering operating conditions of rail circuits is urgent. Purpose: the search for more informative forms of rail line control signal, the search for improved methods of signal processing of control current. Methods: For carrying out researches the method of signals decomposition in Fourier series and the simulation with the method of statistical tests have been sed. Results: Spectrum of frequency shift signal is wider than the spectrum of amplitude shift and phase shift signals by an amount equal to twice the frequency deviation. The research of correlation coherent and incoherent reception methods of rail line control signal has been carried out. In this case, we used the following models of interferences: white Gaussian noise, random pulse sequence and sinusoidal interference. It has been found that the transition from amplitude to frequency shift keying reduces signal/noise ratio by 2.4 dB, depending from the signal level and the type of noise. Losing amplitude shift keying receiver compared with the phase shift keying receiver is 5.6 dB. Conclusions: The use of more advanced methods of modulation and demodulation improves noise stability of rail circuit. When using correlation methods reception phase shift keying provides the best noise stability, and amplitude – the worst.
Impact of loading and unloading productivity on service user satisfaction

This study is aimed at analyzing the effect of port service performance, occupational safety, and health, and work safety on loading and unloading productivity and its impact on service user satisfaction at Yos Sudarso Tual Port, Maluku province, in Eastern Indonesia in 2020. What was found was the limited-service performance delivered to service users, resulting in dissatisfaction. This could have implications for the less-than-optimal loading and unloading performance at Yos Sudarso Tual Port. The study uses a quantitative method, with a path analysis model, with a total of 40 samples. Research respondents are users of loading and unloading services. The findings, in general, indicate that there is an effect of port service performance, occupational safety, and health and work security on loading and unloading productivity which in turn has an impact on increasing user satisfaction of Yos Sudarso Tual Port services. The key finding is that new investments are needed which will require the ongoing capacity building and development of several port authorities who are civil servants who will oversee port planning and operations and regulate access to key port services and facilities.
The Relationships between Depression and Physical Activity with Medication Adherence among Patients with Type 2 Diabetes Mellitus in Klang Valley, Malaysia

The Relationships between Depression and Physical Activity with Medication Adherence among Patients with Type Diabetes Abstract Studies among the Type 2 Diabetes Mellitus (T2DM) patients have shown that optimal mental health status is associated with better medication adherence leading to a good blood glucose control. This research aims to determine the relationship between psychological well-being and physical activity levels with medication adherence among the T2DM patients. A cross-sectional study was conducted on 240 diabetic patients at an outpatient primary care clinic from a tertiary teaching hospital in Klang Valley, Malaysia. The questionnaires used in the study were Medication Compliance Questionnaire (MCQ), Hospital Anxiety and Depression Scale (HADS), and Godine-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ). The research hypotheses were tested by the Pearson Chi-Square Test of Independence. Physical activity shows a non-significant relationship with medication adherence Concur with the previous study, depression is one of the crucial factors that correlate with poor medication adherence among diabetic patients in Malaysia. There is a statistically significant relationship between poor medication adherence with depression. Thus, appropriate psychological assessment must be offered to all T2DM patients as part of the treatment management in all Malaysian primary care services. This study proves that in managing a patient with T2DM, psychological well-being must be addressed at follow-ups by the attending doctor to ensure a favourable patient outcome and to reduce morbidity.
Title: Efficient and Reliable Control of coupled Stochastic and Partial differential Equations (ERC–SPE)

Stochastic differential equations (SDEs), which are coupled with (possibly non-linear) partial differential equations (PDEs), are a class of systems that naturally arise when modeling processes whose dominant probabilistic dynamics are affected by destabilizing second-order convection-diffusion-reaction effects. We refer to such control systems as SDE+PDE systems. A relevant example of a SDE+PDE system is provided by District Heating and Cooling Systems (DHCSs) [1, 2]. DHCSs deliver thermal energy to a network of buildings from an outside source. They offer numerous advantages over individual building apparatus, including greater safety and reliability, reduced emissions, and reliance on alternative fuels such as biomass or waste. However, due to low operating temperatures and limited flow capacity, customer demand can be met only if: 1) non-linear SDE-based dynamical models are leveraged to capture as many uncertain weather fluctuations as possible, and 2) the aforementioned stochastic models are coupled with sophisticated non-linear PDEs to accurately capture energy losses which often occur along pipelines and hinder performance. In turn, the overall setting require to efficiently and reliably control a complex SDE+PDE system. Unfortunately, still too few and specific works on SDE+PDE systems appear in the literature, hence calling for the design of novel tools for the efficient modelization and control of such systems.
Theme Park Storytelling: Deconstructing Immersion in Chinese Theme Parks

Little is known about how visitors become immersed in theme park storytelling. As the first of its kind, this study investigated visitors’ immersion through storytelling experiences in Chinese domestic theme parks. This research pursued participants’ subjective interpretation of immersion and the elements that were essential in their experiences. Thirty visitors were interviewed and data were analyzed using an inductive-deductive approach. The findings revealed that the immersion through theme park storytelling involves four stages, namely spatial-temporal immersion, sensory immersion, conceptual-imaginative immersion, and emotional immersion. Drawing on the findings, this study developed a framework depicting the mechanism of immersion. Achieving an in-depth understanding of how visitors interact with stories in a themed and experiential context, this research makes theoretically meaningful additions to both narrative and psychological literature. It also enables the industry to better understand and manage the visitor experience at theme parks.
Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models

Mobile application (app) reviews contain valuable information for app developers. A plethora of supervised and unsupervised techniques have been proposed in the literature to synthesize useful user feedback from app reviews. However, traditional supervised classification algorithms require extensive manual effort to label ground truth data, while unsupervised text mining techniques, such as topic models, often produce suboptimal results due to the sparsity of useful information in the reviews. To overcome these limitations, in this paper, we propose a fully automatic and unsupervised approach for extracting useful information from mobile app reviews. The proposed approach is based on keyATM, a keyword-assisted approach for generating topic models. keyATM overcomes the prob-lem of data sparsity by using seeding keywords extracted directly from the review corpus. These keywords are then used to generate meaningful domain-specific topics. Our approach is evaluated over two datasets of mobile app reviews sampled from the domains of Investing and Food Delivery apps. The results show that our approach produces significantly more coherent topics than traditional topic modeling techniques.
Are Experts Blinded by Feasibility? Experimental Evidence from a NASA Robotics Challenge

Resource allocation decisions play a dominant role in shaping a firm’s technological trajectory and competitive advantage. Recent work indicates that innovative firms and scientific institutions tend to exhibit an anti-novelty bias when evaluating new projects and ideas. In this paper, we focus on shedding light into this observed pattern by examining how evaluator expertise in the problem’s focal domain shapes the relationship between novelty and feasibility in evaluations of quality for technical solutions. To estimate relationships, we partnered with NASA and Freelancer.com, an online labor marketplace, to design an evaluation challenge, where we recruited 374 evaluators from inside and outside the technical domain to rate 101 solutions drawn from nine robotics challenges. This resulted in 3,869 evaluator-solution pairs, in which evaluators were randomly assigned to solutions to facilitate experimental comparisons. Our experimental findings, complemented with text analysis of the evaluators’ comments, indicate that domain experts exhibit a feasibility preference, focusing first on the feasibility of a solution as the primary indicator of its quality, while discounting riskier but more novel solutions. This results in a tradeoff in which highly feasible but less novel solutions are judged as being higher in quality, shedding light into why experts prefer more incremental ideas over more radical but untested ideas.
Ultra-small low power temperature-to-digital converter and verification methods of analog circuit with Trojan states

Accurate, small and low-power CMOS temperature sensors designed for multi-position temperature monitoring of power management in multi-core processors are proposed. The temperature sensors utilize the temperature characteristics of the threshold voltage of a MOS transistor to sense temperature and are highly linear from 60°C to 90°C. This is the temperature range needed for the power management applications where temperature sensors are strategically placed at multiple locations in each core to protect the processor from temperature-induced reliability degradation. A temperature-to-digital converter (TDC) that does not require either a reference generator or an ADC is also introduced, and it exhibits low supply sensitivity, small die area, and low power consumption. Both analog threshold voltage based temperature sensor and a prototype TDC designed to support multi-position thermalsensing for power management applications from 60°C to 90°C are implemented in an IBM 0. 13μm CMOS process with a 1.2V power supply. A new verification approach with several variants for identifying the number of stable equilibrium points in supply-insensitive bias generators, references, and temperature sensors based upon self-stabilized feedback loops is introduced. This provides a simple and practical method for determining if these circuits require a “start-up” circuit and, if needed, for verifying that the startup circuit is effective at eliminating undesired stable equilibrium points in the presence of process and temperature variations. These undesired stable equilibrium points are often referred to as Trojan states. It will be shown that some widely used approaches for verification do not guarantee Trojan states have been removed. Some of the methods introduced appear to be more practical to work with than others. A group of
VulCNN: An Image-inspired Scalable Vulnerability Detection System

Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intend to process the source code directly by treating them as text. To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and using them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming. In this paper, we aim to achieve both scalability and accuracy on scanning large-scale source code vulnerabilities. Inspired by existing DL-based image classification which has the ability to analyze millions of images accurately, we prefer to use these techniques to accomplish our purpose. Specifically, we propose a novel idea that can efficiently convert the source code of a function into an image while preserving the program details. We implement Vul-CNN and evaluate it on a dataset of 13,687 vulnerable functions and 26,970 non-vulnerable functions. Experimental results report that VulCNN can achieve better accuracy than eight state-of-the-art vul-nerability detectors (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, VulDeePecker, SySeVR, VulDeeLocator, and Devign). As for scalability, VulCNN is about four times faster than VulDeePecker and SySeVR, about 15 times faster than VulDeeLocator, and about six times faster than Devign. Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN can detect large-scale vulnerability. Through the scanning reports, we finally discover 73 vulnerabilities that are not reported in NVD.
Multi-layer bipartite structural features to analyze YouTube Social Network Maria Oikonomidou

This work investigates interactions on YouTube, concerning predicting missing or unseen interactions on multi-layer bipartite networks. More precisely, given a set of own interactions between YouTube users and videos, we measure how accurately we can predict comment interactions. We propose structural bipartite features, which enhance the performance of simple prediction models, to ﬁnd missing or unseen links. Experimental validation of the proposed approach is carried out on multi-layer networks formed on YouTube. We have crawled an extensive dataset of YouTube videos, the channels that own them, and the authors of their comments. Using a machine learning framework, we ﬁnd that we can predict future and unseen comment interactions on YouTube videos with precision 99%. We also show that to predict a day’s comment interactions it suﬃces to account network information generated 1 day prior. Our set-up is implemented on the MapReduce model. We propose two MapReduce algorithms, one that counts the bitruss number of an edge and one that clusters edges into blooms in a bipartite network.
THE EFFECT OF POSTPARTUM COPING SKILL CLASSES (PCSC) ON STRESS LEVEL, CORTISOL LEVELS, MATERNAL SELF- EFFICACY, AND BABY’S GROWTH AND DEVELOPMENT IN SEMARANG, CENTRAL JAVA

Postpartum stress incidence rate is quite high (26% - 85%). Several studies reveal that more than 50% of women experience stress and depression after giving birth and almost 80% of new mothers experience terrible sadness feelings after giving birth, often called postpartum stress. This study aims to determine the effect of postpartum coping skill classes (PCSC) on stress level, cortisol level, maternal self-efficacy (MSE), and baby's growth and development. This study employed an experimental design method with a randomized study pre-test post-test control group design. The study was conducted on 18 postpartum women who were given PCSC treatment which was integrated into postpartum classes and 17 women were given postpartum classes (PC) only. The study was conducted in 4 weeks. The data were analyzed statistically using a t-test method. Ethical clearance for the study was obtained. The results disclose that PCSC significantly decrease the PSS score (-2.66 ± 4.74, p = 0,029). Similarly, the cortisol level drops significantly (-38.53 ± 74.60, p = 0.031). In other respect, the MSE score between groups have significant mean difference (p = 0.013), but the weight gain and development of the baby between the two groups have no significant mean difference (p >0.05). The provision of coping skill classes through maternal classes for postpartum women was effective in decreasing stress levels, cortisol levels, and increasing MSE scores although it did not affect the baby's growth and development.
In vitro and in vivo wound healing-promoting activities of -lapachone

Kung H-N, Yang M-J, Chang C-F, Chau Y-P, Lu K-S. In vitro and in vivo wound healing-promoting activities of -lapachone. Am J Physiol Cell Physiol 295: C931–C943, 2008. First published July 23, 2008; doi:10.1152/ajpcell.00266. 2008. —Impaired wound healing is a serious problem for diabetic patients. Wound healing is a complex process that requires the cooperation of many cell types, including keratinocytes, fibroblasts, endothelial cells, and macrophages. -Lapachone, a natural compound extracted from the bark of the lapacho tree (Tabebuia avellanedae), is well known for its antitumor, antiinflammatory, and antineoplastic effects at different concentrations and conditions, but its effects on wound healing have not been studied. The purpose of the present study was to investigate the effects of -lapachone on wound healing and its underlying mechanism. In the present study, we demonstrated that a low dose of -lapachone enhanced the proliferation in several cells, facilitated the migration of mouse 3T3 fibroblasts and human endothelial EAhy926 cells through different MAPK signaling pathways, and accelerated scrape-wound healing in vitro. Application of ointment with or without -lapachone to a punched wound in normal and diabetic (db/db) mice showed that the healing process was faster in -lapachone-treated animals than in those treated with vehicle only. In addition, -lapachone induced macrophages to release VEGF and EGF, which are beneficial for growth of many cells. Our results showed that -lapachone can increase cell proliferation, including keratinocytes, fibroblasts, and endothelial cells, and migration of fibroblasts and endothelial cells and thus accelerate wound healing. Therefore, we suggest that -lapachone may have potential for therapeutic use for wound healing.