Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptations to Opioid Use Disorder Care During the COVID-19 Pandemic: A National Survey of Prescribers

Objectives Among opioid use disorder (OUD)-treating providers, to characterize adaptations used to provide medications for OUD (MOUD) and factors associated with desire to continue virtual visits post-COVID-19 pandemic. Methods In a national electronic survey of OUD-treating prescribers (July-August 2020), analyses restricted to X-waivered buprenorphine prescribers providing outpatient, longitudinal care for adults with OUD, quantitative and qualitative analyses of survey items and free text responses were conducted. Results Among 797 respondents, 49% were men, 57% ≥50 years, 76% White, 68% physicians. Respondents widely used virtual visits to continue prescribing existing MOUD regimens (79%), provide behavioral healthcare (71%), and initiate new MOUD prescriptions (49%). Most prescribers preferred to continue/expand use of virtual visits after COVID-19. In multivariable models, factors associated with preference to continue/expand virtual visits to initiate MOUD postpandemic were treating a moderate number of patients prepandemic (aOR = 1.67; 95%[CI] = 1.06,2.62) and practicing in an urban setting (aOR = 2.17; 95%[CI] = 1.48,3.18). Prescribing buprenorphine prepandemic (aOR = 2.06; 95%[CI] = 1.11,3.82) and working in an academic medical center (aOR = 2.47; 95%[CI] = 1.30,4.68) were associated with preference to continue/expand use of virtual visits to continue MOUD postpandemic. Prescribing naltrexone extended-release injection prepandemic was associated with preference to continue/expand virtual visits to initiate and continue MOUD (aOR = 1.51; 95%[CI] = 1.10,2.07; aOR = 1.74; 95%[CI] = 1.19,2.54). Qualitative findings suggest that providers appreciated virtual visits due to convenience and patient accessibility, but were concerned about liability and technological barriers. Conclusions Surveyed prescribers widely used virtual visits to provide MOUD with overall positive experiences. Future studies should evaluate the impact of virtual visits on MOUD access and retention and clinical outcomes.
Usability Design of Human-Machine Interaction Interface of Child Companion Robot in Wireless Network

With the core and small-scale family structure, parents and children have less time and opportunities to interact, which leads to the lack of care and emotional companionship for preschool children, which is easy to cause physical and mental disorders. In response to this phenomenon, a series of child care robot products have emerged on the market and have continued to be the focus of attention in the past two years. However, such products are still generally deficient in terms of interaction and content. It is difficult for users to choose and use. This paper takes the design and application of remote parent-child interaction of preschool children’s escort machine as the research object. First, it examines the physical, psychological, and behavioral characteristics of escort robots, namely, preschool children. To sum up, the remote parent-child interaction design strategy of children’s escort robot products is proposed for the design practice and industry reference of this paper. The experimental results show that the children in the experimental group pay more attention to the main content and off-topic content than the control group, and the attention rate is more than 95%. To a certain extent, it can be said that the games in the system have the ability of parent-child interaction. This paper abandons the traditional research direction of pursuing human-computer interaction between childcare products and high technology. Instead, it studies parent-child emotional interaction, so that the emotional interaction between parents and children is not replaced by technology products but helps parents deepen the relationship with their children. Emotional interaction allows parents to truly accompany their children to grow up.
How Transferable are Video Representations Based on Synthetic Data?

Action recognition has improved dramatically with massive-scale video datasets. Yet, these datasets are accompanied with issues related to curation cost, privacy, ethics, bias, and copyright. Compared to that, only minor efforts have been devoted toward exploring the potential of synthetic video data. In this work, as a stepping stone towards addressing these shortcomings, we study the transferability of video representations learned solely from synthetically-generated video clips, instead of real data. We propose SynAPT, a novel benchmark for action recognition based on a combination of existing synthetic datasets, in which a model is pre-trained on synthetic videos rendered by various graphics simulators, and then transferred to a set of downstream action recognition datasets, containing different categories than the synthetic data. We provide an extensive baseline analysis on SynAPT revealing that the simulation-to-real gap is minor for datasets with low object and scene bias, where models pre-trained with synthetic data even outperform their real data counterparts. We posit that the gap between real and synthetic action representations can be attributed to contextual bias and static objects related to the action, instead of the temporal dynamics of the action itself. The SynAPT benchmark is available at https://github.com/mintjohnkim/SynAPT.
New Magellanic Cloud R Coronae Borealis and DY Persei type stars from the EROS-2 database: the connection between RCBs, DYPers, and ordinary carbon stars

Context: R Coronae Borealis stars (RCB) are a rare type of evolved carbon-rich supergiant stars that are increasingly thought to result from the merger of two white dwarfs, called the Double degenerate scenario. This scenario is also studied as a source, at higher mass, of type Ia Supernovae (SnIa) explosions. Therefore a better understanding of RCBs composition would help to constrain simulations of such events. Aims: We searched for and studied RCB stars in the EROS Magellanic Clouds database. We also extended our research to DY Per type stars (DYPers) that are expected to be cooler RCBs (T ˜ 3500 K) and much more numerous than their hotter counterparts. With the aim of studying possible evolutionary connections between RCBs and DYPers, and also ordinary carbon stars, we compared their publically available broad band photometry in the optical, near, and mid-infrared. Methods: The light curves of ~70 millions stars, monitored for 6.7 years (from July 1996 to February 2003), have been analysed to search for the main signature of RCBs and DYPers: a large (up to 9 mag) drop in luminosity. Carbon stars with fading episodes were also found by inspecting numerous light curves of objects that presented an infrared excess in the 2MASS and Spitzer- SAGE and S^3MC databases. Follow-up optical spectroscopy was used to confirm each photometric candidate found. Results: We have discovered and confirmed 6 new Magellanic Cloud RCB stars and 7 new DYPers, but also listed new candidates: 3 RCBs and 14 DYPers. Optical and infrared colour magnitude diagrams that give new insights into these two sets of stars are discussed. We estimated a range of Magellanic RCB shell temperatures between 360 and 600 K. Conclusions: We confirm the wide range of absolute luminosity known for RCB stars, MV ˜ -5.2 to -2.6. Our study further shows that mid-infrared surveys are ideal to search for RCB stars, since they have thinner and cooler circumstellar shells than classical post-AGB stars. In addition, by increasing the number of known DYPers by ~400%, we have been able to shed light on the similarities in the spectral energy distribution between DYPers and ordinary carbon stars. We also observed that DYPer circumstellar shells are fainter and hotter than those of RCBs. This suggests that DYPers may simply be ordinary carbon stars with ejection events, but more abundance analysis is necessary to give a status on a possible evolutionnary connexion between RCBs and DYPers. Based on observations made with the CNRS/INSU MARLY telescope at the European Southern Observatory, La Silla, Chile. Figures 7-13 are only available in electronic form at http://www.aanda. org
Deep Learning and Classification Algorithms for COVID-19 Detection

The imaging modalities of chest X-rays and computed tomography (CT) are commonly utilized to quickly and accurately diagnose COVID-19. Due to time and human error, it is exceedingly difficult to manually identify the infection using radio imaging. COVID-19 identification is being mechanized and improved with the use of artificial intelligence (AI) tools that have already showed promise. This study employs the following methodology: The chest footage was pre-processed by setting equalizing the histogram, sharpening it, and so on. The transformed chest images are then retrieved through shallow and high-level feature mapping over the backbone network. To further improve the classification performance of the convolutional neural network, the model uses self-attained mechanism through feature maps. Numerous simulations show that CT image classification and augmentation may be accomplished with higher efficiency and flexibility using the Inception-Resnet convolutional neural network than with traditional segmentation methods. The experiment illustrates the association between model accuracy, model loss, and epoch. Inception-statistical Resnet's measurement results are 98%, 91%, 91%. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
Optimization of isothermal amplification method for Optimization of isothermal amplification method for Mycobacterium tuberculosis detection and visualization method Mycobacterium tuberculosis detection and visualization method for fieldwork for fieldwork

reliable, point-of-care, and economical methods for the laboratory diagnosis of tuberculosis. Nucleic amplification one of the most effective methods detecting infectious diseases and genetic Background/aim: Tuberculosis is still one of the most contagious diseases around the world. Key factors of tuberculosis control are rapid diagnostic, efficient treatment, and prevention of contamination by surveillance and monitoring. However, culture is the gold standard method for laboratory diagnosis of tuberculosis; the results are several weeks to obtain. In order to prevent contamination of tuberculosis, diagnosis must be made in short time and treatment should be started as soon as possible. The aim of this study is to optimize the loop-mediated isothermal amplification (LAMP) method, which provides a much faster and more sensitive result than the polymerase chain reaction (PCR) method and allows the replication of target nucleic acid sequences under isothermal conditions without the need for laboratory infrastructure. Materials and methods: Sputum samples were homogenized with 5% trypsin solution in CaCl 2 to obtain DNA. DNA was purified using QIAGEN QIAamp DNA mini kit. LAMP primers were design using Primer explorer V5 program according to IS6110 gene of Mycobacterium tuberculosis . NEB Bst 3.0 DNA polymerase kit was used for LAMP reactions. Besides, LAMP reactions were compared with TaqMan based RT-PCR method using NEB’s Taq polymerase kit. Finally, for visualization of LAMP products, lateral flow dipsticks that produced by Milenia Biotec, colorimetric 2X LAMP master mix that produced by NEB and 2% w/v agarose gel electrophoresis methods were used. Results: Optimum amplification temperature for LAMP was found to be 71.4 °C. The detection limit of the method was 10 2 CFU/mL and sensitivity was determined 100% compared to five different Mycobacterium species. Conclusion: The current study indicated that the LAMP-LFD and colorimetric LAMP protocol optimized with sputum samples can be reliable used as a rapid, sensitive and specific assay in the diagnosis of tuberculosis in the field.
COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function

The globe was rocked by unprecedented levels of disruption, which had devastating effects on daily life, global health, and global economy. Since the COVID-19 epidemic started, methods for delivering accurate diagnoses for multi-category classification have been proposed in this work (COVID vs. normal vs. pneumonia). XceptionNet and Dense Net, two transfer learning pre-trained model networks, are employed in our CNN model. The low-level properties of the two DCNN structures were combined and used to a classifier for the final prediction. To get better results with unbalanced data, we used the GEV activation function (generalized extreme value) to augment the training dataset using data augmentation for validation accuracy, which allowed us to increase the training dataset while still maintaining validation accuracy with the output classifier. The model has been put through its paces in two distinct scenarios. In the first instance, the model was tested using Image Augmentation for train data and the GEV (generalized extreme value) function for output class, and it got a 94% accuracy second instance Model evaluations were conducted without data augmentation and yielded an accuracy rating of 95% for the output class © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.
Predicting Academic Performance using a Multiclassification Model: Case Study

—Now-a-days predicting the academic performance of students is increasingly possible, thanks to the constant use of computer systems that store a large amount of student information. Machine learning uses this information to achieve big goals, such as predicting whether or not a student will pass a course. The main purpose of the work was to make a multiclassifier model that exceeds the results obtained from the machine learning models used independently. For the development of our proposed predictive model, the methodology was used, which consists of several phases. For the first step, 557 records with 25 characteristics related to academic performance were selected, then the preprocessing was applied to said data set, eliminating the attributes with the lowest correlation and those records with inconsistencies, leaving 500 records and 9 attributes. For the transformation, it was necessary to convert categorical to numerical data of four attributes, being the following: SEX, ESTATUS_lab_padre, ESTATUS_lab_madre and CONDITION. Having the data set clean, we proceeded to balance the data, where 1,167 data were generated, using the 2/3 for training and the remaining 1/3 for validation, then the following techniques were applied: Extra Tree, Random Forest, Decision Tree, Ada Boost and XGBoost, each obtained an accuracy of 57.41%, 61.96%, 91.44%, 59.65% and 83.3% respectively. Then the proposed model was applied, combining the five algorithms mentioned above, which reached an accuracy of 92.86%, concluding that the proposed model provides better accuracy than when the models are used independently meaning that it was the one that obtained the best result.
Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset

—Network intrusion detection is a key step in securing today’s constantly developing networks. Various experiments have been put forward to propose new methods for resisting harmful cyber behaviors. Though, as cyber-attacks turn out to be more complex, the present methodologies fail to adequately solve the problem. Thus, network intrusion detection is now a significant decision-making challenge that requires an effective and intelligent approach. Various machine learning algorithms such as decision trees, neural networks, K nearest neighbor, logistic regression, support vector machine, and Naive Bayes have been utilized to detect anomalies in network traffic. However, such algorithms require adequate datasets to train and evaluate anomaly-based network intrusion detection systems. This paper presents a testbed that could be a model for building real-world datasets, as well as a newly generated dataset, derived from real network traffic, for intrusion detection. To utilize this real dataset, the paper also presents an ensemble intrusion detection model using a meta-classification approach enabled by stacked generalization to address the issue of detection accuracy and false alarm rate in intrusion detection systems.
MOOC Dropout Prediction using FIAR-ANN Model based on Learner Behavioral Features

Massive Open Online Courses (MOOCs) are a transformative technology in digital learning that incorporates new techniques through video sessions, exams, activities, and conversations. Everyone leads a successful life in their professional and personal skills learning courses during COVID-19. The research concentrated on employing video interaction analysis to characterize crucial MOOC tasks, including predicting dropouts and student achievement. Our work consists of merely generating and picking the best characteristics based on the learner behavior for evaluating the dropout measure. To locate the frequent objects for feature creation, an association rule-FP growth approach is applied. The neural network is implemented using frequent itemset-3, which is used for feature selection. The evaluation metrics are calculated by using the Multilayer Perceptron (MLP) method. The metric values were then compared to the proposed model and some base supervised machine learning models namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The FIAR (Feature Importance Association Rule)-ANN(Artificial Neural Network) dropout prediction model was tested on the KDD Cup 2015 dataset and it had a high accuracy of over 92.42, which is approximately 18% better than the MLP-NN model. With the optimized parameters, we are solely focused on lowering dropout rates and increasing learner retention. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
CaM-Gen: Causally-aware Guided Text Generation

Content is created for a well-defined purpose, 001 often described by a metric or signal repre-002 sented in the form of structured information. 003 The relationship between the goal (metrics) 004 of target content and the content itself is non-005 trivial. While large-scale language models 006 show promising text generation capabilities, 007 guiding the generated text with external met-008 rics is challenging. These metrics and con-009 tent tend to have inherent relationships and 010 not all of them may be of consequence. We 011 introduce CaM-Gen: Causally-aware Gener-012 ative Networks guided by user-defined target 013 metrics incorporating the causal relationships 014 between the metric and content features. We 015 leverage causal inference techniques to identify 016 causally significant aspects of a text that lead 017 to the target metric and then explicitly guide 018 generative models towards these by a feedback 019 mechanism. We propose this mechanism for 020 variational autoencoder and Transformer-based 021 generative models. The proposed models beat 022 baselines in terms of the target metric control 023 while maintaining fluency and language qual-024 ity of the generated text. To the best of our 025 knowledge, this is one of the early attempts 026 at controlled generation incorporating a metric 027 guide using causal inference. 028
A Comprehensive Review and Application of Interpretable Deep Learning Model for ADR Prediction

—Drug safety is a pressing need in today's healthcare. Minimizing drug toxicity and improving the individual’s health and society is the key objective of the healthcare domain. Drugs are clinically tested in laboratories before marketing as medicines. However, the unintended and harmful effects of drugs are called Adverse Drug Reactions (ADRs). The impact of ADRs can range from mild discomfort to more severe health hazards leading to hospitalization and in some cases death. Therefore, the objective of this research paper is to design a framework based on which research papers are collected from both ADR detection and prediction domain. Around 172 research articles are collected from the sites like ResearchGate, PubMed, etc. After applying the elimination criteria the author categorized them into ADR detection and prediction themes. Further, common data sources and algorithms as well as the evaluation metrics were analyzed and their contribution to their respective domains is stated in terms of percentages. A deep learning framework is also designed and implemented based on the research gaps identified in the existing ADR detection and prediction models. The performance of the deep learning model with two hidden layers was found to be optimum for ADR prediction and further, the non-interpretability part of the model is addressed using a global surrogate model. The proposed architecture has successfully addressed multiple limitations of existing models and also highlights the importance of early detection & prediction of adverse drug reactions in the healthcare industry.
Decentralized Access Control using Blockchain Technology for Application in Smart Farming

The application of the Internet of Things (IoT) plays a crucial role in the fourth industrial revolution. The sophistication of technology due to the integration of heterogenous smart devices open a new threat from various aspects. Access control is the first line of defence to ensure that IoT resources are secure by preventing illegitimate users from gaining access to these resources. However, access control mechanisms face the limitation of technology in large scale IoT deployments since they are based on a centralized architecture. Significant research concerning decentralized access control solutions for securing IoT resources using combined techniques, such as blockchain, have caught much research attention in recent years. Nevertheless, research for decentralized access control for application in smart farming domain remain as a gap. Thus, this study presented a structured literature review on 81 articles related to the field of access control in IoT and blockchain technology to understand the challenges of centralized access control in securing IoT resources. This study serves as a foundation for decentralized access control using blockchain technology and its application to ensure the IoT actuators and sensors security with the aim to be applied in smart farming. This paper was deliberated based on systematic literature review that was searched from four different database platforms between 2018 and 2021. This study mostly addresses the relevant techniques/approaches including blockchain technology, access control model, key management mechanism and the combination of all three methods. The possible impacts, gap, procedures and evaluation of the decentralized access control are highlighted along with major trends and challenges. Keywords—Blockchain; access control; smart contract; internet of thing