Miracle-dz/UniFAR-FTUs huggingface dataset:
Live and Kicking: Digital Live Broadcasting Technologies, Participating Strangers and News Mobility

This study investigates the interconnections between digital live broadcasting technologies and participating strangers in the news context, from a sociotechnical perspective. This is done by focusing on the LiveU Solo device, designed for live broadcasting by produsers on social media. Based on a thematic analysis of users’ comments on three leading video gadgets’ reviews of the Solo device on YouTube and on data gathered through semistructured in-depth interviews with a LiveU manager and several “prodnewsers” (news media produsers), findings point to financial and technical considerations as the main factors that propel the assessment of innovative digital live broadcast technologies by users. In so doing, they illustrate a particular form of actor participation in the news.
Improved Automated Essay Scoring using Gaussian Multi-Class SMOTE for Dataset Sampling

Automated Essay Scoring (AES) research efforts primarily focus on feature engineering and the building of machine learning models to attain higher consensus with human graders. In academic grading such as essay scoring, the scores will naturally result in a normal distribution, more commonly referred to as the bell curve. However, the datasets used do not always have such distribution and are often overlooked in most machine learning environments. This paper proposes a Gaussian Multi-Class Synthetic Minority Over-sampling Technique (GMC-SMOTE) for imbalanced datasets. The proposed GMC-SMOTE generates new synthetic data to complement the existing datasets to produce scores that are in a normal distribution. Using several labeled essay sets, some of which already have a substantial agreement between the machine learning model and human graders, learning from normal distribution datasets yields significant improvements. Improvements of 0.038 QWK score (5.8%) over the imbalanced dataset were observed. The experimental result has also shown that naturally occurring distribution in the automated essay scoring domain contributes to the most appropriate training dataset for machine learning purposes.
Supervised Machine Learning for Modelling STEM Career and Education Interest in Irish School Children

The number of unfilled jobs in Science, Technology, Engineering and Mathematics (STEM) is predicted to rise while young people’s interest in STEM careers and education is declining. Efforts to understand this decline have identified some potentially contributing factors based on statistical correlation analysis. However, these correlations can sometimes have relatively low effect-sizes. In these cases, Machine Learning (ML) techniques may provide an alternative by uncovering more complex patterns that provide stronger predictive accuracy. In this pilot study of Irish school children aged 9-13, supervised ML techniques were applied to model interest in pursuing education and careers in STEM fields. Despite the rather low coefficients from Pearson Correlation, the ML techniques were able to predict an individual’s interest in STEM careers and education with accuracies of 72.79% and 79.88% respectively. Our results suggest that ML techniques could be an important tool in understanding young people’s interest in STEM careers and education by providing models that derive more complex relationships.
Modeling Cognitive Load and Affect to Support Adaptive Online Learning

Online learning has been spreading with the increasing availability and diversity of digital resources. Understanding how students’ cognitive load and affect changes when using learning technologies will help us decipher the learning process and understand student needs. In this research, we focus on modeling learner’s cognitive load and affect using real-time physiological reactions. We explore what affect modeling contributes to the modeling of cognitive load, and how real-time cognitive load changes alongside learning activities. We want to further investigate if cognitive load modeling helps diagnose learner knowledge and facilitates improvement. We have designed two case studies: one where students are learning python with an e-learning system and another where they are practicing literacy skills with a web-based learning game. To collect learner data, we have implemented a sensing prototype consisting of an eye tracker and a wireless wristband.
Improving Zonal Fairness While Maintaining Efficiency in Rideshare Matching

Order dispatching algorithms, which match passenger requests with vehicles (agents) in ridesharing systems, are able to achieve high service rates (percentage of requests served) using deep reinforcement learning techniques to estimate the relative values of the different combinations of passenger-vehicle matches. While the goal of such algorithms is to maximize the service rate, this may lead to unintended fairness issues (e.g., high disparity between the service rates of geographic zones in a city). To remedy this limitation, researchers have recently proposed deep reinforcement learning based techniques that incorporates fairness components in the value function approximated. However, this approach suffers from the need to retrain should one wish to tune the degree of fairness or optimize for a different fairness function, which can be computationally expensive. Towards this end, we propose a simpler online approach that uses state-of-art deep reinforcement learning techniques and augments their value functions with fairness components during the matching optimization step. As no additional training is needed, this approach can be adapted to use any existing value function approximator and benefits from improved flexibility in evaluating different fairness objectives efficiently. In this paper, we describe several fairness functions that can be used by this approach and evaluate them against existing state-of-the-art deep RL based fairness techniques on standard ridesharing benchmarks. Our experiments show that our fairness functions outperform existing fairness techniques (i.e., it finds matching solutions that result in higher service rates and lower service rate disparity across zones), demonstrating the practical promise of this approach.
Adversarial bandits for drawing generalizable conclusions in non-adversarial experiments: an empirical study

Online educational technologies facilitate pedagogical experimentation, but typical experimental designs assign a fixed proportion of students to each condition, even if early results suggest some are ineffective. Experimental designs using multi-armed bandit (MAB) algorithms vary the probability of condition assignment for a new student based on prior results, placing more students in more effective conditions. While stochastic MAB algorithms have been used for educational experiments, they collect data that decreases power and increases false positive rates [22]. Instead, we propose using adversarial MAB algorithms, which are less exploita-tive and thus may exhibit more robustness. Through simulations involving data from 20+ educational experiments [29], we show data collected using adversarial MAB algorithms does not have the statistical downsides of that from stochastic MAB algorithms. Further, we explore how differences in condition variability (e.g., performance gaps between students being narrowed by an intervention) impact MAB versus uniform experimental design. Data from stochastic MAB algorithms systematically reduce power when the better arm is less variable, while increasing it when the better arm is more variable; data from the adversarial MAB algorithms results in the same statistical power as uniform assignment. Overall, these results demonstrate that adversarial MAB algorithms are a viable “off-the-shelf” solution for researchers who want to preserve the statistical power of standard experimental designs while also benefiting student participants.
on the use of alcohol and drugs: an integrative review

Objective : to present an integrative review of the scientific literature about the possible influences of religiosity and spirituality as a protection factor on alcohol and drug abuse. Method : this is an integrative literature review, based on the following databases: LILACS - Latin American and Caribbean Literature in Health Sciences; MEDLINE - Online Medical Literature Analysis and Recovery System; Nursing Database, covering the period from January 2009 to December 2019. Results : after reading and analysis, 12 articles were found. The influence of spirituality and religiosity was significant, acting in a protective way for healthy individuals and as a support in the treatment of addiction. Spiritual well-being must be holistically treated in patient care. However, a deficit was found in academic education regarding spirituality and its relationship with mental health. Conclusion : spirituality and religion are factors that exert a positive influence on drug addicts undergoing treatment and provide protection for the mental health of vulnerable people, requiring integration of this content in the academic training of health professionals.
Combining domain modelling and student modelling techniques in a single automated pipeline

In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a concept. We conducted an evaluation using six large datasets from a Python programming course, evaluating the performance of diﬀerent domain and student modelling techniques. The results showed that it is possible to develop a successful and fully automated pipeline which learns from raw data. The best results were achieved using alternating least squares on hill-climbing Q-matrices as domain modelling and exponential moving average as student modelling. This method outperformed all baselines in terms of accuracy and showed excellent run time.
Lexical Frequency in Heritage Speakers of Spanish: The Role of Language Exposure

This study examines the impact of lexical frequency on grammatical agreement in heritage speakers of Spanish and a Spanish monolingual control group. Research has provided evidence of frequency effects when accessing nouns and this effect was proven to be more prominent in bilingual speakers. This investigation expands on the antecedent psycholinguistic research on lexical access through agreement operations carried out on monolingual speakers of Spanish by examining this effect in two populations of heritage speakers of Spanish that differ in relation to their dominance in Spanish. Experiment 1 was a Elicit Production Task and Experiment 2 was a Picture Description Task. Retrieval of grammatical gender features is needed in both tasks; therefore, lexical frequency plays a role in accessing this information. Reaction times analysis showed frequency effects in both bilingual populations and, as predicted by the Frequency-Lag Hypothesis, larger frequency effects in the less Spanish-dominant group. Results contribute to the understanding of processing mechanisms in adult bilingualism, particularly in heritage speakers of Spanish in the United States. English and results showed that the more Spanish-dominant bilingual group (D+) self-reported a higher frequency of use of Spanish than the less Spanish-dominant bilingual group (D-) Spanish group. In the LEAP-Q, heritage speakers (D+) self-reported as having a higher competence in Spanish than the HSs (D-) group. As per exposure of Spanish in different contexts, HSs (D+) mean rates were also higher in all linguistic contexts than the HSs (D-). The DELE results show that mean rates of the more Spanish-dominant HSs (D+) were numerically higher than the less Spanish-dominant group (D-), but p -values showed no statistical significance between the bilingual groups.
From Messaging to Behavioral Strategy: Constructing a Model of Relationship- and Action-Focused Crisis Communication Principles

Existing crisis communication theories are useful in guiding organizations to choose crisis responses that help buffer them from a crisis by shaping how publics interpret the crisis. However, in crises, publics who suffer from negative consequences expect organizations to focus on problem-solving behaviors and eventual restoration of relationships. As a reflective theorizing of the perspectives of publics, this study introduces six relationship-and action-focused principles—relationship, accountability, promptness, inclusivity, disclosure, and symmetry (RAPIDS)—that emphasize how organizations should redress the need for problem solving and bridge with publics to build and maintain organization– public relationships before, during, and after a crisis. A survey ( N = 436) was conducted in the United States to test the construct reliability and validity of RAPIDS for three crises. The findings show conceptual and operational adequacy with an overarching latent construct (a second-order factor structure) encompassing all six principles. Furthermore, the construct is positively associated with forgiveness.
Insta-Reviewer: A Data-Driven Approach for Generating Instant Feedback on Students' Project Reports

Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains, including novice programming, short-essay writing, and open-ended questions. However, to the best of our knowledge, no previous study has investigated automated feedback generation on students’ project reports. In this paper, we present a novel data-driven system, named Insta-Reviewer , for automatically generating instant feedback on students’ project reports, using state-of-the-art natural language processing (NLP) models. We also propose a framework for manually evaluating system-generated feedback. Experimental results show that feedback generated by Insta-Reviewer on real students’ project reports can achieve near-human performance. Our work demonstrates the feasibility of automatic feedback generation for students’ project reports while highlighting several prominent challenges for future research.
Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process

Self-regulated learning (SRL) is a critical component of mathematics problem solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model, proposes that five cognitive operations (i.e., searching, monitoring, assembling, rehearsing, and translating) play a key role in SRL. However, these categories encompass a wide range of behaviors, making measurement challenging – often involving observing individual students and recording their think-aloud activities or asking students to complete labor-intensive tagging activities as they work. In the current study, we develop machine-learned indicators of SMART operations, in order to achieve better scalability than other measurement approaches. We analyzed student’s textual responses and interaction data collected from a mathematical learning platform where students are asked to thoroughly explain their solutions and are scaffolded in communicating their problem-solving process to their peers and teachers. We built detectors of four indicators of SMART operations (namely, assembling and translating operations). Our detectors are found to be reliable and generalizable, with AUC ROCs ranging from . 76-. 89. When applied to the full test set, the detectors are robust against algorithmic bias, performing well across different student populations.
A Comparative Cross-Sectional Psychological analysis of psychosocial and mental health issues faced by frontline healthcare professionals during COVID- 19 pandemic across various countries

Background: COVID-19 was declared as global pandemic by WHO by March 2020. Since then, overwhelming workload, inadequate human resources, technology, personal protective gear and workplace harassment, could cause stress, anxiety or depression among the healthcare professionals. Being care givers to the society it was imperative to evaluate and assess the impact of the pandemic to find a potential ground to make adequate amendments to ensure good mental health of our professionals. Aim: The present study was designed with the objectives to evaluate and compare levels stress, anxiety and depression in healthcare professionals working in India and countries other than India. Setting and Design: This was a cross-sectional study conducted among 200 participants (100 Indians and 100 from other countries-USA, Canada. Method: A questionnaire link through Google form was distributed among healthcare professionals after taking consent. The study was approved by the institutional ethical committee. Statistical Analysis: The results of the two groups were compared using chi square test to observe a difference of significance among them. Result and questionnaire regarding mental health of health care professionals, Severe stress and anxiety were significantly higher among Indian female HCPs (17% and 50% respectively) compared to other countries (6% and 22% respectively) while borderline stress (69%), anxiety (39%) and depression(26%) was more prevalent among healthcare professionals of other countries. Media projection about workplace violence and workplace job security needs to be taken well care of to protect the mental health of HCPS in India.
Examining the Inﬂuence of Spanish Heritage Language Learners’ Critical Language Awareness (CLA) Development on Ethnic Identity Formation

: As critical language pedagogies are being implemented in heritage language (HL) settings, there is an increasing need to examine the impact of critical approaches in Spanish HL speakers. The present study examines how the development of HL learners’ critical language awareness (CLA) inﬂuences ethnic identity formation in a university-level course that adopts a critical approach to HL instruction. As part of the curricular content, a CLA instructional intervention, consisting of a 4-week unit (10 h), was implemented. First, to measure ethnic identity, at the beginning (pre) and at the end of the semester (post), students completed the Ethnic Identity Scale (EIS) and provided comments with their answers. Additionally, in order to examine CLA development before and after the intervention, participants completed an existing questionnaire, which addresses topics such as language variation, language ideologies, and bilingualism. Overall, the results show that students’ CLA levels increased from “somewhat high” to “high”. Furthermore, participants reported different ethnic–racial identity statuses, which moved toward ethnic identity achievement. Higher CLA levels were associated with an achieved positive status. These ﬁndings can contribute to a better understanding of the link between students’ CLA and ethnic identity in HL educational settings, where a critical language pedagogy is applied.
Task management in IoT-Fog-Cloud environment employing static scheduling Techniques

In a distributed computing system, there are limited resources, which needs to be utilized effectively. Then for improving QoS Fog computing paradigm is an effective way, with suitable allocations. Thus, different resource scheduling and optimization algorithms exist. However, still, there is a scope to improve bandwidth, latency, energy consumption, and total communication cost in the Fog environment. In this work investigation is done to show significance of task management in such resource constrained environment. Various heuristics and meta-heuristic algorithms are evaluated using simulations, to show the task placement and their impacts by using 5 different Montage datasets from work flow sim tool kit for Fog-Computing environment. Then QoS parameters like cost, makespan, and energy consumptions are computed for various state-of-the-art techniques like Min-max, PSO, GA, ACO, and BLA. This shows the behaviour of these techniques with such different tasks and allocation environment configurations. Evaluated result parameters are collected and presented in the result section. This work shows the effectiveness of heuristics and meta-heuristics techniques to manage the tasks and their allocations in the Fog environment.
Network Intrusion Detection Using UNSW-NB15 Dataset: Stacking Machine Learning Based Approach

Network intrusion has become a prime concern issue for the industry and government organizations in the domain of the cyber-threat landscape. To counter this threat, a network intrusion detection system has been considered to be vital in identifying network traffic as normal or anomaly. Correct identification of the potential threat as an anomaly depends on the accuracy of the Network Intrusion Detection System (NIDS). Several approaches like single classical, hybrid, and ensemble methods are in practice to develop a network intrusion detection model. In this paper, two different stacking Machine Learning (ML) models with Extra Tree (ET) Classifier and Mutual Information Gain feature selection methods are proposed for better accuracy of the NIDS. We applied the models on the UNSW-NB15 packet-based dataset which contains the most recent attack types and experimentally proved that the testing accuracy of the stacking models is better than all individual models. Comparative results also depict that one of our proposed models shows better accuracy (96.24%) than any other existing competing models.
Liquid Metal-Based Microfluidic Metasurface for Controllable Electromagnetic Wave Reflection Attenuation

We demonstrated a liquid metal based microfluidic chip that enabled consistent, continuous and large liquid metal unit structure array reconfiguration. The chips were assembled into a metasurface, which preliminary achieved controllable electromagnetic wave reflection attenuation. The chip contained a $5{\times }6$ Galinstan split-ring resonator (SRR) array, and the SRRs could be reconfigured continuously by NaOH solution pressure driven. A fin shaped microvalve was designed and integrated onto the chip, which could withstand a high fluid driving pressure (over 210 kPa), so the SRRs could be reconfigured with a large split angle. The flow resistance of each SRR chamber was analyzed for consistent and robust SRRs deformation. The SRRs enabled more than 250° reconfiguration and showed consistent deformation with a standard derivation less than 8.5°. The metasurface showed 7.5 dB and 13.5 dB attenuation at 3 GHz and 4 GHz respectively. Also, with the different split angles, there could be three attenuation peaks that reach −10 dB when the frequency ranged from 2.9–3.1 GHz, 3.7–4.2 GHz and 4.8–5.0 GHz, respectively. The metasurface we proposed can be potentially used in multiband controllable electromagnetic wave absorption.