Miracle-dz/UniFAR-FTUs huggingface dataset:
A Qualitative Investigation Into What Parents Want From an Online Behavioural Sleep Intervention for Children With Epilepsy

Many of the same sleep problems seen in typically developing (TD) children are frequently experienced by children with epilepsy (CWE). Behavioural sleep interventions (BSIs) are commonly and successfully used to treat these sleep problems in TD children and in some neurodevelopmental disorder populations. Therefore, BSIs should be effective in CWE, however, there are special seizure-related considerations for CWE and their parents which may be salient to consider in any future BSI development for this group. The current study sought to identify, from parents, if there were special considerations for the content and delivery of an online BSI for parents of CWE. Semi-structured interviews were conducted with nine mothers of CWE and thematic analysis was conducted on the interview data. Ten themes were apparent which represented what parents wanted from any online BSI for CWE. Parents wanted (i) other parents’ views and real-life experiences to be included, (ii) recognition of how changes over time may influence the appropriateness of using various sleep-management options, (iii) to be presented with a range of sleep management options from which they could select, (iv) personalised information and suggestions for behaviour-change options, (v) help to address child anxiety around sleep, (vi) for the advice and behaviour-change options to be practical, (vii) general educational information about sleep and the relationship between sleep and epilepsy, (viii) for parental worries and concerns to be acknowledged, (ix) to receive help, support, and reassurance around children’s sleep; and (x) to include the child in the intervention. It was clear that any online BSI would require specific adaptations and additions (to content and delivery format) to best meet the needs of parents of CWE. It is hoped that having identified what parents want from on online BSI for CWE will allow these factors to be acknowledged in future intervention development, with the intention to optimise parental engagement and intervention effectiveness. Practical suggestions for how these aspects could be integrated into any online BSI are suggested.
Volume Resuscitation in the Acutely Hemorrhaging Patient: Historic Use to Current Applications

Acute hemorrhage in small animals results from traumatic and non-traumatic causes. This review seeks to describe current understanding of the resuscitation of the acutely hemorrhaging small animal (dog and cat) veterinary patient through evaluation of pre-clinical canine models of hemorrhage and resuscitation, clinical research in dogs and cats, and selected extrapolation from human medicine. The physiologic dose and response to whole blood loss in the canine patient is repeatable both in anesthetized and awake animals and is primarily characterized clinically by increased heart rate, decreased systolic blood pressure, and increased shock index and biochemically by increased lactate and lower base excess. Previously, initial resuscitation in these patients included immediate volume support with crystalloid and/or colloid, regardless of total volume, with a target to replace lost vascular volume and bring blood pressure back to normal. Newer research now supports prioritizing hemorrhage control in conjunction with judicious crystalloid administration followed by early consideration for administration of platelets, plasma and red blood during the resuscitation phase. This approach minimizes blood loss, ameliorates coagulopathy, restores oxygen delivery and correct changes in the glycocalyx. There are many hurdles in the application of this approach in clinical veterinary medicine including the speed with which the bleeding source is controlled and the rapid availability of blood component therapy. Recommendations regarding the clinical approach to volume resuscitation in the acutely hemorrhaging veterinary patient are made based on the canine pre-clinical, veterinary clinical and human literature reviewed.
Changing Patterns of Salmonella enterica Serovar Rissen From Humans, Food Animals, and Animal-Derived Foods in China, 1995–2019

Salmonellosis represents a growing threat to global public health. Salmonella enterica remains the leading cause of bacterial foodborne diseases in China. Salmonella enterica serovar Rissen (S. Rissen) has been recognized as one of the emerging serovars among humans in different countries worldwide. However, knowledge on the prevalence of S. Rissen in China is largely lacking. To address essential epidemiological information for S. Rissen in China, a total of 1,182 S. Rissen isolates recovered from samples across the food chain were collected from 16 provinces or province-level cities between 1995 and 2019. Risk factors due to the consumption of animal-derived food products were also analyzed. We found S. Rissen is widely distributed, especially in the Eastern and Southern parts of China, and there is an increasing frequency in recent years as evidenced by the greater number of isolates recovered in 2016, 2017, and 2018. Interestingly, the majority of S. Rissen isolates recovered in this study were from human samples (63.4%; 749/1182), remarkably, 58.4% (438/749) were from asymptomatic carriers. We obtained most of the S. Rissen isolates from humans from Guangxi (59.5%; 446/749) and Shanghai (29.5%; 221/749). Among 302 human diarrheal isolates (40.3%; 302/749), we found 44.6% (139/311) of S. Rissen in children with diarrhea (age below 10 years old). This is of clinical significance as diarrhea is one of the crucial causes of child mortality globally and our findings here highlighted the importance of Salmonella infections in Chinese children. Additionally, S. Rissen isolates were also found to be associated with pork and poultry products in China. This study projected the most updated national-wide study of S. Rissen isolates obtained from different sources in China over the past two decades. Continued surveillance is warranted to further monitor this emerging serovar in China and elsewhere over the world.
Early Identification of Prolonged QT Interval for Prevention of Sudden Infant Death

Introduction: Long QT syndrome is the main arrhythmogenic disease responsible for sudden death in infants, especially in the first days of life. Performing an electrocardiogram in newborns could enable early diagnosis and adoption of therapeutic measures focused on preventing lethal arrhythmogenic events. However, the inclusion of an electrocardiogram in neonatal screening protocols still remains a matter of discussion. To comprehensively analyse the potential clinical value of performing an electrocardiogram and subsequent follow-up in a cohort of newborns. Methods: Electrocardiograms were performed in 685 neonates within the first week of life. One year follow-up was performed if QTc > 450 ms identified. Comprehensive genetic analysis using massive sequencing was performed in all cases with QTc > 470 ms. Results: We identified 54 neonates with QTc > 450 ms/ <470 ms; all normalized QTc values within 6 months. Eight cases had QTc > 480 ms at birth and, if persistent, pharmacological treatment was administrated during follow-up. A rare variant was identified as the potential cause of long QT syndrome in five cases. Three cases showed a family history of sudden arrhythmogenic death. Conclusions: Our prospective study identifies 0.14% of cases with a definite long QT, supporting implementation of electrocardiograms in routine pediatric protocols. It is an effective, simple and non-invasive approach that can help prevent sudden death in neonates and their relatives. Genetic analyses help to unravel the cause of arrhythmogenic disease in diagnosing neonates. Further, clinical assessment and genetic analysis of relatives allowed early identification of family members at risk of arrhythmias helping to adopt preventive personalized measures.
Proof-of-Concept of the Virtual Reality Comprehensive Balance Assessment and Training for Sensory Organization of Dynamic Postural Control

Accurate quantification of the impact of visual, somatosensory, and vestibular systems on postural control may inform tailor-made balance intervention strategies. The aim of this proof-of-concept study was to determine the safety, sense of presence, system usability, and face validity of a newly developed Virtual Reality Comprehensive Balance Assessment and Training (VR-ComBAT) in healthy young individuals. The VR-ComBAT included six balance condition: (1) stable surface with fixed virtual reality (VR) surroundings; (2) stable surface with blacked out VR surroundings; (3) stable surface with VR visual conflict; (4) unstable surface with fixed VR surroundings; (5) unstable surface with blacked out VR surroundings; and (6) unstable surface with VR visual conflict. Safety was evaluated using the number of adverse events, including scores on the Simulator Sickness Questionnaire. Sense of presence was evaluated using the igroup Presence Questionnaire (iPQ). System usability was assessed using the Systems Usability Scale (SUS). Friedman analyses with post hoc Wilcoxon Signed Rank tests were employed to demonstrate face validity by quantifying center of pressure (COP) changes in mean distance, mean velocity, and mean frequency in the anteroposterior (AP) and mediolateral (ML) direction across the six conditions. Twenty-three participants (27.4 ± 8.0 years old; 13 women) reported no adverse events. Participants scores on average 44.9 ± 9.6 on the iPQ and 79.7 ± 9.9 on the SUS. Post hoc analyses showed significant changes in COP-based measures when compared to baseline. The mean frequency change of COP showed direction-dependence in which increased frequency change in AP was observed while decreased change in ML was noted. The VR-ComBAT provides a safe, feasible, and cost-effective VR environment that demonstrates consistent sensory re-weighting between visual, somatosensory, and vestibular systems. Future studies should investigate whether VR-ComBAT can be used to inform precision rehabilitation of balance and fall prevention in older adults without and with neurological conditions.
Mining User Personality from Music Listening Behavior in Online Platforms Using Audio Attributes

Music and emotions are inherently intertwined. Humans leave hints of their personality everywhere, and particularly their music listening behavior shows conscious and unconscious diametric tendencies and influences. So, what could be more elegant than finding the underlying character given the attributes of a certain music piece and, as such, identifying the likelihood that music preference is also imprinted or at least resonating with its listener? This thesis focuses on the music audio attributes or the latent song features to determine human personality. Based on unsupervised learning, we cluster several large music datasets using multiple clustering techniques known to us. This analysis led us to classify song genres based on audio attributes, which can be deemed a novel contribution in the intersection of Music Information Retrieval (MIR) and human psychology studies. Existing research found a relationship between Myers-Briggs personality models and music genres. Our goal was to correlate audio attributes with the music genre, which will ultimately help us to determine user personality based on their music listening behavior from online music platforms. This target has been achieved as we showed the users’ spectral personality traits from the audio feature values of the songs they listen to online and verified our decision process with the help of a customized Music Recommendation System (MRS). Our model performs genre classification and personality detection with 78% and 74% accuracy, respectively. The results are promising compared to competitor approaches as they are explainable via statistics and visualizations. Furthermore, the RS completes and validates our pursuit through 81.3% accurate song suggestions. We believe the outcome of this thesis will work as an inspiration and assistance for fellow researchers in this arena to come up with more personalized song suggestions. As music preferences will shape specific user personality parameters, it is expected that more such elements will surface that would portray the daily activities of individuals and their underlying mentality.
Casino-induced satisfaction of needs and casino customer loyalty: the moderating role of subjective norms and perceived gaming value

ABSTRACT This study assesses the relationship between casino-induced satisfaction of needs and casino customer loyalty, and explores the moderating role of gaming attitude, subjective norms, perceived behavioral control, perceived gaming value, and perceived personal luck in this relationship. With 425 valid responses collected from Macau casino goers, the results indicate that casinos can enhance customer loyalty by satisfying their customers’ inner needs, including their self-actualization, esteem, belonging, safety and security, and physiological needs. In strengthening the relationship between casino-induced satisfaction of needs and customer loyalty, casinos should pay special attention to subjective norms and perceived gaming value.
Effects of Video Lecture Design and Production Quality on Student Outcomes: A Quasi-Experiment Exploiting Change in Online Course Development Principles

In seeking competitive advantage, many online graduate programs have turned to improving the quality of video lectures by investing in instructional designers and in-studio production. However, it is unclear how much video lecture design and production quality improve student outcomes. We used a regression discontinuity to evaluate how video lecture design and production practices that adhere to principles of multimedia learning affect perceived learning and student satisfaction. The study involved 300 students taking an online graduate course at a large, public research university, where 194 students were exposed to video lectures designed and produced by the instructor and 106 students were exposed to video lectures designed in collaboration between the instructor and instructional designers and produced in studio. Our findings indicate that designing and producing video lectures in accordance with principles of multimedia learning has a meaningful causal effect on students’ perceived learning and a marginal effect on student satisfaction. We discuss the theoretical and practical implications of our findings for video lecture development and design in the context of online business education and the COVID-19 pandemic.
Dynamic Transitions between Neural States Are Associated with Flexible Task Switching during a Memory Task

Abstract Flexible behavior requires switching between different task conditions. It is known that such task switching is associated with costs in terms of slowed RT, reduced accuracy, or both. The neural correlates of task switching have usually been studied by requiring participants to switch between distinct task conditions that recruit different brain networks. Here, we investigated the transition of neural states underlying switching between two opposite memory-related processes (i.e., memory retrieval and memory suppression) in a memory task. We investigated 26 healthy participants who performed a think/no-think task while being in the fMRI scanner. Behaviorally, we show that it was more difficult for participants to suppress unwanted memories when a no-think was preceded by a think trial instead of another no-think trial. Neurally, we demonstrate that think–no-think switches were associated with an increase in control-related and a decrease in memory-related brain activity. Neural representations of task condition, assessed by decoding accuracy, were lower immediately after task switching compared with the nonswitch transitions, suggesting a switch-induced delay in the neural transition toward the required task condition. This suggestion is corroborated by an association between condition-specific representational strength and condition-specific performance in switch trials. Taken together, we provided neural evidence from the time-resolved decoding approach to support the notion that carryover of the previous task set activation is associated with the switching cost, leading to less successful memory suppression.
On the Gap between Adoption and Understanding in NLP

There are some issues with current research trends in NLP that can hamper the free development of scientiﬁc research. We identify ﬁve of particular concern: 1) the early adoption of methods without sufﬁcient understanding or analysis; 2) the preference for computational methods regardless of risks associated with their limitations; 3) the resulting bias in the papers we publish; 4) the impossibility of re-running some experiments due to their cost; 5) the dangers of unexplainable methods. If these issues are not addressed, we risk a loss of reproducibility, reputability, and subsequently public trust in our ﬁeld. In this position paper, we outline each of these points and suggest ways forward.
Multilingual Translation from Denoising Pre-Training

Recent work demonstrates the potential of training one model for multilingual machine translation. In parallel, denoising pretraining using unlabeled monolingual data as a starting point for ﬁnetuning bitext machine translation systems has demonstrated strong performance gains. However, little has been explored on the potential to combine denoising pretraining with multilingual machine translation in a single model. In this work, we ﬁll this gap by studying how multilingual translation models can be created through multilingual ﬁnetuning . Fintuning multilingual model from a denoising pretrained model incorporates the beneﬁts of large quantities of unlabeled monolingual data, which is particularly important for low resource languages where bitext is rare. Further, we create the ML50 benchmark to facilitate re-producible research by standardizing training and evaluation data. On ML50, we show that multilingual ﬁnetuning signiﬁcantly improves over multilingual models trained from scratch and bilingual ﬁnetuning for translation into English. We also ﬁnd that multilingual ﬁne-tuning can signiﬁcantly improve over multilingual models trained from scratch for zero-shot translation on non-English directions. Finally, we discuss that the pretraining and ﬁnetuning paradigm alone is not enough to address the challenges of multilingual models for to-Many directions performance.
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs

Knowledge Graphs (KGs) provide human knowledge with nodes and edges being entities and relations among them, respectively. Multihop question answering over KGs—which aims to find answer entities of given questions through reasoning paths in KGs—has attracted great attention from both academia and industry recently. However, this task remains challenging, as it requires to accurately identify answers in a large candidate entity set, of which the size grows exponentially with the number of reasoning hops. To tackle this problem, we propose a novel Deep Cognitive Reasoning Network (DCRN), which is inspired by the dual process theory in cognitive science. Specifically, DCRN consists of two phases—the unconscious phase and the conscious phase. The unconscious phase first retrieves informative evidence from candidate entities by leveraging their semantic information. Then, the conscious phase accurately identifies answers by performing sequential reasoning according to the graph structure on the retrieved evidence. Experiments demonstrate that DCRN significantly outperforms state-of-the-art methods on benchmark datasets.
Predicting Shopping Intent of e-Commerce Users using LSTM Recurrent Neural Networks

: An e-commerce web site is effective if it turns visitors into buyers achieving a high conversion rate. To this realm, it is useful to predict each user’s purchase intent and understand their navigation behavior. Such predictions may be utilized to improve web design and to personalize shopper’s experience, hopefully leading to increased conversion rates. Additionally, if such predictions can be done in real-time, during the ongoing navigation of an e-commerce user, the e-commerce application can take proactive stimuli actions to offer incentives with a view to increase the probability that a user will finally make a purchase. This paper presents a method for predicting in real-time the shopping intent of e-commerce users using LSTM recurrent neural networks. We test several variants of our method in a dataset created from the processing of Web server logs of an industry e-commerce web application, dividing user sessions in three different classes: browsing, cart abandonment, purchase. The best classifier achieves a predictive accuracy of almost 98%. This result is competitive with other state-of-the-art methods, which affirms that accurate and scalable purchasing intention prediction for e-commerce, using only session-based data, is feasible without any intense feature engineering.
Towards CRYSTALS-Kyber VHDL Implementation

Kyber is one of the three finalists of the National Institute of Standards and Technology (NIST) post-quantum cryptography competition. This article presents an optimized Very High Speed Integrated Circuit Hardware Description Language (VHDL)-based implementation of the main components of the Kyber scheme, namely Number-Theoretic Transform (NTT) and Keccak. We focus specifically on NTT, Keccak and their derivatives since they largely determine Kyber’s performance due to their wide involvement in each step of the scheme. Our high-speed implementation also takes into account the trade-off between the degree of parallelization and the resources utilization. The NTT component is more than 27% faster than the state-of-the-art implementations. Furthermore, the optimization helps the algorithm to achieve 1 572 839 NTT operations per second.
Towards an Approach for Translation Validation of Thread-level Parallelizing Transformations using Colored Petri Nets

Software applications often require the transformation of an input source program into a translated one for optimization. In this process, preserving the semantics across the transformation also called equivalence checking is essential. In this paper, we present ongoing work on a novel translation validation technique for handling loop transformations such as loop swapping and distribution, which cannot be handled by stateof-the-art equivalence checkers. The method makes use of a reduced size Petri net model integrating SMT solvers for validating arithmetic transformations. The approach is illustrated with two simple programs and further validated with a programs benchmark.
Can a TLS Certificate Be Phishy?

: This paper investigates the potential of using digital certiﬁcates for the detection of phishing domains. This is motivated by phishing domains that have started to abuse the (erroneous) trust of the public in browser padlock symbols, and by the large-scale adoption of the Certiﬁcate Transparency (CT) framework. This publicly accessible evidence trail of Transport Layer Security (TLS) certiﬁcates has made the TLS landscape more transparent than ever. By comparing samples of phishing, popular benign, and non-popular benign domains, we provide insight into the TLS certiﬁcates issuance behavior for phishing domains, focusing on the selection of the certiﬁcate authority, the validation level of the certiﬁcates, and the phenomenon of certiﬁcate sharing among phishing domains. Our results show that phishing domains gravitate to a relatively small selection of certiﬁcate authorities, and disproportionally to cPanel , and tend to rely on certiﬁcates with a low, and cheap, validation level. Additionally, we demonstrate that the vast majority of certiﬁcates issued for phishing domains cover more than only phishing domains. These results suggest that a more pro-active role of CAs and putting more emphasis on certiﬁcate revocation can have a crucial impact in the defense against phishing attacks.