Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Culturally-appropriate Persuasive Technology to Promote Positive Work Attitudes among Workers in Public Workplaces

This research aims to design a mobile persuasive technology (PT) to promote acceptable pro-workplace behaviors and etiquette. As a first step to achieving this, we conducted a user study of 252 subjects from an African organization, to uncover what strategies could be used to model proper behaviors and promote employee's commitment to the ideals, visions and missions of an organization. Leveraging existing workplace behavioral procedures, and socio-cultural strategies, we mapped our findings to their corresponding persuasive techniques. Presently, we employed the iterative design process in developing the mobile PT and the design is informed by our findings. Finally, we will deploy our mobile PT and conduct a large-scale evaluation of public workers in a Nigerian workplace to determine its efficacy to promoting positive workplace etiquette and attitudes. We will employ a mixed-method approach involving both quantitative and qualitative (interview and focus group) for this study.
Outcome of ClosureFAST radiofrequency ablation for large-diameter incompetent great saphenous vein

Purpose There is limited data on the outcomes of radiofrequency ablation (RFA) for large diameter saphenous veins. This study aimed to determine whether the large-diameter great saphenous vein (GSV) affected closure rate, complications, and clinical and quality of life (QoL) improvement. Methods From January 2012 to September 2016, a total of 722 limbs were treated with ClosureFAST RFA in a single center. Patients were divided into 2 groups according to the vein diameter measured 3 cm below the saphenofemoral junction (group A ≤ 12 mm, group B > 12 mm). Vein closure was evaluated with duplex scan at 3–5 days, 1, 3, 6, and 12 months postoperatively. The incidence of complications, improvements in symptoms (measured by the Venous Clinical Severity Score [VCSS]) and QoL (measured by the Aberdeen Varicose Vein Symptom Severity Score [AVSS]) were evaluated. Results Groups consisted of 663 GSVs in group A (mean diameter, 6.00 ± 1.74 mm) and 59 in group B (mean diameter, 13.17 ± 1.28 mm). Vein closure rates at 12 months were 98.9% in group A and 100% in group B (P = 0.428). There was no significant difference in the incidence of complications. Both groups showed marked improvements in the VCSS and the AVSS with no significant differences. Conclusion For large-diameter veins, RFA showed comparable outcomes in terms of closure rate, complications, clinical and QoL improvements.
Land cover drives large scale productivity-diversity relationships in Irish vascular plants

The impact of productivity on species diversity is often studied at small spatial scales and without taking additional environmental factors into account. Focusing on small spatial scales removes important regional scale effects, such as the role of land cover heterogeneity. Here, we use a regional spatial scale (10 km square) to establish the relationship between productivity and vascular plant species richness across the island of Ireland that takes into account variation in land cover. We used generalized additive mixed effects models to relate species richness, estimated from biological records, to plant productivity. Productivity was quantified by the satellite-derived enhanced vegetation index. The productivity-diversity relationship was fitted for three land cover types: pasture-dominated, heterogeneous, and non-pasture-dominated landscapes. We find that species richness decreases with increasing productivity, especially at higher productivity levels. This decreasing relationship appears to be driven by pasture-dominated areas. The relationship between species richness and heterogeneity in productivity (both spatial and temporal) varies with land cover. Our results suggest that the impact of pasture on species richness extends beyond field level. The effect of human modified landscapes, therefore, is important to consider when investigating classical ecological relationships, particularly at the wider landscape scale.
Adaptive E-Learning: Motivating Learners whilst Adapting Feedback to Cultural Background

The personalization of feedback by an Intelligent Tutoring System has the potential to greatly improve learner motivation. This PhD investigates how an Intelligent Tutoring System can adapt to the cultural background of learners when giving feedback. The research uses the user-as-wizard method for investigation. To convey the cultural background of the learner in user studies, validated cultural stories (using Hofstede cultural dimensions) are required. These stories are then used to conduct qualitative and empirical studies to investigate how participants from a range of different cultures believe the culture of a learner should affect the kind of feedback given. The insights gathered from these studies will be unified to inspire an algorithm to allow an intelligent tutoring system to utilise these adaptations, and the effects tested on real learners.
Application of an Australian Dietary Guideline Index to Weighed Food Records

The Dietary Guideline Index (DGI) is a validated diet quality index that reflects adherence to the Australian Dietary Guidelines. The aim of the current study was to establish a novel methodology that applied the DGI to dietary data collected via gold standard, weighed food records (WFR). Consisting of 10 components with a maximal score of 120, the DGI reflected the food-based recommendations of the current Australian Dietary Guidelines and included indicators to score adequacy and quality of core food components and discretionary choices within the diet. The DGI was applied to WFR collected from a sample of 141 adults (84 women, 57 men). Differences between gender for each indicator, as well as subscores for core and noncore components of the DGI were examined. Construct validity was assessed by evaluating the relationship between total DGI score and intake of key nutrients of interest. Overall, the median DGI score was low, 50.87 (range 20.6–104.1). Higher DGI scores were associated with lower intakes of saturated fat, added sugars and sodium (P < 0.05). This methodological approach of applying the DGI to WFR may improve our ability to quantify diet quality, thereby providing a tool to assess changes in dietary intake over time and allow the quantification of diet quality as a variable in relation to health outcomes.
High-throughput mapping of CoA metabolites by SAMDI-MS to optimize the cell-free biosynthesis of HMG-CoA

A chemical approach enables the rapid mapping of CoA metabolites in complex biological systems. Metabolic engineering uses enzymes to produce small molecules with industrial, pharmaceutical, and energy applications. However, efforts to optimize enzymatic pathways for commercial production are limited by the throughput of assays for quantifying metabolic intermediates and end products. We developed a multiplexed method for profiling CoA-dependent pathways that uses a cysteine-terminated peptide to covalently capture CoA-bound metabolites. Captured metabolites are then rapidly separated from the complex mixture by immobilization onto arrays of self-assembled monolayers and directly quantified by SAMDI mass spectrometry. We demonstrate the throughput of the assay by characterizing the cell-free synthesis of HMG-CoA, a key intermediate in the biosynthesis of isoprenoids, collecting over 10,000 individual spectra to map more than 800 unique reaction conditions. We anticipate that our rapid and robust analytical method will accelerate efforts to engineer metabolic pathways.
Psychometric properties and cultural adaptation of sleep disturbance measures in Arabic‐speaking populations: A systematic review

The aim of this review was to evaluate the psychometric properties and cross‐cultural adaptation of sleep disturbance scales that have been translated into Arabic or originally developed in Arabic, and to identify appropriate scales that can be used in research and clinical practice intended for Arabic‐speaking participants. The following databases were searched: CINAHL (2003–2019), MEDLINE (1946–2019), EMBASE (1980–2019), PsycINFO (1806–2019) and Cochrane Library (1806–2019). This review was conducted following PRISMA guidelines. Terwee et al. (J. Clin. Epidemiol., 60, 2007, 34) quality assessment was used to evaluate the psychometric properties of the studies, and cross‐cultural adaptation was assessed using criteria from Guillemin, Bombardier, and Beaton (J. Clin. Epidemiol., 46, 1993, 1417). Seven studies met the inclusion criteria, which included four scales: the Epworth Sleepiness Scale, Insomnia Severity Index, Pittsburgh Sleep Quality Index, and Arabic Scale of Insomnia. Cross‐cultural adaptations scored between good and poor; psychometric properties information was missing for most scales. The review suggested that Pittsburgh Sleep Quality Index may be a useful scale to measure sleep disturbance, as the scale showed good cultural adaptation and acceptable psychometric properties in an Arabic population. Furthermore, the scales measure seven different aspects of sleep quality. This review provides options to help researchers and clinicians select the most appropriate instrument for their practice. Further psychometric testing and cultural adaptation is required for sleep scales used in Arabic clinical populations to ensure validity and reliability in outcome measurement for research studies.
Multi-faceted Trust-based Collaborative Filtering

Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.
Nonlinear Pose Filters on the Special Euclidean Group SE(3) With Guaranteed Transient and Steady-State Performance

Two novel nonlinear pose (i.e., attitude and position) filters developed directly on the Special Euclidean Group $\mathbb {SE}(3)$ able to guarantee prescribed characteristics of transient and steady-state performance are proposed. The position error and normalized Euclidean distance of attitude error are trapped to arbitrarily start within a given large set and converge systematically and asymptotically to the origin from almost any initial condition. The transient error is guaranteed not to exceed a prescribed value while the steady-state error is bounded by a predefined small value. The first pose filter operates based on a set of vectorial measurements coupled with a group of velocity vectors and requires preliminary pose reconstruction. The second filter, on the contrary, is able to perform its function using a set of vectorial measurements and a group of velocity vectors directly. Both proposed filters provide reasonable pose estimates with superior convergence properties while being able to use measurements obtained from low-cost inertial measurement, landmark measurement, and velocity measurement units. The simulation results demonstrate the effectiveness and robustness of the proposed filters considering large error in initialization and high level of uncertainties in velocity vectors as well as in the set of vector measurements.
Real or Fake? Learning to Discriminate Machine from Human Generated Text

Energy-based models (EBMs), a.k. a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult. In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete. Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models. The EBM then works in the residual of the language model; and is trained to discriminate real text from text generated by the auto-regressive models. We investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications. We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives. Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives. However, EBMs exhibit more sensitivity to the training set used by such generators.
HGC: Hierarchical Group Convolution for Highly Efficient Neural Network

Group convolution works well with many deep convolutional neural networks (CNNs) that can effectively compress the model by reducing the number of parameters and computational cost. Using this operation, feature maps of different group cannot communicate, which restricts their representation capability. To address this issue, in this work, we propose a novel operation named Hierarchical Group Convolution (HGC) for creating computationally efficient neural networks. Different from standard group convolution which blocks the inter-group information exchange and induces the severe performance degradation, HGC can hierarchically fuse the feature maps from each group and leverage the inter-group information effectively. Taking advantage of the proposed method, we introduce a family of compact networks called HGCNets. Compared to networks using standard group convolution, HGCNets have a huge improvement in accuracy at the same model size and complexity level. Extensive experimental results on the CIFAR dataset demonstrate that HGCNets obtain significant reduction of parameters and computational cost to achieve comparable performance over the prior CNN architectures designed for mobile devices such as MobileNet and ShuffleNet.
Inference and uncertainty quantification for noisy matrix completion

Significance Matrix completion finds numerous applications in data science, ranging from information retrieval to medical imaging. While substantial progress has been made in designing estimation algorithms, it remains unknown how to perform optimal statistical inference on the unknown matrix given the obtained estimates—a task at the core of modern decision making. We propose procedures to debias the popular convex and nonconvex estimators and derive distributional characterizations for the resulting debiased estimators. This distributional theory enables valid inference on the unknown matrix. Our procedures 1) yield optimal construction of confidence intervals for missing entries and 2) achieve optimal estimation accuracy in a sharp manner. Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite remarkable progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained estimates and how to perform efficient statistical inference on the unknown matrix (e.g., constructing a valid and short confidence interval for an unseen entry). This paper takes a substantial step toward addressing such tasks. We develop a simple procedure to compensate for the bias of the widely used convex and nonconvex estimators. The resulting debiased estimators admit nearly precise nonasymptotic distributional characterizations, which in turn enable optimal construction of confidence intervals/regions for, say, the missing entries and the low-rank factors. Our inferential procedures do not require sample splitting, thus avoiding unnecessary loss of data efficiency. As a byproduct, we obtain a sharp characterization of the estimation accuracy of our debiased estimators in both rate and constant. Our debiased estimators are tractable algorithms that provably achieve full statistical efficiency.
Loop quantum cosmology from an alternative Hamiltonian

In this paper, a new Hamiltonian constraint operator for loop quantum cosmology is constructed by using the Chern-Simons action. The quantum dynamics of the $k=0$ cosmological model with respect to a massless scalar field as an emergent time is determined by a difference equation. The semiclassical analysis shows that the new quantum dynamics has the correct classical limit, and the classical big-bang singularity is again replaced by the quantum bounce. Interestingly, the inverse time evolution of the flat Friedmann-Robertson-Walker cosmological model determined by the effective Hamiltonian will be bounced to an asymptotic de Sitter universe.
Effects of an Internet-based informational video on preoperative anxiety in patients with colorectal cancer

Purpose Surgery is the primary curative treatment for colorectal cancer; however, it remains a frightening procedure that can cause stress and pain in affected patients. Therefore, patients typically experience significant anxiety during the preoperative period, which has been associated with poorer outcome after surgery. This study aimed to evaluate the effect of an Internet-based informational video on preoperative anxiety level in patients with colorectal cancer. Methods This prospective, single-arm, observational study included patients scheduled to undergo elective colorectal cancer surgery, who did not have a history of previous surgery or major cognitive impairment. The primary outcome measure was the change in Amsterdam Preoperative Anxiety and Information Scale - Anxiety (APAIS-A) before and after watching a 5-min informational video (https://youtu.be/VzhtOMPUe4Q) during the preoperative period. Secondary outcome measures were the change in Hospital Anxiety and Depression Scale (HADS), length of postoperative hospital day, and postoperative morbidity. Results Thirty-two patients were enrolled. Anxiety was significantly decreased after watching the video (APAIS-A score: from 10.8 ± 3.7 to 8.2 ± 3.2, P < 0.001, mean reduction: 22.2%). HADS score was also significantly decreased (from 5.8 ± 4.4 to 4.0 ± 3.3, P = 0.001, mean reduction: 26.5%). All preoperative anxiety level did not significantly differ between patients who developed postoperative complication and those who did not. Conclusion The informational video was an effective tool to reduce preoperative anxiety. Viewing this video may confer a higher level of confidence and realistic expectations, as well as reducing patients' preoperative anxiety.
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling

Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo’s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.
Data Efficient and Safe Learning for Locomotion via Simplified Model

In this letter, we formulate a novel Markov Decision Process (MDP) for data efficient and safe learning for locomotion via a simplified model. In our previous studies on biped locomotion, we relied on a low-dimensional robot model, e.g., the Linear Inverted Pendulum Model (LIPM), commonly used in Walking Pattern Generators (WPG). However, employing low-level control cannot precisely track desired footstep locations due to the discrepancies between the real system and the simplified model. In this work, we propose an approach for mitigating this problem by complementing model-based policies with machine learning. We formulate an MDP process incorporating dynamic properties of robots, desired walking directions, and footstep features. We iteratively update the policy to determine footstep locations based on the previous MDP process aided by a deep reinforcement learning process. The policy of the proposed approach consists of a WPG and a parameterized stochastic policy. In addition, a Control Barrier Function (CBF) process applies corrections the above policy to prevent exploration of unsafe regions during learning. Our contributions include: 1) reduction of footstep tracking errors resulting from employing LIPM; 2) efficient exploration of the data driven process, and; 3) scalability of the procedure to any humanoid robot.
Cell Image Classification: A Comparative Overview

Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. The article also reviews three main approaches for cell image classification most often used: numerical feature extraction, end‐to‐end classification with neural networks (NNs), and transport‐based morphometry (TBM). In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. The results computed using four publicly available datasets show that numerical features tend to carry the best discriminative information for most of the classification tasks. Results also show that NN‐based methods produce state‐of‐the‐art results in the dataset that contains a relatively large number of training samples. Data augmentation or the choice of a more recently reported architecture does not necessarily improve the classification performance of NNs in the datasets with limited number of training samples. If understanding and visualization are desired aspects, TBM methods can offer the ability to invert classification functions, and thus can aid in the interpretation of results. These and other comparison outcomes are discussed with the aim of clarifying the advantages and disadvantages of each method. © 2020 International Society for Advancement of Cytometry