Miracle-dz/UniFAR-FTUs huggingface dataset:
Covid-19: From Pandemic to Epidemic an Ostrich Syndrome Causing Desensitization among Urban and Suburban Masses

Background: After declaration of the Covid-19 as pandemic by WHO, like every country of the world Pakistan also took exceptional precautionary measure to control the spread and transmission of this virus. These measures included strict lockdowns, shutting down educational institutions, markets, shopping malls, airports and masjids. The study primarily aimed to assess the individuals' knowledge, attitude, behavioral practices to prevent coronavirus and its psychological impacts on their mental health but later researcher also conducted another post study. Factually, the economic crisis, holy month of Ramadan and Eid-ul-Fitar forced the authorities soften the lockdown. Consequently, after this relaxation, the patients of Covid-19 as well as the death rates increased exponentially in Punjab. Methods: A non-equivalent quasi-experimental research designed was used to conduct pre and post study. An online survey having standardized questions about the peoples' knowledge regarding Covid-19, attitudes, their behavioral practices to prevent it and in what manner covid effected their mental health was conducted. Moreover, it also includes the information for demographics of sample. The link was sent to the participants through email, WhatsApp, Social media, Twitter, Facebook and LinkedIn. Similarly, same procedure was followed in post-study, with the same participants, but after a gap of three weeks. Results: Data was analyzed by SPSS and t-test was applied to draw comparisons among the responses of pre and post study. Results revealed that peoples' knowledge and attitude towards Covid-19 remained same in both studies, however, their behavioral practices to prevent Covid-19 and psychological impacts on their mental health (i.e., stress, depression, fear) greatly reduced in post study as compared to the prestudy. Conclusion: The authorities should initiate the health education programs to improve the knowledge, attitude and behavioral practices regarding Covid-19 and to combat its psychological impact on mental health. Besides, it is highly recommended that authorities should take bold steps to enforce formal, or smart lockdown to take control over the situation or else get ready to filled up the graveyards. The situation can also be controlled by enforcing precautionary measures and re-inviting awareness.
An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN

In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks to virtual cloud network (VCN) are increased. The protection of VCN is required to maintain the faith of the cloud users. Intrusion detection is essential to secure any network. The existing approaches that use the conventional neural network cannot utilize all information for identifying the intrusions. In this paper, the anomaly-based NIDS for VCN is proposed. For feature selection, grey wolf optimization (GWO) is hybridized with a bald eagle search (BES) algorithm. For classification, a deep learning approach—deep sparse auto-encoder (DSAE)—is employed. In this way, this paper proposes a NIDS model for VCN named GWO-DES-DSAE. The proposed system is simulated in the python programming environment. The proposed NIDS model’s performance is compared with other recent approaches for both binary and multi-class classification on the considered datasets—NSL-KDD, UNSW-NB15, and CICIDS 2017—and found better than other methods. Deep Sparse Autoencoder (DSAE) has been utilized to learn the underlying traffic data structure. The proposed system improves performance and, hence producing reliable predictions. Evaluation of the results shows the quality and effectiveness of the proposed NIDS model, and the main contributions of this work are as follows:
Reflections on Remote Learning and Teaching of Inclusive Design in HCI

Impacted by the disruptions due to the pandemic as students, teaching assistants, and faculty, in this paper we employ a refexive self-study to share our perspectives and experiences of engaging in an HCI course on Inclusive Design. We fnd that we were able to overcome some of the anticipated challenges of transitioning in-person experiential learning components. However, the timing was critical with course meetings being too long for a Zoom setting but too short to ft all desired interactions. The lack of impromptu interactions and the steep learning curve of new technologies for blind students in the class were also identifed as critical obstacles.
Who will lead? Principal succession in New Zealand’s faith-based integrated schools

Abstract One of the most challenging tasks for any school’s board of trustees is leading the process to employ a new principal when a principal change is impending. The importance of this task cannot be underestimated as there is an integral relationship between school leadership and student achievement. Globally, research indicates that this task is becoming increasingly difficult as an aging population of principals is heading towards retirement and fewer teachers and middle managers are aspiring to lead a school. This challenge is further complicated if the school has specific employment criteria such as those found in integrated faith-based schools. The pool of applicants is smaller than for state schools and this requires that strategic succession planning occurs to develop a well-prepared leadership pipeline for future leadership roles. This paper draws on doctoral research focused on principal succession in faith-based schools in New Zealand and suggests a model to successfully manage the principal succession process.
Uncovering food security value networks in West-Africa using a value modeling

Despite worldwide efforts, many countries around the globe still face food insecurity and undernourishment. Many of these countries lay within the SubSaharan African region, of which Mali is an example. Within these countries, the value chains for cereal seeds form a major factor for food security. Problematically, the Malian seed value chains face many obstacles and challenges. To gain insights into these value chains and their difficulties, this research has opted for a business informatics perspective, resulting in applying a value modeling approach. The modeling language used for this is e3value(1). This is a visual business informatics model suitable for analyzing value networks. Creating two different e3value models revealed major differences between the two main different seed value chains. The core of the traditional value chain lies in subsistence and bartering (using unimproved seeds). In contrast, the other more commercial chain is based on profitability and economic activity (using improved seeds). Conclusively, the process of creating these models, along with gathering relevant information, has led to critical questions and observations. At the moment, the business infrastructure of Mali seems inadequate, whereas the traditional chain is not officially recognized and is even somewhat oppressed by privatization and commercial motives. It can, therefore, be challenged whether the (inter)national push for the commercial seed system comes too early or is even justifiable at all.
Exploring multinomial naïve Bayes for Yorùbá text document classification

The recent increase in the emergence of Nigerian language text online motivates this paper in which the problem of classifying text documents written in Yorùbá language into one of a few predesignated classes is considered. Text document classification/categorization research is well established for English language and many other languages; this is not so for Nigerian languages. This paper evaluated the performance of a multinomial Naive Bayes model learned on a research dataset consisting of 100 samples of text each from business, sporting, entertainment, technology and political domains, separately on unigram, bigram and trigram features obtained using the bag of words representation approach. Results show that the performance of the model over unigram and bigram features is comparable but significantly better than a model learned on trigram features. The results generally indicate a possibility for the practical application of NB algorithm to the classification of text documents written in Yorùbá language.
Audit Quality and Environment, Social, and Governance Risks

This study examines the association between a firm’s environment, social and governance (ESG) risks and audit quality. We measure audit quality using two proxies: audit fees and discretionary accruals. ESG risk is measured using Representative Risk Index from the RepRisk AG database. Using a sample of public U.S. firms from the period between 2007 and 2016, we find that there is positive association between audit fess and ESG risk implying that firms pay higher audit fees when their ESG risk increase in order obtain higher quality audit services. We also find that there is a negative relationship between ESG risk measures and discretionary accruals suggesting firms assessed having high ESG risks do not manage their earnings as much. Overall, our results indicate that auditors take ESG risks of a firm into account when performing financial statements audit.
Causal Associations of Self-Reported Walking Pace with Telomere Length in 405,981 middle-aged adults: a UK Biobank study

Objectives: Walking pace is a strong marker of functional and health status. We investigated whether walking pace is also associated with leucocyte telomere length (LTL), which is causally associated with several diseases and has been proposed as a marker of biological age. Methods: We used baseline data from UK Biobank participants recruited from March-2006 to July-2010. Walking pace was self-reported as slow, steady/average, or brisk. Accelerometer-assessed measures of total physical activity and intensity were included to support interpretation of walking pace data. LTL was measured by qPCR assay. Bi-directional Mendelian randomization (MR) analyses were undertaken to inform likely causal directions. Results: The analysed cohort comprised 405,981 adults (54% women) with mean age of 56.5 years (SD, 8.1) and body mass index 27.2 kg/m2 (SD, 4.7). Steady/average and brisk walkers had significantly longer LTL compared with slow walkers, with a Z-standardised LTL difference of 0.066 (0.053-0.078) and 0.101 (0.088-0.113), respectively. Associations remained but were attenuated following full covariate adjustment: 0.038 (0.025-0.051) and 0.058 (0.045-0.072), respectively. Accelerometer data (n=86,002) demonstrated a non-linear association between LTL and habitual movement intensity, but not total activity. MR analysis supported a causal association of walking pace on LTL, with an increase in Z-standardised LTL of 0.192 (0.077, 0.306) for each difference in walking pace category. No evidence of a causal association was observed for LTL on walking pace. Conclusion: Faster walking pace may be causally associated with longer LTL, which could explain some of the beneficial effects of brisk walking on health status.
Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

Abstract Background: Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories. Methods: This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions. Results: Younger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates. Conclusions: This study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.
N-(6-((2r,3s)-3,4-dihydroxybutan-2-yloxy)-2-(4-fluorobenzylthio)pyrimidin-4-yl)-3-methylazetidine-1-sulfonamide as chemokine receptor modulator

(A) it contains a pharmaceutical intermediate, and the compound used in the preparation of pyrimidin-sulfonamide, or (b) a salt compounds acceptable pharmaceutically, crystalline form of the compound, a method to obtain the above compound, the compound of formula (I) the pharmaceutical composition is provided. The compounds are useful for the treatment of a disease beneficial / conditioning of chemokine receptor activity.
Financial Literacy and Payment Behaviour: Evidence from Payment Diary Survey Data

This paper examines the effect of financial literacy on payment behaviour. We use data from a payment diary and an online survey conducted in 2018 in Switzerland. We estimate a two-step Heckman selection model to separately assess payment instrument adoption and usage decisions. We analyse the payment instruments cash, debit card, credit card, prepaid credit card, contactless debit card, contactless credit card and mobile payment. We introduce a novel payment-related literacy index. The results suggest that financial literacy exerts no effect on payment instrument choice. Payment literacy is a stronger predictor of payment behaviour than financial literacy. Traditional and contactless debit and credit card adoption are positively associated with payment literacy. We show that literacy levels impact payment instrument adoption more than subsequent usage decisions.
ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection

The reasonable employment of RGB and depth data show great significance in promoting the development of computer vision tasks and robot-environment interaction. However, there are different advantages and disadvantages in the early and late fusion of the two types of data. Besides, due to the diversity of object information, using a single type of data in a specific scenario tends to result in semantic misleading. Based on the above considerations, we propose an adaptively-cooperative fusion network (ACFNet) with ResinRes structure for salient object detection. This structure is designed to flexibly utilize the advantages of feature fusion in early and late stages. Secondly, an adaptively-cooperative semantic guidance (ACG) scheme is designed to suppress inaccurate features in the guidance phase. Further, we proposed a type-based attention module (TAM) to optimize the network and enhance the multi-scale perception of different objects. For different objects, the features generated by different types of convolution are enhanced or suppressed by the gated mechanism for segmentation optimization. ACG and TAM optimize the transfer of feature streams according to their data attributes and convolution attributes, respectively. Sufficient experiments conducted on RGB-D SOD datasets illustrate that the proposed network performs favorably against 18 state-of-the-art algorithms.
Jammkle: Fibre jamming 3D printed multi-material tendons and their application in a robotic ankle

Fibre jamming is a new and understudied soft robotic mechanism that has previously found success in stiffness-tunable arms and fingers. However, to date researchers have not fully taken advantage of the freedom offered by contemporary fabrication techniques including multi-material 3D printing in the creation of fibre jamming structures. In this research, we present a novel, modular, multi-material, 3D printed, fibre jamming tendon unit for use in a stiffness-tunable compliant robotic ankle, or Jammkle. Its multimaterial printed design offers unparalleled design freedom, enabling application specific tendon design. We develop analytical and finite element models of the tendon unit, showing good agreement with experimental data and numerically explore the design space. Finally, we demonstrate a practical application by integrating multiple tendon units into a robotic ankle and perform extensive testing and characterisation. We show that the Jammkle outperforms comparative leg structures in terms of compliance, damping, and slip prevention.
On 3‐graphs with no four vertices spanning exactly two edges

Let D2$D_2$ denote the 3‐uniform hypergraph with 4 vertices and 2 edges. Answering a question of Alon and Shapira, we prove an induced removal lemma for D2$D_2$ having polynomial bounds. We also prove an Erdős–Hajnal‐type result: Every induced D2$D_2$ ‐free hypergraph on n$n$ vertices contains a clique or an independent set of size nc$n^{c}$ for some absolute constant c>0$c > 0$ . In the case of both problems, D2$D_2$ is the only nontrivial k$k$ ‐uniform hypergraph with k⩾3$k\geqslant 3$ which admits a polynomial bound.
Property {A} and duality in linear programming

Property A is a form of weak amenability for groups and metric spaces introduced as an approach to the famous Novikov higher signature conjecture, one of the most important unsolved problems in topology. We show that property A can be reduced to a sequence of linear programming optimization problems on finite graphs. We explore the dual problems, which turn out to have interesting interpretations as combinatorial problems concerning the maximum total supply of flows on a network. Using isoperimetric inequalities, we relate the dual problems to the Cheeger constant of the graph and explore the role played by symmetry of a graph to obtain a striking characterization of the difference between an expander and a graph without property A. Property A turns out to be a new measure of connectivity of a graph that is relevant to graph theory. The dual linear problems can be solved using a variety of methods, which we demonstrate on several enlightening examples. As a demonstration of the power of this linear programming approach we give elegant proofs of theorems of Nowak and Willett about graphs without property A.
Fairness without the sensitive attribute via Causal Variational Autoencoder

In recent years, most fairness strategies in machine learning have focused on mitigating unwanted biases by assuming that the sensitive information is available. However, in practice this is not always the case: due to privacy purposes and regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. Yet, only a few prior works address the issue of mitigating bias in such a difficult setting, in particular to meet classical fairness objectives such as Demographic Parity and Equalized Odds. By leveraging recent developments for approximate inference, we propose in this paper an approach to fill this gap. To infer a sensitive information proxy, we introduce a new variational auto-encoding-based framework named SRCVAE that relies on knowledge of the underlying causal graph. The bias mitigation is then done in an adversarial fairness approach. Our proposed method empirically achieves significant improvements over existing works in the field. We observe that the generated proxy’s latent space correctly recovers sensitive information and that our approach achieves a higher accuracy while obtaining the same level of fairness on two real datasets.
CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization

Visual localization is the problem of estimating the position and orientation from which a given image (or a sequence of images) is taken in a known scene. It is an important part of a wide range of computer vision and robotics applications, from self-driving cars to augmented/virtual reality systems. Visual localization techniques should work reliably and robustly under a wide range of conditions, including seasonal, weather, illumination and man-made changes. Recent benchmarking efforts model this by providing images under different conditions, and the community has made rapid progress on these datasets since their inception. However, they are limited to a few geographical regions and often recorded with a single device. We propose a new benchmark for visual localization in outdoor scenes, using crowd-sourced data to cover a wide range of geographical regions and camera devices with a focus on the failure cases of current algorithms. Experiments with state-of-the-art localization approaches show that our dataset is very challenging, with all evaluated methods failing on its hardest parts. As part of the dataset release, we provide the tooling used to generate it, enabling efficient and effective 2D correspondence annotation to obtain reference poses.
3D Real-Time Supercomputer Monitoring

Supercomputers are complex systems producing vast quantities of performance data from multiple sources and of varying types. Performance data from each of the thousands of nodes in a supercomputer tracks multiple forms of storage, memory, networks, processors, and accelerators. Optimization of application performance is critical for cost effective usage of a supercomputer and requires efficient methods for effectively viewing performance data. The combination of supercomputing analytics and 3D gaming visualization enables real-time processing and visual data display of massive amounts of information that humans can process quickly with little training. Our system fully utilizes the capabilities of modern 3D gaming environments to create novel representations of computing hardware which intuitively represent the physical attributes of the supercomputer while displaying real-time alerts and component utilization. This system allows operators to quickly assess how the supercomputer is being used, gives users visibility into the resources they are consuming, and provides instructors new ways to interactively teach the computing architecture concepts necessary for efficient computing.