Miracle-dz/UniFAR-FTUs huggingface dataset:
Way forward for post pandemic online teaching: A case of higher education in Pakistan

This small-scale research was conducted to propose a way forward for the post pandemic online teaching in universities of Sindh. Data from 15 university representatives were gathered to identify issues and best practices concerning online teaching and learning in higher education settings of Sindh, Pakistan during the spread of the COVID-19 Pandemic. The data revealed that universities in Sindh face student related, instructor related and online interaction related issues to a significant level while adopting the online teaching modality. The data revealed that universities resolve issues in an ad hoc manner. Therefore, Anderson’s online learning model was adapted and proposed for universities to manage online teaching and resolve issues concerning them. The paper also proposes a conceptual framework developed on the theory of reasoned action and the Technology Acceptance Model (TAM) to be adopted for future researcher to further inquire the effect of instructors’ perceived usefulness of online teaching and their perceived ease of technology use on instructors’ attitudes towards online teaching and their adoption of online teaching.
The Long-Term Effects of Financial Aid and Career Education: Evidence from a Randomized Experiment

I study the effects of the Future to Discover Project, a randomized experiment in which Canadian high school students were either invited to participate in career planning workshops or were made eligible for an $8,000 college grant. By matching the experimental data to post-secondary institution records and income tax files, I am able to examine the effects of the interventions on college enrollment, graduation, and earnings in adulthood. I show that the career education intervention greatly improved students’ outcomes in the long run by improving academic matching. In contrast, the college grant had no long-term monetary benefits despite increasing college enrollment, which is consistent with classical models of human capital investment in the absence of credit constraints. My findings suggest that informational frictions and behavioral obstacles—rather than financial constraints—represent the primary barrier to four-year college enrollment faced by low-income students. And that they explain a large part of the gap in four-year college enrollment between highand low-income students. JEL Codes: I22, I23, I24, D8, D31 *Department of Economics, McGill University, laetitia.renee@mail.mcgill. ca. I am grateful to Fabian Lange and Fernando Saltiel for their continued guidance and support. I would also like to thank Sonia Laszlo, Christopher Rauh, Nicolas Gendron-Carrier, Stephen Ross, Andrei Munteanu, Marc Law, and participants at the CIREQ Lunch Seminar, the 2021 Conferences of the ESPE, CEA, SOLE and EEA, the 2021 Summer and Winter Meetings of the Econometric Society, the GEEZ Seminar, the AMIE Workshop, and the RCI Workshop for valuable input. I am indebted to the Social Research and Demonstration Corporation team, and especially to Reuben Ford, for research support and for making the Future to Discover project data available via the Canadian Research Data Centre Network. I acknowledge the financial support of the Fonds du Recherche du Quebec, the Quebec Interuniversity Centre for Social Statistics and the Research Initiative, Education + Skills. The analysis was conducted at the Quebec Interuniversity Centre for Social Statistics, part of the Canadian Research Data Centre Network. This service is provided through the support of Quebec Universities, the province of Quebec, the Canadian Foundation for Innovation, the Canadian Institutes of Health Research, the Social Science and Humanity Research Council, the Fonds du Recherche du Québec, and Statistics Canada. All views expressed in this work are my own.
Effects of associative inference on individuals' susceptibility to misinformation.

Associative inference is an adaptive process of memory that allows people to recombine associated information and make novel inferences. We report two online human-subject experiments investigating an associative inference version in which participants viewed overlapping real-news pairs (AB&BC) that could later be linked to support inferences of misinformation (AC). In each experiment, we examined participants' recognition and perceived accuracy of snippets of news articles presented as tweets across two phases. At Phase 1, only real-news tweets were presented, which were associated with political news of Phase 2 at three levels: real, fake, and fake with inference. In Experiment 2, participants' cognitive ability was also assessed. Participants recognized more but gave lower accuracy ratings for the fake news with inference than the fake news in both experiments. The effect of associative inference was more evident in the perceived accuracy ratings for participants of higher cognitive ability than those of lower cognitive ability. We conclude that associative inference can make people become susceptible to misinformation. We also discuss the results in terms of why associative inference made people susceptible to misinformation in the relatively automatic familiarity judgment (i.e., recognition) but not the relatively controlled and effortful semantic judgment (i.e., accuracy rating). (PsycInfo Database Record (c) 2022 APA, all rights reserved).
INNOVATION, DIGITAL PLATFORM TECHNOLOGIES AND EMPLOYMENT: AN OVERVIEW OF KEY ISSUES AND EMERGING TRENDS IN SOUTH AFRICA

This paper provides an analytical profile of the South African labour market, along with a descriptive overview of the nature and extent of digital platform labour in the country. The paper also discusses the conceptual links between different types of innovation and employment, before reflecting on the implications of new forms of digital labour relations on labour organization and regulation. The literature on South Africa’s digital platform labour is nascent. Some estimates suggest that there are about 135 000 platform workers in the country. There are, however, important concerns about the quality of platform work, and recent research suggests that many platforms do not provide workers with a living wage or decent working conditions. There are several challenges to regulating platform work, in part due to workers being classified as independent contractors. Despite this, there are new emerging forms of worker organization amongst precarious workers in South Africa that go beyond traditional trade unions to incorporate broader worker associations.
EXOGENOUSLY APPLIED ALUMINUM INDUCED GROWTH INHIBITION AND APOPTOSIS IN WHEAT SEEDLINGS

Aluminum (Al) toxicity significantly limits plant productivity, hinders plant growth, and reduces food production in most crops, including wheat. The present study explored the effects of AlCl3 on wheat seedlings at the physiological and cytological levels. The results showed that Al stress affected chlorophyll synthesis in wheat seedling leaves. We found that chlorophyll content and Al concentration are negatively correlated in wheat seedlings. Root length, number of adventitious roots, and cell division index at the root tip showed a negative correlation with Al concentration. Besides, Al stress caused chromosomal aberrations in wheat seedlings, such as micronuclei and polynuclear formation, chromosomal breakage, the formation of a chromosomal bridge, and chromosomal loop. Exogenous application of excess Al could cause H2O2 accumulation in meristematic and elongation regions of the root, membrane oxidation, programmed cell death, and DNA damage. These results showed that excessive application of exogenous Al inhibited the normal growth of wheat and affected various physiological activities in wheat. Therefore, Al toxicity severely affects the growth and development of wheat seedlings.
Industrial Relatedness in MNE Spillovers over Geographical Space

In this paper, we explore how spillovers from multinational enterprises (MNEs) spread and impact domestic firms through di↵erent channels and at various spatial scales. Taking a firm-level approach, we test whether industrial relatedness mediates spillover e↵ects from MNEs over and above horizontal and vertical linkages traditionally identified by the literature. Thanks to finegrained geographical information, we further investigate the spatial reach of the spillovers and how they are associated with domestic firms’ characteristics such as absorptive capacity and technological sophistication. Our hypotheses are tested on a panel data set of Indonesian manufacturing firms census between 2002 to 2009. We find that domestic firms have higher total factor productivity when being exposed to a higher share of output from multinational firms in related industries, on top of the widely acknowledged horizontal and vertical MNE spillovers. We also show that MNE spillovers are sensitive to distance, with relatedness-mediated ones being detected between 30 and 60 km from the municipality of the MNE. Regarding heterogeneity, large firms benefit from productivity-enhancing relatedness spillovers at a wider spatial ⇤ Economic Geography Group, Utrecht University. † Erasmus School of Economics, Erasmus University Rotterdam. ‡ Department of Spatial Economics, Vrije Universiteit Amsterdam and LPEM-FEB, Universitas
UDC: 616.127: 616.132.2–005.4-089 Myocardial stunning in patients with coronary artery disease before and after treatment by method of coronary artery bypass grafting (own studies)

Aim of the study: Determination of quantitative radiological indicators of myocardial revascularization effectiveness in patients with coronary artery disease in the early postoperative period. Materials and methods of research: For the implementation of the clinical objectives, 62 patients with coronary artery disease, heart failure, with preserved systolic function and systolic dysfunction were examined in the early postoperative period (up to 7 days). The patients' age ranged from 40 to 79 years, the average age of the examined was 59.6 ± 8.2 years. 35 (56%) patients had HF with LV systolic dysfunction with EF of 49% or less.
The Populist Dimension of Mediated Discourses About Corruption in Romania

Understanding populism as a communication strategy (Aalberg et al. 2017) that is based on both messages and styles (Engesser et. al., 2017), the present research investigates the populist elements of political discourses articulating the subject of “corruption” in the context of the attempts to change the anti-corruption legislation by the Romanian government in 2017– 2018. Based on audience preferences and on the density of political communication conducted by both political and media actors, the study conducted content analysis articles from three major Romanian TV-related news portals during February 2017 and May 1 – June 5, 2018 (N=548). In addition, computer-assisted content analysis was performed on social media posts of political parties (N=875) and the posts of their leaders (N=540). The research not only identified elements of populist political communication both in the content and the style of corruption-discourses but also reflected the polarization of the Romanian media system.
Assessment of importance of YKL-40 protein as biomarker in colorectal cancer and its relation to selected clinical and pathological parameters

1Oddział Kliniczny Chirurgii Ogólnej, Kolorektalnej i Urazów Wielonarządowych, Wydział Nauk o Zdrowiu w Katowicach, Śląski Uniwersytet Medyczny w Katowicach 2Zakład Pielęgniarstwa Chirurgicznego i Propedeutyki Chirurgii, Wydział Nauk o Zdrowiu w Katowicach, Śląski Uniwersytet Medyczny w Katowicach 3Katedra i Zakład Biologii Medycznej i Molekularnej, Wydział Nauk Medycznych w Zabrzu, Śląski Uniwersytet Medyczny w Katowicach 4Zakład Profilaktyki Chorób Żywieniowozależnych, Wydział Nauk o Zdrowiu w Bytomiu, Śląski Uniwersytet Medyczny w Katowicach 5Klinika Chirurgii Wad Rozwojowych Dzieci i Traumatologii, Wydział Nauk Medycznych w Zabrzu, Śląski Uniwersytet Medyczny w Katowicach 6Zakład Farmakologii Klinicznej, Wydział Nauk Medycznych w Zabrzu, Śląski Uniwersytet Medyczny w Katowicach
Sentiment Analysis behind Text with Different Length and Formality

The purpose of the project is to evaluate the capability of sentiment analysis for different models on dataset with various text length and formality. Three models, logistic regression with TF-IDF, RNN with LSTM layer and word embedding, RNN with LSTM layer, word embedding and an average pooling, are tested on each of the three different datasets. The result shows that for twitter tweets, RNN with LSTM and pre-trained embedding has the highest accuracy, for IMDB reviews, logistic regression with TF-IDF has the highest accuracy, and for Yelp reviews, RNN with LSTM and trainable embedding has the highest accuracy
A Research on an Improved Intranet Users' Behavior Audit Model

Currently,most of the security audit systems are based on the audit of wide area network(WAN) data,and the audit of intranet users' behavior is still in the primary stage of research.The traditional audit model of users' behavior has low intelligence and the centralized structure audit has heavy burden.To deal with this deficiency,this thesis proposes a distributed intranet users' behavior audit model.This model adopts three-level Agent structure and incorporates improved neural network analysis of IGA.Finally,the distributed data acquisition,filtration and transmission,and intelligent audit of users' behavior are realized.
Convolutional Neural Networks Based Active SLAM and Exploration *

Mobile robots are high-performance robots that are used to perform a specific function in environments such as land, air and water, with free movement options and are equipped with many sensors for different processing capabilities. Today, it is used in many tasks such as object detection, tracking and mapping. Mobile robots used in mapping implementations are usually guided by user inputs. However, in some cases, this guidance is autonomously implemented through exploration algorithms that are examined under the active Simultaneous Localization and Mapping (SLAM) keyword. These algorithms are usually based on Laser Imaging Detection and Ranging (LIDAR) sensor. Since this sensor has a bulky structure and occupancy grid maps require heavy computing time, it is needed to develop new kinds of algorithms. In this study, we propose a novel Convolutional Neural Network (CNN) based algorithm that can create a map of an environment with a mobile robot that is independent of user inputs and move autonomously. For the first stage, the CNN structure is trained using the data set consisting of the environment image and the wheel angles related to these images so that the CNN model learns how to guide the robot. For the second stage, the robot is navigated autonomously through the trained network in an environment which is different from the first one, and the map of the environment is acquired simultaneously. Training and testing processes have been realized on a real-time implementation and the advantages of the developed method have been verified.
A CORPUS-BASED STUDY OF ENGLISH SYNONYMS: ADVISE, RECOMMEND, AND SUGGEST BY MS. WATCHARAPORN PHITAYAKORN AN INDEPENDENT STUDY PAPER SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN ENGLISH FOR CAREERS

Knowledge of vocabulary is vital for effective English communication and “synonyms” should be used to avoid repeated usage. Most English synonyms are near-synonyms rather than strict synonyms. Near-synonyms cannot substitute for each other because of differences in meaning. This study examined the similarities and differences of three synonymous verbs, i.e. advise, recommend, and suggest, focusing on grammatical patterns and collocation usage within British English. The data was drawn from the Longman Dictionary of Contemporary English, 6th edition (2014) and one corpus, namely, the British National Corpus (BNC). Taking a corpus of 450 concordance lines from one corpus, the data was put into an Excel worksheet. It was searched and analyzed manually using the aforementioned two criteria. The results showed that three synonymous verbs cannot be used interchangeably in every context due to the differences in grammatical patterns and collocations. Therefore, they should be considered as near-synonyms. Furthermore, this study revealed that the dictionary does not provide all the possible patterns. As a result, concordance lines may be employed as a secondary source to provide supplementary patterns that are not given in the dictionary. L2 learners should not only investigate the meaning of each word in dictionaries but also use concordance lines as an additional source to learn how to use these synonymous verbs naturally.
EFFECT OF STRUCTURAL STEEL PARAMETER AND QUALITY OF CONSTRUCTION UNCERTAINTIES ON SEISMIC PERFORMANC OF A SPACIAL MOMENT RESISTING FRAME

Seismic excitations are one of the most hazardous loadings encountered during the life time of structures. Seismic evaluation of Steel Moment Frames, which are used often as lateral seismic system subjected to earthquake must account for the structural steel parameter and workmanship uncertainties, is of high importance. In this study, the uncertainties ,which involve the quality of workmanship (quality of construction and weld fabrication) that is affected in the behavior of the beam-to-column connectionsas well as mechanical properties such as Young modulus and yield-strength, are parameters for considering those associated with structural steel framing parameters. Incremental dynamic analysis is utilized to assess the structural dynamic behavior of the frames and to generate the required data for performance based evaluations. A probabilistic framework for seismic assessment of a structural system,which takes into account the uncertainty in the mentioned variables, is used to examine the variation of the probability of exceeding a limit state capacity under seismic excitations. In this study, seismic evaluation of structure has been accomplished in two modes, before construction (the designed structure with no uncertainty) and after construction (the structure with uncertainty). This confidence level is assesable and obtaianble through evaluation of the factored demand-to-capacity, namely DCFD format. SMF at the IO performance level, as affected by uncertainties, shows few changs in DCFD values as well as in confidence level in comparisonwith the structure with no uncertaintywhile, at CP, result shows more changes, increase ofthe DCFD parameter and consequently decrease of confidense levelof the structue affected by unertainrties.
Selecting wavelet basis in wavelet transform analyses of cutting tool wear acoustic emission signal

In order to solve the problem that different wavelet bases have an important impact on the results when the wavelet theory is used to analyze the tool wear acoustic emission(AE) signal,the suitable wavelet basis selection in wavelet analysis from the cutting tool wear acoustic emission(AE) signals was theoretically analyzed through researching the properties of wavelet basis and the features of cutting tool wear AE signal.In the process of experimental verification,the cutting tool wear AE signal was decomposed into 3 level wavelet packets with four different wavelet bases according to the theory of signal energy conservation.When the signal energy in frequency band was taken as characteristic parameter,the theoretical analysis accuracy was confirmed after comparing the contrast of characteristic parameter in these four situations.
Comparison of novel SCADA Data Cleaning Technique for Wind Turbine Electric Pitch System

Wind turbines typically do not operate in the ideal operating conditions, leading to abnormal behaviour that is reflected in their power curves. This abnormal behaviour can affect the performance of condition monitoring processes, as it may mask faulty behaviour. By cleaning other abnormal data, such as curtailment, models can learn the normal behaviour of the turbines. This paper presents a novel cleaning technique that utilises a combination of data binning and the Mahalanobis distance. This removes between 5 to 6% of the data, without great loss of normal data. When compared against other data cleaning techniques, the one presented in this paper produces a more ideal power curve. This technique could improve the performance of data-based condition monitoring techniques.
Permission Watcher Tool: a sandbox tool based static and Dynamic analysis for Android Apps

Android security has become a very important issue with regard to mobile phone development: Android gives great freedom to developers to create and publish their apps for free in the PlayStore. The security mechanism of Android is based on an instrument that gives users the information about permissions that the application requests before installing it. This authorization system provides an overview of the application, and this can help to raise awareness of its risks. However, standard users still do not have enough information to understand clearly these requested authorizations and their implications on their security. In this article, we present a tool called “Permission watcher” that combines dynamic and static analysis. Our proposed tool allows users to install any application with only the necessary permissions instead of accepting all permissions requested or cancel the installation completely. To cite this document: Er-rajy Latifa, and El Kiram My Ahmed, " PERMISSION WATCHER TOOL: A SANDBOX TOOL-BASED STATIC AND DYNAMIC ANALYSIS FOR ANDROID APPS ", International Journal of Electronic Commerce Studies, Vol.9, No.2, pp.209-238, 2018. Permanent link to this document: http://dx.doi. org/10.7903/ijecs.1651
CMAR_A_338788 237..247

1Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People’s Republic of China; 2Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People’s Republic of China; 3Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People’s Republic of China; 4Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People’s Republic of China
Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors

African smallholder farmers have struggled with low agricultural productivity for decades, partly due to their inability to proactively assess irrigation needs in their farms in the face of long-term climate change. In this paper, we tackle this challenge by employing data-driven techniques to develop forecasting tools for three widely used crop-productivity related variables (i.e., actual evapotranspiration, reference evapotranspiration, and net primary production), which can then be used by farmers to take corrective actions on their farms. Prior work in this domain, despite using data-driven methods, suffers from two major limitations: (i) they mainly focus on estimating variable values (as opposed to forecasting the future); and (ii) they mostly use classical Machine Learning (ML) prediction models, despite the abundance of data sufficient to train sophisticated deep learning models. To fill this research gap, we collaborate with PlantVillage, the world’s leading non-profit agricultural knowledge delivery platform for African farmers, to identify ∼2,200 smallholder farm locations, and gather remote-sensed data of these farms over a period of five years. Next, we propose CLIMATES, a meta-algorithm leveraging structural insights about temporal patterns of this time-series data to accurately forecast their future values. We conduct extensive experiments to evaluate its performance in this domain. Our experimental results show that CLIMATES outperforms several state-of-the-art time-series forecasting models. We also provide insights about the poor performance of some competing models. Our work is being evaluated by officials at PlantVillage for potential future deployment as an early warning system in East Africa. We release the code at https://github.com/maryam-tabar/CLIMATES.