UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu

This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language. This is a binary classification problem in which the task is to classify a given news article into two classes: (i) real news, or (ii) fake news. In this shared task, 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the shared task, 18 teams submitted their experimental results and 11 teams submitted their technical reports. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The stochastic gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score

Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021

Automatic detection of fake news is a highly important task in the contemporary world. This study reports the 2nd shared task called UrduFake@FIRE2021 on identifying fake news detection in Urdu. The goal of the shared task is to motivate the community to come up with efficient methods for solving this vital problem, particularly for the Urdu language. The task is posed as a binary classification problem to label a given news article as a real or a fake news article. The organizers provide a dataset comprising news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business, split into training and testing sets. The training set contains 1300 annotated news articles -- 750 real news, 550 fake news, while the testing set contains 300 news articles -- 200 real, 100 fake news. 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the UrduFake@FIRE2021 shared task. Out of those, 18 teams submitted their experimental results, and 11 of those submitted their technical reports, which is substantially higher compared to the UrduFake shared task in 2020 when only 6 teams submitted their technical reports. The technical reports submitted by the participants demonstrated different data representation techniques ranging from count-based BoW features to word vector embeddings as well as the use of numerous machine learning algorithms ranging from traditional SVM to various neural network architectures including Transformers such as BERT and RoBERTa. In this year's competition, the best performing system obtained an F1-macro score of 0.679, which is lower than the past year's best result of 0.907 F1-macro. Admittedly, while training sets from the past and the current years overlap to a large extent, the testing set provided this year is completely different

DOES CORPORATE SOCIAL RESPONSIBILITY INFLUENCE JOB STRESS AND TURNOVER OF EMPLOYEE IN PRIVATE COLLEGES OF PESHAWAR, KP-PAKISTAN?

The main aim of this research is to fill the gap by accomplish a realistic study in Private colleges of Peshawar-Pakistan, by knowing the impact of Corporate Social Responsibility on job stress and turnover of employee. Adopted questionnaires were used and Data was collected from existing literature through extensive study. Data analysis was perform using SPSS. Corporate Social Responsibility, job stress and turnover have negative correlation. This study will give a base for planning out strategy for establishing corporate social Responsibility in Private Colleges of Peshawar for maintainable developments besides decreasing job stress level and employee turnover rate. There is less research done on corporate social Responsibility, job stress and turnover relation in educational sector particularly in private colleges of Peshawar, Pakistan

DOES CORPORATE SOCIAL RESPONSIBILITY INFLUENCE JOB STRESS AND TURNOVER OF EMPLOYEE IN PRIVATE COLLEGES OF PESHAWAR, KP-PAKISTAN?

The main aim of this research is to fill the gap by accomplish a realistic study in Private colleges of Peshawar-Pakistan, by knowing the impact of Corporate Social Responsibility on job stress and turnover of employee. Adopted questionnaires were used and Data was collected from existing literature through extensive study. Data analysis was perform using SPSS. Corporate Social Responsibility, job stress and turnover have negative correlation. This study will give a base for planning out strategy for establishing corporate social Responsibility in Private Colleges of Peshawar for maintainable developments besides decreasing job stress level and employee turnover rate. There is less research done on corporate social Responsibility, job stress and turnover relation in educational sector particularly in private colleges of Peshawar, Pakistan

Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021

With the growth of social media platform influence, the effect of their misuse becomes more and more impactful. The importance of automatic detection of threatening and abusive language can not be overestimated. However, most of the existing studies and state-of-the-art methods focus on English as the target language, with limited work on low- and medium-resource languages. In this paper, we present two shared tasks of abusive and threatening language detection for the Urdu language which has more than 170 million speakers worldwide. Both are posed as binary classification tasks where participating systems are required to classify tweets in Urdu into two classes, namely: (i) Abusive and Non-Abusive for the first task, and (ii) Threatening and Non-Threatening for the second. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. The abusive dataset contains 2400 annotated tweets in the train part and 1100 annotated tweets in the test part. The threatening dataset contains 6000 annotated tweets in the train part and 3950 annotated tweets in the test part. We also provide logistic regression and BERT-based baseline classifiers for both tasks. In this shared task, 21 teams from six countries registered for participation (India, Pakistan, China, Malaysia, United Arab Emirates, and Taiwan), 10 teams submitted their runs for Subtask A, which is Abusive Language Detection and 9 teams submitted their runs for Subtask B, which is Threatening Language detection, and seven teams submitted their technical reports. The best performing system achieved an F1-score value of 0.880 for Subtask A and 0.545 for Subtask B. For both subtasks, m-Bert based transformer model showed the best performance

The Moderating Role of Employee Empowerment and Distributive Justice in Transformational Leadership with Its Impact on Organizational Commitment in Islamic Banks at Pakistan

The basic of the current studywas to discover the influence of transformational leadership on organizational commitment,distributive justice, and employee empowerment as moderate variables in the Islamic banking company of DistrictSwat, KP-Pakistan.Adopted questionnaires were used to have five pointsLikert scalesand containing 24 items. 18 samples were taken with Random Sampling Techniquesfrom the Islamic bank's sector.A total of 305 questionnaires were distributedamong employees and the feedback rate was 80 % which 245questionnaires received.Data analysis used multiple linear regression. The study found thatorganizational commitment is positively and significantly affect empowerment. Distributive justice has a positive influence on organization commitment but not significant. There was also found that the leadership of transformational influences organization commitment positively and significantly.The variance of transformational leadership and organizational commitments were 16% which indicates a weaksignificant influence

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate predictions however the XGBoost algorithm surpasses others, followed by AdaBoost, RF, DT, and SVM. The sensitivity analysis result shows that the SPT blow count along the pile shaft has the greatest effect on the Pu

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate predictions however the XGBoost algorithm surpasses others, followed by AdaBoost, RF, DT, and SVM. The sensitivity analysis result shows that the SPT blow count along the pile shaft has the greatest effect on the Pu