Performance Appraisal and Promotion Practices of Public Commercial Banks in Bangladesh- A Case Study on ACR Method

Purpose- Performance appraisal is crucial for enhancing the productivity of employees working in any sector. This paper investigates the prevailing performance appraisal system and promotion policy of selected public commercial banks (Sonali Bank Ltd, Rupali Bank Ltd, and Agrani Bank Ltd) in Bangladesh. Design/Methodology- The study is a descriptive case of three banks. The researchers interviewed six experienced banking professionals who provided rich data about their respective banks' performance appraisal and feedback systems. The findings of the study are based on the thematic analysis of the respondents’ statements. However, the study also utilized secondary sources for other relevant information. Findings- The study found that all the banks have an established system for evaluating employee performances through ACR with structured criteria for the promotion. The standard criteria include ACR, academic degree, banking diploma, length of service, etc. However, the grading scale for promoting to different positions is differing among the banks. The study also addressed some limitations of the ACR method as rating only by the manager, biasness on ratings, no appraisal feedback to employees etc. Practical Implications- The study seeks to drive the attention of policymakers to utilize their existing performance appraisal system better and design a more effective one with a well-established promotion policy to encourage the employees to give their best effort at the workplace

Code-Switching and Social Media in Bangladesh: Emergence of a New English

Using two or more languages in a singular context or conversation is similar to going with the flow these days, specifically on social sites. This is referred to as "code-switching" by sociolinguists. The study aims at finding out the ratio and scenario of code-switching on social media in Bangladesh, and whether a New English has emerged or not for that. The study was conducted on a randomly chosen sample population of 40 participants across the country. It applied a mixed-method approach consisting of both qualitative and quantitative research methods to conduct this study. The study collected data through a close-ended questionnaire sent to the sample population via email, Messenger, WhatsApp, and Google Docs and some relevant data in the form of screenshots from Facebook posts, comments, and messenger chats. The findings of the study show that more than half of the participants in this research does not have the proper knowledge and intention regarding code-switching, and they perform it for sheer convenience in colloquial practice and communication. However, most of the participants do not support code-switching because they are concerned about their English language efficiency. And they also assert that Code-switching does not play a major role in the emergence of new sorts of English language because there are, in truth, other reasons behind this

BLP 2023 Task 2: Sentiment Analysis

We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluation phases, respectively. In total, participants submitted 597 runs. However, a total of 15 teams submitted system description papers. The range of approaches in the submitted systems spans from classical machine learning models, fine-tuning pre-trained models, to leveraging Large Language Model (LLMs) in zero- and few-shot settings. In this paper, we provide a detailed account of the task setup, including dataset development and evaluation setup. Additionally, we provide a brief overview of the systems submitted by the participants. All datasets and evaluation scripts from the shared task have been made publicly available for the research community, to foster further research in this domainComment: Accepted in BLP Workshop at EMNLP-2

Technology-Mediated Task-Based Language Teaching at Private Universities in Bangladesh: Students' and Teachers' Perceptions

Task-based language teaching (TBLT) facilitates the process of language learning by focusing on communicative skills in real life. The English language teaching (ELT) system at the private universities of Bangladesh is trying to integrate TBLT with technology nowadays. This study aims to explore the proportion of successful implications of technology and tasks, the perceptions of teachers and learners regarding technology-mediated tasks, the challenges faced by both sides and recommendations to overcome limitations at three private universities in Bangladesh. Besides, this study identifies the task patterns used by teachers as well as the types of tasks preferred by students. Both qualitative and quantitative methods were applied to conduct this study. Data were collected from the teachers and learners of Pundra University of Science & Technology, Bogura; Varendra University, Rajshahi; and Rabindra Maitree University, Kushtia by providing them with Google Docs semi-structured questionnaires. The teachers of the department of English, and the students of the department of English, Computer Science Engineering (CSE) and Business Administration participated in the data collection procedure. The study found that technology-mediated TBLT is implemented alongside traditional teaching methods in language teaching. Students appreciate technology-based tasks in learning a target language. They recommend more skilled and experienced teachers. Teachers suggest that financial funding and skilled IT officers are needed to implement TBLT. Poor internet connection, inadequate technological support, faulty projectors and sound systems are the difficulties faced by teachers and students

Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis

The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,605 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community. In the spirit of further research, we plan to make this dataset and our experimental resources publicly accessible to the wider research community.Comment: Zero-Shot Prompting, Few-Shot Prompting, LLMs, Comparative Study, Fine-tuned Models, Bangla, Sentiment Analysi

Faster Evacuation after Disaster: Finding Alternative Routes using Probable Human Behavior

This poster presents an app that can help disaster affected communities find efficient and safe evacuation routes to reduce the loss of human and resources, both during and after a disaster has hit. This proposed app will navigate people seeking evacuation through suitable routes based on geographical condition, structural vulnerability, disaster severity, traffic density, human mobility, etc. The choice of most effective and safe evacuation paths primarily relies on stochastic probability of human movement and requires frequently updated data. In order to achieve this, the app uses real time GPS data by simulating the movement pattern of its users connected to network as well as their previous movement patterns when they are found offline. This simulation process will find out the less congested and safer routes for faster traversal. Users can use these path suggestions to safely drive themselves out of the disaster stricken area. In case of a user being offline, this app will use data stored on the device to suggest evacuation routes based on human mobility pattern. The implementation of this idea will help the app users evacuate safely and quickly, thus minimizing human casualty due to disaster fatality

Do Intrinsic Rewards Matter on Motivation? Evidence from Primary School Teachers of Bangladesh

Purpose- This study investigates the impact of intrinsic rewards (Recognition, Training and Development, Work Environment, Participation in Decision Making, and Workplace Flexibility) on primary school teachers’ motivation at the workplace. Design/Methodology- A survey was conducted using a 5 Likert scale questionnaire among the teachers of different primary schools across Bangladesh. A total of 200 data was gathered through random sampling. SPSS 22.0 is used for analyzing the data. The study employed multiple regression and ANOVA, correlation, reliability test, and descriptive statistics to draw the findings. Findings- The study revealed that intrinsic rewards have a significant effect on teachers’ motivation. All the studied variables (Recognition, Training and Development, Work Environment, Participation in Decision Making, and Workplace Flexibility) have found a statistically significant relationship with Motivation. Moreover, recognition and work environment showed the most robust relationships with teachers’ motivation. Practical Implications- Organizations can use this study's results to comprehend the effect of intrinsic or non-financial rewards on employee motivation. Thus, the research findings could help similar institutions design an appropriate reward package with adequate intrinsic rewards to motivate their employees and ensure better performance at work

Introduction to Medical Imaging Informatics

Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.Comment: 17 pages, 11 figures, 2 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging