This 3-day workshop will be open to all senior undergrad and graduate students, Researcher, and faculty members. The aim of this workshop is to encourage participants in interdisciplinary research. For more details see the flyer below
They developed PACE a Python AI companion for Enhanced Engagement. For this work they generated synthetic data from GPT 3.5 turbo for scaffolding and conversion fine-tuning. The LORA fine tuned Gemma 2B model was used for making the system relatively lightweight. This trains the LLM model to breakdown complex problems into subproblems and generate hints and structured steps for the students. On the other hand the conversation data allows the LLM to engage with users using natural, human-like dialogue, to avoid hallucinations, to supports error correction and detailed feedback, and to enhances motivation and interest through interaction with users with different learning styles, and pace. Evaluation of the system was also performed.
A wider evaluation of the system is underway and we plan to make the version 2 of the system available to our intro to python programming students. Interaction datasets collected from students (with their consent of course) will be valuable for making such a system more reliable.
We hope to see more exciting work from Ibteda and Ashfaq in the near future.
The paper presents FabricSpotDefect dataset which is an annotated dataset for identifying spot defects in different fabric types.
Here is a short description of the paper:
The FabricSpotDefect dataset is, to the best of our knowledge, the first dataset specifically designed to accurately challenge computer vision in detecting fabric spots. There are a total of 1,014 raw images and manually annotated 3,288 different categories of spots. This dataset expands to 2,300 augmented images after applying six categories of augmentation techniques like flipping, rotating, shearing, saturation adjustment, brightness adjustment, and noise addition. We manually conducted annotations on original images to provide real-world essence rather than augmented images. Two versions are considered for augmented images, one is YOLOv8 resulting in 7,641 annotations and another one is COCO format resulting in 7,635 annotations. This dataset consists of various types of fabrics such as cotton, linen, silk, denim, patterned textiles, jacquard fabrics, and so on, and spots like stains, discolorations, oil marks, rust, blood marks, and so on. These kinds of spots are quite difficult to detect manually or using traditional methods. The images were snapped in home lights, using basic everyday clothes, and in normal conditions, making this FabricSpotDefect dataset established in real-world applications.
The figure below shows different spot samples with annotated bounding boxes and polygon annotation in red color 109 where (a) ink stain (b) paint spot (c) marker spot (d) makeup stain (e) rust stain (f) glue spot 110 (g)detergent stain (h) oil stain (i) coffee stain (j) food spot (k) blood spot, and (l) sweat stain.
link to download the dataset will be shared soon.
A paper titled “Capturing the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using Nighttime Lights” has been accepted for publication in the Journal of the Asia Pacific Economy (SJR Q2, H-index 38, (Scopus) CiteScore 3.7 in 2023).
The research work was led by Dr Amin Masud Ali, Professor, Dept of Economics, JU and a co-director / supervisor of Data Science wing, CCDS. The paper is co-authored by Muntasir Wahed (then RA of DnDLab and Data Science wing, currently PhD student at UIUC), Dr Amin Ahsan Ali (Dept of CSE, IUB, and Director, AI & ML Wing, CCDS), and Dr Moinul I Zaber (Dept of CSE, DU and a collaborator of the Data Science wing, CCDS).
This paper examines the spatiotemporal inequality in electricity consumption at the subnational level (Zila and Upazila/Thana) of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values.
These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality. First, these findings provide a clear picture of the NTL inequality pattern at the subnational level of the country. The findings should contribute to the process of ensuring electricity for all (SDG, Goal 7) producing and monitoring the evolution of inequalities within the country (SDG, Goal 10) to achieve the sustainable development goal of reducing inequalities. Secondly, this investigation also captures the inequality in regional economic development as NTL is a recognized proxy indicator of poverty, public service coverage, and economic activity.
Exciting News!
Congratulations to Our Talented Students!
We are thrilled to announce that Two Groups of our BSc Senior Project Students of HCI Wing have been selected for Innovation Cohort of the LaunchPad by the University Innovation Hub Program (UIHP) at UIU! The ideas are the outcome of their Senior Project Research!
Group-1: “GorbhoShongi: Empowering mothers with AI-driven mental health support” by Istiaq, Faiza and Niaz from CSE, IUB!
Group-2: “MatrikalinDiabetes: A Bangla-language based mHealth app for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women” by Ratul, Tunisha, and Noorjahan from CSE, IUB!
This prestigious Innovation Cohort selection is a testament to their hard work, creativity, and passion for driving positive change. 
Joining this cohort is a significant accomplishment, given the competitive selection process, and we couldn’t be prouder of their achievements. This opportunity will allow them to collaborate with other innovators, gain invaluable mentorship, and develop projects that could make a real impact. 
Orientation for this exciting journey begins on November 8th, and we can’t wait to see what they’ll accomplish next. Please join us in congratulating them and wishing them the best as they embark on this transformative experience! 
This can be a great opportunity to participate in our research projects and study sessions to prepare yourself for higher studies. We are looking for students who are truly enthusiastic about research and mathematics.
PROJECTS
ELIGIBILITY
WHY BE AN INTERN?
HOW TO APPLY?
Apply through this form by Nov 15, 2024
https://forms.gle/PcEaSM8Uwt76wfeB7
contact ccds@iub.edu.bd and visit https://ccds.ai/ & fb page CCDS.IUB for more information
(full-time positions to open in July ‘25)
Be ready for higher studies
ATTENTION
Five unpaid internship positions are open.
Dr AKM Mahbubur Rahman presents out paper on Improving the Performance of Transformer-based Models Over Classical Baselines in Multiple Transliterated Languages in one of the reputed AI conferences, European Conference on Artificial Intelligence (ECAI). This year it was held in Santiago de Compostela, Spain.
Authors include Fahim Ahmed (undergrad CSE, IUB ), Md. Fahim (RA, CCDS), and Drs. AKM Mahabubur Rahman, M Ashraful Amin, and Amin Ahsan Ali.
Huge Congratulations to Our Senior RAs!
We are thrilled to announce that two papers by CCDS Senior RAs Nabarun Halder, Jahanggir Hossain Setu, Tanjina Piash Proma, and Syed Tangim Pasha have been accepted at the prestigious IEEE 23rd International Conference on Machine Learning and Applications (ICMLA), USA! 
Accepted Titles:
“ECGInsight: A Web Application-Based Approach to Myocardial Infarction Detection From ECG Image Reports Utilizing ResNet”
and
“Using Transformers for Emotion Recognition in Bangla Text: A Comparative Study of MultiBERT and BanglaBERT with Data Augmentation”
With an impressive acceptance rate of just 24.3% this year, this is an excellent achievement. The hard work and dedication of our talented RAs, under the supervision of Dr. Ashraful Islam, have truly paid off.
Congratulations to the team for this remarkable success! 
We are incredibly proud of you all and excited to see your contributions making waves in the world of machine learning! 
He is currently working as Graduate Assistant under Dr. Henry Hexmoor.
His research topic is focused on applying AI for ensuring security of Video Conferencing softwares like Google zamboard, Whiteboard etc. This is a NSF funded project.
He joined the CCDS around 2022 after completing his bachelor ( in CSE) from RUET.
We wish him all the best for his future endeavors.
The two images below are from the article titled – Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structure, by Sanborn et al. (https://arxiv.org/abs/2407.09468v1)
The paper talk about how “the mathematics of topology, geometry and algebra provide a conceptual framework to categorize the nature of data found in machine learning.” The two figures, present a “graphical taxonomy, to categorize the structures of data.” The article the authors discuss two types of data that we generally encounter: “either data as coordinates in space—for example the coordinate of the position of an object in a 2D space; or data as signals over a space—for example, an image is a 3D (RGB) signal defined over a 2D space. In each case, the space can either be a Euclidean space or it can be equipped with topological, geometric and algebraic structures.”
The paper goes on to then “review a large and disparate body of literature of non-Euclidean generalizations of algorithms classically defined for data residing in Euclidean spaces.” The algorithms presented assume that certain topological, algebraic, or geometric structures of the data / problem are known. However, it does not go into discussion on methods where such structures are not known. For example, methods that fall into the category of topological data analysis, metric learning, or group learning are not covered.
