Young-Koo Lee

Irfan Ullah, Sharifullah Khan, Muhammad Imran et al.

Catastrophic events create uncertain situations for humanitarian organizations locating and providing aid to affected people. Many people turn to social media during disasters for requesting help and/or providing relief to others. However, the majority of social media posts seeking help could not properly be detected and remained concealed because often they are noisy and ill-formed. Existing systems lack in planning an effective strategy for tweet preprocessing and grasping the contexts of tweets. This research, first of all, formally defines request tweets in the context of social networking sites, hereafter rweets, along with their different primary types and sub-types. Our main contributions are the identification and categorization of rweets. For rweet identification, we employ two approaches, namely a rule-based and logistic regression, and show their high precision and F1 scores. The rweets classification into sub-types such as medical, food, and shelter, using logistic regression shows promising results and outperforms existing works. Finally, we introduce an architecture to store intermediate data to accelerate the development process of the machine learning classifiers.

Muhammad Umair, Tangina Sultana, Young-Koo Lee

Keyphrase Prediction (KP) is essential for identifying keyphrases in a document that can summarize its content. However, recent Natural Language Processing (NLP) advances have developed more efficient KP models using deep learning techniques. The limitation of a comprehensive exploration jointly both keyphrase extraction and generation using pre-trained language models spotlights a critical gap in the literature, compelling our survey paper to bridge this deficiency and offer a unified and in-depth analysis to address limitations in previous surveys. This paper extensively examines the topic of pre-trained language models for keyphrase prediction (PLM-KP), which are trained on large text corpora via different learning (supervisor, unsupervised, semi-supervised, and self-supervised) techniques, to provide respective insights into these two types of tasks in NLP, precisely, Keyphrase Extraction (KPE) and Keyphrase Generation (KPG). We introduce appropriate taxonomies for PLM-KPE and KPG to highlight these two main tasks of NLP. Moreover, we point out some promising future directions for predicting keyphrases.

Muhammad Umair, Syed Jalaluddin Hashmi, Young-Koo Lee

Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrases. We propose a novel approach using Submodular Function Optimization (SFO) to balance relevance and diversity in keyphrase ranking. By framing the task as submodular maximization, our method selects diverse and representative keyphrases. Experiments on benchmark datasets show that our approach outperforms existing methods in both relevance and diversity metrics, achieving SOTA performance in execution time. Our code is available online.

Irfan Ullah, Young-Koo Lee

Graph Neural Networks (GNNs) are powerful tools for learning graph-structured data, but their scalability is hindered by inefficient mini-batch generation, data transfer bottlenecks, and costly inter-GPU synchronization. Existing training frameworks fail to overlap these stages, leading to suboptimal resource utilization. This paper proposes MQ-GNN, a multi-queue pipelined framework that maximizes training efficiency by interleaving GNN training stages and optimizing resource utilization. MQ-GNN introduces Ready-to-Update Asynchronous Consistent Model (RaCoM), which enables asynchronous gradient sharing and model updates while ensuring global consistency through adaptive periodic synchronization. Additionally, it employs global neighbor sampling with caching to reduce data transfer overhead and an adaptive queue-sizing strategy to balance computation and memory efficiency. Experiments on four large-scale datasets and ten baseline models demonstrate that MQ-GNN achieves up to \boldmath $\bm{4.6\,\times}$ faster training time and 30% improved GPU utilization while maintaining competitive accuracy. These results establish MQ-GNN as a scalable and efficient solution for multi-GPU GNN training.

Irfan Ullah, Young-Koo Lee

Graph Neural Networks (GNNs) are becoming an essential tool for learning from graph-structured data, however uniform neighbor sampling and static fanout settings frequently limit GNNs' scalability and efficiency. In this paper, we propose the Dynamic Adaptive Fanout Optimization Sampler (DAFOS), a novel approach that dynamically adjusts the fanout based on model performance and prioritizes important nodes during training. Our approach leverages node scoring based on node degree to focus computational resources on structurally important nodes, incrementing the fanout as the model training progresses. DAFOS also integrates an early stopping mechanism to halt training when performance gains diminish. Experiments conducted on three benchmark datasets, ogbnarxiv, Reddit, and ogbn-products, demonstrate that our approach significantly improves training speed and accuracy compared to a state-of-the-art approach. DAFOS achieves a 3.57x speedup on the ogbn-arxiv dataset and a 12.6x speedup on the Reddit dataset while improving the F1 score from 68.5% to 71.21% on ogbn-arxiv and from 73.78% to 76.88% on the ogbn-products dataset, respectively. These results highlight the potential of DAFOS as an efficient and scalable solution for large-scale GNN training.

Umair Qudus, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo et al.

Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.

Tariq Habib Afridi, Aftab Alam, Muhammad Numan Khan et al.

Memes are graphics and text overlapped so that together they present concepts that become dubious if one of them is absent. It is spread mostly on social media platforms, in the form of jokes, sarcasm, motivating, etc. After the success of BERT in Natural Language Processing (NLP), researchers inclined to Visual-Linguistic (VL) multimodal problems like memes classification, image captioning, Visual Question Answering (VQA), and many more. Unfortunately, many memes get uploaded each day on social media platforms that need automatic censoring to curb misinformation and hate. Recently, this issue has attracted the attention of researchers and practitioners. State-of-the-art methods that performed significantly on other VL dataset, tends to fail on memes classification. In this context, this work aims to conduct a comprehensive study on memes classification, generally on the VL multimodal problems and cutting edge solutions. We propose a generalized framework for VL problems. We cover the early and next-generation works on VL problems. Finally, we identify and articulate several open research issues and challenges. This is the first study that presents the generalized view of the advanced classification techniques concerning memes classification to the best of our knowledge. We believe this study presents a clear road-map for the Machine Learning (ML) research community to implement and enhance memes classification techniques.