arXiv subjects

Mohammad Al Hasan

Publications and source records attributed to Mohammad Al Hasan.

At least 19 recordsLinked to original sources

arXiv2026

SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition

Noisy-label learning in small-scale medical image classification is challenging and hinders the superiority of deep neural networks. Recent studies suggest that quantum neural networks (QNNs) have shown potential in limited-data regimes, yet their use for noisy-label learning remains under-explored. A key obstacle is QNNs' intrinsic "natural smoothness", which may regularize training but also obscure high-confidence samples needed for noise-transition estimation. We propose Supermartingale-based Label Transition (SLT), an anchor-free loss correction framework for robust QNN-based medical image classification under noisy labels. SLT models entropy reduction in predictive distributions as a supermartingale and uses its monotonic behavior to identify stable transition-matrix refinement steps. This enables dynamic transition updates while reducing noise-driven oscillations during QNN training. We further provide a convergence analysis showing that the proposed transition-refinement process reaches a steady state. Experiments on multiple public small-scale medical image datasets demonstrate that SLT consistently improves QNN-based classification and stably outperforms classic noise-label learning baselines under synthetic and real-world label noise.

cs.CV↗

arXiv2025

Supervised Link Prediction in Co-Authorship Networks Based on Author Node-Based Features

Predicting the emergence of future research collaborations between authors in academic social networks (SNs) is a very effective example that demonstrates the link prediction problem. This problem refers to predicting the potential existence or absence of a link between a pair of nodes (authors) on the co-authorship network. Various similarity and aggregation metrics were proposed in the literature for predicting the potential link between two authors on such networks. However, the relevant research did not investigate the impact of similarity of research interests of two authors or the similarity of their affiliations on the performance of predicting the potential link between them. Additionally, the impact of the aggregation of the research performance indices of two authors on link prediction performance was not highlighted. To this end, in this paper we propose an integrative supervised learning framework for predicting potential collaboration in co-authorship network based on similarity of the research interests and the similarity of the affiliations of each pair of authors in this network. Moreover, our proposed framework integrates the aggregation of research performance indices of each author pair and the similarity between the two authors nodes with the research interest and affiliation similarity as four metrics for predicting the potential link between each two authors. Our experimental results obtained from applying our proposed link prediction approach to the two largest connected graphs of two huge academic co-authorship networks, namely ArnetMiner and DBLP, show the great performance of this approach in predicting potential links between two authors on large-scale academic SNs.

cs.SI↗

arXiv2025

Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model

Extracting cause and effect phrases from a sentence is an important NLP task, with numerous applications in various domains, including legal, medical, education, and scientific research. There are many unsupervised and supervised methods proposed for solving this task. Among these, unsupervised methods utilize various linguistic tools, including syntactic patterns, dependency tree, dependency relations, etc. among different sentential units for extracting the cause and effect phrases. On the other hand, the contemporary supervised methods use various deep learning based mask language models equipped with a token classification layer for extracting cause and effect phrases. Linguistic tools, specifically, dependency tree, which organizes a sentence into different semantic units have been shown to be very effective for extracting semantic pairs from a sentence, but existing supervised methods do not have any provision for utilizing such tools within their model framework. In this work, we propose DepBERT, which extends a transformer-based model by incorporating dependency tree of a sentence within the model framework. Extensive experiments over three datasets show that DepBERT is better than various state-of-the art supervised causality extraction methods.

cs.LG↗

arXiv2025

Hierarchical Job Classification with Similarity Graph Integration

In the dynamic realm of online recruitment, accurate job classification is paramount for optimizing job recommendation systems, search rankings, and labor market analyses. As job markets evolve, the increasing complexity of job titles and descriptions necessitates sophisticated models that can effectively leverage intricate relationships within job data. Traditional text classification methods often fall short, particularly due to their inability to fully utilize the hierarchical nature of industry categories. To address these limitations, we propose a novel representation learning and classification model that embeds jobs and hierarchical industry categories into a latent embedding space. Our model integrates the Standard Occupational Classification (SOC) system and an in-house hierarchical taxonomy, Carotene, to capture both graph and hierarchical relationships, thereby improving classification accuracy. By embedding hierarchical industry categories into a shared latent space, we tackle cold start issues and enhance the dynamic matching of candidates to job opportunities. Extensive experimentation on a large-scale dataset of job postings demonstrates the model's superior ability to leverage hierarchical structures and rich semantic features, significantly outperforming existing methods. This research provides a robust framework for improving job classification accuracy, supporting more informed decision-making in the recruitment industry.

cs.LG↗

arXiv2025

Extracting Important Tokens in E-Commerce Queries with a Tag Interaction-Aware Transformer Model

The major task of any e-commerce search engine is to retrieve the most relevant inventory items, which best match the user intent reflected in a query. This task is non-trivial due to many reasons, including ambiguous queries, misaligned vocabulary between buyers, and sellers, over- or under-constrained queries by the presence of too many or too few tokens. To address these challenges, query reformulation is used, which modifies a user query through token dropping, replacement or expansion, with the objective to bridge semantic gap between query tokens and users' search intent. Early methods of query reformulation mostly used statistical measures derived from token co-occurrence frequencies from selective user sessions having clicks or purchases. In recent years, supervised deep learning approaches, specifically transformer-based neural language models, or sequence-to-sequence models are being used for query reformulation task. However, these models do not utilize the semantic tags of a query token, which are significant for capturing user intent of an e-commerce query. In this work, we pose query reformulation as a token classification task, and solve this task by designing a dependency-aware transformer-based language model, TagBERT, which makes use of semantic tags of a token for learning superior query phrase embedding. Experiments on large, real-life e-commerce datasets show that TagBERT exhibits superior performance than plethora of competing models, including BERT, eBERT, and Sequence-to-Sequence transformer model for important token classification task.

cs.LG↗

arXiv2025

Retrieval Augmented Generation based Large Language Models for Causality Mining

Causality detection and mining are important tasks in information retrieval due to their enormous use in information extraction, and knowledge graph construction. To solve these tasks, in existing literature there exist several solutions -- both unsupervised and supervised. However, the unsupervised methods suffer from poor performance and they often require significant human intervention for causal rule selection, leading to poor generalization across different domains. On the other hand, supervised methods suffer from the lack of large training datasets. Recently, large language models (LLMs) with effective prompt engineering are found to be effective to overcome the issue of unavailability of large training dataset. Yet, in existing literature, there does not exist comprehensive works on causality detection and mining using LLM prompting. In this paper, we present several retrieval-augmented generation (RAG) based dynamic prompting schemes to enhance LLM performance in causality detection and extraction tasks. Extensive experiments over three datasets and five LLMs validate the superiority of our proposed RAG-based dynamic prompting over other static prompting schemes.

cs.CL↗

arXiv2024

Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs

As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optimizing their day to day activities. But, most of published ML based methodologies in this area have been limited to the tasks like candidate matching, job to skill matching, job classification and normalization. In this work, we discuss a novel task in the recruitment domain, namely, application count forecasting, motivation of which comes from designing of effective outreach activities to attract qualified applicants. We show that existing auto-regressive based time series forecasting methods perform poorly for this task. Henceforth, we propose a multimodal LM-based model which fuses job-posting metadata of various modalities through a simple encoder. Experiments from large real-life datasets from CareerBuilder LLC show the effectiveness of the proposed method over existing state-of-the-art methods.

cs.LG↗

arXiv2024

A Survey on E-Commerce Learning to Rank

In e-commerce, ranking the search results based on users' preference is the most important task. Commercial e-commerce platforms, such as, Amazon, Alibaba, eBay, Walmart, etc. perform extensive and relentless research to perfect their search result ranking algorithms because the quality of ranking drives a user's decision to purchase or not to purchase an item, directly affecting the profitability of the e-commerce platform. In such a commercial platforms, for optimizing search result ranking numerous features are considered, which emerge from relevance, personalization, seller's reputation and paid promotion. To maintain their competitive advantage in the market, the platforms do no publish their core ranking algorithms, so it is difficult to know which of the algorithms or which of the features is the most effective for finding the most optimal search result ranking in e-commerce. No extensive surveys of ranking to rank in the e-commerce domain is also not yet published. In this work, we survey the existing e-commerce learning to rank algorithms. Besides, we also compare these algorithms based on query relevance criterion on a large real-life e-commerce dataset and provide a quantitative analysis. To the best of our knowledge this is the first such survey which include an experimental comparison among various learning to rank algorithms.

cs.IR↗

arXiv2024

Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors

For natural language understanding and generation, embedding concepts using an order-based representation is an essential task. Unlike traditional point vector based representation, an order-based representation imposes geometric constraints on the representation vectors for explicitly capturing various semantic relationships that may exist between a pair of concepts. In existing literature, several approaches on order-based embedding have been proposed, mostly focusing on capturing hierarchical relationships; examples include vectors in Euclidean space, complex, Hyperbolic, order, and Box Embedding. Box embedding creates region-based rich representation of concepts, but along the process it sacrifices simplicity, requiring a custom-made optimization scheme for learning the representation. Hyperbolic embedding improves embedding quality by exploiting the ever-expanding property of Hyperbolic space, but it also suffers from the same fate as box embedding as gradient descent like optimization is not simple in the Hyperbolic space. In this work, we propose Binder, a novel approach for order-based representation. Binder uses binary vectors for embedding, so the embedding vectors are compact with an order of magnitude smaller footprint than other methods. Binder uses a simple and efficient optimization scheme for learning representation vectors with a linear time complexity. Our comprehensive experimental results show that Binder is very accurate, yielding competitive results on the representation task. But Binder stands out from its competitors on the transitive closure link prediction task as it can learn concept embeddings just from the direct edges, whereas all existing order-based approaches rely on the indirect edges.

cs.CL↗

arXiv2023

Robust Node Representation Learning via Graph Variational Diffusion Networks

Node representation learning by using Graph Neural Networks (GNNs) has been widely explored. However, in recent years, compelling evidence has revealed that GNN-based node representation learning can be substantially deteriorated by delicately-crafted perturbations in a graph structure. To learn robust node representation in the presence of perturbations, various works have been proposed to safeguard GNNs. Within these existing works, Bayesian label transition has been proven to be more effective, but this method is extensively reliant on a well-built prior distribution. The variational inference could address this limitation by sampling the latent node embedding from a Gaussian prior distribution. Besides, leveraging the Gaussian distribution (noise) in hidden layers is an appealing strategy to strengthen the robustness of GNNs. However, our experiments indicate that such a strategy can cause over-smoothing issues during node aggregation. In this work, we propose the Graph Variational Diffusion Network (GVDN), a new node encoder that effectively manipulates Gaussian noise to safeguard robustness on perturbed graphs while alleviating over-smoothing issues through two mechanisms: Gaussian diffusion and node embedding propagation. Thanks to these two mechanisms, our model can generate robust node embeddings for recovery. Specifically, we design a retraining mechanism using the generated node embedding to recover the performance of node classifications in the presence of perturbations. The experiments verify the effectiveness of our proposed model across six public datasets.

cs.LG↗

arXiv2023

Force-directed graph embedding with hops distance

Graph embedding has become an increasingly important technique for analyzing graph-structured data. By representing nodes in a graph as vectors in a low-dimensional space, graph embedding enables efficient graph processing and analysis tasks like node classification, link prediction, and visualization. In this paper, we propose a novel force-directed graph embedding method that utilizes the steady acceleration kinetic formula to embed nodes in a way that preserves graph topology and structural features. Our method simulates a set of customized attractive and repulsive forces between all node pairs with respect to their hop distance. These forces are then used in Newton's second law to obtain the acceleration of each node. The method is intuitive, parallelizable, and highly scalable. We evaluate our method on several graph analysis tasks and show that it achieves competitive performance compared to state-of-the-art unsupervised embedding techniques.

cs.LG↗

arXiv2022

Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation

Node classification using Graph Neural Networks (GNNs) has been widely applied in various real-world scenarios. However, in recent years, compelling evidence emerges that the performance of GNN-based node classification may deteriorate substantially by topological perturbation, such as random connections or adversarial attacks. Various solutions, such as topological denoising methods and mechanism design methods, have been proposed to develop robust GNN-based node classifiers but none of these works can fully address the problems related to topological perturbations. Recently, the Bayesian label transition model is proposed to tackle this issue but its slow convergence may lead to inferior performance. In this work, we propose a new label inference model, namely LInDT, which integrates both Bayesian label transition and topology-based label propagation for improving the robustness of GNNs against topological perturbations. LInDT is superior to existing label transition methods as it improves the label prediction of uncertain nodes by utilizing neighborhood-based label propagation leading to better convergence of label inference. Besides, LIndT adopts asymmetric Dirichlet distribution as a prior, which also helps it to improve label inference. Extensive experiments on five graph datasets demonstrate the superiority of LInDT for GNN-based node classification under three scenarios of topological perturbations.

cs.LG↗

arXiv2022

ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction

Query similarity prediction task is generally solved by regression based models with square loss. Such a model is agnostic of absolute similarity values and it penalizes the regression error at all ranges of similarity values at the same scale. However, to boost e-commerce platform's monetization, it is important to predict high-level similarity more accurately than low-level similarity, as highly similar queries retrieves items according to user-intents, whereas moderately similar item retrieves related items, which may not lead to a purchase. Regression models fail to customize its loss function to concentrate around the high-similarity band, resulting poor performance in query similarity prediction task. We address the above challenge by considering the query prediction as an ordinal regression problem, and thereby propose a model, ORDSIM (ORDinal Regression for SIMilarity Prediction). ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values. We evaluate ORDSIM on a dataset of over 10 millions e-commerce queries from eBay platform and show that ORDSIM achieves substantially smaller prediction error compared to the competing regression methods on this dataset.

cs.LG↗

arXiv2022

Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns

Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as, causality between disease and symptoms, between medications and side effects, between genes and diseases, etc. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, PatternCausality, which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, PatternCausality is substantially better than the existing methods with an order of magnitude improvement in the F-score metric over the best of the existing methods.

cs.CL↗

arXiv2022

Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision

In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be vulnerable to adversarial attacks on label-scarce dynamic graphs. Many existing works aim to strengthen the robustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. However, these works fail on dynamic graphs for which label scarcity is a pressing issue. To overcome label scarcity, self-training attempts to iteratively assign pseudo-labels to highly confident unlabeled nodes but such attempts may suffer serious degradation under dynamic graph perturbations. In this paper, we generalize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian self-supervision model, namely GraphSS, to address the issue. Extensive experiments demonstrate that GraphSS can not only affirmatively alert the perturbations on dynamic graphs but also effectively recover the prediction of a node classifier when the graph is under such perturbations. These two advantages prove to be generalized over three classic GCNs across five public graph datasets.

cs.LG↗

arXiv2022

Deperturbation of Online Social Networks via Bayesian Label Transition

Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such a task can be solved effectively using Graph Convolutional Networks (GCNs). However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task. Existing works in this direction defend GCNs either by adversarial training or by identifying the attacker nodes followed by their removal. However, both of these approaches require that the attack patterns or attacker nodes be identified first, which is difficult in the scenario when the number of perturbator nodes is very small. In this work, we develop a GCN defense model, namely GraphLT, which uses the concept of label transition. GraphLT assumes that perturbators' random activities deteriorate GCN's performance. To overcome this issue, GraphLT subsequently uses a novel Bayesian label transition model, which takes GCN's predicted labels and applies label transitions by Gibbs-sampling-based inference and thus repairs GCN's prediction to achieve better node classification. Extensive experiments on seven benchmark datasets show that GraphLT considerably enhances the performance of the node classifier in an unperturbed environment; furthermore, it validates that GraphLT can successfully repair a GCN-based node classifier with superior performance than several competing methods.

cs.LG↗

arXiv2021

Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Supervised learning, while deployed in real-life scenarios, often encounters instances of unknown classes. Conventional algorithms for training a supervised learning model do not provide an option to detect such instances, so they miss-classify such instances with 100% probability. Open Set Recognition (OSR) and Non-Exhaustive Learning (NEL) are potential solutions to overcome this problem. Most existing methods of OSR first classify members of existing classes and then identify instances of new classes. However, many of the existing methods of OSR only makes a binary decision, i.e., they only identify the existence of the unknown class. Hence, such methods cannot distinguish test instances belonging to incremental unseen classes. On the other hand, the majority of NEL methods often make a parametric assumption over the data distribution, which either fail to return good results, due to the reason that real-life complex datasets may not follow a well-known data distribution. In this paper, we propose a new online non-exhaustive learning model, namely, Non-Exhaustive Gaussian Mixture Generative Adversarial Networks (NE-GM-GAN) to address these issues. Our proposed model synthesizes Gaussian mixture based latent representation over a deep generative model, such as GAN, for incremental detection of instances of emerging classes in the test data. Extensive experimental results on several benchmark datasets show that NE-GM-GAN significantly outperforms the state-of-the-art methods in detecting instances of novel classes in streaming data.

cs.LG↗

arXiv2021

FS^3: A Sampling based method for top-k Frequent Subgraph Mining

Mining labeled subgraph is a popular research task in data mining because of its potential application in many different scientific domains. All the existing methods for this task explicitly or implicitly solve the subgraph isomorphism task which is computationally expensive, so they suffer from the lack of scalability problem when the graphs in the input database are large. In this work, we propose FS^3, which is a sampling based method. It mines a small collection of subgraphs that are most frequent in the probabilistic sense. FS^3 performs a Markov Chain Monte Carlo (MCMC) sampling over the space of a fixed-size subgraphs such that the potentially frequent subgraphs are sampled more often. Besides, FS^3 is equipped with an innovative queue manager. It stores the sampled subgraph in a finite queue over the course of mining in such a manner that the top-k positions in the queue contain the most frequent subgraphs. Our experiments on database of large graphs show that FS^3 is efficient, and it obtains subgraphs that are the most frequent amongst the subgraphs of a given size.

cs.DB↗