arXiv subjects

Mohammad Ishrak Abedin

Publications and source records attributed to Mohammad Ishrak Abedin.

4 recordsLinked to original sources

arXiv2026

LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification

Code comment classification is a critical task for automated software documentation and analysis. In the context of the NLBSE'26 Tool Competition, we present LoRA-MME, a Multi-Model Ensemble architecture utilizing Parameter-Efficient Fine-Tuning (PEFT). Our approach addresses the multi-label classification challenge across Java, Python, and Pharo by combining the strengths of four distinct transformer encoders: UniXcoder, CodeBERT, GraphCodeBERT, and CodeBERTa. By independently fine-tuning these models using Low-Rank Adaptation(LoRA) and aggregating their predictions via a learned weighted ensemble strategy, we maximize classification performance without the memory overhead of full model fine-tuning. Our tool achieved an F1 Weighted score of 0.7906 and a Macro F1 of 0.6867 on the test set. However, the computational cost of the ensemble resulted in a final submission score of 41.20%, highlighting the trade-off between semantic accuracy and inference efficiency.

cs.SE↗

arXiv2026

Auxilio and Beyond: Comparative Evaluation, Usability, and Design Guidelines for Head Movement-based Assistive Mouse Controllers

Upper limb disability due to neurological disorders or other factors restricts computer interaction for affected individuals using a generic optical mouse. This work reports the findings of a comparative evaluation of Auxilio, a sensor-based wireless head-mounted Assistive Mouse Controller (AMC), that facilitates computer interaction for such individuals. Combining commercially available, low-cost motion and infrared sensors, Auxilio utilizes head movements and cheek muscle twitches for mouse control. Its performance in pointing tasks with subjects without motor impairments has been juxtaposed against a commercially available and patented vision-based head-tracking AMC developed for similar stakeholders. Furthermore, our study evaluates the usability of Auxilio using the System Usability Scale, supplemented by a qualitative analysis of participant interview transcripts to identify the strengths and weaknesses of both AMCs. Experimental results demonstrate the feasibility and effectiveness of Auxilio, and we summarize our key findings into design guidelines for the development of similar future AMCs.

cs.HC↗

arXiv2024

Renovo: Sensor-Based Visual Assistive Technology for Physiotherapists in the Rehabilitation of Stroke Patients with Upper Limb Motor Impairments

Stroke patients with upper limb motor impairments are re-acclimated to their corresponding motor functionalities through therapeutic interventions. Physiotherapists typically assess these functionalities using various qualitative protocols. However, such assessments are often biased and prone to errors, reducing rehabilitation efficacy. Therefore, real-time visualization and quantitative analysis of performance metrics, such as range of motion, repetition rate, velocity, etc., are crucial for accurate progress assessment. This study introduces Renovo, a working prototype of a wearable motion sensor-based assistive technology that assists physiotherapists with real-time visualization of these metrics. We also propose a novel mathematical framework for generating quantitative performance scores without relying on any machine learning model. We present the results of a three-week pilot study involving 16 stroke patients with upper limb disabilities, evaluated across three successive sessions at one-week intervals by both Renovo and physiotherapists (N=5). Results suggest that while the expertise of a physiotherapist is irreplaceable, Renovo can assist in the decision-making process by providing valuable quantitative information.

cs.HC↗

arXiv2021

ANTASID: A Novel Temporal Adjustment to Shannon's Index of Difficulty for Quantifying the Perceived Difficulty of Uncontrolled Pointing Tasks

Shannon's Index of Difficulty ($ID$), reputable for quantifying the perceived difficulty of pointing tasks as a logarithmic relationship between movement-amplitude ($A$) and target-width ($W$), is used for modelling the corresponding observed movement-times ($MT_O$) in such tasks in controlled experimental setup. However, real-life pointing tasks are both spatially and temporally uncontrolled, being influenced by factors such as - human aspects, subjective behavior, the context of interaction, the inherent speed-accuracy trade-off where, emphasizing accuracy compromises speed of interaction and vice versa, and so on. Effective target-width ($W_e$) is considered as spatial adjustment for compensating accuracy. However, no significant adjustment exists in the literature for compensating speed in different contexts of interaction in these tasks. As a result, without any temporal adjustment, the true difficulty of an uncontrolled pointing task may be inaccurately quantified using Shannon's ID. To verify this, we propose the ANTASID (A Novel Temporal Adjustment to Shannon's ID) formulation with detailed performance analysis. We hypothesized a temporal adjustment factor ($t$) as a binary logarithm of $MT_O$, compensating for speed due to contextual differences and minimizing the non-linearity between movement-amplitude and target-width. Considering spatial and/or temporal adjustments to ID, we conducted regression analysis using our own and Benchmark datasets in both controlled and uncontrolled scenarios of pointing tasks with a generic mouse.ANTASID formulation showed significantly superior fitness values and throughput in all the scenarios while reducing the standard error. Furthermore, the quantification of ID with ANTASID varied significantly compared to the classical formulations of Shannon's ID, validating the purpose of this study.

cs.HC↗