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International Journal of Graphic Design
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29880343     EISSN : 29879434     DOI : 10.51903
Core Subject : Science, Art,
Arts Computer Science & IT
This journal is a peer-reviewed and open Visual Communication Design Publication Journal. The fields of study in this journal include the sub-groups of Performing Arts, Arts, journalistic, Crafts, Media, and Design. The Art, Design, and Media Research
Arjuna Subject : Ilmu Komputer (semua kategori) - Grafik Komputer dan Desain Berbantu Komputer
Articles 53 Documents
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Trust-Calibrated Multilingual RAG for Humanitarian Information Platforms: Empirical Evaluation on OMoS-QA for Migration Information Access Chen, Yushan; Xu, Haosen
International Journal of Graphic Design Vol. 4 No. 1 (2026): April | IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v4i1.3552

Abstract

Humanitarian information platforms increasingly serve migrants, refugees, and crisis-affected users who need correct answers about housing, schooling, legal procedures, benefits, health, and emergency services. In this setting, a wrong answer is more harmful than a missing answer, so multilingual question-answering systems must not only retrieve and summarize relevant content but also calibrate when to answer, when to abstain, and how to communicate uncertainty to the user. This paper develops a trust-calibrated multilingual retrieval-augmented generation (RAG) design for humanitarian information platforms and evaluates it on the public OMoS-QA benchmark for migration information access. The study combines two empirical layers. First, we run a direct page-retrieval evaluation over the full public corpus and compare BM25, word-level TF-IDF, character-level TF-IDF, and a lexical-character hybrid retriever. Second, we reanalyze the officially scored benchmark outputs released with OMoS-QA for sentence-level answer extraction, question-level no-answer detection, multilingual transfer, and cross-language transfer. All numerical results are empirically measured; no illustrative placeholders are used. The hybrid retriever reaches 69.4% recall at rank 1, 82.6% at rank 3, and 86.1% at rank 5, outperforming the sparse baselines. On same-language answer extraction, DeBERTa achieves the strongest balanced F1 (62.5 German, 64.9 English), while Llama-3-70B and GPT-3.5-Turbo obtain the strongest no-answer detection results. Explicit answerability prompting raises Llama-3-70B recall on unanswerable questions to 83.6% in German and 78.2% in English. Multilingual experiments show moderate degradation for French and larger losses for Arabic and Ukrainian, while cross-language transfer remains surprisingly robust. Based on these findings, the paper formulates a design contribution for graphic and interaction design: a trust-calibrated evidence-card pattern that combines evidence highlighting, citation links, uncertainty cues, and escalation to human support. The result is a benchmark-grounded interface logic for safer public-interest LLM applications rather than a user-validated final interface.
Computer-Vision-Informed Visual Explanation Cards for Autonomous-Driving Traffic-Sign Alerts: Localization, Classification, and Retrieved Evidence on GTSDB Ruiyan Ma; Long Zhang; Tiffany Song
International Journal of Graphic Design Vol. 3 No. 2 (2025): October| IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v3i2.3991

Abstract

This paper develops a computer-vision-informed visual explanation card for traffic-sign alerts in autonomous-driving and driver-assistance interfaces. The card organizes a detected sign crop, predicted class, confidence, default semantic display-priority tier, retrieved visual precedents, scene-location cue, and concise action prompt. The empirical study uses the complete German Traffic Sign Detection Benchmark (GTSDB), comprising 900 road scenes in the standard 600-scene development and 300-scene evaluation portions. The same scenes support localization, crop classification, retrieval, calibration, and end-to-end analysis. The first 600 scenes were divided at the scene level into training and validation subsets; the 300 evaluation scenes were held out until model choices, retrieval depth, fusion weight, and detector threshold had been fixed. A learned class-agnostic localizer filters color-connected-component proposals with a histogram-of-oriented-gradients and color classifier. Five crop classifiers and four nearest-neighbor settings were evaluated, with retrieval treated primarily as example-based explanation support. On the held-out scenes, the selected localizer achieved an AP at IoU 0.50 of 0.211, an AP averaged over IoU 0.50–0.95 of 0.098, and a recall of 0.260 at the validation-selected operating point. The selected crop classifier achieved 0.784 accuracy and 0.574 macro-F1 on ground-truth crops. With predicted crops, correct-class end-to-end coverage was 0.177, and correct-tier end-to-end coverage was 0.244. These results define the information that the proposed card can receive from the evaluated vision pipeline. They do not measure driver comprehension, glance behavior, response time, trust, usability, or deployment safety, which require separate human-centred evaluation.
Data-Informed UI Decision Cards for Emergency Department Renovation: Linking Patient-Flow Simulation to Configurable Spatial Scenarios Huichao Dong; Xiaoming Xiao; Sarah Li
International Journal of Graphic Design Vol. 3 No. 2 (2025): October| IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v3i2.4014

Abstract

Emergency department renovation requires designers to connect operational indicators with spatial planning choices through a clear visual interface. This study developed and analytically demonstrated a data-informed UI decision-card prototype linking patient-flow analysis to configurable wayfinding, nurse-station, and staff-support scenarios. The analysis used public CSV files from the 2024 Synthetic Dataset of Emergency Healthcare Services, with the California Emergency Department Volume and Capacity dataset providing facility-level context. Dataset-derived variables included waiting time, queue count, resource utilization, length of stay (LOS), and satisfaction; wayfinding friction, walking load, visibility, collaboration, station count, and respite-room count were prespecified scenario inputs. Composite indices represented ED pressure and operational staff-support review. The outpatient analysis set contained 236 records. Mean total waiting time was 36.42 minutes, mean LOS was 50.79 minutes, mean simulated satisfaction was 82.84%, and mean ED Pressure Index was 0.298. Gradient boosting achieved a mean five-fold RMSE of 4.119 and an R² of 0.944 on the synthetic data; its output served as a secondary-priority cue for congestion and provider-load cards. CheckPatientType had the highest mean wait, and Triage the highest mean provider utilization. Under the prespecified scenario inputs and base-case weights, Scenario C received the highest support score (66.95) and highest collaboration risk. Evidence labels indicate whether each card is based on dataset variables, model outputs, or configured scenario inputs, making the basis of each renovation trade-off explicit.

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