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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 57 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.
Evidence-Grounded Visual Explanation Design for E-commerce Recommendation Cards: Complementary and Substitute Relations, Image Availability, and Interface Hierarchy Hailin Zhou
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.v4i1.3992

Abstract

E-commerce recommendation cards must explain why an item is related while maintaining a compact, consistent visual hierarchy. This study presents an evidence-grounded card framework and evaluates its retrieval inputs with the final TREC Product Recommendation 2025 topics, a 48,616-product corpus, 7,633 NIST judgments for 47 topics, and the SQID supplementary image-URL table. Fixed lexical methods generated separate top-10 complement and substitute rankings over the full corpus before assessor judgments were applied. Under the official scoring procedure, the relation-aware lexical method achieved an average nDCG@10 of 0.1840, with a complement nDCG@10 of 0.0562 and a substitute nDCG@10 of 0.3119. Its average difference from full-text TF–IDF was not statistically reliable (difference = 0.0111, 95% CI [-0.0090, 0.0321]). The complement-minus-substitute gap was -0.2557 (95% CI [-0.3151, -0.1937]), confirming that complementary recommendation remains the weaker relation. Requiring a confirmed supplementary image URL increased confirmed URL coverage among displayed items to 100% but reduced average nDCG@10 to 0.0138. The findings support relation-qualified reasons, confirmed-image and text-led fallback states, and conservative language for complementary suggestions. The framework specifies evidence and layout behavior; shopper responses and different verbalization methods require direct comparative evaluation.
Beyond CTR and VCR: LLM-Assisted Design Evaluation with Eye-Tracking Validation for Graphic Advertising Qiwen Zheng; Xiaochen Li; Fiona Wang
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.v4i1.3994

Abstract

Digital advertising design is often judged with delivery or response metrics such as click-through rate (CTR), video-completion rate (VCR), and viewability, although these measures do not describe whether a banner communicates through a clear visual hierarchy. This study evaluates whether large language model (LLM)-assisted scores and lightweight image features can support graphic advertising review while keeping design-quality prediction distinct from measured visual attention. GraphicDesignEvaluation provided 1,200 principle–image rating rows for alignment, overlap, and white space. Under grouped five-fold cross-validation by base design, the GPT-only Ridge model reached R2 = 0.408, RMSE = 1.566, MAE = 1.257, Pearson = 0.639, and Spearman = 0.634. Direct GPT–human agreement was uneven: overlap was strong (Spearman = 0.769), white space was moderate (0.637), and alignment was weaker (0.519). An external construct analysis used 1,000 advertisements from ADD1000 with aggregate human fixation-density maps and matched EAID ratings. A combined gradient and local-contrast proxy modestly exceeded a center prior (CC = 0.457, SIM = 0.562, KL divergence = 0.637), while lower fixation entropy was associated with higher aesthetic ratings (Spearman = -0.319). BannerRequest400 was used only to describe brief, CTA-language, logo, and format constraints because it contains no human design-quality or gaze labels. The findings support a preliminary, human-supervised design-review framework: LLM scores are useful screening evidence, especially for overlap, but they do not replace human creative judgment, eye tracking, or advertising-performance measures.
From 8-K to Deal Cards: A Source-Grounded Visual Triage Framework for FinTech M&A Filings Sisi Meng; Dingyuan Zhang; Eric Li
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.v4i1.4001

Abstract

Form 8-K is a timely public channel through which U.S. registrants disclose material events, including acquisition agreements, completed transactions, shareholder communications, and related exhibits. The density of this filing stream creates a visual communication problem: analysts need to identify source-linked deal cues without losing the legal and temporal provenance of each record. This study presents a source-grounded deal-card workflow for FinTech M&A filing triage. The analysis used the official SEC quarterly master indexes and included 16,439 Form 8-K and 8-K/A filings in Q4 2025, together with 276,261 contextual records in the complete Q4 index. Symmetric companion-form windows were protected against quarter-boundary truncation with the last 14 days of Q3 2025 and the first 14 days of Q1 2026. A seven-day window identified 222 rule-defined FinTech M&A targets across 94 company names, or 1.35% of the Q4 corpus. In five-fold cross-validation grouped by CIK, text-only logistic triage reached F1 = 0.224 and M&A-context logistic triage reached F1 = 0.322. Integrated logistic and linear SVM configurations reproduced the rule-defined target exactly (F1 = 1.000). Because the target was constructed from the same FinTech and M&A-neighborhood cues available to those configurations, this exact agreement is interpreted as rule-consistency validation rather than predictive superiority. A structural interface audit found that the deal-card schema exposed 10 documented fields, nine visual anchors, and six configured decision cues. These author-defined attributes describe interface structure; they do not measure comprehension, trust, or review speed. The contribution is a method for organizing SEC filing evidence for subsequent visual review.
Grounded AI Pointer Cards for Browser Agents: Evidence-Aware Web Element Reranking and Action Explanation on WebLINX Yinchen Shi; Yuxuan Ren
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.v4i1.4002

Abstract

AI browser agents increasingly promise to turn the mouse pointer into a situated interface for selecting page content, asking follow-up questions, scrolling to relevant sections, and opening contextual panels. For graphic design research, this shift matters because the pointer card is both a computational output and a visual communication device: it must make the target, supporting evidence, and next action legible at the moment of use. This paper evaluates evidence-aware web element reranking as the grounding layer for AI pointer cards. The empirical study uses the WebLINX candidate-reranking data, including validation, same-distribution test, and unseen-website test partitions. Each query is paired with candidate DOM/page elements and binary relevance labels. Nine reranking methods were evaluated, ranging from random and DOM-salience controls to BM25 variants, a transparent hybrid formula, a logistic reranker, and a depth-limited decision-tree reranker. Across 5,883 queries and 1,556,655 query–element pairs, the tree reranker achieved the highest same-distribution nDCG@10 (0.272) and Recall@10 (0.473), while the fixed hybrid achieved the highest unseen-website Top-1 accuracy (0.104). Combining lexical and DOM evidence improved ranking, whereas layout features transferred less consistently across websites. Because the first-ranked target was correct in only 10.4% of unseen-website queries, the findings support a confirm-before-act grounding prototype with visible alternatives and correction controls rather than autonomous execution. The reported metrics evaluate target ranking rather than card usability.

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