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Journal of Technology Informatics and Engineering
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29619068     EISSN : 29618215     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Power Engineering Telecommunication Engineering Computer Engineering Control and Computer Systems Electronics Information technology Informatics Data and Software engineering Biomedical Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 197 Documents
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Distilling VMAF into an Edge-Deployable Quality Predictor: A Pilot Shot-Level Proxy with LLM-Ready Quality Tokens Xiaohan Chang; Heyu Wang
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.522

Abstract

This pilot study evaluates whether a compact student model can approximate VMAF well enough to support low-latency release guarding on edge-class CPU environments. The corpus comprises a 62.31-second Big Buck Bunny excerpt at 1280 × 720 and 25 fps, segmented into 13 shots. Twelve distorted variants were generated by crossing H.264/AVC and H.265/HEVC with 180p, 240p, and 360p delivery resolutions and two quality levels per codec-resolution pair, yielding 156 shot-level samples. Frame-level VMAF scores were aggregated into shot-level teacher labels, and a student proxy consumed 14 low-cost no-reference features derived from decoded frames and stream metadata. Shot-grouped five-fold cross-validation was used to prevent content leakage across train-test splits. On this corpus, a 50-tree gradient-boosted decision tree achieved MAE = 6.56 VMAF points, RMSE = 8.32, and Pearson r = 0.913. Relative to simple regressors, the student reduced MAE by approximately 21.5% versus bitrate-only regression and 10.7% versus metadata-only regression. In a single CPU-only benchmark, predictor latency was 0.484 ms per sample and the full decode-feature-predict chain averaged 42.61 ms versus 1117.41 ms for the teacher, corresponding to a 26.22× end-to-end speed-up. As a thresholded guard, the same student reached F1 = 0.826, 0.893, and 0.900 at 60, 70, and 80 VMAF respectively. These findings support the feasibility of a practical edge proxy on this specific pilot corpus, but they should not be interpreted as broad generalization across content classes or production ladders. The paper also introduces an LLM-ready token interface intended for downstream reporting rather than for replacing the underlying quality measurement
Layout-Aware Progressive PDF Rendering: AI Prioritization of PDF Slices to Reduce Time-to-Functional-First-Frame on FUNSD Heyu Wang; Yuxuan Ren
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.523

Abstract

Progressive PDF rendering is attractive because users rarely need every visible pixel at once; they need the semantically useful parts of the current viewport early enough for reading and interaction. This paper studies whether layout-aware AI can prioritize PDF slices more effectively than geometric or density-based heuristics. We reconstruct vector PDFs from official FUNSD form annotations and evaluate a tile scheduler that predicts tile utility from inexpensive layout and preview features before high-resolution rendering begins. The empirical study covers 26 reconstructed documents from the FUNSD test split that were fully processed in the present environment, four viewport scenarios, and measured clip-render timings for all visible tiles. The main configuration uses an 8×10 grid and a random-forest regressor trained with page-level 5-fold GroupKFold, then compares the learned scheduler with row-major visible-first, center-first, ink-density, text-density, a hand-tuned layout heuristic, full-page rendering, and an oracle upper bound. The proposed model reaches TTFF-90 in 14.21 ms, compared with 15.18 ms for the best non-AI heuristic, 20.48 ms for full-page rendering, and 24.09 ms for row-major rendering. It also achieves Utility@20ms of 0.941, AUC@25ms of 0.730, NDCG@10 of 0.963, and Recall@10 of 0.969. The results show that slice rendering is not inherently beneficial: the summed visible-tile cost in the main 8×10 setting is 28.80 ms, which is higher than the full-page cost of 20.48 ms, so scheduling quality determines whether slicing improves or harms TTFF. A coarser 6×8 grid reduces AI TTFF-90 to 10.58 ms, while the densest pages favor a full-page fallback. Paired Wilcoxon signed-rank tests over the page-scenario cases yield p < .001 for TTFF-90 improvements of the proposed model over every non-AI baseline. However, those tests should be interpreted as case-level rather than document-level evidence.
From General Human Activity Recognition to Volleyball-Oriented Wearable Transfer Learning: Cross-Dataset Evidence from UCI HAR and WISDM for Domain Adaptation and Edge Deployment Jubin Zhang
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.524

Abstract

Wearable human activity recognition has become a practical foundation for coaching analytics, workload monitoring, and interactive sports training, yet volleyball-specific labelled inertial datasets remain much smaller than general-purpose public HAR corpora. This study addressed that gap through a transfer-learning design in which public HAR benchmarks were treated as representation sources and a smartwatch target domain was used as a volleyball-oriented wrist proxy. Full experimental evaluations were conducted on UCI HAR and on a public WISDM-derived smartwatch subset, using three baseline families: logistic regression, a lightweight one-dimensional convolutional network, and a tiny Transformer. The study also measured modality ablation and unsupervised domain adaptation through Deep CORAL in a common four-class transfer space. On UCI HAR, the final measured accuracies were 0.7940 for logistic regression, 0.8039 for CNN-Lite, and 0.3621 for Transformer-Tiny. On the WISDM smartwatch subset, the corresponding accuracies were 0.8207, 0.8682, and 0.7632. Modality ablation on WISDM showed that accelerometer-only input reached 0.8486 accuracy, gyroscope-only input reached 0.6543, and fused accelerometer-plus-gyroscope input reached 0.8505. For cross-dataset transfer from UCI to WISDM, source-only training achieved 0.3376 accuracy, Deep CORAL improved accuracy to 0.4134, and the fully supervised target-only upper bound reached 0.8374. The results establish three concrete conclusions: lightweight convolutional sequence encoders are more reliable than the tested tiny Transformer under these data conditions, accelerometer channels carry most of the discriminative value for wrist-worn deployment, and domain adaptation is necessary when general smartphone HAR is transferred to smartwatch sports analytics. These findings provide a reproducible public-data foundation for volleyball-oriented wearable modelling and for subsequent fine-tuning on sport-specific action labels.
The Impact of Generative AI on University Students’ Academic Performance: A Quantitative and Predictive Analysis Sazeed Hossain; Shahria Zzaman; Afsana Mimi
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.527

Abstract

This research is concerned with assessing the effects of generative AI on the academic performance of university students in terms of usage, benefits, and challenges. Quantitative research methodology was used for data collection via structured questionnaires from 256 university students. Data analysis was performed through IBM SPSS (version 27.0) and Stata software utilizing non-parametric statistics considering the type of data. The reliability test showed that the instrument was highly reliable with a Cronbach's Alpha score of 0.842. The results show a statistically significant positive association between the different variables of AI use, with research assistance being the most significant predictor of academic performance. Exploratory factor analysis indicated three important factors affecting academic performance, namely academic integration, perception of challenges, and skill development. Regression analysis showed a positive relationship between academic integration and skill development and academic performance. However, perception of challenges had a negative relationship. In addition to this, another strong relationship between the geographical position of students and awareness about risks related to generative AI was observed. In general, the research offers a balanced look at the role of generative AI in academia as a very helpful technology, as well as a risky one, which has to be approached thoughtfully.
Calibrated Resume-Job Matching for Trustworthy LLM-Assisted Recruiter Screening: Pairwise Matching, Probability Calibration, and Selective Refusal on Two Public Recruitment Datasets Binghua Zhou; Jiaying Jin; David Zhao
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.529

Abstract

Recruiter screening increasingly relies on large language model (LLM)-assisted workflows, but high-stakes applications require reproducible matching, calibrated probabilities, and reliable handling of uncertain cases. This study evaluates a screening framework combining matching, calibration, and selective refusal using two public datasets: resume-job-description-fit for supervised pairwise learning and Resume-Screening-Dataset for benchmarking and external generalization. After deterministic preprocessing, we compared cosine similarity, alignment features, TF-IDF pairwise models, and hybrid models integrating text, alignment, and title information. The strongest probabilistic models were calibrated with Platt scaling and isotonic regression and evaluated under confidence-based refusal. On the resume-job-description-fit test set, the best three-class model achieved a macro-F1 of 0.450. For binary shortlist-versus-reject screening, the title-augmented hybrid model obtained 0.654 balanced accuracy, 0.647 F1, and 0.699 AUROC. Platt calibration improved probability estimates by reducing the Brier score from 0.232 to 0.226 and negative log-likelihood from 0.772 to 0.675. Selective refusal further improved in-domain accuracy, while cross-dataset transfer remained weak (AUROC 0.47–0.51). These results indicate that matching, calibration, and selective refusal enhance trustworthy within-domain screening, although human review remains essential under distribution shift.
Uncertainty-Aware Medical Vision–Language Classification on a Lightweight MedMNIST-Compatible Biomedical Patch Benchmark Shenghan Lu; Xiaohan Chang; Tracey Zou
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.530

Abstract

Medical image classifiers can be accurate while still being unsafe to use when their confidence values are poorly calibrated or when their predictions are communicated in language that overstates diagnostic certainty. This paper presents an uncertainty-aware medical vision-language classification workflow for lightweight 28×28 biomedical images. The target setting is MedMNIST-style classification, where images are standardized to small spatial sizes and where compact CNN, residual, and transformer models can be trained on ordinary hardware. The official MedMNIST v2 collection contains 12 two-dimensional and 6 three-dimensional biomedical image subsets; however, the execution environment used for this manuscript could read the official documentation but could not fetch binary Zenodo files. Three lightweight models were trained and evaluated across three random seeds: a 53,380-parameter CNN, a 392,092-parameter tiny residual network, and a 77,956-parameter tiny Vision Transformer. Each model used the same 2,240/320/640 train/validation/test split, AdamW optimization, and validation-set temperature scaling. The evaluated metrics were top-1 accuracy, macro one-vs-rest ROC-AUC, negative log likelihood, multiclass Brier score, expected calibration error, predictive entropy, and confusion-matrix/class-level metrics. TinyViT achieved the highest mean calibrated top-1 accuracy, 0.9906 ± 0.0016, while SmallCNN achieved the best mean macro ROC-AUC, 0.9993 ± 0.0005, and the best mean post-calibration ECE, 0.0115 ± 0.0028. Temperature scaling reduced ECE for all models, with reductions of 0.1153 for SmallCNN, 0.0853 for TinyResNet, and 0.1189 for TinyViT. A deterministic language-card module converted calibrated predictions into patient-friendly decision-support text that explicitly includes confidence, uncertainty, visual cue wording, and a non-diagnostic safety caveat.
Language-Guided Feature Selection for DDoS and Intrusion Detection on CICIDS2017 Yunhe Li; Shenghan Lu
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.531

Abstract

This paper reports a complete empirical study of language-guided feature selection for DDoS and intrusion detection on the CICIDS2017 MachineLearningCSV flow data. The central question is whether an LLM-style semantic reading of CICFlowMeter feature names can reduce the feature set while preserving detection performance and lowering false alarms. The experiment used the eight labeled CICIDS2017 CSV sessions, removed only non-finite numeric rows, and retained 2,827,876 flows with 78 original numeric features. A semantic feature screen selected 32 features describing service context, duration, packet and byte volume, flow rates, inter-arrival timing, TCP flags, window sizes, and active/idle behavior. The evaluation compared all features with the language-selected set under full-corpus binary and multiclass stochastic logistic regression, DDoS-specific Random Forest, DDoS-specific stochastic logistic regression, and a compact multilayer perceptron. The best DDoS result was obtained by Random Forest with the selected features: F1 = 0.999896, false-positive rate = 0.000068, and eight errors on 67,714 test flows. The selected features reduced the DDoS Random Forest training time by 23.78% and reduced full-corpus SGD training time by about one half, although the full feature set was stronger for the full binary linear model. Ablation showed that TCP flag/window and destination-port semantics produced the largest DDoS degradation when removed. The findings support language-guided feature selection as a practical compression step for latency-sensitive DDoS mitigation, while retaining all features remains advisable for broad multiclass intrusion detection when a linear learner is used.

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