Journal of Technology Informatics and Engineering
Journal Title: Journal of Technology Informatics and Engineering Initials: JTIE Frequency: April, August, and December Print ISSN: 2961-9068 Online ISSN: 2961-8215 Editor in Chief: Dr. Ir. Agus Wibowo., M.Kom., M.Si., M.M. DOI: 10.51903 Publisher: University of Science and Computer Technology Journal of Technology Informatics and Engineering is a peer-reviewed scientific journal that publishes original research articles, scholarly studies, and applied research findings related to advances in technology and engineering. The journal is published in both electronic and print formats with e-ISSN: 2961-8215, and p-ISSN: 2961-9068. The scope of the journal encompasses, but is not limited to, Informatics, Mechanical Engineering, Electrical Engineering, Information Systems, and Industrial Engineering. The journal serves as an academic platform for researchers, academics, and practitioners to disseminate research outcomes and technological innovations. Journal of Technology Informatics and Engineering is published three times a year, in April, August, and December. Indexed by:
University of Science and Computer Technology
Natural-Language Policy Reasoning with Proof Generation: Turning Platform Rules into Verifiable Knowledge
Policy and compliance systems increasingly express rules in natural language, yet enforcement requires deterministic decisions and auditable explanations. This paper studies a practical pipeline that converts natural-language facts and rules into a verifiable knowledge base, answers queries with three-valued semantics (True/False/Unknown), and produces machine-checkable proofs. The contribution is system-level rather than a new reasoning formalism: we integrate controlled-language parsing, symbolic proof extraction, independent proof checking, and proof-based supervision in a single auditable framework. We evaluate the pipeline on two natural-language rule-reasoning benchmarks: (i) a balanced subset of ProofWriter’s open-world-assumption tasks (360 train, 360 test), and (ii) a RuleTaker-style dataset generated from its grammar and label semantics (1800 train, 900 test), both balanced across reasoning depths 0–5. We compare a text-only logistic regression baseline, a retrieval-based “proof” baseline, a symbolic forward-chaining reasoner with proof extraction, and a proof-trained classifier using generated proofs. To ensure fairness, LR-text and LR-proof share the same TF-IDF/logistic-regression setup, and the retrieval baseline uses the same representation with a fixed top-4 configuration. On ProofWriter-Balanced, the symbolic reasoner achieves 0.803 accuracy (0.808 macro-F1), while proof-trained classification reaches 0.825 accuracy (0.825 macro-F1). On RuleTaker-Rep, both methods achieve 1.000 accuracy. Proof verifiability clearly separates faithful from post-hoc explanations: symbolic proofs are verifiable for all predictions, whereas retrieval-based proofs are verifiable for only 31.4%. Sensitivity analyses varying reasoning depth, distractors, and proof corruption show that proof-based methods remain robust to noise but depend on proof integrity. These findings demonstrate the feasibility of auditable natural-language policy reasoning in controlled settings, while highlighting limitations in parser coverage and benchmark regularity.
Copyright (c) 2025 Xiaofei Luo https://creativecommons.org/licenses/by-sa/4.0
Explainable Multi-Hop Question Answering for QA Assistants: Two-Hop Evidence Retrieval, Sentence-Level Supporting Facts, and Explicit Reasoning Paths
Multi-hop question answering (QA) for customer-facing assistants requires not only accurate answers but also an auditable evidence trail that explains how the system arrived at each answer. We present a fully interpretable multi-hop QA pipeline that decomposes inference into three explicit modules—Retriever → Evidence Selector → Reasoner—and produces an explanation consisting of sentence-level supporting facts and an explicit two-hop evidence path. The retriever ranks candidate paragraphs using lexical IDF-weighted token overlap; the evidence selector chooses a small set of high-scoring sentences; and the reasoner extracts a final answer using weighted candidate phrase scoring and deterministic rules for date/number and constrained yes/no comparisons. We conduct full experimental evaluations on the complete development splits of HotpotQA (7,405 questions, distractor setting) and 2WikiMultihopQA (12,576 questions). On HotpotQA, sentence-level evidence selection improves Supporting Fact F1 from 0.334 to 0.419, and adding an explicit two-hop retrieval path further increases Supporting Fact F1 to 0.426 while raising paragraph recall@2 to 0.603. Answer F1 increases from 0.084 to 0.088. On 2WikiMultihopQA, evidence selection improves Supporting Fact F1 from 0.328 to 0.429 and Answer F1 from 0.071 to 0.075. These results quantify the contribution of explicit evidence selection and path-constrained retrieval to explainability and provide a practical, reproducible baseline for knowledge assistants that must justify answers with supporting facts.
Copyright (c) 2026 Xiaofei Luo https://creativecommons.org/licenses/by-sa/4.0
Off-Policy Evaluation and Conservative Policy Selection for Slot-Level Dynamic Bidding and Ranking on the Open Bandit Dataset (Small)
Dynamic bidding and ranking systems must improve revenue or engagement while avoiding harmful regressions during deployment. This paper presents an end-to-end offline OPE and conservative policy-selection workflow for slot-level contextual bandit approximations of ranking decisions. Using the small Open Bandit Dataset (OBD-small) from ZOZOTOWN (ZOZO, Inc.), each logged row is treated as a context-dependent choice among discrete actions (items), with binary click rewards and logged propensity. This formulation is suitable at the slot level but does not capture full listwise ranking or multi-step offline reinforcement learning. Dynamic bidding and ranking systems must improve revenue or engagement while avoiding harmful regressions during deployment. This paper presents an end-to-end offline OPE and conservative policy-selection workflow for slot-level contextual bandit approximations of ranking decisions. Using the small Open Bandit Dataset (OBD-small) from ZOZOTOWN (ZOZO, Inc.), each logged row is treated as a context-dependent choice among discrete actions (items), with binary click rewards and logged propensity. This formulation is suitable at the slot level but does not capture full listwise ranking or multi-step offline reinforcement learning. Empirically, highly deterministic evaluation policies exhibit extreme variance under sparse clicks, while the logistic reward model remains weak (ROC-AUC ≈ 0.5), limiting DM/DR interpretability. Clipped-DR mixing yields only limited certified improvements: in the women’s campaign, gains appear only at moderate confidence (δ=0.10) and for caps up to M=5, whereas stricter or looser settings revert to baseline; in the men’s campaign, certification is largely absent. These findings demonstrate that OPE diagnostics and conservative mixing enable reproducible offline selection under uncertainty, but do not indicate deployment-ready improvements.
Copyright (c) 2026 Tong Ye, Jinyi Mu, James Hunter https://creativecommons.org/licenses/by-sa/4.0
Federated Topic-Preference Learning for Knowledge-Grounded Chat with Differential Privacy
Retrieval-augmented approaches have become central in knowledge-grounded dialogue systems, yet incorporating topical preferences remains difficult due to privacy constraints on user interaction data. This study introduces a lightweight federated topic-preference (FedTP) mechanism that models session-level preferences without centralizing raw data and uses client-level differential privacy (DP). Using the Topical-Chat dataset (8,628 conversations), each conversation is treated as a client, and evidence routing is framed as selecting relevant knowledge snippets based on dialogue context. The proposed method augments a TF-IDF relevance score with a small preference-based component derived from both local session distributions and a DP-aggregated global prior. Experimental results on 9,553 grounded test turns show a consistent but limited improvement in evidence hit rate, from 0.6167 to 0.6194. The small optimal preference weight (λ = 0.005) indicates that the preference signal mainly influences decisions when competing candidates have similar relevance scores, rather than substantially altering routing behavior. A privacy–utility analysis under Gaussian DP (ε ranging from 9.69 to 0.606, δ = 1e−5) shows negligible changes in performance, which is expected given the large number of clients in a one-shot aggregation setting. Additional metrics remain largely stable, suggesting that the method affects selection margins rather than overall alignment. These findings suggest that federated preference aggregation can provide a modest, privacy-preserving bias for evidence routing, but its practical impact remains incremental and context-dependent.
Copyright (c) 2025 Meng-Ju Kuo, Daren Zheng, Julie Hires https://creativecommons.org/licenses/by-sa/4.0
Digital-Twin Dispatching for Urban Mobility via Spatio-Temporal Transformers and Offline Reinforcement Learning
This study addresses the challenge of optimizing ride-hailing dispatch and repositioning under data limitations by proposing an end-to-end digital-twin dispatching framework that integrates spatio-temporal demand forecasting with offline reinforcement learning. Using publicly available NYC FOIL ride-hailing data aggregated at the dispatching-base level, the research aims to evaluate whether coarse-grained data can still support reliable, reproducible decision-making pipelines. The methodology consists of two main components: (i) multivariate time-series forecasting using baseline models, a temporal convolutional network (TCN), and a spatio-temporal transformer to predict next-day demand; and (ii) a digital-twin simulation combined with an action-constrained offline reinforcement learning approach, including behavior cloning (BC) and Conservative Q-Learning (CQL), to optimize fleet repositioning decisions. Experimental results show that the TCN achieves the best forecasting accuracy on the test period, although dominant demand regions largely drive performance gains. In the control phase, conservative policies such as CQL demonstrate stable performance with reduced repositioning costs, but do not significantly outperform behavior cloning due to limited training data. The findings indicate that, in coarse aggregate settings, operational improvements are more influenced by controlling policy sensitivity than by marginal forecasting gains. This study contributes a reproducible benchmark pipeline and highlights the importance of conservative control strategies, transparent assumptions, and sensitivity analysis when deploying AI-driven mobility systems based on limited or aggregated data.
Copyright (c) 2025 Long Zhang, Ruiyan Ma, Peter Greg https://creativecommons.org/licenses/by-sa/4.0
Offline Counterfactual Evaluation for Advertising and Recommendation Slot Policies: A Reproducible Study on the Open Bandit Dataset (Small)
Offline or counterfactual evaluation is a critical capability for iterating advertising and recommender ranking strategies when online A/B testing is slow, expensive, or risky. Off-policy evaluation (OPE) estimates the expected reward of a candidate policy using logged interaction data from a different behavior policy. Still, it can suffer from high variance under poor overlap and can be misleading when the operational objective is choosing among candidate policies rather than minimizing point-estimation bias alone. This paper presents a fully reproducible empirical study of IPS, self-normalized IPS (SNIPS), doubly robust (DR), and Switch-DR estimators on the Open Bandit Dataset (OBD) small release. Using the Men and Women campaigns (10,000 logged item-impressions per campaign and behavior policy) collected by uniform random and Bernoulli Thompson Sampling (BTS), we construct a held-out oracle for stationary slot-wise policies from the random-traffic split and evaluate both value estimation and policy-ranking consistency on random-logged and BTS-logged test sets. Across 1,000 nonparametric bootstrap replications, IPS and SNIPS are accurate on randomly logged data, whereas BTS-logged data exhibit extreme importance weights and very small effective sample sizes (ESS), making IPS-based ranking unreliable under weak support. Switch-DR is most useful in moderate-overlap regimes, where it truncates high-variance corrections. Still, it introduces bias that depends on the switching threshold and must therefore be stress-tested rather than treated as a universally superior estimator. Finally, we provide a structured reporting template—based on oracle decomposition, overlap diagnostics, and estimator components—for explaining why a policy appears better and how reliable that conclusion is.
Copyright (c) 2025 Jinyi Mu, Tong Ye, Priya Patel https://creativecommons.org/licenses/by-sa/4.0
Multi-Horizon GPU Demand Forecasting with Workload Semantics and Operational Risk Curves: An Empirical Study on Alibaba Clusterdata GPU Trace
This study addresses the operational challenge of multi-horizon GPU demand forecasting in large-scale computing clusters, where GPUs are costly resources and demand fluctuates under constraint-driven scheduling. The objective is to evaluate whether integrating workload semantics improves forecasting performance across horizons up to 72 hours. A reproducible empirical benchmark is developed using the Alibaba Clusterdata GPU trace (cluster-trace-gpu-v2023), comprising 8,152 pods over approximately 149 days with a total capacity of 6,212 GPUs. The study compares two statistical baselines, ARIMA(48,0,0) and a seasonal-trend additive model, with three lightweight deep learning models: Temporal Convolutional Network (TCN), Informer-lite, and TFT-lite. Workload semantics are approximated by converting hourly job metadata into textual summaries, embedding them with TF-IDF and truncated SVD (8 dimensions), and incorporating them as exogenous covariates. Evaluation uses SMAPE and MASE across multiple horizons (1–72 hours), along with peak-aware metrics and operational risk curves. Results show that the seasonal-trend model achieves the best overall accuracy (15.34% sMAPE), while TCN is the strongest deep model (17.20% sMAPE). Semantic embeddings do not improve short horizons (1–48 hours) but reduce 72-hour sMAPE by 11.1% and improve peak-window error. These findings indicate that autoregressive signals dominate short-term forecasting, whereas semantic context becomes beneficial at longer horizons. The study emphasizes that combining point accuracy with risk-based evaluation is essential for effective GPU capacity planning under dynamic and uncertain demand conditions.
Copyright (c) 2025 Siming Zhao, Jingwen Bai, Drew Roberson https://creativecommons.org/licenses/by-sa/4.0
A Decision-Support Analytics Framework of Strategic HR Practices and Employee Performance in Islamic Banking
This research analyzes Islamic banking's strategic human resource strategies by examining how compensation, work incentives, and job satisfaction affect employee performance. 210 Islamic finance personnel were quantitatively surveyed and evaluated using Partial Least Squares–Structural Equation Modeling (PLS-SEM) using SmartPLS. Bootstrapping. 5,000 subsamples were used to evaluate direct and indirect impacts. Research indicates that financial remuneration and work motivation negatively affect employee performance (β = −0.280 and β = −0.182, respectively). Job happiness has a low but positive impact on employee performance (β = 0.087). Minimal indirect effects and variation accounted for (VAF) values below 20%. Job satisfaction does not moderate the links between financial compensation and employee performance, nor between work motivation and performance. The findings suggest that direct processes, rather than emotional measures such as job happiness, affect the performance of Islamic banking employees. The Islamic banking strategic human resource management literature is enhanced by emotional assessments, such as job satisfaction. Strategic human resource management in Islamic banking is informed by research, with managerial implications for fair remuneration systems and sustainable motivational techniques that reflect Islamic ideals.
Copyright (c) 2026 Melyani Melyani, Yulianto Yulianto, Wasilatun Nikmah, Henny Armaniah, Herudini Subariyanti, Andri Rizko Yulianto, Daniel Daniel https://creativecommons.org/licenses/by-sa/4.0
Optimizing Regional Financial Management through the Transformation of the Digital Financial Information System in the Bekasi City Government
This research adopts an applied research approach conducted in November 2023 at the Bekasi City Government. Data were collected through observation, interviews, and literature study. The system development methodology follows the System Development Life Cycle (SDLC), including system analysis, system design, implementation, and maintenance. The proposed digital financial information system is developed in Java and integrated with a MySQL database to manage financial data, including revenue and expenditure, SPP (Payment Request Letters), and SPJ (Accountability Reports). The system design incorporates tools such as Context Diagram (CD), Data Flow Diagram (DFD), Entity Relationship Diagram (ERD), and flowcharts to ensure systematic and structured development. The implementation of the digital system is expected to improve the accuracy, timeliness, and relevance of financial information, facilitate faster report generation, and enhance financial transparency and accountability in regional governance. The findings indicate that the transformation of a digital financial information system significantly supports the optimization of regional financial management processes, minimizes reporting errors, accelerates data processing, and strengthens decision-making processes within the Bekasi City Government. Therefore, the digital transformation of financial systems is essential in supporting good governance and sustainable regional development.
Copyright (c) 2026 Dian Mohamad Anwar, Isyana Emita, Melyani Melyani, Indra Riyana Rahadjeng, Wahyu Indrarti, Ahmad Rafik, Dian Indah Sari https://creativecommons.org/licenses/by-sa/4.0
Ranking AODV Routing Characteristics in MANETs: Fuzzy Delphi Method Application
Optimization of Mobile Ad hoc Networks (MANET) routing has the potential to improve performance. Enhancing the Ad-hoc On-demand Distance Vector (AODV) protocol, which is predominantly used in MANET routing, would lead to performance improvements. This study sought to provide a structured approach to identifying which AODV protocol characteristics should be prioritized for modification to improve performance in MANETs. This would provide guidance for future research on AODV prioritization. Thirteen AODV characteristics were identified through a systematic review. The Fuzzy Delphi Method was employed to analyze and prioritize the study characteristics. Route discovery characteristics were prioritized and ranked as number one, with a threshold value of 0.097, 100% agreement among experts, and a fuzzy score of 0.72. The experts unanimously agreed that modifying the route discovery characteristic has the greatest potential to improve performance in MANET. This was because a large amount of control traffic in the network is generated during the route discovery process. The other characteristics were route selection, route maintenance, route table management, routing updates, number of routes discovered, hop count, time to live, and use of hello messages. However, some characteristics were perceived to be structurally critical for AODV operation but less responsive to performance-optimization modifications, namely hop-by-hop communication, identification of route request messages, sequence numbers, and neighbor lists. Future studies should complement the expert-based prioritization of AODV characteristics with experimental and simulation evaluations to quantify performance gains.
Copyright (c) 2026 Patrick Njoroge, John G. Ndia, Daniel Makupi https://creativecommons.org/licenses/by-sa/4.0
Calibration-Light Subject-Independent Motor Imagery BCI via Self-Supervised Pretraining and Conformer
Motor imagery (MI) electroencephalography (EEG) is a foundational paradigm for non-invasive brain–computer interfaces (BCIs). However, its practical adoption is constrained by time-consuming per-user calibration and limited cross-subject generalization. This study evaluates a calibration-light MI-BCI framework that combines self-supervised masked EEG pretraining with a lightweight Conformer fine-tuning model. Experiments were conducted on BCI Competition IV Dataset 2b using only the labeled sessions 01T–03T, with artifact-annotated trials removed according to the official 1023 markers. Three deployment-relevant settings were examined: within-subject evaluation (01T–02T → 03T), strict leave-one-subject-out (LOSO) evaluation, and few-shot adaptation with k = 1/5/10 trials per class from the held-out subject’s screening sessions. Full within-subject benchmarking included CSP+LDA, EEGNet, DeepConvNet, ShallowFBCSPNet, supervised Conformer, and SSL+Conformer, while the subject-independent and few-shot analyses focused on CSP+LDA, EEGNet, supervised Conformer, and SSL+Conformer. In the fully calibrated setting, the best mean accuracy was obtained by ShallowFBCSPNet (62.23% ± 14.16%), whereas SSL+Conformer achieved 54.85% ± 11.15% and slightly outperformed the supervised Conformer (53.56% ± 8.81%). Under strict LOSO, EEGNet achieved the highest mean accuracy (52.92% ± 8.25%), while SSL+Conformer reached 51.56% ± 7.18%. In few-shot adaptation, SSL+Conformer achieved the highest mean accuracy at k = 10 (52.84% ± 7.64%) among the core calibration-light methods. The proposed model had a size of 0.1329 MB, a median CPU latency of 0.8777 ms/trial, and LOSO calibration values of ECE = 0.0630 and Brier = 0.4995. These results indicate that masked EEG pretraining provides a competitive lightweight baseline and is most useful when a modest amount of target-subject calibration data is available.
Copyright (c) 2025 Qiyou Wu, Gaotian Mi, Dan Wood https://creativecommons.org/licenses/by-sa/4.0
A Lightweight Medical Foundation Model for Cross-Modal Multi-Task Pretraining and Parameter-Efficient Few-Shot Transfer on MedMNIST
Medical imaging has rapidly adopted pre-trained backbones, yet many transfer-learning pipelines remain expensive to train and difficult to adapt when data, compute, or privacy constraints limit full fine-tuning. We present STMedFM, a lightweight medical multi-task backbone baseline designed for fast prototyping across 2D images and 3D volumes. STMedFM uses modality-specific convolutional stems (2D and 3D) and a shared low-depth encoder, and it supports parameter-efficient transfer via Low-Rank Adaptation (LoRA) and bottleneck adapters. We pretrain STMedFM with supervised multi-task learning on four MedMNIST tasks (PathMNIST, BloodMNIST, DermaMNIST, and OrganMNIST3D) using official train/validation/test splits. We then compare (i) training from scratch, (ii) full fine-tuning from the multi-task checkpoint, and (iii) parameter-efficient fine-tuning (LoRA or adapters) that updates only a small fraction of parameters. Under a fixed compute budget (200 pretraining steps; 120 fine-tuning steps for 2D tasks; 50 steps for the 3D task), multi-task pretraining improved performance on PathMNIST (test accuracy 0.568 → 0.634; macro AUROC 0.886 → 0.914) and preserved most gains under PEFT (LoRA AUROC 0.909; Adapter AUROC 0.913) while training only 4,041–5,225 parameters versus 160,105 for full fine-tuning. For DermaMNIST, pretraining increased macro AUROC from 0.746 (Scratch, weighted) to 0.756 (Pretrain+Full), with similar AUROC under LoRA (0.760) and Adapter (0.763). In contrast, BloodMNIST and OrganMNIST3D showed mixed behavior, including cases where Scratch outperformed pretrained variants, indicating that transfer in this compact shared encoder is task-dependent and budget-sensitive. Calibration results were similarly non-monotonic: methods with better AUROC did not always achieve lower ECE. Overall, our results show that a small cross-modal multi-task model can serve as a practical MedMNIST-scale transfer baseline and that LoRA/adapters offer substantial parameter savings when task alignment is favorable. STMedFM should therefore be viewed as a lightweight supervised multi-task backbone on benchmark-scale tasks rather than a broadly general medical foundation model.
Copyright (c) 2025 Gaotian Mi, Tong Ye, Dan Wood https://creativecommons.org/licenses/by-sa/4.0
Automatic Detection and Explanation of Dark Patterns from Interface Microcopy: Empirical Comparison of BERT-Style Encoders, RoBERTa-Style Encoders, and LLM-Style Decoders on the ec-darkpattern Dataset
Dark patterns (also called deceptive design patterns) are interface choices that steer or pressure users into unintended actions such as rushed purchases, unnecessary disclosures, or hard-to-cancel subscriptions. In e-commerce, many dark patterns are expressed directly in microcopy (e.g., button labels, banners, and inline messages), which makes text-only detection attractive for scalable auditing. This paper presents a fully reproducible experimental study on ec-darkpattern, a text-based dataset of e-commerce interface strings with balanced binary labels (1,178 dark pattern vs. 1,178 non-dark pattern) and seven dark pattern categories. We compare (i) a rule-based lexicon baseline, (ii) hashed n-gram linear models, (iii) a lightweight BERT-style bidirectional transformer encoder with word tokenization, (iv) a lightweight RoBERTa-style bidirectional transformer encoder with character tokenization, and (v) an LLM-style causal decoder trained as a classifier on the same inputs. On a fixed 80/10/10 split with seed 42, the best-performing model is a hashing + linear SVM baseline (F1=0.9437, ROC-AUC=0.9810), while the BERT-style encoder achieves F1=0.9038 (ROC-AUC=0.9695), the RoBERTa-style encoder achieves F1=0.8907 (ROC-AUC=0.9573), and the LLM-style decoder achieves F1=0.7884 (ROC-AUC=0.8808). These results should be interpreted as a controlled comparison under low-resource, no-pretraining conditions on a single fixed split, rather than as a general ranking of encoder-style versus decoder-style transformers. To support explainability, we generate token-level attributions using gradient-based saliency, summarize them as key phrases, and estimate explanation consistency via top-k token overlap on an exploratory 20-instance sample (mean Jaccard up to 0.7482 between the two character-based transformers). Finally, we curate an error-case library that links misclassifications to their most influential phrases. Within this short-microcopy setting, the findings show that lexical baselines are especially strong, while transformer directionality and tokenization change both accuracy and the stability of highlighted cues.
Copyright (c) 2025 Yushan Chen https://creativecommons.org/licenses/by-sa/4.0
From Hand-Drawn Sketches to Interactive Web Prototypes: A Reproducible Vision-Language Approach with Structural and Visual Consistency Evaluation
Service design workflows often begin with low-fidelity sketches that must be quickly translated into interactive prototypes. This paper studies the Sketch-to-Web problem: generating HTML/CSS prototypes from hand-drawn UI sketches and evaluating fidelity with both structural and visual metrics. Because the original Sketch2Code benchmark is distributed primarily as compressed artifacts that are not executable in our restricted runtime, we construct Sketch2Code-Synth, a size-matched and protocol-matched instantiation containing 731 hand-drawn-style sketches paired with 484 webpage prototypes while preserving the same sketch-to-HTML task interface. We implement a lightweight constrained sketch-to-HTML baseline (ProtoVLM) that combines HOG-based template recognition with template-conditioned HTML/CSS instantiation. We compare ProtoVLM against three baselines (kNN retrieval, heuristic computer vision layout extraction, and majority-template generation) and an oracle upper bound. Evaluation uses (i) DOM tree edit distance computed on a containment-induced layout tree, (ii) element-level IoU with Hungarian matching, and (iii) wireframe SSIM on 200×150 rasterized layouts. On the held-out test split (97 pages, 147 sketches), ProtoVLM achieves a mean tree edit distance of 2.224, mean element IoU of 0.755, and mean SSIM of 0.474. Relative to kNN retrieval, the main gain is in localization stability (IoU 0.755 vs. 0.697), while structural distance is similar (TED 2.224 vs. 2.422). Because the benchmark uses a controlled template library and wireframe renderings, the results should be interpreted as evidence on constrained layout recognition and prototype normalization rather than unconstrained real-world sketch understanding. In this setting, SSIM measures layout resemblance only, not interface realism or usability.
LiDAR–Camera Object-Level Fusion for Multi-Target Tracking Using JPDA and EKF: A Reproducible Empirical Study on a PandaSet-Parameterised Five-Sequence Dataset
Multi-target tracking in cluttered scenes is essential for automated driving, where downstream planning requires stable object identities and accurate state estimates. This paper provides a fully reproducible empirical and sensitivity study of a classical object-level LiDAR–camera fusion tracker that combines Joint Probabilistic Data Association (JPDA) with an Extended Kalman Filter (EKF) under a constant-velocity state model. Because the MathWorks PandaSet subset is distributed as a ZIP archive that cannot be ingested into our execution environment, we generate a PandaSet-parameterised five-sequence synthetic dataset with explicitly specified sampling rates, measurement noise, detection probabilities, and Poisson clutter, and report end-to-end results with fixed random seeds. Using sequential fusion (LiDAR JPDA–EKF update followed by a camera bearing update), we obtain a mean MOTA of 0.880 and a mean position RMSE of 0.361 m, compared with LiDAR-only JPDA–EKF MOTA of 0.883 and RMSE of 0.395 m. Fusion, therefore, improves localization accuracy while sometimes reducing MOTA due to additional association ambiguity introduced by camera clutter; this trade-off is discussed in terms of downstream use cases that prioritize state accuracy. Sensitivity sweeps show that probabilistic association degrades more gracefully than hard nearest-neighbor assignment as clutter increases and delineate regimes where camera information is beneficial. A camera-only bearing tracker is included as a diagnostic baseline (not as a competitive approach); as expected, given the observability limits, it is not reliable under the studied clutter conditions. The dataset specification, parameters, and reporting artefacts form a reproducible template for diagnosing JPDA/EKF tracking and object-level fusion.
Copyright (c) 2026 Qi Xin https://creativecommons.org/licenses/by-sa/4.0
Uncertainty-Aware Late Fusion for 3D Perception (Confidence Calibration + Fusion Rule Learning)
Late fusion remains attractive for multi-sensor 3D perception because it preserves independent sensor pipelines, enables modular upgrades, and supports rigorous ablation experiments. This paper presents an uncertainty-aware late-fusion framework that combines per-modality confidence calibration with learning a fusion rule. We conduct full experimental evaluations on a PandaSet-style LiDAR+camera subset comprising 10 multi-frame sequences and 2,200 synchronized frames, with 49,549 annotated 3D objects across the Car, Pedestrian, and Cyclist classes. The framework calibrates LiDAR and camera confidence using temperature scaling and isotonic regression, estimates uncertainty-conditioned localization variance, and fuses associated candidates using multiple rules (max, mean, product/odds, and Dempster-Shafer) as well as a learned fusion rule (logistic regression trained on association features). On the test split, isotonic calibration reduces LiDAR Expected Calibration Error from 0.260 to 0.006 and Negative Log-Likelihood from 0.410 to 0.110, and it similarly improves camera confidence quality. Although mean Average Precision (mAP) remains similar to a LiDAR-only baseline in this controlled setting, calibrated late fusion provides substantially better decision reliability at fixed confidence thresholds and maintains conservative high-precision behavior under camera dropout. These results support an engineering conclusion: confidence calibration is the highest-leverage upgrade for late fusion in safety-critical stacks, and fusion rule choice can be tuned to downstream risk preferences.
Copyright (c) 2025 Qi Xin https://creativecommons.org/licenses/by-sa/4.0
LLM-Driven CI Failure Diagnosis and Automated Repair: From GitHub Actions Logs to Patch Recommendation
Continuous Integration (CI) pipelines surface regressions early but also produce long, noisy logs. Diagnosing a failing GitHub Actions run and drafting a safe repair patch can be time-consuming, especially when dealing with dependency drift or configuration errors. We study a practical CI-repair pipeline decomposed into three measurable tasks: (1) coarse failure-type classification, (2) retrieval-based repair (log similarity reuse the closest historical fix diff), and (3) constrained patch generation that emits a unified diff via template+slot filling. The pipeline follows the schema and task framing of JetBrains-Research’s lca-ci-builds-repair dataset from Long Code Arena (212 samples). Because runtime restrictions in our environment prevent downloading the original Hugging Face-hosted parquet files, all quantitative results in this paper are evaluated on a locally generated proxy dataset, CI-Repair-Sim212, which matches the benchmark’s field schema and evaluation protocol. On CI-Repair-Sim212, failure-type classification reaches a ceiling (Macro-F1=1.000), whereas repair-pattern prediction remains harder (Macro-F1=0.796 with log+workflow). For patch recommendation, retrieval achieves Token-F1@1=0.898 and Pattern@1=0.783 when combining logs with workflow context, and constrained generation further improves diff similarity to Token-F1=0.923. Across tasks, adding workflow YAML context yields consistent gains, motivating hybrid CI assistants that prioritize retrieval when near-duplicate failures exist and fall back to constrained generation when close matches are absent.
Copyright (c) 2025 Hanqi Zhang https://creativecommons.org/licenses/by-sa/4.0
A Comparative Study on Self-Organization in Wireless Sensor Networks
Wireless sensor networks (WSNs) have emerged as a critical infrastructure for distributed sensing platforms in recent years. Their effective implementation requires self-organizing features that can adapt to rapidly changing ecological conditions. We have noticed in the comparative study that despite extensive research on individual self-organizing mechanisms, e.g., clustering, routing, and topology management. We believe there exists a significant analytical gap in systematically comparing these approaches across key performance metrics. Our study addresses this gap by conducting a comprehensive comparative analysis of four primary self-organization or autonomious mechanisms: clustering-based organization, dynamic routing protocols, topology adjustment strategies, and coverage reinforcement methods. In our work, using a simulation-based methodology with the NS-3 network simulator, we thoroughly tested these frameworks across networks with 50 to 500 nodes under varying traffic loads and mobility patterns. We assessed the performance using three key KPIs (key performance indicators). Reliability is measured by packet delivery ratio, scalability by convergence time, and energy efficiency by network lifetime parameters. Our results demonstrate that clustering approaches achieve 23% better energy efficiency in static deployments, whereas distributed routing protocols provide 34% better scalability in dynamic conditions. We also observed that topology adjustment mechanisms improve reliability by 18% under high node failure rates. These findings provide clear, evidence-based guidance for selecting the right self-organization technique for specific deployment scenarios and application requirements. We recommend that future research investigate hybrid mechanisms that combine multiple approaches and explore integrating machine learning to support adaptive strategy selection under heterogeneous network conditions.
Copyright (c) 2026 Michael Simon, Salwa M. Din, Raja Jamal Chib https://creativecommons.org/licenses/by-sa/4.0
A Novel Three-phase integrated inverter for Hybrid Electric Vehicle Applications
Multilevel inverters (MLIs) have become essential in power electronics due to their capability to improve power quality, minimize harmonic distortion and enhance efficiency in medium and HV applications. It achieves smoother waveform, reducing total harmonic distortion (THD). Hybrid electric vehicles (HEVs) are increasingly recognized for their ability to reduce fuel consumption and emissions in modern transportation. Their performance largely depends on the efficiency of power conversion systems, particularly in inverter setup. In this study a novel 3-phase 25-level MLI configuration is proposed with a fewer switches. The primary objective is to reduce harmonics through high-level MLI implementation and motor optimization in electric vehicle applications. Proposed research suggests a novel three-phase 25-level hybrid inverter tailored for hybrid electric vehicles, featuring innovative hybrid DC-DC and DC-AC topologies that utilize fewer switches to enhance cost-effectiveness. Simulation results indicate, the three-phase 25-level MLI improves voltage stability and further mitigates harmonics compared to single-phase configuration. While comparing with conventional converters, MLIs offer significant benefits, including high-frequency switching and advanced modulation techniques for precise control. It generates output voltage with minimal harmonics, lower dv/dt stress on switches and introduces common-mode voltage. In particular, the 3-phase 25-level inverter system, achieves a smoother waveform, reducing total harmonic distortion (THD). The proposed system has been validated using MATLAB/Simulink. Additionally, the comparative analysis between proposed 1-ph. and 3-ph. 25-level MLI topologies also presented.
Copyright (c) 2026 Tatarao Donepudi, Anupalli Immanuel https://creativecommons.org/licenses/by-sa/4.0
IoT-Driven in the Banking Application Platforms Using a Real-Time SQL Injection Mitigative Measures
The Internet of Things (IoT) integration into banking systems has revolutionized banking operations while also posing threats, including SQL injection (SQLi) attacks. Thus, the defenses of the existing system, such as access control mechanisms, firewalls, and signature-based Intrusion Detection Systems (IDSs), failed to detect both novel and obfuscated SQLi attempts. Hence, this research developed a machine-learning-based detection framework capable of identifying SQLi attacks on IoT-driven banking platforms. The model was trained on a Random Forest (RF) classifier and evaluated in a Python environment. Streamlit was used to deploy the model for real-time prediction, while performance visualization was through the Power BI dashboard. However, the results from the model’s evaluation were highly impressive, with 99.53% accuracy, 99.96% precision, and 98.78% recall. This demonstrated the model's ability to detect both known and unknown SQL patterns. However, the research concluded that combining behavioural analytics with a machine-learning approach is highly effective for securing IoT banking platforms and recommended periodic retraining using a deep-learning approach.
Copyright (c) 2026 Amaka Eugenia Ngozi, Oji Victor Kalu, Ezea Jonathan Ikechukwu, Okpalla Chidimma Lilian, Gloria Ngozi Ezeh https://creativecommons.org/licenses/by-sa/4.0
Automation in Cybersecurity using Machine Learning: A CaseStudy on Anomaly Detection with Isolation Forest
The escalating sophistication of cyber threats necessitates advanced anomaly detection techniques that transcend traditional signature-based methods. This paper presents an automated cybersecurity framework leveraging the Isolation Forest algorithm for unsupervised anomaly detection in network traffic. Using the NSL-KDD dataset, we demonstrate that Isolation Forest achieves 95.2% detection accuracy with a 4.7% false-positive rate, outperforming conventional methods such as One-Class SVM (88.1% accuracy) and Local Outlier Factor (82.3% accuracy) in both computational efficiency and precision. Key advantages include: (1) real-time processing capability (8.2s training time, 4× faster than density-based approaches), (2) effective identification of rare attack types (U2R/R2L), and (3) elimination of dependency on labeled training data. The proposed system integrates dynamic threshold tuning and SHAP-based feature weighting to enhance detection stability and reduce false alarms. The results validate Isolation Forest as a scalable and reliable solution for modern intrusion detection systems, with strong implications for SIEM integration and real-time cybersecurity automation. Challenges in parameter tuning and encrypted traffic analysis are discussed, alongside future directions involving hybrid deep learning architectures.
Copyright (c) 2025 Noorul Hassan S., Sandhiya L., Kavya S., Priyadharshini E., Vanmathi T. https://creativecommons.org/licenses/by-sa/4.0
An IoT-Based Smart Feeding System for Koi Fish Using Mamdani Fuzzy Logic
Koi feeding management requires precision in both feeding timing and feed quantity to maintain fish health and reduce mortality rates. Manual feeding practices are often inconsistent due to human limitations, leading to overfeeding or underfeeding. This study proposes an IoT-based smart feeding system for koi fish that integrates Mamdani fuzzy logic to determine adaptive feeding durations based on feed stock conditions. The system employs a NodeMCU ESP8266 microcontroller, an ultrasonic sensor for feed-level monitoring, a servo motor for feed dispensing, and the Blynk platform for real-time remote monitoring and control over the internet. Mamdani fuzzy inference is utilized to classify feed levels into linguistic variables (low, medium, and high) and generate appropriate feeding actions. Experimental results demonstrate that the proposed system operates reliably, with an average measurement error of 1.59%, indicating high accuracy in feed-level detection. The fuzzy logic controller effectively adjusts feeding duration according to feed availability, enabling consistent and controlled feeding schedules. The proposed system offers a practical and low-cost solution for intelligent koi fish feeding management and can be extended to broader applications in smart aquaculture systems.
Copyright (c) 2026 April Firman Daru, Alauddin Maulana Hirzan, Galuh Ardiansyah Putra, Paminto Agung Christianto https://creativecommons.org/licenses/by-sa/4.0
Intelligent Image Rights Protection System Using Machine Learning, Cloud Services and IoT Alerts
The rapid growth of digital media platforms has intensified the misuse of images through unauthorized manipulation, morphing, and non-consensual redistribution, posing significant threats to individual privacy and intellectual property rights. Despite the availability of reporting and takedown mechanisms, their effectiveness remains limited due to procedural complexity, delayed response times, and concerns regarding user anonymity. This paper presents the Smart Image Rights Protection (SIRP) system, a user-centric framework designed to detect, monitor, and respond to unauthorized use of images in online environments. The proposed system utilizes perceptual hashing to generate resilient digital fingerprints for registered images, enabling accurate identification after common transformations such as resizing, cropping, and minor visual alterations. A cloud-ready similarity analysis module is designed to support scalable matching in future deployments, while the current evaluation is conducted on a controlled dataset. An IoT-enabled hardware interface provides real-time alerts to users upon detection of potential misuse. Experimental results on controlled manipulation scenarios show that SIRP achieves detection accuracy of 95.6% for resized images and 94.3% for cropped images, outperforming traditional pixel-based comparison methods. Furthermore, automated evidence logging and instant notifications substantially reduce the latency between detection and user response actions. By combining robustness under common transformations, cloud-assisted processing, and timely user engagement, SIRP offers a practical solution for protecting digital image ownership and personal privacy.
Copyright (c) 2026 S. P. Yaamini, K. Sivaranjani, B. Nujithra, V. Anitha https://creativecommons.org/licenses/by-sa/4.0
Bias and Hallucination Evaluation in LLMs
The largest failure modes of LLMs to-date, bias and hallucination, have measurable harms in contexts where factuality and fairness are paramount. Both areas have experienced significant research growth; however, prior work on each generally operates as a disparate body of research, and there is a gap in a methodological framework for jointly measuring, tracing, and reducing both under the same experimental conditions. We provide that framework through an empirical evaluation (not a survey) of bias propagation and hallucination generation on four illustrative domains (medical, legal, finance, human resources) through a framework that addresses the three research questions: how can bias and hallucination be measured simultaneously through a replicable, domain-specific protocol; which techniques yield statistically meaningful improvements and a consistency of effectiveness; and how do causally informed methods fare against retrieval methods when tested for factual error reduction. We report new experiments using the GPT-4, LLaMA-2, and Falcon-7B models on the MIMIC-III, CrowS-Pairs, Yahoo Finance Q3 and XNLI-HR benchmarks while keeping our prompts uniform and our random seeds fixed. Methods included structural causal modeling, retrieval-augmented generation, uncertainty-aware RLHF, and hallucination-specific fine-tuning, with experiments on each method separately before merging them into combined frameworks. We observe that RAG achieved a 45% reduction in hallucination rates and that our causally guided active learning method reduced bias disparity by 25%; together, they substantially outperform either method alone. This contributes to a repeatable method for auditing bias and hallucinations, helping ensure alignment with EU AI Act standards and similar requirements.
Copyright (c) 2026 R Sathiyaseelan, A. B. Reshma, P. Ganga https://creativecommons.org/licenses/by-sa/4.0
AI-Driven Multi- Modal Fake Content Detection System Using Audio-Text Fusion and Transformer Network
The rapid proliferation of AI-generated synthetic media has posed substantial threats to digital trust, particularly through audio deepfakes and manipulated text. Existing unimodal detection systems that analyze either audio or text in isolation remain insufficient to counter advanced generative attacks that exploit both modalities simultaneously. This paper proposes an AI-driven multimodal fake content detection framework that jointly leverages acoustic and linguistic signals to enable robust deepfake identification. Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-Spectrograms are extracted from raw audio to capture spectral and temporal vocal patterns. At the same time, BERT-based transformer embeddings encode semantic and contextual information from transcripts generated via Automatic Speech Recognition (ASR). An attention-based fusion layer dynamically weights and integrates both feature streams, and a Random Forest–XGBoost ensemble classifier performs the final authenticity prediction. Experiments conducted on the ASVspoof 2019 benchmark demonstrate a classification accuracy of 95%, with precision of 93%, recall of 94%, and F1-score of 95%, outperforming standalone audio-only and text-only baselines by approximately 4–7%. These findings confirm that cross-modal feature fusion substantially reduces false-detection rates and improves generalization over single-modality approaches. The proposed system offers practical applicability in cybersecurity, voice biometrics, and digital forensics.
Copyright (c) 2026 S. Jeeva, J. S. Trisha, S. Keerthana https://creativecommons.org/licenses/by-sa/4.0
Self-Supervised Representation Learning for Criminology: Detecting Anomalies, Classifying Reports, and Mapping Networks
Crime analysis using various types of data, such as video surveillance, crime reports, and criminal networks, has been widely investigated in digital criminology. Most of the available data are unlabelled. In this work, we introduce a self-supervised learning framework for multimodal criminology, which enables the fully automatic learning of effective features for unlabelled video, text, and graph datasets and the completion of crime analysis tasks, including anomaly detection, crime report classification, and high-risk node prediction via contrastive learning, masked prediction, and graph self-supervised learning. The experimental results show that our SSL model learns high-quality features and achieves better performance than its supervised counterpart and baseline models. Unlike traditional deep learning-based models that require large amounts of labeled data, our proposed SSL model is label-efficient, scalable, and robust to artificial or anonymous data. Our work aims to develop an AI-based multimodal self-supervised learning approach for efficient, accurate, reliable, and safe crime analysis
Copyright (c) 2026 Noorul Hassan S., Sivalakshmi S., Janani M., Fouziya A., Thirisha S. https://creativecommons.org/licenses/by-sa/4.0
IoT-Enabled Multimodel Emotion Detection and Assistive Smartwatch for Special-Needs Children
Children with neurodevelopmental and communication challenges often find it difficult to express emotional discomfort, stress, or urgent needs, which can delay timely support and increase anxiety. To address this issue, this paper presents an IoT-enabled assistive smartwatch designed to support near real-time emotion inference and basic communication for children with special needs. The proposed system integrates physiological sensing, including heart rate variability, galvanic skin response, and skin temperature, with camera-based facial expression analysis using a multimodal data fusion approach. In addition to emotion-aware monitoring, the smartwatch provides a simple icon-based communication interface and a routine reminder module to support daily activities. The framework enables caregiver-side visualization of emotional trends, communication events, and adherence to routines through a cloud-based dashboard. The system is evaluated through a design-level feasibility assessment using simulated experiments and analysis of benchmark datasets. Overall, this work presents a technically feasible and ethically conscious assistive framework that highlights the potential of combining IoT and emotion-aware computing for supportive care applications.
Copyright (c) 2026 A. Samrin, S. Saidharshini, S. Shalima, B. Moohambiga https://creativecommons.org/licenses/by-sa/4.0
Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment
Measuring advertising incrementality in the absence of user-level identifiers is increasingly constrained by platform policies and privacy regulations. In cookieless environments, practitioners often observe only aggregated or weak signals (e.g., cohort-level conversion counts) and must still estimate the causal lift of an intervention while quantifying uncertainty. This paper studies cookieless incrementality evaluation through the lens of uplift and individual treatment effect (ITE) modeling under explicit privacy constraints. We conduct full experimental evaluations on the MineThatData (Hillstrom) E-Mail Analytics Challenge dataset (64,000 customers in a randomized controlled experiment with three arms). We cast the task as a binary treatment problem—sending any e-mail campaign versus sending none—and compare six ITE estimators (S-, T-, X-, R-, and doubly robust learners, plus transformed-outcome regression) against cohort-only estimators that emulate cookieless measurement. The cohort estimator uses only aggregated counts and a Bayesian beta–binomial model to shrink noisy rates, and we evaluate robustness under k-anonymity thresholds and Laplace-noised differentially private aggregates. Across held-out test data, the best ID-level model (T-learner with logistic regression) achieves a Qini coefficient of 6.675 and improves the estimated policy conversion rate when targeting the top 20% of customers by predicted uplift. Cohort-only estimation retains a weaker and more variable signal; its point estimate is sensitive to privacy constraints but yields valid uncertainty intervals with 0.892 empirical coverage for a 95% interval in cohort-level validation. The results demonstrate that (i) causal lift is estimable without identifiers when randomized experimentation is available, (ii) doubly robust estimators provide strong performance and fast scoring, and (iii) privacy-preserving aggregation introduces an accuracy–privacy trade-off that can be quantified and monitored using bootstrap and Bayesian uncertainty.
Copyright (c) 2026 Jingwen Bai, Haozhe Wang, Qiyou Wu, Boning Zhang https://creativecommons.org/licenses/by-sa/4.0
A Constrained, Data-Driven Budgeting Framework Integrating Macro Demand Forecasting and Marketing Response Modeling
Budgeting and financial planning & analysis (FP&A) increasingly require combining macroeconomic signals, channel-level marketing effectiveness, and hard accounting constraints into a single, auditable decision process. This paper proposes and empirically evaluates an end-to-end framework that (i) forecasts category-level demand from public macro data, (ii) learns diminishing-returns marketing response curves, and (iii) solves a constrained portfolio optimization problem to allocate marketing spend while satisfying SG&A and cash-flow guardrails consistent with real public-company statements. Using quarterly Personal Consumption Expenditures (PCE) components from FRED (durable goods, nondurable goods, and services) as a proxy for market demand, we compare seasonal naïve, SARIMAX, gradient boosting, and a multivariate VAR model in a rolling backtest (2018Q1-2025Q3). In parallel, we estimate marketing response from the Advertising dataset (TV, radio, and newspaper spend) via linear models, gradient boosting, and a Hill-function saturation model. We then calibrate financial constraints-gross margin, SG&A ratio, and operating cash-flow coverage-directly from Apple Inc.’s FY2025 Form 10-K filed with the SEC, and integrate all components into a Monte Carlo-evaluated budgeting optimizer. Results show that multivariate models improve total-demand accuracy (≈2.85% MAPE) and that nonlinear response curves indicate strong diminishing returns and negligible incremental value for newspaper spend. The constrained optimizer produces stable allocations that trade off expected operating profit and downside risk, and it highlights a practical insight: budgets that exactly meet a ratio-based cap under point forecasts may violate constraints under realistic demand uncertainty. The proposed workflow is fully reproducible from public data sources and provides a template for transparent, constraint-aware budgeting.
Copyright (c) 2025 Yifei Lu, Hailin Zhou, Yitian Zhang https://creativecommons.org/licenses/by-sa/4.0
User-Centered Mobile Navigation: Evaluating Local Usability for Improved UX
Mobile navigation interfaces continue to be plagued by the key usability problem, particularly where widely accepted design traditions fail to address the specific local user population requirements and mental models. This research addresses this significant limitation by using a thorough user-focused measurement framework to search for and investigate context-dependent navigation problems in mobile applications. The research employed a multi-method qualitative approach, using in-depth questionnaires, semi-structured interviews, and laboratory-based usability testing of 15 participants on an interactive Figma prototype that simulated real-world navigation tasks. Our analysis step by step revealed significant navigation issues within the local context, quantified using a 73% task completion rate and a 2.8 participant error average for the core navigation tasks. The most significant usability issues were ambiguous iconography, inconsistent application of platform design patterns, and insufficient system feedback mechanisms. The results conclusively demonstrate that localized usability testing is not merely beneficial but necessary while creating genuinely good and accessible mobile experiences. The study provides a replicable, practical context-aware evaluation approach and tangible, right-now applicable design recommendations, for example, the need to enhance icons with descriptive text labels and use instant visual feedback mechanisms. The study provides developers and designers tangible take-away recommendations for significantly enhancing navigation user experience without trading off methodological availability and implementation convenience in different development environments.
Copyright (c) 2025 Sofia Petrova, Ken Watanabe https://creativecommons.org/licenses/by-sa/4.0