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All Journal Management and Economics Journal (MEC-J) Journal of Technology Informatics and Engineering
Qi Xin
Management Information Systems, University of Pittsburgh, PA, USA
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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LiDAR–Camera Object-Level Fusion for Multi-Target Tracking Using JPDA and EKF: A Reproducible Empirical Study on a PandaSet-Parameterised Five-Sequence Dataset Qi Xin
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.v5i1.486

Abstract

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.
LLM-Based Strategic Risk Q & A over SEC Filings for Top Management Decision Support: A Reproducible Evaluastion on the SecQue Benchmark Qi Xin
MEC-J (Management and Economics Journal) Vol 10, No 2 (2026)
Publisher : Faculty of Economics, State Islamic University of Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mec-j.v10i2.41794

Abstract

This paper studies strategic question answering over SEC filings for top-management decision support through a comprehensive evaluation on the SecQue benchmark, encompassing expert-written comparison, ratio, risk, and analysis tasks. The study operationalizes a board-facing retrieval-control stack with four integrated components: evidence retrieval, answer composition, refusal control, and explanation tracing. The benchmark contains 565 expert-written questions distributed across 220 comparison questions, 188 ratio questions, 85 risk questions, and 72 analysis questions. Empirical results demonstrate that metadata-aware retrieval significantly outperforms conventional lexical baselines, while specialized synthesis models achieve superior textual alignment and numerical fidelity. Furthermore, negative-testing and explanation diagnostics confirm robust cross-company refusal mechanisms and complete metadata provenance tracing. By filtering ungrounded assertions and highlighting precise metadata provenance, these automated control layers significantly reduce cognitive overload for corporate directors evaluating dense filings under tight time constraints. Ultimately, grounding board-level synthesis in auditable, trace-backed financial evidence mitigates informational asymmetry between executive teams and non-executive board members
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