Journal of Computing Theories and Applications

CAMMRF: A Conflict-Aware Missing-Modality Robust Fusion Framework for Multimodal Sentiment Analysis under Heterogeneous Observability

2026-07-16T07:53:22+00:00

Multimodal sentiment analysis combines linguistic, acoustic, and visual evidence, yet deployment commonly violates the benchmark assumption that every modality is present and mutually consistent. Missing-modality systems generally reconstruct absent channels while conflict-aware systems generally assume full observation, even though absence and disagreement both change how far an observed modality should be trusted. We therefore treat the two jointly and hypothesise that conditioning modality reliability on both availability and cross-modal compatibility, and applying it across aligned and disagreement-preserving representations, yields more consistent performance across observability regimes than matched fusion controls without this coupling. We introduce CAMMRF, a framework that couples a mask- and peer-conditioned reliability estimator, an alignment/conflict subspace decomposition, and stochastic modality dropout with cross-view consistency. We evaluate one frozen protocol on the official CMU-MOSI train/validation/test partitions under eight observability regimes; five independent training seeds (42, 43, 44, 45, and 46) completed, each retaining predictions, checkpoints, and logs. In the full-modality regime CAMMRF is best on all four metrics simultaneously—MAE 1.050 ± 0.028, Pearson correlation 0.593 ± 0.009, weighted F1 75.01 ± 1.72%, and Acc-2 74.91 ± 1.80%—rather than on accuracy alone, and it ranks first by mean Acc-2 in 5 of eight regimes. The advantage persists when audio or vision is missing and under the controlled conflict stress (mean Acc-2 73.81%, a 1.10-point decrease, with matching small changes in MAE, weighted F1, and correlation); however, all four metrics degrade together when text is absent—correlation most steeply—so robustness is regime-dependent rather than universal. Full-budget ablations show that several component removals improve full-observation point estimates, supporting an accuracy–robustness trade-off rather than an independent-component claim. The evidence is limited to one corpus and controlled surrogate baselines and does not establish external generalisation. A single Colab notebook regenerates the protocol and exports a machine-readable result registry.

Exhaustive Entanglement-Topology Enumeration and Edge-Vertex-Level Attribution in Quantum Feature Map Design for Classification

2026-07-13T09:21:23+00:00

Quantum kernel methods encode classical data into quantum states using specially designed feature-map circuits and then train a classical SVM on the resulting kernel matrix. Prior studies of entanglement structure typically compare only a few basic topologies, such as linear, circular, and full entanglement, leaving the broader space of entanglement graphs largely unexplored. This study exhaustively evaluates all possible entanglement topologies of a TwoLocal feature map and benchmarks them against classical methods and standard quantum feature maps on two synthetic datasets (Adhoc3_150 and Adhoc4_150) and two real-world datasets (Blood and Banknote). On Adhoc4_150, the best TwoLocal topology achieves an accuracy of 0.7556, matching Pauli Z; on Blood, it reaches 0.6889, exceeding RBF SVM and Pauli Z at 0.6667; and on Banknote, it achieves 1.0000, compared with 0.9778 for RBF SVM. In contrast, Random Forest achieves the highest accuracy on Adhoc3_150 at 0.8222, exceeding all evaluated quantum configurations. Across all four datasets, increasing the number of entangling pairs does not consistently improve accuracy, while the standard linear, circular, and full Pauli ZZ topologies never outperform Pauli Z. To further characterize this behavior, we propose a factorial linear modeling approach that quantifies the contribution of individual entanglement edges and qubit connectivity to classification accuracy. On Adhoc4_150, two edges exhibit significant negative effects, whereas Blood and Banknote show both significant positive and negative edge effects. The q0q1 edge is significant across all three four-feature datasets but reverses direction across datasets. These results indicate that the effect of entanglement depends jointly on the feature-map architecture, dataset, and specific qubit connections rather than on entanglement density alone. The proposed framework can also be applied to sampled topology sets, providing a basis for targeted entanglement analysis beyond exhaustively enumerable low-qubit systems.

Beyond Reported Accuracy: A Verifiability-Focused, Multi-Axis Review of Content and Context Based Fake News Detection

2026-07-31T02:22:38+00:00

Fake news undermines information integrity, and the proliferation of large language models (LLMs) has intensified both its generation and its detection. Prior surveys catalogue methods but rarely verify the performance figures they synthesize, restrict comparison to same-benchmark settings, or assess risk of bias, so reported accuracies are often over-read. This verifiability-focused review analyzes an enumerated, non-exhaustive corpus of 45 content- and context-based detection studies (2018-2025; 42 quantitative-core) in which each reported headline metric is traced to its primary source and confirmed against it wherever the source is accessible (36 of 45; the nine paywalled studies are flagged as reported-but-unconfirmed), and released with the corpus. Studies are grouped into five method families, Transformer/PLM (31.0%), multimodal (26.2%), LLM-based (21.4%), graph/propagation (16.7%), and traditional machine learning (4.8%), and interpreted through a four-axis framework of evidence source, detection time, data dependence, and deployment objective. Without metric conversion or pooling, reported accuracies are strongly heterogeneous and not comparable across families (Transformer 74.8-99.36%, multimodal 82.4-99.97%, graph 84.4-94.36% or 0.75-0.93 AUC, LLM-based 48.6-89.0%); within Weibo, seven multimodal models span 82.4-91.8%, and GPT-4 attains 68.2% (not the ~95% often cited) on LIAR-binary. Three findings follow. First, reported performance appears shaped as much by data and evaluation design as by architecture, so architecture-only interpretations can be misleading; near-perfect results may reflect dataset-specific separability, leakage, or favorable evaluation designs rather than superior transferable capability. Second, the field has evolved cumulatively from static content cues toward multi-source evidence integration and reasoning, so the families are complementary evidence sources rather than exclusive choices. Third, no family is optimal across scenarios, and a central open problem is sustaining reliability under drift, new languages, attacks, and AI-generated content. We derive a dependency-ordered research agenda and, from a Vietnamese case study, transferable design principles for low-resource languages.

Explainable Machine Learning for Predicting Indonesian Vocational School Accreditation: Geographic Validation, Probability Calibration, and Subgroup Auditing

2026-08-26T06:31:15+00:00

Vocational school accreditation is a high-stakes institutional classification, yet the geographic transferability and probability reliability of models based on routinely collected administrative data remain underexplored. This study developed a reproducible, leakage-controlled machine-learning framework for classifying the recorded accreditation classes C, B, and A among Indonesian vocational schools. Its main contribution is an integrated evaluation design that combines province-disjoint validation, probability calibration, cluster-aware uncertainty, and subgroup auditing to test reliability beyond conventional random-validation performance. A cross-sectional dataset containing 149,376 program-level records was deterministically aggregated into 14,391 schools, including 14,134 with valid labels. Eight provinces comprising 1,536 schools were locked before feature selection, model comparison, tuning, and calibration; 12,598 schools from 31 provinces supported development. The selected CatBoost model used 35 structural and vocational features with grouped out-of-fold temperature scaling. On the locked holdout, the calibrated model achieved balanced accuracy of 0.586, Macro F1 of 0.533, quadratic weighted kappa of 0.499, Macro ROC-AUC of 0.757, and expected calibration error of 0.049. School size, teacher resources, vocational staffing, program diversity, and status were most in-fluential. Class B had the lowest recall, and performance varied across school groups and provinces. The framework supports calibrated, auditable preliminary screening, but not automated accreditation or replacement of professional assessors.

Machine Learning Framework for SHM Alarm Log Triage under Alarm Flood and Limited Labeled Data: A Case Study on a Tropical Cable-Stayed Bridge

2026-07-16T09:13:06+00:00

Structural Health Monitoring (SHM) systems on long-span bridges routinely generate alarm volumes exceeding operators' manual review capacity, a condition known as alarm flood, so that genuine structural anomalies risk being buried among far more numerous instrumentation faults. The problem is acute in developing-country settings, where monitoring falls to small operational teams rather than dedicated staff. This study proposes an alarm-log triage framework that classifies events directly from tabular database records and static sensor metadata, requiring no raw sensor time-series, offering an alternative to the raw-signal paradigm dominating bridge SHM. The dataset comprises 63,934 alarm records logged over 2.5 years on a 641.8-m tropical cable-stayed bridge: labeling collapses as volume rises, leaving only 401 usable labeled records from the pre-flood period, a weakly-supervised task under severe label scarcity. The pipeline integrates alarm-log attributes, causally-ordered temporal window features, and as-built sensor metadata, with fold-internal resampling and recall-oriented operating-point selection. Seven classifiers were benchmarked under identical stratified 5-fold cross-validation, including TabNet alongside linear, kernel-based, tree-ensemble, and neural baselines. Gradient-boosted tree ensembles separate clearly from all other families (F1-Macro 0.7991–0.8860 outside the tree family versus 0.9354–0.9432 within it; McNemar and DeLong tests confirm the separation, p < 0.05). Differences among the tree ensembles themselves are not consistently significant, so LightGBM is reported as the selected deployment model on consistent top-tier performance (F1-Macro 0.9432, AUC-ROC 0.9885 at the default threshold) rather than as demonstrably superior. A two-part ablation shows temporal alarm-rate features supply most of the discriminative power, while explicit imbalance handling yields no measurable benefit at the observed ratio, indicating that such mechanisms should be calibrated to imbalance severity rather than applied by default. The results establish operational alarm logs as a viable, lightweight input for structural event triage and document alarm flood quantitatively in a real bridge SHMS.

A Heterogeneous Multi-Task Deep Learning Approach for Geopolitical Risk Prediction and Cross-Country Heterogeneity in ASEAN Equity Markets

2026-08-16T15:04:45+00:00

Modeling the asymmetric transmission of geopolitical shocks across structurally diverse emerging markets presents a severe computational challenge. Traditional homogeneous deep learning approaches often suffer from negative transfer and base-rate bias when applied to cross-country financial prediction. To address this, we propose the Heterogeneous Multi-Task Spatiotemporal Network (HMT-STN), a structurally adaptive framework that integrates country-specific embeddings, heterogeneous task decoupling, and a Focal Loss mechanism. Evaluated on a daily panel dataset of ASEAN-4 equity markets (2016–2026), the HMT-STN is designed to simultaneously optimize directional return classification and volatility regression. Comprehensive evaluations using Precision, Recall, F1-score, and AUC-ROC demonstrate the framework's superior discriminative power, notably achieving 66.21% accuracy and an AUC-ROC of 0.68 in Thailand, a highly volatile transitional market. Rigorous ablation studies confirm that Focal Loss prevents catastrophic performance collapse in noisy environments, while country embeddings successfully isolate market-specific idiosyncrasies. Furthermore, SHAP (SHapley Additive exPlanations) analysis reveals a tripartite spectrum of cross-country heterogeneity, providing model-based feature attributions that are consistent with established economic theories of market segmentation and geopolitical risk sensitivity. Collectively, this study demonstrates that explicitly modeling cross-country heterogeneity through heterogeneous multi-task learning offers a robust, interpretable, and computationally efficient paradigm for geopolitical risk assessment in fragmented global economies.

Clinically Weighted Feature Fusion for Unsupervised Identification of Postpartum Depression and Anxiety Risk Subgroups Among Breastfeeding Mothers

2026-08-11T06:35:13+00:00

Postpartum depression and anxiety often go undetected, and breastfeeding experience is among the factors most strongly linked to both. However, previous research has largely been supervised, dependent on biomarkers, and rarely captures the heterogeneity of risk profiles among mothers with similar symptom levels. This study proposes Clinically Weighted Feature Fusion (CWFF), a clustering framework that weights clinical composite features based on their out-of-fold association with EPDS and GAD-7 scores, to map postpartum depression and anxiety risk subgroups among 2,010 breastfeeding mothers in the United Kingdom.

As a foundation, 22 composite features representing five clinical domains were compared with 30 raw variables using K-Means (k = 4). The engineered approach with equal weighting (equal-weight) showed better clustering quality, marked by a 28.2% increase in the Silhouette Score, a 21.8% decrease in the Davies–Bouldin Index, and a 26.8% increase in the Calinski–Harabasz Index; all significant based on bootstrap testing (p < 0.0001). Ablation analysis of leave-one-domain-out and leave-one-interaction-term-out (Friedman χ² = 794.15; p < 0.001) shows that this improvement mainly comes from Interaction Features and Mental Health History, not from all domains uniformly. This advantage is also only consistent for K-Means and Agglomerative Clustering, not for Gaussian Mixture Model (GMM) or HDBSCAN. Then, applying CWFF, the results show that CWFF raises Silhouette from 0.1537 to 0.2418 while also raising clinical validity (η²_EPDS from 0.071 to 0.096; η²_GAD-7 from 0.090 to 0.123), with all improvements significant (bootstrap 95% CI does not include zero). These findings indicate that clinically informed weighting improves risk-subgroup identification compared to equal-weight, potentially supporting low-cost postpartum screening in primary healthcare settings.