arXiv subjects

Amir H. Gandomi

Publications and source records attributed to Amir H. Gandomi.

At least 19 recordsLinked to original sources

arXiv2026

Fine-Grained Instruction-Guided Graph Reasoning for Vision-and-Language Navigation

Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demanding accurate alignment between visual observations and linguistic guidance. To address these challenges, we propose a fine grained instruction guided graph reasoning framework (FIGR) that enhances both spatial representation and instruction understanding during navigation. Specifically, an observation graph interaction mechanism is introduced to disentangle angular and visual cues while strengthening directed edge representations through geometric embedding, enabling more reliable spatial reasoning within the navigation graph. The key detail guidance module is implemented as Adaptive Open Vocabulary Guidance (AOVG), where a contextual role parser dynamically identifies location, object, spatial relation, and other contextual cues. This design avoids exact string matching and supports previously unseen entities and compositional expressions. For multilingual instructions, a Multilingual Semantic Adapter (MSA) maps language-specific representations into a shared navigation-semantic space. By jointly integrating structured graph reasoning with instruction critical semantic cues, the proposed approach significantly improves the agent ability to follow complex navigation instructions. On the validation-unseen splits, FIGR achieves 67 SPL on R2R and 64.8 sDTW on RxR, exceeding SPENav by 1 percentage point in SPL and PRET by 2.4 points in sDTW, respectively.

cs.CV↗

arXiv2026

Cross-Modal Visuo-Tactile Representation Learning with Action Chunking Transformers for Contact-Rich Manipulation

Tactile feedback is important for contact-rich robotic manipulation, yet effective use of tactile observations remains challenging when tactile signals are image-like, hardware-dependent, and only weakly aligned with external visual observations. This study addresses this representation-learning problem by proposing a visuo-tactile contrastive learning framework for imitation-based manipulation. The method aligns external RGB observations and calibrated tactile images in a shared embedding space using a CLIP-style objective, and integrates the resulting representation into an Action Chunking Transformer (ACT) policy. A low-cost visuo-tactile gripper (LVTG) is proposed to provide a modular and durable sensing platform for reproducible data collection, supplying tactile observations that can be used by downstream manipulation algorithms. Experiments on contact-rich manipulation tasks show that tactile feedback improves the average task completion rate from 30% for a vision-only ACT baseline to 42%, and that the proposed contrastive pretraining further increases the completion rate to 54%. These results indicate that explicitly aligning visual and tactile observations provides more useful contact-aware features for downstream policy learning than directly adding tactile images without pretraining.

cs.RO↗

arXiv2026

ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow

Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across seasons, cities, and sensors. Despite recent progress, many incremental approaches still treat training steps as isolated updates, which leaves representation drift and forgetting insufficiently controlled. We present ProtoFlow, a time-aware prototype dynamics framework that models class prototypes as trajectories and learns their evolution with an explicit temporal vector field. By jointly enforcing low-curvature motion and inter-class separation, ProtoFlow stabilizes prototype geometry throughout incremental learning. Experiments on standard class- and domain-incremental remote sensing benchmarks show consistent gains over strong baselines, including up to 1.5-2.0 points improvement in mIoUall, together with reduced forgetting. These results suggest that explicitly modeling temporal prototype evolution is a practical and interpretable strategy for robust continual remote sensing segmentation. Open-source code:https://github.com/dudududke/protoflow.

cs.CV↗

arXiv2026

Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs

The rapid spread of online misinformation has led to increasingly complex detection models, including large language models and hybrid architectures. However, their computational cost and deployment limitations raise concerns about practical applicability. In this work, we benchmark graph neural networks (GNNs) against non-graph-based machine learning methods under controlled and comparable conditions. We evaluate lightweight GNN architectures (GCN, GraphSAGE, GAT, ChebNet) against Logistic Regression, Support Vector Machines, and Multilayer Perceptrons across seven public datasets in English, Indonesian, and Polish. All models use identical TF-IDF features to isolate the impact of relational structure. Performance is measured using F1 score, with inference time reported to assess efficiency. GNNs consistently outperform non-graph baselines across all datasets. For example, GraphSAGE achieves 96.8% F1 on Kaggle and 91.9% on WELFake, compared to 73.2% and 66.8% for MLP, respectively. On COVID-19, GraphSAGE reaches 90.5% F1 vs. 74.9%, while ChebNet attains 79.1% vs. 66.4% on FakeNewsNet. These gains are achieved with comparable or lower inference times. Overall, the results show that classic GNNs remain effective and efficient, challenging the need for increasingly complex architectures in misinformation detection.

cs.CL↗

arXiv2026

Learning to Attack: A Bandit Approach to Adversarial Context Poisoning

Neural contextual bandits are vulnerable to adversarial attacks, where subtle perturbations to rewards, actions, or contexts induce suboptimal decisions. We introduce AdvBandit, a black-box adaptive attack that formulates context poisoning as a continuous-armed bandit problem, enabling the attacker to jointly learn and exploit the victim's evolving policy. The attacker requires no access to the victim's internal parameters, reward function, or gradient information; instead, it constructs a surrogate model using a maximum-entropy inverse reinforcement learning module from observed context-action pairs and optimizes perturbations against this surrogate using projected gradient descent. An upper confidence bound-aware Gaussian process guides arm selection. An attack-budget control mechanism is also introduced to limit detection risk and overhead. We provide theoretical guarantees, including sublinear attacker regret and lower bounds on victim regret linear in the number of attacks. Experiments on three real-world datasets (Yelp, MovieLens, and Disin) against various victim contextual bandits demonstrate that our attack model achieves higher cumulative victim regret than state-of-the-art baselines.

cs.LG↗

arXiv2025

A Generalized and Configurable Benchmark Generator for Continuous Unconstrained Numerical Optimization

As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives, such as the systematic evaluation of algorithms under controllable conditions. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for singleobjective, box-constrained, continuous numerical optimization. Unlike the commonly used test suites that rely on multiple baseline functions and transformations, GNBG utilizes a single, parametric, and configurable baseline function. This design allows for control over various problem characteristics. Researchers using GNBG can generate instances that cover a broad range of morphological features, from unimodal to highly multimodal functions, various local optima patterns, and symmetric to highly asymmetric structures. The generated problems can also vary in separability, variable interaction structures, dimensionality, conditioning, and basin shapes. These customizable features enable the systematic evaluation and comparison of optimization methods, allowing researchers to examine the strengths and weaknesses of algorithms under diverse and controllable conditions.

cs.NE↗

arXiv2025

Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification

Reliable uncertainty quantification is critical in high-stakes applications, such as medical diagnosis, where confidently incorrect predictions can erode trust in automated decision-making systems. Traditional uncertainty quantification methods rely on a predefined confidence threshold to classify predictions as confident or uncertain. However, this approach assumes that predictions exceeding the threshold are trustworthy, while those below it are uncertain, without explicitly assessing the correctness of high-confidence predictions. As a result, confidently incorrect predictions may still occur, leading to misleading uncertainty assessments. To address this limitation, this study proposed an uncertainty-aware stacked neural network, which extends conventional uncertainty quantification by learning when predictions should be trusted. The framework consists of a two-tier model: the base model generates predictions with uncertainty estimates, while the meta-model learns to assign a trust flag, distinguishing confidently correct cases from those requiring expert review. The proposed approach is evaluated against the traditional threshold-based method across multiple confidence thresholds and pre-trained architectures using the COVIDx CXR-4 dataset. Results demonstrate that the proposed framework significantly reduces confidently incorrect predictions, offering a more trustworthy and efficient decision-support system for high-stakes domains.

eess.IV↗

arXiv2025

Uncertainty-Aware Post-Hoc Calibration: Mitigating Confidently Incorrect Predictions Beyond Calibration Metrics

Despite extensive research on neural network calibration, existing methods typically apply global transformations that treat all predictions uniformly, overlooking the heterogeneous reliability of individual predictions. Furthermore, the relationship between improved calibration and effective uncertainty-aware decision-making remains largely unexplored. This paper presents a post-hoc calibration framework that leverages prediction reliability assessment to jointly enhance calibration quality and uncertainty-aware decision-making. The framework employs proximity-based conformal prediction to stratify calibration samples into putatively correct and putatively incorrect groups based on semantic similarity in feature space. A dual calibration strategy is then applied: standard isotonic regression calibrated confidence in putatively correct predictions, while underconfidence-regularized isotonic regression reduces confidence toward uniform distributions for putatively incorrect predictions, facilitating their identification for further investigations. A comprehensive evaluation is conducted using calibration metrics, uncertainty-aware performance measures, and empirical conformal coverage. Experiments on CIFAR-10 and CIFAR-100 with BiT and CoAtNet backbones show that the proposed method achieves lower confidently incorrect predictions, and competitive Expected Calibration Error compared with isotonic and focal-loss baselines. This work bridges calibration and uncertainty quantification through instance-level adaptivity, offering a practical post-hoc solution that requires no model retraining while improving both probability alignment and uncertainty-aware decision-making.

cs.LG↗

arXiv2025

Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey

This review surveys the rapid evolution of Meta AI's LLaMA (Large Language Model Meta AI) series - from LLaMA 1 through LLaMA 4 and the specialized parameter-efficient fine-tuning (PEFT) methods developed for these models. We first describe the LLaMA family of foundation models (7B-65B to 288B parameters), their architectures (including native multimodal and Mixtureof-Experts variants), and key performance characteristics. We then describe and discuss the concept of PEFT, which adapts large pre-trained models by updating only a small subset of parameters, and review five PEFT methods that have been applied to LLaMA: LoRA (Low-Rank Adaptation), LLaMA-Adapter V1 and V2, LLaMA-Excitor, and QLoRA (Quantized LoRA). We discuss each method's mechanism, parameter savings, and example application to LLaMA (e.g., instruction tuning, multimodal tasks). We provide structured discussion and analysis of model and adapter architectures, parameter counts, and benchmark results (including examples where fine-tuned LLaMA models outperform larger baselines). Finally, we examine real-world use cases where LLaMA-based models and PEFT have been successfully applied (e.g., legal and medical domains), and we discuss ongoing challenges and future research directions (such as scaling to even larger contexts and improving robustness). This survey paper provides a one-stop resource for ML researchers and practitioners interested in LLaMA models and efficient fine-tuning strategies.

cs.AI↗

arXiv2025

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams

This study presents domain-informed genetic superposition programming (DIGSP), a symbolic regression framework tailored for engineering systems governed by separable physical mechanisms. DIGSP partitions the input space into domain-specific feature subsets and evolves independent genetic programming (GP) populations to model material-specific effects. Early evolution occurs in isolation, while ensemble fitness promotes inter-population cooperation. To enable symbolic superposition, an adaptive hierarchical symbolic abstraction mechanism (AHSAM) is triggered after stagnation across all populations. AHSAM performs analysis of variance- (ANOVA) based filtering to identify statistically significant individuals, compresses them into symbolic constructs, and injects them into all populations through a validation-guided pruning cycle. The DIGSP is benchmarked against a baseline multi-gene genetic programming (BGP) model using a dataset of steel fiber-reinforced concrete (SFRC) beams. Across 30 independent trials with 65% training, 10% validation, and 25% testing splits, DIGSP consistently outperformed BGP in training and test root mean squared error (RMSE). The Wilcoxon rank-sum test confirmed statistical significance (p < 0.01), and DIGSP showed tighter error distributions and fewer outliers. No significant difference was observed in validation RMSE due to limited sample size. These results demonstrate that domain-informed structural decomposition and symbolic abstraction improve convergence and generalization. DIGSP offers a principled and interpretable modeling strategy for systems where symbolic superposition aligns with the underlying physical structure.

cs.NE↗

arXiv2025

Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification

This paper introduces Multi-population Ensemble Genetic Programming (MEGP), a computational intelligence framework that integrates cooperative coevolution and the multiview learning paradigm to address classification challenges in high-dimensional and heterogeneous feature spaces. MEGP decomposes the input space into conditionally independent feature subsets, enabling multiple subpopulations to evolve in parallel while interacting through a dynamic ensemble-based fitness mechanism. Each individual encodes multiple genes whose outputs are aggregated via a differentiable softmax-based weighting layer, enhancing both model interpretability and adaptive decision fusion. A hybrid selection mechanism incorporating both isolated and ensemble-level fitness promotes inter-population cooperation while preserving intra-population diversity. This dual-level evolutionary dynamic facilitates structured search exploration and reduces premature convergence. Experimental evaluations across eight benchmark datasets demonstrate that MEGP consistently outperforms a baseline GP model in terms of convergence behavior and generalization performance. Comprehensive statistical analyses validate significant improvements in Log-Loss, Precision, Recall, F1 score, and AUC. MEGP also exhibits robust diversity retention and accelerated fitness gains throughout evolution, highlighting its effectiveness for scalable, ensemble-driven evolutionary learning. By unifying population-based optimization, multi-view representation learning, and cooperative coevolution, MEGP contributes a structurally adaptive and interpretable framework that advances emerging directions in evolutionary machine learning.

cs.NE↗

arXiv2025

From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification

We study symbolic surrogate modeling of frozen Transformer embeddings to obtain compact, auditable classifiers with calibrated probabilities. For five benchmarks (SST2G, 20NG, MNIST, CIFAR10, MSC17), embeddings from ModernBERT, DINOv2, and SigLIP are partitioned on the training set into disjoint, information-preserving views via semantic-preserving feature partitioning (SPFP). A cooperative multi-population genetic program (MEGP) then learns additive, closed-form logit programs over these views. Across 30 runs per dataset we report F1, AUC, log-loss, Brier, expected calibration error (ECE), and symbolic complexity; a canonical model is chosen by a one-standard-error rule on validation F1 with a parsimony tie-break. Temperature scaling fitted on validation yields substantial ECE reductions on test. The resulting surrogates achieve strong discrimination (up to F1 around 0.99 on MNIST, CIFAR10, MSC17; around 0.95 on SST2G), while 20NG remains most challenging. We provide reliability diagrams, dimension usage and overlap statistics, contribution-based importances, and global effect profiles (PDP and ALE), demonstrating faithful, cross-modal explanations grounded in explicit programs.

cs.NE↗

arXiv2025

Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks

This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making that replaces a single global cutoff with an evidence-conditioned, instance-adaptive criterion. For each test instance, proximal exemplars are retrieved in an embedding space; their predictive distributions are fused via Dempster-Shafer theory. The resulting fused belief acts as a per-instance thresholding mechanism. Because the supporting evidences are explicit, decisions are transparent and auditable. Experiments on CIFAR-10/100 with BiT and ViT backbones show higher or comparable uncertainty-aware performance with materially fewer confidently incorrect outcomes and a sustainable review load compared with applying threshold on prediction entropy. Notably, only a few evidences are sufficient to realize these gains; increasing the evidence set yields only modest changes. These results indicate that evidence-conditioned tagging provides a more reliable and interpretable alternative to fixed prediction entropy thresholds for operational uncertainty-aware decision-making.

cs.CV↗

arXiv2025

The Role of Orthographic Consistency in Multilingual Embedding Models for Text Classification in Arabic-Script Languages

In natural language processing, multilingual models like mBERT and XLM-RoBERTa promise broad coverage but often struggle with languages that share a script yet differ in orthographic norms and cultural context. This issue is especially notable in Arabic-script languages such as Kurdish Sorani, Arabic, Persian, and Urdu. We introduce the Arabic Script RoBERTa (AS-RoBERTa) family: four RoBERTa-based models, each pre-trained on a large corpus tailored to its specific language. By focusing pre-training on language-specific script features and statistics, our models capture patterns overlooked by general-purpose models. When fine-tuned on classification tasks, AS-RoBERTa variants outperform mBERT and XLM-RoBERTa by 2 to 5 percentage points. An ablation study confirms that script-focused pre-training is central to these gains. Error analysis using confusion matrices shows how shared script traits and domain-specific content affect performance. Our results highlight the value of script-aware specialization for languages using the Arabic script and support further work on pre-training strategies rooted in script and language specificity.

cs.CL↗

arXiv2025

A Language Model-Driven Semi-Supervised Ensemble Framework for Illicit Market Detection Across Deep/Dark Web and Social Platforms

Illegal marketplaces have increasingly shifted to concealed parts of the internet, including the deep and dark web, as well as platforms such as Telegram, Reddit, and Pastebin. These channels enable the anonymous trade of illicit goods including drugs, weapons, and stolen credentials. Detecting and categorizing such content remains challenging due to limited labeled data, the evolving nature of illicit language, and the structural heterogeneity of online sources. This paper presents a hierarchical classification framework that combines fine-tuned language models with a semi-supervised ensemble learning strategy to detect and classify illicit marketplace content across diverse platforms. We extract semantic representations using ModernBERT, a transformer model for long documents, finetuned on domain-specific data from deep and dark web pages, Telegram channels, Subreddits, and Pastebin pastes to capture specialized jargon and ambiguous linguistic patterns. In addition, we incorporate manually engineered features such as document structure, embedded patterns including Bitcoin addresses, emails, and IPs, and metadata, which complement language model embeddings. The classification pipeline operates in two stages. The first stage uses a semi-supervised ensemble of XGBoost, Random Forest, and SVM with entropy-based weighted voting to detect sales-related documents. The second stage further classifies these into drug, weapon, or credential sales. Experiments on three datasets, including our multi-source corpus, DUTA, and CoDA, show that our model outperforms several baselines, including BERT, ModernBERT, DarkBERT, ALBERT, Longformer, and BigBird. The model achieves an accuracy of 0.96489, an F1-score of 0.93467, and a TMCC of 0.95388, demonstrating strong generalization, robustness under limited supervision, and effectiveness in real-world illicit content detection.

cs.CL↗

arXiv2025

Effective Self-Attention-Based Deep Learning Model with Evolutionary Grid Search for Robust Wave Farm Energy Forecasting

Achieving carbon neutrality, a key focus of UN SDG #13, drives the exploration of wave energy, a renewable resource with the potential to generate 30,000 TWh of clean electricity annually, surpassing global demand. However, wave energy remains underdeveloped due to technical and economic challenges, particularly in forecasting wave farm power output, which is vital for grid stability and commercial viability. This study proposes a novel predictive framework to enhance wave energy integration into power grids. It introduces a hybrid sequential learning model combining Self-Attention-enhanced Convolutional Bi-LSTM with hyperparameter optimization. The model leverages spatial data from Wave Energy Converters (WECs) and is validated using datasets from wave farms in Adelaide, Sydney, Perth, and Tasmania, Australia. Benchmarked against ten machine learning algorithms, the model achieves superior accuracy, with R2 scores of 91.7% (Adelaide), 88.0% (Perth), 82.8% (Tasmania), and 91.0% (Sydney). It outperforms conventional ML and deep learning methods, offering robust and scalable predictions for wave energy output across diverse marine environments, supporting reliable integration into energy systems.

cs.NE↗

arXiv2025

Smart Buildings Energy Consumption Forecasting using Adaptive Evolutionary Ensemble Learning Models

Smart buildings are gaining popularity because they can enhance energy efficiency, lower costs, improve security, and provide a more comfortable and convenient environment for building occupants. A considerable portion of the global energy supply is consumed in the building sector and plays a pivotal role in future decarbonization pathways. To manage energy consumption and improve energy efficiency in smart buildings, developing reliable and accurate energy demand forecasting is crucial and meaningful. However, extending an effective predictive model for the total energy use of appliances at the building level is challenging because of temporal oscillations and complex linear and non-linear patterns. This paper proposes three hybrid ensemble predictive models, incorporating Bagging, Stacking, and Voting mechanisms combined with a fast and effective evolutionary hyper-parameters tuner. The performance of the proposed energy forecasting model was evaluated using a hybrid dataset comprising meteorological parameters, appliance energy use, temperature, humidity, and lighting energy consumption from various sections of a building, collected by 18 sensors located in Stambroek, Mons, Belgium. To provide a comparative framework and investigate the efficiency of the proposed predictive model, 15 popular machine learning (ML) models, including two classic ML models, three NNs, a Decision Tree (DT), a Random Forest (RF), two Deep Learning (DL) and six Ensemble models, were compared. The prediction results indicate that the adaptive evolutionary bagging model surpassed other predictive models in both accuracy and learning error. Notably, it achieved accuracy gains of 12.6%, 13.7%, 12.9%, 27.04%, and 17.4% compared to Extreme Gradient Boosting (XGB), Categorical Boosting (CatBoost), GBM, LGBM, and Random Forest (RF).

cs.NE↗

arXiv2025

Hybrid Wave-wind System Power Optimisation Using Effective Ensemble Covariance Matrix Adaptation Evolutionary Algorithm

Floating hybrid wind-wave systems combine offshore wind platforms with wave energy converters (WECs) to create cost-effective and reliable energy solutions. Adequately designed and tuned WECs are essential to avoid unwanted loads disrupting turbine motion while efficiently harvesting wave energy. These systems diversify energy sources, enhancing energy security and reducing supply risks while providing a more consistent power output by smoothing energy production variability. However, optimising such systems is complex due to the physical and hydrodynamic interactions between components, resulting in a challenging optimisation space. This study uses a 5-MW OC4-DeepCwind semi-submersible platform with three spherical WECs to explore these synergies. To address these challenges, we propose an effective ensemble optimisation (EEA) technique that combines covariance matrix adaptation, novelty search, and discretisation techniques. To evaluate the EEA performance, we used four sea sites located along Australia's southern coast. In this framework, geometry and power take-off parameters are simultaneously optimised to maximise the average power output of the hybrid wind-wave system. Ensemble optimisation methods enhance performance, flexibility, and robustness by identifying the best algorithm or combination of algorithms for a given problem, addressing issues like premature convergence, stagnation, and poor search space exploration. The EEA was benchmarked against 14 advanced optimisation methods, demonstrating superior solution quality and convergence rates. EEA improved total power output by 111%, 95%, and 52% compared to WOA, EO, and AHA, respectively. Additionally, in comparisons with advanced methods, LSHADE, SaNSDE, and SLPSO, EEA achieved absorbed power enhancements of 498%, 638%, and 349% at the Sydney sea site, showcasing its effectiveness in optimising hybrid energy systems.

cs.NE↗