31 citations · 186 across the 67 of their papers we have counts for
71 papers
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction
Arshia Abolghasemi, Seyed Reza Tavakoli, Parsa Naderi +7
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time ser…
GhostWord: A Fine-Grained Backdoor Attack on Automatic Speech Recognition
Mojtaba Nafez, Mobina Poulaei, Kiarash Kiani Feriz +4
Automatic Speech Recognition (ASR) systems are widely deployed in safety-critical settings but remain vulnerable to data-poisoning backdoor attacks. Existing ASR backdoors typicall…
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia +4
Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evi…
When Linear RUL Labels Disagree with Vibration Degradation: A Stage-Aware Target and Dual-Scale Predictor Evaluated on XJTU-SY and IMS
Behrad Mousaei Shir-Mohammad, Seyed Reza Tavakoli, Mohammad Mohammadi +3
Remaining useful life (RUL) studies commonly treat the label as fixed, although clock-linear labels may decline while measured vibration remains nearly stable and then changes rapi…
The Judge Who Never Admits: Hidden Shortcuts in LLM-based Evaluation
Arash Marioriyad, Omid Ghahroodi, Ehsaneddin Asgari +2
Large language models (LLMs) are increasingly used as automatic judges to evaluate system outputs in tasks such as reasoning, question answering, and creative writing. A faithful j…
Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Framework
Hamid Reza Akbari, Mohammad Hossein Sameti, Amir M. Mansourian +2
The pursuit of Artificial General Intelligence (AGI) is a central goal in language model development, in which consciousness-like processing could serve as a key facilitator. While…
DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline
Houman Kazemzadeh, Kiarash Mokhtari Dizaji, Seyed Reza Tavakoli +12
In our study, we evaluated large language model (LLM) performance on pharmacy licensure-style question-answering tasks and developed an external knowledge integration method to imp…
All Centers Are at most a Few Tokens Apart: Knowledge Distillation with Domain Invariant Prompt Tuning
Amir Mohammad Ezzati, Alireza Malekhosseini, Armin Khosravi +1
Domain generalization is critical in computational pathology (CPath) due to inherent domain shifts caused by variations in staining protocols, scanner devices, and imaging settings…
Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey
Fatemeh Shahhosseini, Arash Marioriyad, Ali Momen +3
Scientific idea generation is central to discovery, requiring the joint satisfaction of novelty and scientific soundness. Unlike standard reasoning or general creative generation,…
Real-Time Knee Angle Prediction Using EMG and Kinematic Data with an Attention-Based CNN-LSTM Network and Transfer Learning Across Multiple Datasets
Mojtaba Mollahossein, Gholamreza Vossoughi, Mohammad Hossein Rohban
Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learning (DL) methods. However, these approaches often face c…
FrameShield: Adversarially Robust Video Anomaly Detection
Mojtaba Nafez, Mobina Poulaei, Nikan Vasei +3
Weakly Supervised Video Anomaly Detection (WSVAD) has achieved notable advancements, yet existing models remain vulnerable to adversarial attacks, limiting their reliability. Due t…
The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge
Arash Marioriyad, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Large language models (LLMs) are increasingly deployed as automatic judges to evaluate system outputs in tasks such as summarization, dialogue, and creative writing. A faithful jud…
Unspoken Hints: Accuracy Without Acknowledgement in LLM Reasoning
Arash Marioriyad, Shaygan Adim, Nima Alighardashi +2
Large language models (LLMs) increasingly rely on chain-of-thought (CoT) prompting to solve mathematical and logical reasoning tasks. Yet, a central question remains: to what exten…
No Concept Left Behind: Test-Time Optimization for Compositional Text-to-Image Generation
Mohammad Hossein Sameti, Amir M. Mansourian, Arash Marioriyad +3
Despite recent advances in text-to-image (T2I) models, they often fail to faithfully render all elements of complex prompts, frequently omitting or misrepresenting specific objects…
Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation
Seyed Amir Kasaei, Mohammad Hossein Rohban
In language and vision-language models, hallucination is broadly understood as content generated from a model's prior knowledge or biases rather than from the given input. While th…
Evaluating the Evaluators: Metrics for Compositional Text-to-Image Generation
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad +4
Text-image generation has advanced rapidly, but assessing whether outputs truly capture the objects, attributes, and relations described in prompts remains a central challenge. Eva…
CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad +5
Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional alignment, particularly when prompts d…
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment
Omid Ghahroodi, Arshia Hemmat, Marzia Nouri +8
Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEE…
Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models
Parham Rezaei, Arash Marioriyad, Mahdieh Soleymani Baghshah +1
Despite the ability of text-to-image models to generate high-quality, realistic, and diverse images, they face challenges in compositional generation, often struggling to accuratel…
Attention-Based Convolutional Neural Network Model for Human Lower Limb Activity Recognition using sEMG
Mojtaba Mollahossein, Farshad Haghgoo Daryakenari, Mohammad Hossein Rohban +1
Accurate classification of lower limb movements using surface electromyography (sEMG) signals plays a crucial role in assistive robotics and rehabilitation systems. In this study,…
Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
Reihaneh Zohrabi, Hosein Hasani, Mahdieh Soleymani Baghshah +3
Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications, where they frequently face data distri…
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies
Mojtaba Nafez, Amirhossein Koochakian, Arad Maleki +2
Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and…
CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
Reza Abbasi, Ali Nazari, Aminreza Sefid +3
Contrastive Language-Image Pre-training (CLIP) models excel in zero-shot classification, yet face challenges in complex multi-object scenarios. This study offers a comprehensive an…
Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Study
Reza Abbasi, Ali Nazari, Aminreza Sefid +3
Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable performance in zero-shot classification tasks, yet their efficacy in handling complex multi-objec…