arXiv subjects

Mohammad Ali

Publications and source records attributed to Mohammad Ali.

At least 19 recordsLinked to original sources

arXiv2026

Learning-Based Modeling of Soft Robots via Cosserat Rod Theory

Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order principles is typically time-demanding, and their expressiveness is limited, whereas data-driven models lack interpretability and physical consistency. This work aims to overcome these challenges by introducing a port-Hamiltonian Gaussian Process Regression framework for learning and simulating the dynamics of planar, rod-like soft robots. In detail, the proposed model integrates Cosserat rod theory and Hamiltonian physics with data-driven inference to preserve the system's energy structure while accurately learning the rod dynamics. Numerical simulations show that we can achieve accurate and energy-consistent representations of a rod-like soft robot, showing the potential for a robust and interpretable pathway for modeling complex continuum mechanics.

cs.RO↗

arXiv2025

Interpretable Geometry Sensitivity for Inverse Design of Integrated Photonics

As an increasingly powerful technique in integrated photonics, inverse design uses optimization algorithms to automatically create compact, high-performance photonic structures, often yielding non-intuitive layouts far more compact than conventional designs. While adjoint-based inverse design is a prominent optimization method, the resulting free-form layouts are difficult to interpret or diagnose under fabrication variability, even for experienced photonic device designers. We present an experimentally validated interpretability workflow that produces pixel-level sensitivity maps directly on the binary mask of an inverse-designed device. Using wavelength-division demultiplexers (WDMs) at 1310/1550 nm as examples, we train a lightweight convolutional surrogate to regress figures of merit (FoMs) and apply Integrated Gradients (IG) to attribute predicted transmission to individual pixels. We demonstrate that high-attribution hotspots correspond to physically meaningful substructures, such as splitter hubs and high-curvature edges. Experimental results show that controlled perturbations in these high-sensitivity regions result in up to an 11x higher excess insertion loss compared to perturbations in non-sensitive regions, consistent with full-wave simulations. This approach adds a practical explainability layer to existing pipelines, offering a clear pathway for foundry-compatible design-rule checking and fabrication-aware constraint allocation without modifying the underlying electromagnetic solver.

physics.optics↗

arXiv2025

Signal Classification Recovery Across Domains Using Unsupervised Domain Adaptation

Signal classification models based on deep neural networks are typically trained on datasets collected under controlled conditions, either simulated or over-the-air (OTA), which are constrained to specific channel environments with limited variability, such as fixed signal-to-noise ratio (SNR) levels. As a result, these models often fail to generalize when deployed in real-world scenarios where the feature distribution significantly differs from the training domain. This paper explores unsupervised domain adaptation techniques to bridge the generalization gap between mismatched domains. Specifically, we investigate adaptation methods based on adversarial learning, statistical distance alignment, and stochastic modeling to align representations between simulated and OTA signal domains. To emulate OTA characteristics, we deliberately generate modulated signals subjected to realistic channel impairments without demodulation. We evaluate classification performance under three scenarios, i.e., cross-SNR, SNR-matched cross-domain, and stepwise adaptation involving both SNR and domain shifts. Experimental results show that unsupervised domain adaptation methods, particularly stochastic classifier (STAR) and joint adaptive networks (JAN), enable consistent and substantial performance gains over baseline models, which highlight their promise for real-world deployment in wireless systems.

cs.CE↗

arXiv2025

Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis

This research presents a novel approach to computational framing analysis, called Semantic Relations-based Unsupervised Framing Analysis (SUFA). SUFA leverages semantic relations and dependency parsing algorithms to identify and assess entity-centric emphasis frames in news media reports. This innovative method is derived from two studies -- qualitative and computational -- using a dataset related to gun violence, demonstrating its potential for analyzing entity-centric emphasis frames. This article discusses SUFA's strengths, limitations, and application procedures. Overall, the SUFA approach offers a significant methodological advancement in computational framing analysis, with its broad applicability across both the social sciences and computational domains.

cs.CL↗

arXiv2024

Enumeration algorithms for combinatorial problems using Ising machines: When should we stop exploring energy landscapes?

Combinatorial problems such as combinatorial optimization and constraint satisfaction problems arise in decision-making across various fields of science and technology. In real-world applications, when multiple optimal or constraint-satisfying solutions exist, enumerating all these solutions is often desirable, as it provides flexibility in decision-making. However, combinatorial problems and their enumeration versions pose significant computational challenges due to combinatorial explosion. To address these challenges, we propose enumeration algorithms for combinatorial optimization and constraint satisfaction problems using Ising machines. Ising machines are specialized devices designed to efficiently solve combinatorial problems by exploring the energy landscape of an Ising model. Ising machines typically sample lower-energy solutions with higher probability. Our enumeration algorithms repeatedly perform such sampling to collect all desirable solutions. The crux of the proposed algorithms lies in their stopping criteria for sampling-based energy landscape exploration, which are derived from probability theory. In particular, the proposed algorithms have theoretical guarantees that the failure probability of enumeration is bounded above by a user-specified value, provided that lower-cost solutions are sampled more frequently and equal-cost solutions are sampled with equal probability. Many physics-based Ising machines are expected to (approximately) satisfy these conditions. As a demonstration, we applied our algorithm using simulated annealing to maximum clique enumeration on random graphs. We found that our algorithm enumerates all maximum cliques in large, dense graphs faster than a conventional branch-and-bound algorithm specifically designed for maximum clique enumeration. These findings underscore the effectiveness and potential of our proposed approach.

cs.DS↗

arXiv2024

Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines

In the digital age, the prevalence of misleading news headlines poses a significant challenge to information integrity, necessitating robust detection mechanisms. This study explores the efficacy of Large Language Models (LLMs) in identifying misleading versus non-misleading news headlines. Utilizing a dataset of 60 articles, sourced from both reputable and questionable outlets across health, science & tech, and business domains, we employ three LLMs- ChatGPT-3.5, ChatGPT-4, and Gemini-for classification. Our analysis reveals significant variance in model performance, with ChatGPT-4 demonstrating superior accuracy, especially in cases with unanimous annotator agreement on misleading headlines. The study emphasizes the importance of human-centered evaluation in developing LLMs that can navigate the complexities of misinformation detection, aligning technical proficiency with nuanced human judgment. Our findings contribute to the discourse on AI ethics, emphasizing the need for models that are not only technically advanced but also ethically aligned and sensitive to the subtleties of human interpretation.

cs.CL↗

arXiv2023

A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme's security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods.

cs.CR↗

arXiv2023

Federated Online/Offline Remote Data Inspection for Distributed Edge Computing

In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or unintentional events. Given the limited resources of edge servers and prohibitive storage costs incurred by app vendors, designing an efficient approach to inspect and maintain the data over tremendous edge servers is a critical issue. To tackle the problem, we design a novel data inspection approach, named ${\text{O}^2\text{DI}}$, that provides the following services: i) using ${\text{O}^2\text{DI}}$, app vendors can inspect the data cached in edge servers without having the original data, which reduces the incurred I/O and storage overhead significantly; ii) computational operations conducted by both edge servers and app vendors are highly efficient because of a novel online/offline technique; iii) many data files cached in different edge servers can be verified quickly and at once by using a novel batch verification method; iv) corrupted data in edge servers can be localized and repaired efficiently. We analyze the security and performance of ${\text{O}^2\text{DI}}$. We see that it is secure in the random oracle model, much faster, and more cost-effective compared to state-of-the-art approaches.

cs.CR↗

arXiv2021

Model Predictive Control with Infeasible Reference Trajectories

Model Predictive Control (MPC) formulations are typically built on the requirement that a feasible reference trajectory is available. In practical settings, however, references that are infeasible with respect to the system dynamics are used for convenience. In this paper, we prove under which conditions an MPC formulation is Input-to-State Stable~(ISS) in closed-loop when an infeasible reference is used, and that with proper terminal conditions, asymptotic stability towards an optimal reference may be achieved. We illustrate the theoretical results with a four-dimensional robotic joint example.

eess.SY↗

arXiv2021

HUAP: Practical Attribute-based Access Control Supporting Hidden Updatable Access Policies for Resource-Constrained Devices

Attribute-based encryption (ABE) is a promising cryptographic mechanism for providing confidentiality and fine-grained access control in the cloud-based area. However, due to high computational overhead, common ABE schemes are not suitable for resource-constrained devices. Moreover, data owners should be able to update their defined access policies efficiently, and in some cases, applying hidden access policies is required to preserve the privacy of clients and data. In this paper, we propose a ciphertext-policy attribute-based access control scheme which for the first time provides online/offline encryption, hidden access policy, and access policy update simultaneously. In our scheme, resource-constrained devices are equipped with online/offline encryption reducing the encryption overhead significantly. Furthermore, attributes of access policies are hidden such that the attribute sets satisfying an access policy cannot be guessed by other parties. Moreover, data owners can update their defined access policies while outsourcing a major part of the updating process to the cloud service provider. In particular, we introduce blind access policies that enable the cloud service provider to update the data owners' access policies without receiving a new re-encryption key. Besides, our scheme supports fast decryption such that the decryption algorithm consists of a constant number of bilinear pairing operations. The proposed scheme is proven to be secure in the random oracle model and under the hardness of Decisional Bilinear Diffie-Hellman (DBDH) and Decision Linear (D-Linear) assumptions. Also, performance analysis results demonstrate that the proposed scheme is efficient and practical.

cs.CR↗

arXiv2020

Developing Augmented Reality based Gaming Model to Teach Ethical Education in Primary Schools

Education sector is adopting new technologies for both teaching and learning pedagogy. Augmented Reality (AR) is a new technology that can be used in the educational pedagogy to enhance the engagement with students. Students interact with AR-based educational material for more visualization and explanation. Therefore, the use of AR in education is becoming more popular. However, most researches narrate the use of AR technologies in the field of English, Maths, Science, Culture, Arts, and History education but the absence of ethical education is visible. In our paper, we design the system and develop an AR-based mobile game model in the field of Ethical education for pre-primary students. Students from pre-primary require more interactive lessons than theoretical concepts. So, we use AR technology to develop a game which offers interactive procedures where students can learn with fun and engage with the context. Finally, we develop a prototype that works with our research objective. We conclude our paper with future works.

cs.CY↗

arXiv2020

Develop Health Monitoring and Management System to Track Health Condition and Nutrient Balance for School Students

Health Monitoring and Management System (HMMS) is an emerging technology for decades. Researchers are working on this field to track health conditions for different users. Researchers emphasize tracking health conditions from an early stage to the human body. Therefore, different research works have been conducted to establish HMMS in schools. Researchers propose different frameworks and technologies for their HMMS to check student's health condition. In this paper, we introduce a complete and scalable HMMS to track health conditions and nutrient balance for students from primary school. We define procedures step by step to establish a robust HMMS where big data methodologies can be used for further prediction for diseases.

cs.HC↗

arXiv2020

Multi-Dialect Arabic BERT for Country-Level Dialect Identification

Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task. The dialect identification subtask provides 21,000 country-level labeled tweets covering all 21 Arab countries. An unlabeled corpus of 10M tweets from the same domain is also presented by the competition organizers for optional use. Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask at hand. We publicly release the pre-trained language model component of our winning solution under the name of Multi-dialect-Arabic-BERT model, for any interested researcher out there.

cs.CL↗

arXiv2020

Safe Trajectory Tracking in Uncertain Environments

In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility. This is the case, for example, in trajectory tracking applications for mobile systems in the presence of constraints which are not fully known a-priori. In this paper, we propose a new framework called Model Predictive Flexible trajectory Tracking Control (MPFTC), which relaxes the trajectory tracking requirement. Additionally, we accommodate recursive feasibility in the presence of a-priori unknown constraints, which might render the reference trajectory infeasible. In the proposed framework, constraint satisfaction is guaranteed at all times while the reference trajectory is tracked as good as constraint satisfaction allows, thus simplifying the controller design and reducing possibly aggressive tracking behavior. The proposed framework is illustrated with three numerical examples.

eess.SY↗

arXiv2019

Inverse Probability Weighted Estimators of Vaccine Effects Accommodating Partial Interference and Censoring

Estimating population-level effects of a vaccine is challenging because there may be interference, i.e., the outcome of one individual may depend on the vaccination status of another individual. Partial interference occurs when individuals can be partitioned into groups such that interference occurs only within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effects of an exposure or treatment. Tchetgen Tchetgen and VanderWeele (2012) proposed a modified IPW estimator for causal effects in the presence of partial interference. Motivated by a cholera vaccine study in Bangladesh, this paper considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator to the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are estimated using proportional hazards frailty models. The large sample properties of the IPCW estimators are derived, and simulation studies are presented demonstrating the estimators' performance in finite samples. The methods are then used to analyze data from the cholera vaccine study.

stat.ME↗

arXiv2019

Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control

In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on model predictive control. Traffic is simulated with numerous predefined driver behaviors and intentions, and the performance of the proposed decision algorithm was evaluated against another controller. The results show that the proposed decision algorithm yields shorter training episodes and an increased performance in success rate compared to the other controller.

cs.RO↗

arXiv2019

Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving With Road Users

For motion planning and control of autonomous vehicles to be proactive and safe, pedestrians' and other road users' motions must be considered. In this paper, we present a vehicle motion planning and control framework, based on Model Predictive Control, accounting for moving obstacles. Measured pedestrian states are fed into a prediction layer which translates each pedestrians' predicted motion into constraints for the MPC problem. Simulations and experimental validation were performed with simulated crossing pedestrians to show the performance of the framework. Experimental results show that the controller is stable even under significant input delays, while still maintaining very low computational times. In addition, real pedestrian data was used to further validate the developed framework in simulations.

eess.SY↗

arXiv2018

Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning

This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently. Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions. The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98% of the time, while at the same time not being too passive. Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%.

cs.LG↗