Showing 1–33 of 33 results for author: Abrar, A

MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification

Authors: Kazi Samin Yasar Alam, Md Tanbir Chowdhury, Tamim Ahmed, Ajwad Abrar, Md Rafid Haque

Abstract: Bangla-English code-mixing is widespread across South Asian social media, yet resources for implicit meaning identification in this setting remain scarce. Existing sentiment and sarcasm models largely focus on monolingual English or high-resource languages and struggle with transliteration variation, cultural references, and intra-sentential language switching. To address this gap, we introduce Mi… ▽ More Bangla-English code-mixing is widespread across South Asian social media, yet resources for implicit meaning identification in this setting remain scarce. Existing sentiment and sarcasm models largely focus on monolingual English or high-resource languages and struggle with transliteration variation, cultural references, and intra-sentential language switching. To address this gap, we introduce MixSarc, the first publicly available Bangla-English code-mixed corpus for implicit meaning identification. The dataset contains 9,087 manually annotated sentences labeled for humor, sarcasm, offensiveness, and vulgarity. We construct the corpus through targeted social media collection, systematic filtering, and multi-annotator validation. We benchmark transformer-based models and evaluate zero-shot large language models under structured prompting. Results show strong performance on humor detection but substantial degradation on sarcasm, offense, and vulgarity due to class imbalance and pragmatic complexity. Zero-shot models achieve competitive micro-F1 scores but low exact match accuracy. Further analysis reveals that over 42\% of negative sentiment instances in an external dataset exhibit sarcastic characteristics. MixSarc provides a foundational resource for culturally aware NLP and supports more reliable multi-label modeling in code-mixed environments. △ Less

Submitted 25 February, 2026; originally announced February 2026.

Comments: Under Review

BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Authors: Ahmed Rafid, Rumman Adib, Fariya Ahmed, Ajwad Abrar, Mohammed Saidul Islam

Abstract: Evaluating factual consistency is essential for reliable text summarization, particularly in high-stakes domains such as healthcare and news. However, most existing evaluation metrics overlook Bangla, a widely spoken yet under-resourced language, and often depend on reference summaries. We introduce BanglaSummEval, a reference-free, question-answering-based framework for evaluating factual consist… ▽ More Evaluating factual consistency is essential for reliable text summarization, particularly in high-stakes domains such as healthcare and news. However, most existing evaluation metrics overlook Bangla, a widely spoken yet under-resourced language, and often depend on reference summaries. We introduce BanglaSummEval, a reference-free, question-answering-based framework for evaluating factual consistency in Bangla summarization. The proposed method assesses both factual accuracy and content coverage through automatically generated questions and answers derived from the source document and the summary. A single multilingual instruction-tuned language model handles question generation, question answering, candidate answer extraction, and question importance weighting. This unified design reduces system complexity and computational cost. To capture semantic consistency beyond surface-level overlap, we use BERTScore-Recall for answer comparison. We validate BanglaSummEval on 300 human-written summaries from educational and medical domains, demonstrating strong correlation with expert human judgments (Pearson's $r = 0.694$, Spearman's $ρ= 0.763$). By providing interpretable, step-wise diagnostics alongside reliable evaluation scores, BanglaSummEval offers a practical and transparent solution for factual consistency evaluation in low-resource language settings. △ Less

Submitted 18 February, 2026; originally announced February 2026.

Comments: Accepted in 2nd LoResLM at EACL 2026

Assessing Large Language Models for Medical QA: Zero-Shot and LLM-as-a-Judge Evaluation

Authors: Shefayat E Shams Adib, Ahmed Alfey Sani, Ekramul Alam Esham, Ajwad Abrar, Tareque Mohmud Chowdhury

Abstract: Recently, Large Language Models (LLMs) have gained significant traction in medical domain, especially in developing a QA systems to Medical QA systems for enhancing access to healthcare in low-resourced settings. This paper compares five LLMs deployed between April 2024 and August 2025 for medical QA, using the iCliniq dataset, containing 38,000 medical questions and answers of diverse specialties… ▽ More Recently, Large Language Models (LLMs) have gained significant traction in medical domain, especially in developing a QA systems to Medical QA systems for enhancing access to healthcare in low-resourced settings. This paper compares five LLMs deployed between April 2024 and August 2025 for medical QA, using the iCliniq dataset, containing 38,000 medical questions and answers of diverse specialties. Our models include Llama-3-8B-Instruct, Llama 3.2 3B, Llama 3.3 70B Instruct, Llama-4-Maverick-17B-128E-Instruct, and GPT-5-mini. We are using a zero-shot evaluation methodology and using BLEU and ROUGE metrics to evaluate performance without specialized fine-tuning. Our results show that larger models like Llama 3.3 70B Instruct outperform smaller models, consistent with observed scaling benefits in clinical tasks. It is notable that, Llama-4-Maverick-17B exhibited more competitive results, thus highlighting evasion efficiency trade-offs relevant for practical deployment. These findings align with advancements in LLM capabilities toward professional-level medical reasoning and reflect the increasing feasibility of LLM-supported QA systems in the real clinical environments. This benchmark aims to serve as a standardized setting for future study to minimize model size, computational resources and to maximize clinical utility in medical NLP applications. △ Less

Submitted 16 February, 2026; originally announced February 2026.

Comments: Accepted in 28th ICCIT, 2025

Post-Quantum Cryptography for Intelligent Transportation Systems: An Implementation-Focused Review

Authors: Abdullah Al Mamun, Akid Abrar, Mizanur Rahman, M Sabbir Salek, Mashrur Chowdhury

Abstract: As quantum computing advances, the cryptographic algorithms that underpin confidentiality, integrity, and authentication in Intelligent Transportation Systems (ITS) face increasing vulnerability to quantum-enabled attacks. To address these risks, governments and industry stakeholders are turning toward post-quantum cryptography (PQC), a class of algorithms designed to resist adversaries equipped w… ▽ More As quantum computing advances, the cryptographic algorithms that underpin confidentiality, integrity, and authentication in Intelligent Transportation Systems (ITS) face increasing vulnerability to quantum-enabled attacks. To address these risks, governments and industry stakeholders are turning toward post-quantum cryptography (PQC), a class of algorithms designed to resist adversaries equipped with quantum computing capabilities. However, existing studies provide limited insight into the implementation-focused aspects of PQC in the ITS domain. This review addresses that gap by evaluating the readiness of vehicular communication and security standards for adopting PQC. It examines in-vehicle networks and vehicle-to-everything (V2X) interfaces, and investigates vulnerabilities at the physical implementation layer of cryptographic hardware and embedded platforms, primarily exposure to side-channel and fault injection attacks. The review identifies thirteen research gaps: non-PQC-ready standards; constraints in embedded implementation and hybrid cryptography; interoperability and certificate-management barriers; a lack of real-world PQC deployment data in ITS; and physical-attack vulnerabilities in PQC-enabled vehicular communication. We present several future research directions, including updating vehicular communication and security standards, optimizing PQC for low-power devices, enhancing interoperability and certificate-management frameworks for PQC integration, conducting real-world evaluations of PQC-enabled communication and control functions across ITS deployments, and strengthening defenses against AI-assisted physical attacks. A phased roadmap is presented that aligns PQC deployment with regulatory, performance, and safety requirements, thereby guiding the secure evolution of ITS in the quantum computing era. △ Less

Submitted 14 February, 2026; v1 submitted 2 January, 2026; originally announced January 2026.

Comments: This is a preprint version of a manuscript currently under second-round peer review

Flow-Based Path Planning for Multiple Homogenous UAVs for Outdoor Formation-Flying

Authors: Mahmud Suhaimi Ibrahim, Shantanu Rahman, Muhammad Samin Hasan, Minhaj Uddin Ahmad, Abdullah Abrar

Abstract: Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any g… ▽ More Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any graph-based path-finding algorithm, and 3) Implementing the Ford-Fulkerson Method to find the paths with the maximum flow (no collision). Simulations of up to 64 UAVs were conducted for various formations, followed by a practical experiment with 3 quadcopters for testing physical plausibility and feasibility. The results of these tests show the efficacy of this method's ability to produce safe, collision-free paths. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Comments: 9 pages, 15 figures, conference

Journal ref: 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR)

Distinguishing Repetition Disfluency from Morphological Reduplication in Bangla ASR Transcripts: A Novel Corpus and Benchmarking Analysis

Authors: Zaara Zabeen Arpa, Sadnam Sakib Apurbo, Nazia Karim Khan Oishee, Ajwad Abrar

Abstract: Automatic Speech Recognition (ASR) transcripts, especially in low-resource languages like Bangla, contain a critical ambiguity: word-word repetitions can be either Repetition Disfluency (unintentional ASR error/hesitation) or Morphological Reduplication (a deliberate grammatical construct). Standard disfluency correction fails by erroneously deleting valid linguistic information. To solve this, we… ▽ More Automatic Speech Recognition (ASR) transcripts, especially in low-resource languages like Bangla, contain a critical ambiguity: word-word repetitions can be either Repetition Disfluency (unintentional ASR error/hesitation) or Morphological Reduplication (a deliberate grammatical construct). Standard disfluency correction fails by erroneously deleting valid linguistic information. To solve this, we introduce the first publicly available, 20,000-row Bangla corpus, manually annotated to explicitly distinguish between these two phenomena in noisy ASR transcripts. We benchmark this novel resource using two paradigms: state-of-the-art multilingual Large Language Models (LLMs) and task-specific fine-tuning of encoder models. LLMs achieve competitive performance (up to 82.68\% accuracy) with few-shot prompting. However, fine-tuning proves superior, with the language-specific BanglaBERT model achieving the highest accuracy of 84.78\% and an F1 score of 0.677. This establishes a strong, linguistically-informed baseline and provides essential data for developing sophisticated, semantic-preserving text normalization systems for Bangla. △ Less

Submitted 17 November, 2025; originally announced November 2025.

Faithful Summarization of Consumer Health Queries: A Cross-Lingual Framework with LLMs

Authors: Ajwad Abrar, Nafisa Tabassum Oeshy, Prianka Maheru, Farzana Tabassum, Tareque Mohmud Chowdhury

Abstract: Summarizing consumer health questions (CHQs) can ease communication in healthcare, but unfaithful summaries that misrepresent medical details pose serious risks. We propose a framework that combines TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs) to enhance faithfulness in medical text summarization. In our experiments, we fine-tuned the LL… ▽ More Summarizing consumer health questions (CHQs) can ease communication in healthcare, but unfaithful summaries that misrepresent medical details pose serious risks. We propose a framework that combines TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs) to enhance faithfulness in medical text summarization. In our experiments, we fine-tuned the LLaMA-2-7B model on the MeQSum (English) and BanglaCHQ-Summ (Bangla) datasets, achieving consistent improvements across quality (ROUGE, BERTScore, readability) and faithfulness (SummaC, AlignScore) metrics, and outperforming zero-shot baselines and prior systems. Human evaluation further shows that over 80\% of generated summaries preserve critical medical information. These results highlight faithfulness as an essential dimension for reliable medical summarization and demonstrate the potential of our approach for safer deployment of LLMs in healthcare contexts. △ Less

Submitted 13 November, 2025; originally announced November 2025.

Comments: Accepted at the 5th Muslims in Machine Learning (MusIML) Workshop, co-located with NeurIPS 2025

BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering

Authors: Sadia Sultana, Saiyma Sittul Muna, Mosammat Zannatul Samarukh, Ajwad Abrar, Tareque Mohmud Chowdhury

Abstract: Developing accurate biomedical Question Answering (QA) systems in low-resource languages remains a major challenge, limiting equitable access to reliable medical knowledge. This paper introduces BanglaMedQA and BanglaMMedBench, the first large-scale Bangla biomedical Multiple Choice Question (MCQ) datasets designed to evaluate reasoning and retrieval in medical artificial intelligence (AI). The st… ▽ More Developing accurate biomedical Question Answering (QA) systems in low-resource languages remains a major challenge, limiting equitable access to reliable medical knowledge. This paper introduces BanglaMedQA and BanglaMMedBench, the first large-scale Bangla biomedical Multiple Choice Question (MCQ) datasets designed to evaluate reasoning and retrieval in medical artificial intelligence (AI). The study applies and benchmarks several Retrieval-Augmented Generation (RAG) strategies, including Traditional, Zero-Shot Fallback, Agentic, Iterative Feedback, and Aggregate RAG, combining textbook-based and web retrieval with generative reasoning to improve factual accuracy. A key novelty lies in integrating a Bangla medical textbook corpus through Optical Character Recognition (OCR) and implementing an Agentic RAG pipeline that dynamically selects between retrieval and reasoning strategies. Experimental results show that the Agentic RAG achieved the highest accuracy 89.54% with openai/gpt-oss-120b, outperforming other configurations and demonstrating superior rationale quality. These findings highlight the potential of RAG-based methods to enhance the reliability and accessibility of Bangla medical QA, establishing a foundation for future research in multilingual medical artificial intelligence. △ Less

Submitted 6 November, 2025; originally announced November 2025.

Comments: Under Review

FirstAidQA: A Synthetic Dataset for First Aid and Emergency Response in Low-Connectivity Settings

Authors: Saiyma Sittul Muna, Rezwan Islam Salvi, Mushfiqur Rahman Mushfique, Ajwad Abrar

Abstract: In emergency situations, every second counts. The deployment of Large Language Models (LLMs) in time-sensitive, low or zero-connectivity environments remains limited. Current models are computationally intensive and unsuitable for low-tier devices often used by first responders or civilians. A major barrier to developing lightweight, domain-specific solutions is the lack of high-quality datasets t… ▽ More In emergency situations, every second counts. The deployment of Large Language Models (LLMs) in time-sensitive, low or zero-connectivity environments remains limited. Current models are computationally intensive and unsuitable for low-tier devices often used by first responders or civilians. A major barrier to developing lightweight, domain-specific solutions is the lack of high-quality datasets tailored to first aid and emergency response. To address this gap, we introduce FirstAidQA, a synthetic dataset containing 5,500 high-quality question answer pairs that encompass a wide range of first aid and emergency response scenarios. The dataset was generated using a Large Language Model, ChatGPT-4o-mini, with prompt-based in-context learning, using texts from the Vital First Aid Book (2019). We applied preprocessing steps such as text cleaning, contextual chunking, and filtering, followed by human validation to ensure accuracy, safety, and practical relevance of the QA pairs. FirstAidQA is designed to support instruction-tuning and fine-tuning of LLMs and Small Language Models (SLMs), enabling faster, more reliable, and offline-capable systems for emergency settings. We publicly release the dataset to advance research on safety-critical and resource-constrained AI applications in first aid and emergency response. The dataset is available on Hugging Face at https://huggingface.co/datasets/i-am-mushfiq/FirstAidQA. △ Less

Submitted 3 November, 2025; originally announced November 2025.

Comments: Accepted at the 5th Muslims in Machine Learning (MusIML) Workshop, co-located with NeurIPS 2025

AI-Driven Post-Quantum Cryptography for Cyber-Resilient V2X Communication in Transportation Cyber-Physical Systems

Authors: Akid Abrar, Sagar Dasgupta, Mizanur Rahman, Ahmad Alsharif

Abstract: Transportation Cyber-Physical Systems (TCPS) integrate physical elements, such as transportation infrastructure and vehicles, with cyber elements via advanced communication technologies, allowing them to interact seamlessly. This integration enhances the efficiency, safety, and sustainability of transportation systems. TCPS rely heavily on cryptographic security to protect sensitive information tr… ▽ More Transportation Cyber-Physical Systems (TCPS) integrate physical elements, such as transportation infrastructure and vehicles, with cyber elements via advanced communication technologies, allowing them to interact seamlessly. This integration enhances the efficiency, safety, and sustainability of transportation systems. TCPS rely heavily on cryptographic security to protect sensitive information transmitted between vehicles, transportation infrastructure, and other entities within the transportation ecosystem, ensuring data integrity, confidentiality, and authenticity. Traditional cryptographic methods have been employed to secure TCPS communications, but the advent of quantum computing presents a significant threat to these existing security measures. Therefore, integrating Post-Quantum Cryptography (PQC) into TCPS is essential to maintain secure and resilient communications. While PQC offers a promising approach to developing cryptographic algorithms resistant to quantum attacks, artificial intelligence (AI) can enhance PQC by optimizing algorithm selection, resource allocation, and adapting to evolving threats in real-time. AI-driven PQC approaches can improve the efficiency and effectiveness of PQC implementations, ensuring robust security without compromising system performance. This chapter introduces TCPS communication protocols, discusses the vulnerabilities of corresponding communications to cyber-attacks, and explores the limitations of existing cryptographic methods in the quantum era. By examining how AI can strengthen PQC solutions, the chapter presents cyber-resilient communication strategies for TCPS. △ Less

Submitted 9 October, 2025; originally announced October 2025.

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems

Authors: Minhaj Uddin Ahmad, Akid Abrar, Sagar Dasgupta, Mizanur Rahman

Abstract: Intelligent Transportation Systems (ITS) have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time traffic monitoring, adaptive signal control, and predictive maintenance. However, such integration s… ▽ More Intelligent Transportation Systems (ITS) have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time traffic monitoring, adaptive signal control, and predictive maintenance. However, such integration significantly broadens the ITS attack surface, exposing critical infrastructures to cyber threats that jeopardize safety, data integrity, and operational resilience. Ensuring robust cybersecurity is therefore essential, yet comprehensive vulnerability assessments, threat modeling, and mitigation validations are often cost-prohibitive and time-intensive when applied to large-scale, heterogeneous transportation systems. Simulation platforms offer a cost-effective and repeatable means for cybersecurity evaluation, and the simulation platform should encompass the full range of ITS dimensions - mobility, sensing, networking, and applications. This chapter discusses an integrated co-simulation testbed that links CARLA for 3D environment and sensor modeling, SUMO for microscopic traffic simulation and control, and OMNeT++ for V2X communication simulation. The co-simulation testbed enables end-to-end experimentation, vulnerability identification, and mitigation benchmarking, providing practical insights for developing secure, efficient, and resilient ITS infrastructures. To illustrate its capabilities, the chapter incorporates a case study on a C-V2X proactive safety alert system enhanced with post-quantum cryptography, highlighting the role of the testbed in advancing secure and resilient ITS infrastructures. △ Less

Submitted 19 September, 2025; originally announced September 2025.

CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI

Authors: Hasin Jawad Ali, Ilhamul Azam, Ajwad Abrar, Md. Kamrul Hasan, Hasan Mahmud

Abstract: The challenge of aligning artificial intelligence (AI) with human values persists due to the abstract and often conflicting nature of moral principles and the opacity of existing approaches. This paper introduces CogniAlign, a multi-agent deliberation framework based on naturalistic moral realism, that grounds moral reasoning in survivability, defined across individual and collective dimensions, a… ▽ More The challenge of aligning artificial intelligence (AI) with human values persists due to the abstract and often conflicting nature of moral principles and the opacity of existing approaches. This paper introduces CogniAlign, a multi-agent deliberation framework based on naturalistic moral realism, that grounds moral reasoning in survivability, defined across individual and collective dimensions, and operationalizes it through structured deliberations among discipline-specific scientist agents. Each agent, representing neuroscience, psychology, sociology, and evolutionary biology, provides arguments and rebuttals that are synthesized by an arbiter into transparent and empirically anchored judgments. As a proof-of-concept study, we evaluate CogniAlign on classic and novel moral questions and compare its outputs against GPT-4o using a five-part ethical audit framework with the help of three experts. Results show that CogniAlign consistently outperforms the baseline across more than sixty moral questions, with average performance gains of 12.2 points in analytic quality, 31.2 points in decisiveness, and 15 points in depth of explanation. In the Heinz dilemma, for example, CogniAlign achieved an overall score of 79 compared to GPT-4o's 65.8, demonstrating a decisive advantage in handling moral reasoning. Through transparent and structured reasoning, CogniAlign demonstrates the feasibility of an auditable approach to AI alignment, though certain challenges still remain. △ Less

Submitted 21 February, 2026; v1 submitted 14 September, 2025; originally announced September 2025.

Comments: Under Review

Report number: Volume 6, article number 243, (2026)

Journal ref: AI and Ethics (2026)

OpenCAMS: An Open-Source Connected and Automated Mobility Co-Simulation Platform for Advancing Next-Generation Intelligent Transportation Systems Research

Authors: Minhaj Uddin Ahmad, Akid Abrar, Sagar Dasgupta, Mizanur Rahman

Abstract: We introduce OpenCAMS (Open-Source Connected and Automated Mobility Co-Simulation Platform), an open-source, synchronized, and extensible co-simulation framework that tightly couples three best-in-class simulation tools: (i) SUMO, (ii) CARLA, and (iii) OMNeT++. OpenCAMS is designed to support advanced research in transportation safety, mobility, and cybersecurity by combining the strengths of each… ▽ More We introduce OpenCAMS (Open-Source Connected and Automated Mobility Co-Simulation Platform), an open-source, synchronized, and extensible co-simulation framework that tightly couples three best-in-class simulation tools: (i) SUMO, (ii) CARLA, and (iii) OMNeT++. OpenCAMS is designed to support advanced research in transportation safety, mobility, and cybersecurity by combining the strengths of each simulation domain. Specifically, SUMO provides large-scale, microscopic traffic modeling; CARLA offers high-fidelity 3D perception, vehicle dynamics, and control simulation; and OMNeT++ enables modular, event-driven network communication, such as cellular vehicle-to-everything (C-V2X). OpenCAMS employs a time-synchronized, bidirectional coupling architecture that ensures coherent simulation progression across traffic, perception, and communication domains while preserving modularity and reproducibility. For example, CARLA can simulate and render a subset of vehicles that require detailed sensor emulation and control logic; SUMO orchestrates network-wide traffic flow, vehicle routing, and traffic signal management; and OMNeT++ dynamically maps communication nodes to both mobile entities (e.g., vehicles) and static entities (e.g., roadside units) to enable C-V2X communication. While these three simulators form the foundational core of OpenCAMS, the platform is designed to be expandable and future-proof, allowing additional simulators to be integrated on top of this core without requiring fundamental changes to the system architecture. The OpenCAMS platform is fully open-source and publicly available through its GitHub repository https://github.com/minhaj6/carla-sumo-omnetpp-cosim, providing the research community with an accessible, flexible, and collaborative environment for advancing next-generation intelligent transportation systems. △ Less

Submitted 24 July, 2025; v1 submitted 12 July, 2025; originally announced July 2025.

From Chat to Checkup: Can Large Language Models Assist in Diabetes Prediction?

Authors: Shadman Sakib, Oishy Fatema Akhand, Ajwad Abrar

Abstract: While Machine Learning (ML) and Deep Learning (DL) models have been widely used for diabetes prediction, the use of Large Language Models (LLMs) for structured numerical data is still not well explored. In this study, we test the effectiveness of LLMs in predicting diabetes using zero-shot, one-shot, and three-shot prompting methods. We conduct an empirical analysis using the Pima Indian Diabetes… ▽ More While Machine Learning (ML) and Deep Learning (DL) models have been widely used for diabetes prediction, the use of Large Language Models (LLMs) for structured numerical data is still not well explored. In this study, we test the effectiveness of LLMs in predicting diabetes using zero-shot, one-shot, and three-shot prompting methods. We conduct an empirical analysis using the Pima Indian Diabetes Database (PIDD). We evaluate six LLMs, including four open-source models: Gemma-2-27B, Mistral-7B, Llama-3.1-8B, and Llama-3.2-2B. We also test two proprietary models: GPT-4o and Gemini Flash 2.0. In addition, we compare their performance with three traditional machine learning models: Random Forest, Logistic Regression, and Support Vector Machine (SVM). We use accuracy, precision, recall, and F1-score as evaluation metrics. Our results show that proprietary LLMs perform better than open-source ones, with GPT-4o and Gemma-2-27B achieving the highest accuracy in few-shot settings. Notably, Gemma-2-27B also outperforms the traditional ML models in terms of F1-score. However, there are still issues such as performance variation across prompting strategies and the need for domain-specific fine-tuning. This study shows that LLMs can be useful for medical prediction tasks and encourages future work on prompt engineering and hybrid approaches to improve healthcare predictions. △ Less

Submitted 17 June, 2025; originally announced June 2025.

Comments: Accepted in 1st IEEE QPAIN 2025

Performance Evaluation of Large Language Models in Bangla Consumer Health Query Summarization

Authors: Ajwad Abrar, Farzana Tabassum, Sabbir Ahmed

Abstract: Consumer Health Queries (CHQs) in Bengali (Bangla), a low-resource language, often contain extraneous details, complicating efficient medical responses. This study investigates the zero-shot performance of nine advanced large language models (LLMs): GPT-3.5-Turbo, GPT-4, Claude-3.5-Sonnet, Llama3-70b-Instruct, Mixtral-8x22b-Instruct, Gemini-1.5-Pro, Qwen2-72b-Instruct, Gemma-2-27b, and Athene-70B,… ▽ More Consumer Health Queries (CHQs) in Bengali (Bangla), a low-resource language, often contain extraneous details, complicating efficient medical responses. This study investigates the zero-shot performance of nine advanced large language models (LLMs): GPT-3.5-Turbo, GPT-4, Claude-3.5-Sonnet, Llama3-70b-Instruct, Mixtral-8x22b-Instruct, Gemini-1.5-Pro, Qwen2-72b-Instruct, Gemma-2-27b, and Athene-70B, in summarizing Bangla CHQs. Using the BanglaCHQ-Summ dataset comprising 2,350 annotated query-summary pairs, we benchmarked these LLMs using ROUGE metrics against Bangla T5, a fine-tuned state-of-the-art model. Mixtral-8x22b-Instruct emerged as the top performing model in ROUGE-1 and ROUGE-L, while Bangla T5 excelled in ROUGE-2. The results demonstrate that zero-shot LLMs can rival fine-tuned models, achieving high-quality summaries even without task-specific training. This work underscores the potential of LLMs in addressing challenges in low-resource languages, providing scalable solutions for healthcare query summarization. △ Less

Submitted 8 May, 2025; originally announced May 2025.

Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs

Authors: Mohsinul Kabir, Ajwad Abrar, Sophia Ananiadou

Abstract: A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment… ▽ More A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment in less constrained settings, where responses are not forced. Additionally, we show that even minor changes, such as reordering survey choices, lead to inconsistent outputs, exposing the limitations of closed-style evaluations. Our findings advocate for more robust and flexible evaluation frameworks that focus on specific cultural proxies, encouraging more nuanced and accurate assessments of cultural alignment in LLMs. △ Less

Submitted 16 September, 2025; v1 submitted 11 February, 2025; originally announced February 2025.

Comments: Accepted at EMNLP 2025 (Main)

Journal ref: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments

Authors: Hasin Jawad Ali, Ajwad Abrar, S. M. Hozaifa Hossain, M. Firoz Mridha

Abstract: In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stances. While prior studies have explored ideological stance detection in general contexts, limited attention has been given to conflict-specific settings. This study addresses this gap by analyzing 9,969 Reddit comments related to the Israel-Palestine conf… ▽ More In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stances. While prior studies have explored ideological stance detection in general contexts, limited attention has been given to conflict-specific settings. This study addresses this gap by analyzing 9,969 Reddit comments related to the Israel-Palestine conflict, collected between October 2023 and August 2024. The comments were categorized into three stance classes: Pro-Israel, Pro-Palestine, and Neutral. Various approaches, including machine learning, pre-trained language models, neural networks, and prompt engineering strategies for open source large language models (LLMs), were employed to classify these stances. Performance was assessed using metrics such as accuracy, precision, recall, and F1-score. Among the tested methods, the Scoring and Reflective Re-read prompt in Mixtral 8x7B demonstrated the highest performance across all metrics. This study provides comparative insights into the effectiveness of different models for detecting ideological stances in highly polarized social media contexts. The dataset used in this research is publicly available for further exploration and validation. △ Less

Submitted 1 February, 2025; originally announced February 2025.

Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies

Authors: Ajwad Abrar, Nafisa Tabassum Oeshy, Mohsinul Kabir, Sophia Ananiadou

Abstract: Note: This paper includes examples of potentially offensive content related to religious bias, presented solely for academic purposes. The widespread adoption of language models highlights the need for critical examinations of their inherent biases, particularly concerning religion. This study systematically investigates religious bias in both language models and text-to-image generation models, a… ▽ More Note: This paper includes examples of potentially offensive content related to religious bias, presented solely for academic purposes. The widespread adoption of language models highlights the need for critical examinations of their inherent biases, particularly concerning religion. This study systematically investigates religious bias in both language models and text-to-image generation models, analyzing both open-source and closed-source systems. We construct approximately 400 unique, naturally occurring prompts to probe language models for religious bias across diverse tasks, including mask filling, prompt completion, and image generation. Our experiments reveal concerning instances of underlying stereotypes and biases associated disproportionately with certain religions. Additionally, we explore cross-domain biases, examining how religious bias intersects with demographic factors such as gender, age, and nationality. This study further evaluates the effectiveness of targeted debiasing techniques by employing corrective prompts designed to mitigate the identified biases. Our findings demonstrate that language models continue to exhibit significant biases in both text and image generation tasks, emphasizing the urgent need to develop fairer language models to achieve global acceptability. △ Less

Submitted 14 January, 2025; originally announced January 2025.

Journal ref: AI & Society (2025)

D-Antimagic Labelings of Oriented Star Forests

Authors: Ahmad Muchlas Abrar, Rinovia Simanjuntak

Abstract: For a distance set $D$, an oriented graph $\overrightarrow{G}$ is $D$-antimagic if there exists a bijective vertex labeling such that the sum of all labels of $D$-out-neighbors is distinct for each vertex. This paper provides all orientations and all possible $D$s of a $D$-antimagic oriented star. We provide necessary and sufficient condition for $D$-antimagic oriented star forest containing isomo… ▽ More For a distance set $D$, an oriented graph $\overrightarrow{G}$ is $D$-antimagic if there exists a bijective vertex labeling such that the sum of all labels of $D$-out-neighbors is distinct for each vertex. This paper provides all orientations and all possible $D$s of a $D$-antimagic oriented star. We provide necessary and sufficient condition for $D$-antimagic oriented star forest containing isomorphic oriented stars. We show that for all possible $D$s, there exists an orientation for a star forest to admit a $D$-antimagic labeling. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Comments: 12 pages, 9 figures

MSC Class: 05C78 05C78 05C78

D-Antimagic Labelings of Oriented 2-Regular Graphs

Authors: Ahmad Muchlas Abrar, Rinovia Simanjuntak

Abstract: Given an oriented graph $\overrightarrow{G}$ and $D$ a distance set of $\overrightarrow{G}$, $\overrightarrow{G}$ is $D$-antimagic if there exists a bijective vertex labeling such that the sum of all labels of the $D$-out-neighbors of each vertex is distinct. This paper investigates $D$-antimagic labelings of 2-regular oriented graphs. We characterize $D$-antimagic oriented cycles, when $|D|=1$;… ▽ More Given an oriented graph $\overrightarrow{G}$ and $D$ a distance set of $\overrightarrow{G}$, $\overrightarrow{G}$ is $D$-antimagic if there exists a bijective vertex labeling such that the sum of all labels of the $D$-out-neighbors of each vertex is distinct. This paper investigates $D$-antimagic labelings of 2-regular oriented graphs. We characterize $D$-antimagic oriented cycles, when $|D|=1$; $D$-antimagic unidirectional odd cycles, when $|D|=2$; and $D$-antimagic $Θ$-oriented cycles. Finally, we characterize $D$-antimagic oriented 2-regular graphs, when $|D|=1$, and $D$-antimagic $Θ$-oriented 2-regular graphs. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Comments: 20 pages, 8 figures

MSC Class: 05C78

D-Antimagic Labelings on Oriented Linear Forests

Authors: Ahmad Muchlas Abrar, Rinovia Simanjuntak

Abstract: Let $\overrightarrow{G}$ be an oriented graph with the vertex set $V(\overrightarrow{G})$ and the arc set $A(\overrightarrow{G})$. Suppose that $D\subseteq \{0,1,\dots,\partial \}$ is a distance set where $\partial=\max \{d(u,v)<\infty|u,v\in V(\overrightarrow{G})\}$. Given a bijection $h:V(\overrightarrow{G}) \rightarrow\{1,2,\dots,|V(\overrightarrow{G})|\}$, the $D$-weight of a vertex… ▽ More Let $\overrightarrow{G}$ be an oriented graph with the vertex set $V(\overrightarrow{G})$ and the arc set $A(\overrightarrow{G})$. Suppose that $D\subseteq \{0,1,\dots,\partial \}$ is a distance set where $\partial=\max \{d(u,v)<\infty|u,v\in V(\overrightarrow{G})\}$. Given a bijection $h:V(\overrightarrow{G}) \rightarrow\{1,2,\dots,|V(\overrightarrow{G})|\}$, the $D$-weight of a vertex $v\in V(\overrightarrow{G})$ is defined as $ω_D(v)=\sum_{u\in N_D(v)}h(u)$, where $N_D(v)=\{u\in V|d(v,u)\in D\}$. A bijection $h$ is called a $D$-antimagic labeling if for every pair of distinct vertices $x$ and $y$, $ω_D(x)\ne ω_D(y)$. An oriented graph $\overrightarrow{G}$ is called $D$-antimagic if it admits such a labeling. In addition to introducing the notion of $D$-antimagic labeling for oriented graphs, we investigate some properties of $D$-antimagic oriented graphs. In particular, we study $D$-antimagic linear forests for some $D$. We characterize $D$-antimagic paths where $1 \in D$, $n-1\in D$, or $\{0,n-2\}\subset D$. We characterize distance antimagic trees and forests. We conclude by constructing $D$-antimagic labelings on oriented linear forests. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Comments: 16 pages, 4 figures, The International Conference on Graph Theory and Information Security VI 2024

MSC Class: 05C78; 05C12

Enhancing Transportation Cyber-Physical Systems Security: A Shift to Post-Quantum Cryptography

Authors: Abdullah Al Mamun, Akid Abrar, Mizanur Rahman, M Sabbir Salek, Mashrur Chowdhury

Abstract: The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems (TCPS). Shor's algorithm poses a significant threat to RSA and ECC, while Grover's algorithm reduces the security of symmetric encryption schemes, such as AES. The objective of this paper is to underscore the urgency of transitioning to post-quantum cryptography (PQC) to m… ▽ More The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems (TCPS). Shor's algorithm poses a significant threat to RSA and ECC, while Grover's algorithm reduces the security of symmetric encryption schemes, such as AES. The objective of this paper is to underscore the urgency of transitioning to post-quantum cryptography (PQC) to mitigate these risks in TCPS by analyzing the vulnerabilities of traditional cryptographic schemes and the applicability of standardized PQC schemes in TCPS. We analyzed vulnerabilities in traditional cryptography against quantum attacks and reviewed the applicability of NIST-standardized PQC schemes, including CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+, in TCPS. We conducted a case study to analyze the vulnerabilities of a TCPS application from the Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) service package, i.e., Electronic Toll Collection, leveraging the Microsoft Threat Modeling tool. This case study highlights the cryptographic vulnerabilities of a TCPS application and presents how PQC can effectively counter these threats. Additionally, we evaluated CRYSTALS-Kyber's performance across wired and wireless TCPS data communication scenarios. While CRYSTALS-Kyber proves effective in securing TCPS applications over high-bandwidth, low-latency Ethernet networks, our analysis highlights challenges in meeting the stringent latency requirements of safety-critical wireless applications within TCPS. Future research should focus on developing lightweight PQC solutions and hybrid schemes that integrate traditional and PQC algorithms, to enhance compatibility, scalability, and real-time performance, ensuring robust protection against emerging quantum threats in TCPS. △ Less

Submitted 19 November, 2024; originally announced November 2024.

Comments: This version has been submitted to ACM Transactions on Cyber-Physical Systems (Special Issue on Security and Privacy in Safety-Critical Cyber-Physical Systems) and is currently under peer review. Please note that the abstract in this version has been revised from the ACM-submitted version to comply with arXiv's 1920-character limit

Direct imaging of asymmetric interfaces and electrostatic potentials inside a hafnia-zirconia ferroelectric nanocapacitor

Authors: Daniel B Durham, Manifa Noor, Khandker Akif Aabrar, Yuzi Liu, Suman Datta, Kyeongjae Cho, Supratik Guha, Charudatta Phatak

Abstract: In hafnia-based thin-film ferroelectric devices, chemical phenomena during growth and processing such as oxygen vacancy formation and interfacial reactions appear to strongly affect device performance. However, the nanoscale structure, chemistry, and electrical potentials in these devices are not fully known, making it difficult to understand their influence on device properties. Here, we directly… ▽ More In hafnia-based thin-film ferroelectric devices, chemical phenomena during growth and processing such as oxygen vacancy formation and interfacial reactions appear to strongly affect device performance. However, the nanoscale structure, chemistry, and electrical potentials in these devices are not fully known, making it difficult to understand their influence on device properties. Here, we directly image the composition and electrostatic potential with nanometer resolution in the cross section of a nanocrystalline W / Hf$_{0.5}$Zr$_{0.5}$O$_{2-δ}$ (HZO) / W ferroelectric capacitor using multimodal electron microscopy. This reveals a 1.4 nm wide tungsten sub-oxide interfacial layer formed at the bottom interface during fabrication which introduces a potential dip and leads to asymmetric switching fields. Additionally, the measured inner potential in HZO is consistent with the presence of about 20% oxygen vacancies and a negative built-in potential in HZO. These chemical and electrostatic details are important to characterize and tune to achieve high performance ferroelectric devices. △ Less

Submitted 19 May, 2024; originally announced May 2024.

Comments: 20 pages, 3 figures, 1 table, 3 supporting figures

Paving the Way for Pass Disturb Free Vertical NAND Storage via A Dedicated and String-Compatible Pass Gate

Authors: Zijian Zhao, Sola Woo, Khandker Akif Aabrar, Sharadindu Gopal Kirtania, Zhouhang Jiang, Shan Deng, Yi Xiao, Halid Mulaosmanovic, Stefan Duenkel, Dominik Kleimaier, Steven Soss, Sven Beyer, Rajiv Joshi, Scott Meninger, Mohamed Mohamed, Kijoon Kim, Jongho Woo, Suhwan Lim, Kwangsoo Kim, Wanki Kim, Daewon Ha, Vijaykrishnan Narayanan, Suman Datta, Shimeng Yu, Kai Ni

Abstract: In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of the applied field by channel at the low-… ▽ More In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of the applied field by channel at the low-${V}_{TH}$ (LVT) state; ii) combined simulations and experimental demonstrations of dual-port design verify the disturb-free operation in a NAND string, overcoming a key challenge in single-port designs; iii) the proposed design can be incorporated in a highly scaled vertical NAND FeFET string and the pass gate can be incorporated into the existing 3D NAND with the negligible overhead of the pass gate interconnection through a global bottom pass gate contact in the substrate. △ Less

Submitted 7 March, 2024; originally announced March 2024.

Comments: 29 pages, 7 figures

Quantitative Ink Analysis: Estimating the Number of Inks in Documents through Hyperspectral Imaging

Authors: Aneeqa Abrar, Hamza Iqbal

Abstract: In the field of document forensics, ink analysis plays a crucial role in determining the authenticity of legal and historic documents and detecting forgery. Visual examination alone is insufficient for distinguishing visually similar inks, necessitating the use of advanced scientific techniques. This paper proposes an ink analysis technique based on hyperspectral imaging, which enables the examina… ▽ More In the field of document forensics, ink analysis plays a crucial role in determining the authenticity of legal and historic documents and detecting forgery. Visual examination alone is insufficient for distinguishing visually similar inks, necessitating the use of advanced scientific techniques. This paper proposes an ink analysis technique based on hyperspectral imaging, which enables the examination of documents in hundreds of narrowly spaced spectral bands, revealing hidden details. The main objective of this study is to identify the number of distinct inks used in a document. Three clustering algorithms, namely k-means, Agglomerative, and c-means, are employed to estimate the number of inks present. The methodology involves data extraction, ink pixel segmentation, and ink number determination. The results demonstrate the effectiveness of the proposed technique in identifying ink clusters and distinguishing between different inks. The analysis of a hyperspectral cube dataset reveals variations in spectral reflectance across different bands and distinct spectral responses among the 12 lines, indicating the presence of multiple inks. The clustering algorithms successfully identify ink clusters, with k-means clustering showing superior classification performance. These findings contribute to the development of reliable methodologies for ink analysis using hyperspectral imaging, enhancing the △ Less

Submitted 9 June, 2023; originally announced June 2023.

Minimum Trotterization Formulas for a Time-Dependent Hamiltonian

Authors: Tatsuhiko N. Ikeda, Asir Abrar, Isaac L. Chuang, Sho Sugiura

Abstract: When a time propagator $e^{δt A}$ for duration $δt$ consists of two noncommuting parts $A=X+Y$, Trotterization approximately decomposes the propagator into a product of exponentials of $X$ and $Y$. Various Trotterization formulas have been utilized in quantum and classical computers, but much less is known for the Trotterization with the time-dependent generator $A(t)$. Here, for $A(t)$ given by t… ▽ More When a time propagator $e^{δt A}$ for duration $δt$ consists of two noncommuting parts $A=X+Y$, Trotterization approximately decomposes the propagator into a product of exponentials of $X$ and $Y$. Various Trotterization formulas have been utilized in quantum and classical computers, but much less is known for the Trotterization with the time-dependent generator $A(t)$. Here, for $A(t)$ given by the sum of two operators $X$ and $Y$ with time-dependent coefficients $A(t) = x(t) X + y(t) Y$, we develop a systematic approach to derive high-order Trotterization formulas with minimum possible exponentials. In particular, we obtain fourth-order and sixth-order Trotterization formulas involving seven and fifteen exponentials, respectively, which are no more than those for time-independent generators. We also construct another fourth-order formula consisting of nine exponentials having a smaller error coefficient. Finally, we numerically benchmark the fourth-order formulas in a Hamiltonian simulation for a quantum Ising chain, showing that the 9-exponential formula accompanies smaller errors per local quantum gate than the well-known Suzuki formula. △ Less

Submitted 1 November, 2023; v1 submitted 13 December, 2022; originally announced December 2022.

Comments: 15 pages, 5 figure

Journal ref: Quantum 7, 1168 (2023)

Patients' Severity States Classification based on Electronic Health Record (EHR) Data using Multiple Machine Learning and Deep Learning Approaches

Authors: A. N. M. Sajedul Alam, Rimi Reza, Asir Abrar, Tanvir Ahmed, Salsabil Ahmed, Shihab Sharar, Annajiat Alim Rasel

Abstract: This research presents an examination of categorizing the severity states of patients based on their electronic health records during a certain time range using multiple machine learning and deep learning approaches. The suggested method uses an EHR dataset collected from an open-source platform to categorize severity. Some tools were used in this research, such as openRefine was used to pre-proce… ▽ More This research presents an examination of categorizing the severity states of patients based on their electronic health records during a certain time range using multiple machine learning and deep learning approaches. The suggested method uses an EHR dataset collected from an open-source platform to categorize severity. Some tools were used in this research, such as openRefine was used to pre-process, RapidMiner was used for implementing three algorithms (Fast Large Margin, Generalized Linear Model, Multi-layer Feed-forward Neural Network) and Tableau was used to visualize the data, for implementation of algorithms we used Google Colab. Here we implemented several supervised and unsupervised algorithms along with semi-supervised and deep learning algorithms. The experimental results reveal that hyperparameter-tuned Random Forest outperformed all the other supervised machine learning algorithms with 76% accuracy as well as Generalized Linear algorithm achieved the highest precision score 78%, whereas the hyperparameter-tuned Hierarchical Clustering with 86% precision score and Gaussian Mixture Model with 61% accuracy outperformed other unsupervised approaches. Dimensionality Reduction improved results a lot for most unsupervised techniques. For implementing Deep Learning we employed a feed-forward neural network (multi-layer) and the Fast Large Margin approach for semi-supervised learning. The Fast Large Margin performed really well with a recall score of 84% and an F1 score of 78%. Finally, the Multi-layer Feed-forward Neural Network performed admirably with 75% accuracy, 75% precision, 87% recall, 81% F1 score. △ Less

Submitted 29 September, 2022; originally announced September 2022.

Comments: 32 pages, 13 figures, and 14 tables

Collision Prediction from UWB Range Measurements

Authors: Alemayehu Solomon Abrar, Anh Luong, Gregory Spencer, Nathan Genstein, Neal Patwari, Mark Minor

Abstract: The ability to predict, and thus react to, oncoming collisions among a set of mobile agents is a fundamental requirement for safe autonomous movement, both human and robotic. This paper addresses systems that use range measurements between mobile agents for the purpose of collision prediction, which involves prediction of the agents' future paths to know if they will collide at any time. One strai… ▽ More The ability to predict, and thus react to, oncoming collisions among a set of mobile agents is a fundamental requirement for safe autonomous movement, both human and robotic. This paper addresses systems that use range measurements between mobile agents for the purpose of collision prediction, which involves prediction of the agents' future paths to know if they will collide at any time. One straightforward system would use known-location static anchors to estimate agent coordinates over time, and use the track to predict collision. Fundamentally, no fixed coordinate system is required for collision prediction, so using only the pairwise range between two agents can be used to predict collision. We present lower bound analysis which shows the limitations of this pairwise method. As an alternative anchor-free method, we propose the friend-based autonomous collision prediction and tracking (FACT) method that uses all measured ranges between nearby (unknown location mobile) agents, in a distributed algorithm, to estimate their relative locations and velocities and predict future collisions between agents. Using analysis and simulation, we show the potential for FACT to achieve equal or better collision detection performance compared to other methods, while avoiding the need for anchors. We then build a network of $N$ ultra wideband (UWB) devices and an efficient multi-node protocol which allows all ${\cal O}(N^2)$ pairwise ranges to be measured in $N$ slots. We run experiments with up to six independent robot agents moving and colliding in a 2D plane and up to four anchor nodes to compare the performance of the collision prediction methods. We show that the FACT method can perform better than either other method but without the need for a fixed infrastructure of anchor nodes. △ Less

Submitted 8 October, 2020; originally announced October 2020.

Quantifying Interference-Assisted Signal Strength Surveillance of Sound Vibrations

Authors: Alemayehu Solomon Abrar, Neal Patwari, Sneha Kumar Kasera

Abstract: A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person's vital signs, activities, and sound in their environment. This article considers an attacker who looks for subtle changes in the RSS in order to eavesdrop sound vibrations. The challenge to the adversary is that sound vib… ▽ More A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person's vital signs, activities, and sound in their environment. This article considers an attacker who looks for subtle changes in the RSS in order to eavesdrop sound vibrations. The challenge to the adversary is that sound vibrations cause very low amplitude changes in RSS, and RSS is typically quantized with a significantly larger step size. This article contributes a lower bound on an attacker's monitoring performance as a function of the RSS step size and sampling frequency so that a designer can understand their relationship. Our bound considers the little-known and counter-intuitive fact that an adversary can improve their sinusoidal parameter estimates by making some devices transmit to add interference power into the RSS measurements. We demonstrate this capability experimentally. As we show, for typical transceivers, the RSS surveillance attacker can monitor sound vibrations with remarkable accuracy. New mitigation strategies will be required to prevent RSS surveillance attacks. △ Less

Submitted 21 February, 2020; originally announced February 2020.

Comments: arXiv admin note: substantial text overlap with arXiv:1905.03939

Quantifying an Interference-Assisted Signal Strength Breathing Surveillance Attack

Authors: Alemayehu Solomon Abrar, Neal Patwari, Aniqua Baset, Sneha Kumar Kasera

Abstract: A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person's vital signs, activities, audio in their environment, and other RF sensing capabilities. This paper considers an attacker who looks for periodic changes in the RSS in order to surveil a person's breathing rate. The challe… ▽ More A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person's vital signs, activities, audio in their environment, and other RF sensing capabilities. This paper considers an attacker who looks for periodic changes in the RSS in order to surveil a person's breathing rate. The challenge to the attacker is that a person's breathing causes a low amplitude change in RSS, and RSS is typically quantized with a significantly larger step size. This paper contributes a lower bound on an attacker's breathing monitoring performance as a function of the RSS step size and sampling frequency so that a designer can understand their relationship. Our bound considers the little-known and counter-intuitive fact that an adversary can improve their sinusoidal parameter estimates by making some devices transmit to add interference power into the RSS measurements. We demonstrate this capability experimentally. As we show, for typical transceivers, the RSS surveillance attack can monitor RSS with remarkable accuracy. New mitigation strategies will be required to prevent RSS surveillance attacks. △ Less

Submitted 10 May, 2019; originally announced May 2019.

Save Our Spectrum: Contact-Free Human Sensing Using Single Carrier Radio

Authors: Alemayehu Solomon Abrar, Anh Luong, Peter Hillyard, Neal Patwari

Abstract: Recent research has demonstrated new capabilities in radio frequency (RF) sensing that apply to health care, smart home, and security applications. However, previous work in RF sensing requires heavy utilization of the radio spectrum, for example, transmitting thousands of WiFi packets per second. In this paper, we present a device-free human sensing system based on received signal strength (RSS)… ▽ More Recent research has demonstrated new capabilities in radio frequency (RF) sensing that apply to health care, smart home, and security applications. However, previous work in RF sensing requires heavy utilization of the radio spectrum, for example, transmitting thousands of WiFi packets per second. In this paper, we present a device-free human sensing system based on received signal strength (RSS) measurements from a low-cost single carrier narrowband radio transceiver. We test and validate its performance in three different applications: real-time heart rate monitoring, gesture recognition, and human speed estimation. The challenges in these applications stem from the very low signal-to-noise ratio and the use of a single-dimensional measurement of the channel. We apply a combination of linear and non-linear filtering, and time-frequency analysis, and develop new estimators to address the challenges in the particular applications. Our experimental results indicate that RF sensing based on single-carrier magnitude measurements performs nearly as well as the state-of-the-art while utilizing three orders of magnitude less bandwidth. △ Less

Submitted 25 November, 2018; originally announced November 2018.

Comments: 13 pages

Application of DenseNet in Camera Model Identification and Post-processing Detection

Authors: Abdul Muntakim Rafi, Uday Kamal, Rakibul Hoque, Abid Abrar, Sowmitra Das, Robert Laganière, Md. Kamrul Hasan

Abstract: Camera model identification has earned paramount importance in the field of image forensics with an upsurge of digitally altered images which are constantly being shared through websites, media, and social applications. But, the task of identification becomes quite challenging if metadata are absent from the image and/or if the image has been post-processed. In this paper, we present a DenseNet pi… ▽ More Camera model identification has earned paramount importance in the field of image forensics with an upsurge of digitally altered images which are constantly being shared through websites, media, and social applications. But, the task of identification becomes quite challenging if metadata are absent from the image and/or if the image has been post-processed. In this paper, we present a DenseNet pipeline to solve the problem of identifying the source camera-model of an image. Our approach is to extract patches of 256*256 from a labeled image dataset and apply augmentations, i.e., Empirical Mode Decomposition (EMD). We use this extended dataset to train a Neural Network with the DenseNet-201 architecture. We concatenate the output features for 3 different sizes (64*64, 128*128, 256*256) and pass them to a secondary network to make the final prediction. This strategy proves to be very robust for identifying the source camera model, even when the original image is post-processed. Our model has been trained and tested on the Forensic Camera-Model Identification Dataset provided for the IEEE Signal Processing (SP) Cup 2018. During testing we achieved an overall accuracy of 98.37%, which is the current state-of-the-art on this dataset using a single model. We used transfer learning and tested our model on the Dresden Database for Camera Model Identification, with an overall test accuracy of over 99% for 19 models. In addition, we demonstrate that the proposed pipeline is suitable for other image-forensic classification tasks, such as, detecting the type of post-processing applied to an image with an accuracy of 96.66% -- which indicates the generality of our approach. △ Less

Submitted 27 May, 2019; v1 submitted 3 September, 2018; originally announced September 2018.

Comparing Respiratory Monitoring Performance of Commercial Wireless Devices

Authors: Peter Hillyard, Anh Luong, Alemayehu Solomon Abrar, Neal Patwari, Krishna Sundar, Robert Farney, Jason Burch, Christina A. Porucznik, Sarah Hatch Pollard

Abstract: This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight slee… ▽ More This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable. △ Less

Submitted 17 July, 2018; originally announced July 2018.