Showing 1–50 of 66 results for author: M, A

CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning

Authors: Ajmal M., Abin Roy, Afthab Salam Kanniyan, Jawadh Abdul Kabeer, Jerin James, Preslav Nakov, Zhuohan Xie

Abstract: Large Language Models (LLMs) achieve strong results on many medical benchmarks, but their clinical reasoning remains difficult to evaluate reliably. A central risk is an evaluation illusion: fluent and well-structured explanations can appear clinically convincing even when the final diagnosis is incorrect. We introduce CLExEval, a human-in-the-loop framework for evaluating LLM clinical reasoning u… ▽ More Large Language Models (LLMs) achieve strong results on many medical benchmarks, but their clinical reasoning remains difficult to evaluate reliably. A central risk is an evaluation illusion: fluent and well-structured explanations can appear clinically convincing even when the final diagnosis is incorrect. We introduce CLExEval, a human-in-the-loop framework for evaluating LLM clinical reasoning under progressive information masking. CLExEval combines 5,600 expert-physician annotations with 200 clinical reasoning traces derived from 40 rare diagnostic cases. Our analysis identifies three recurring failure patterns: (i) verbosity bias, where GPT-4o-mini's diagnostic accuracy drops from 95.0% to 32.5% under information scarcity; (ii) a hidden knowledge paradox, where a specialist model reaches 92.5% maximum diagnostic potential but fails to retrieve that knowledge reliably in verbose contexts; and (iii) a 68.6% reasoning-to-output mismatch, where correct diagnoses appear in reasoning traces but are not reflected in final answers. We further evaluate the LLM-as-a-Judge paradigm on a human-verified failure set (n = 142). GPT-4o-mini approved 47.9% of clinically incorrect outputs, while HuatuoGPT-o1 approved all validly scored failures and showed a positive self-preference bias. These results suggest that standalone automated clinical evaluations can substantially overestimate clinical reliability without expert-grounded validation. △ Less

Submitted 30 June, 2026; originally announced June 2026.

Comments: 21 pages, 12 figures

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Authors: Aritra Roy, Kevin Shen, Andrew MacBride, Awwal Oladipupo, Mudassra Taskeen, Wojtek Treyde, Ruaa A. E. A. Abakar, Ahmad D. Abbas, Elsayed Abdelfatah, Abbas A. Abdullahi, Seham S. Abyah, Chahd Rahyl Adjmi, Fariha Agbere, Savyasanchi Aggarwal, Muhammad Ahmed, Tasnim Ahmed, Motasem Ajlouni, Mattias Akke, Hussein AlAdwan, Anwaar S. Alazani, Zahra A. Alharbi, Wajd A. Aljulyhi, Mohammed A. AlKubaish, Fatima A. Almahri, Sayed A. Almohri , et al. (328 additional authors not shown)

Abstract: Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle. We organize the projects into two complementary categori… ▽ More Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle. We organize the projects into two complementary categories: Knowledge Infrastructure, systems that structure, retrieve, synthesize, and validate scientific information; and Action Systems, systems that execute, coordinate, or automate scientific work across computational and experimental environments. The submissions reveal a shift from single-purpose LLM tools toward integrated, multi-agent workflows that combine retrieval, reasoning, tool use, and domain-specific validation. Prominent themes include retrieval-augmented generation as grounding infrastructure, persistent structured knowledge representations, multimodal and multilingual scientific inputs, and early progress toward laboratory-integrated closed-loop systems. Together, these results suggest that LLMs are evolving from general-purpose assistants into composable infrastructure for scientific reasoning and action. This work provides a community snapshot of that transition and a practical taxonomy for understanding emerging LLM-enabled workflows in materials science and chemistry. △ Less

Submitted 4 May, 2026; originally announced May 2026.

Comments: This paper reflects contributions from hundreds of researchers worldwide through an event, follow-on discussions, and project development exploring LLM applications in materials science and chemistry. While unconventional, it captures a timely, broad, and efficient community exploration of a rapidly evolving field and offers value to the arXiv community

Chaotic CNN for Limited Data Image Classification

Authors: Anusree M, Akhila Henry, Pramod P Nair

Abstract: Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature diversity. In this work, a simple and effective chaos-based feature transformation is proposed to enhance CNN performance without increasing model complexity. The method applies nonlinear transformations using logistic, skew tent, and sine maps to no… ▽ More Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature diversity. In this work, a simple and effective chaos-based feature transformation is proposed to enhance CNN performance without increasing model complexity. The method applies nonlinear transformations using logistic, skew tent, and sine maps to normalised feature vectors before the classification layer, thereby reshaping the feature space and improving class separability. The approach is evaluated on greyscale datasets (MNIST and Fashion-MNIST) and an RGB dataset (CIFAR-10) using CNN architectures of varying depth under limited data conditions. The results show consistent improvement over the standalone (SA) CNN across all datasets. Notably, a maximum performance gain of 5.43% is achieved on MNIST using the skew tent map with a 3-layer CNN at 40 samples per class. A higher gain of 9.11% is observed on Fashion-MNIST using the sine map with a 3-layer CNN at 50 samples per class. Additionally, a strong gain of 7.47% is obtained on CIFAR-10 using the skew tent map at 200 samples per class. The consistent improvements across different chaotic maps indicate that the performance gain is driven by the shared nonlinear and dynamical properties of chaotic systems. The proposed method is computationally efficient, requires no additional trainable parameters, and can be easily integrated into existing CNN architectures, making it a practical solution for data-scarce image classification tasks. △ Less

Submitted 16 April, 2026; originally announced April 2026.

The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview

Authors: Zheng Chen, Kai Liu, Jingkai Wang, Xianglong Yan, Jianze Li, Ziqing Zhang, Jue Gong, Jiatong Li, Lei Sun, Xiaoyang Liu, Radu Timofte, Yulun Zhang, Jihye Park, Yoonjin Im, Hyungju Chun, Hyunhee Park, MinKyu Park, Zheng Xie, Xiangyu Kong, Weijun Yuan, Zhan Li, Qiurong Song, Luen Zhu, Fengkai Zhang, Xinzhe Zhu , et al. (128 additional authors not shown)

Abstract: This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective super-resolution solutions and analyze… ▽ More This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective super-resolution solutions and analyze recent advances in the field. To reflect the evolving objectives of image super-resolution, the challenge includes two tracks: (1) a restoration track, which emphasizes pixel-wise fidelity and ranks submissions based on PSNR; and (2) a perceptual track, which focuses on visual realism and evaluates results using a perceptual score. A total of 194 participants registered for the challenge, with 31 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, main results, and methods of participating teams. The challenge provides a unified benchmark and offers insights into current progress and future directions in image super-resolution. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: NTIRE 2026 webpage: https://cvlai.net/ntire/2026. Code: https://github.com/zhengchen1999/NTIRE2026_ImageSR_x4

Influence of Geometry, Class Imbalance and Alignment on Reconstruction Accuracy -- A Micro-CT Phantom-Based Evaluation

Authors: Avinash Kumar K M, Samarth S. Raut

Abstract: The accuracy of the 3D models created from medical scans depends on imaging hardware, segmentation methods and mesh processing techniques etc. The effects of geometry type, class imbalance, voxel and point cloud alignment on accuracy remain to be thoroughly explored. This work evaluates the errors across the reconstruction pipeline and explores the use of voxel and surface-based accuracy metrics f… ▽ More The accuracy of the 3D models created from medical scans depends on imaging hardware, segmentation methods and mesh processing techniques etc. The effects of geometry type, class imbalance, voxel and point cloud alignment on accuracy remain to be thoroughly explored. This work evaluates the errors across the reconstruction pipeline and explores the use of voxel and surface-based accuracy metrics for different segmentation algorithms and geometry types. A sphere, a facemask, and an AAA were printed using the SLA technique and scanned using a micro-CT machine. Segmentation was performed using GMM, Otsu and RG based methods. Segmented and reference models aligned using the KU algorithm, were quantitatively compared to evaluate metrics like Dice and Jaccard scores, precision. Surface meshes were registered with reference meshes using an ICP-based alignment process. Metrics like chamfer distance, and average Hausdorff distance were evaluated. The Otsu method was found to be the most suitable method for all the geometries. AAA yielded low overlap scores due to its small wall thickness and misalignment. The effect of class imbalance on specificity was observed the most for AAA. Surface-based accuracy metrics differed from the voxel-based trends. The RG method performed best for sphere, while GMM and Otsu perform better for AAA. The facemask surface was most error-prone, possibly due to misalignment during the ICP process. Segmentation accuracy is a cumulative sum of errors across different stages of the reconstruction process. High voxel-based accuracy metrics may be misleading in cases of high class imbalance and sensitivity to alignment. The Jaccard index is found to be more stringent than the Dice and more suitable for accuracy assessment for thin-walled structures. Voxel and point cloud alignment should be ensured to make any reliable assessment of the reconstruction pipeline. △ Less

Submitted 7 February, 2026; originally announced February 2026.

Comments: 22 pages, 13 figures

Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks

Authors: Abanoub M. Girgis, Ibtissam Labriji, Mehdi Bennis

Abstract: In wireless networked control systems, ensuring timely and reliable state updates from distributed devices to remote controllers is essential for robust control performance. However, when multiple devices transmit high-dimensional states (e.g., images or video frames) over bandwidth-limited wireless networks, a critical trade-off emerges between communication efficiency and control performance. To… ▽ More In wireless networked control systems, ensuring timely and reliable state updates from distributed devices to remote controllers is essential for robust control performance. However, when multiple devices transmit high-dimensional states (e.g., images or video frames) over bandwidth-limited wireless networks, a critical trade-off emerges between communication efficiency and control performance. To address this challenge, we propose a Hierarchical Joint-Embedding Predictive Architecture (H-JEPA) for scalable predictive control. Instead of transmitting states, device observations are encoded into low-dimensional embeddings that preserve essential dynamics. The proposed architecture employs a three-level hierarchical prediction, with high-level, medium-level, and low-level predictors operating across different temporal resolutions, to achieve long-term prediction stability, intermediate interpolation, and fine-grained refinement, respectively. Control actions are derived within the embedding space, removing the need for state reconstruction. Simulation results on inverted cart-pole systems demonstrate that H-JEPA enables up to 42.83 % more devices to be supported under limited wireless capacity without compromising control performance. △ Less

Submitted 28 January, 2026; originally announced February 2026.

Metric Hub: A metric library and practical selection workflow for use-case-driven data quality assessment in medical AI

Authors: Katinka Becker, Maximilian P. Oppelt, Tobias S. Zech, Martin Seyferth, Sandie Cabon, Vanja Miskovic, Ivan Cimrak, Michal Kozubek, Giuseppe D'Avenio, Ilaria Campioni, Jana Fehr, Kanjar De, Ismail Mahmoudi, Emilio Dolgener Cantu, Laurenz Ottmann, Andreas Klaß, Galaad Altares, Jackie Ma, Alireza Salehi M., Nadine R. Lang-Richter, Tobias Schaeffter, Daniel Schwabe

Abstract: Machine learning (ML) in medicine has transitioned from research to concrete applications aimed at supporting several medical purposes like therapy selection, monitoring and treatment. Acceptance and effective adoption by clinicians and patients, as well as regulatory approval, require evidence of trustworthiness. A major factor for the development of trustworthy AI is the quantification of data q… ▽ More Machine learning (ML) in medicine has transitioned from research to concrete applications aimed at supporting several medical purposes like therapy selection, monitoring and treatment. Acceptance and effective adoption by clinicians and patients, as well as regulatory approval, require evidence of trustworthiness. A major factor for the development of trustworthy AI is the quantification of data quality for AI model training and testing. We have recently proposed the METRIC-framework for systematically evaluating the suitability (fit-for-purpose) of data for medical ML for a given task. Here, we operationalize this theoretical framework by introducing a collection of data quality metrics - the metric library - for practically measuring data quality dimensions. For each metric, we provide a metric card with the most important information, including definition, applicability, examples, pitfalls and recommendations, to support the understanding and implementation of these metrics. Furthermore, we discuss strategies and provide decision trees for choosing an appropriate set of data quality metrics from the metric library given specific use cases. We demonstrate the impact of our approach exemplarily on the PTB-XL ECG-dataset. This is a first step to enable fit-for-purpose evaluation of training and test data in practice as the base for establishing trustworthy AI in medicine. △ Less

Submitted 30 January, 2026; originally announced January 2026.

Self-Training the Neurochaos Learning Algorithm

Authors: Anusree M, Akhila Henry, Pramod P Nair

Abstract: In numerous practical applications, acquiring substantial quantities of labelled data is challenging and expensive, but unlabelled data is readily accessible. Conventional supervised learning methods frequently underperform in scenarios characterised by little labelled data or imbalanced datasets. This study introduces a hybrid semi-supervised learning (SSL) architecture that integrates Neurochaos… ▽ More In numerous practical applications, acquiring substantial quantities of labelled data is challenging and expensive, but unlabelled data is readily accessible. Conventional supervised learning methods frequently underperform in scenarios characterised by little labelled data or imbalanced datasets. This study introduces a hybrid semi-supervised learning (SSL) architecture that integrates Neurochaos Learning (NL) with a threshold-based Self-Training (ST) method to overcome this constraint. The NL architecture converts input characteristics into chaos-based ring-rate representations that encapsulate nonlinear relationships within the data, whereas ST progressively enlarges the labelled set utilising high-confidence pseudo-labelled samples. The model's performance is assessed using ten benchmark datasets and five machine learning classifiers, with 85% of the training data considered unlabelled and just 15% utilised as labelled data. The proposed Self-Training Neurochaos Learning (NL+ST) architecture consistently attains superior performance gain relative to standalone ST models, especially on limited, nonlinear and imbalanced datasets like Iris (188.66%), Wine (158.58%) and Glass Identification (110.48%). The results indicate that using chaos-based feature extraction with SSL improves generalisation, resilience, and classification accuracy in low-data contexts. △ Less

Submitted 3 January, 2026; originally announced January 2026.

Virtual Garbage Collector (VGC): A Zone-Based Garbage Collection Architecture for Python's Parallel Runtime

Authors: Abdulla M

Abstract: The Virtual Garbage Collector (VGC) proposes a zone-based memory management architecture aimed at improving execution predictability and memory behavior in Python runtimes. The design explores a dual-layer model consisting of an Active VGC, responsible for managing runtime object lifecycles, and a Passive VGC, intended as a compile-time optimization layer for static allocation planning. Rather tha… ▽ More The Virtual Garbage Collector (VGC) proposes a zone-based memory management architecture aimed at improving execution predictability and memory behavior in Python runtimes. The design explores a dual-layer model consisting of an Active VGC, responsible for managing runtime object lifecycles, and a Passive VGC, intended as a compile-time optimization layer for static allocation planning. Rather than relying on traditional heap traversal or generational heuristics, VGC introduces memory zoning and checkpoint-based state evaluation to reduce allocation churn and constrain garbage collection scope. Execution partitioning is experimentally evaluated to isolate workloads and localize memory pressure, enabling more deterministic behavior under loop-intensive, recursive, and compute-heavy workloads. This work presents the architectural principles, execution model, and experimental observations of VGC within a partition-aware runtime context. While the full realization of the dual-layer design is an ongoing effort, the results indicate that zone-based allocation and partitioned execution provide a viable foundation for improving scalability and memory predictability in Python-oriented systems. △ Less

Submitted 1 January, 2026; v1 submitted 29 December, 2025; originally announced December 2025.

Comments: 30 pages, 5 figures. Primary category cs.PL, secondary cs.DC

Quadrupped-Legged Robot Movement Plan Generation using Large Language Model

Authors: Muhtadin, Vincentius Gusti Putu A. B. M., Ahmad Zaini, Mauridhi Hery Purnomo, I Ketut Eddy Purnama, Chastine Fatichah

Abstract: Traditional control interfaces for quadruped robots often impose a high barrier to entry, requiring specialized technical knowledge for effective operation. To address this, this paper presents a novel control framework that integrates Large Language Models (LLMs) to enable intuitive, natural language-based navigation. We propose a distributed architecture where high-level instruction processing i… ▽ More Traditional control interfaces for quadruped robots often impose a high barrier to entry, requiring specialized technical knowledge for effective operation. To address this, this paper presents a novel control framework that integrates Large Language Models (LLMs) to enable intuitive, natural language-based navigation. We propose a distributed architecture where high-level instruction processing is offloaded to an external server to overcome the onboard computational constraints of the DeepRobotics Jueying Lite 3 platform. The system grounds LLM-generated plans into executable ROS navigation commands using real-time sensor fusion (LiDAR, IMU, and Odometry). Experimental validation was conducted in a structured indoor environment across four distinct scenarios, ranging from single-room tasks to complex cross-zone navigation. The results demonstrate the system's robustness, achieving an aggregate success rate of over 90\% across all scenarios, validating the feasibility of offloaded LLM-based planning for autonomous quadruped deployment in real-world settings. △ Less

Submitted 24 December, 2025; originally announced December 2025.

Journal ref: 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

A Novel 8T SRAM-Based In-Memory Computing Architecture for MAC-Derived Logical Functions

Authors: Amogh K M, Sunita M S

Abstract: This paper presents an in-memory computing (IMC) architecture developed on an 8x8 array of 8T SRAM cells. This architecture enables both multi-bit parallel Multiply-Accumulate (MAC) operations and standard memory processing through charge-sharing on dedicated read bit-lines. By leveraging the maturity of SRAM technology, this work introduces an 8T SRAM-based IMC architecture that decouples read an… ▽ More This paper presents an in-memory computing (IMC) architecture developed on an 8x8 array of 8T SRAM cells. This architecture enables both multi-bit parallel Multiply-Accumulate (MAC) operations and standard memory processing through charge-sharing on dedicated read bit-lines. By leveraging the maturity of SRAM technology, this work introduces an 8T SRAM-based IMC architecture that decouples read and write paths, thereby overcoming the reliability limitations of prior 6T SRAM designs. A novel analog-to-digital decoding scheme converts the MAC voltage output into digital counts, which are subsequently interpreted to realize fundamental logic functions including AND/NAND, NOR/OR, XOR/XNOR, and 1-bit addition within the same array. Simulated in a 90 nm CMOS process at 1.8 V supply voltage, the proposed design achieves 8-bit MAC and logical operations at a frequency of 142.85 MHz, with a latency of 0.7 ns and energy consumption of 56.56 fJ/bit per MAC operation and throughput of 15.8 M operations/s. △ Less

Submitted 29 November, 2025; originally announced December 2025.

Comments: 6 pages, 6 figures, Accepted at 39th VLSID 2026 conference

The Riddle of Reflection: Evaluating Reasoning and Self-Awareness in Multilingual LLMs using Indian Riddles

Authors: Abhinav P M, Ojasva Saxena, Oswald C, Parameswari Krishnamurthy

Abstract: The extent to which large language models (LLMs) can perform culturally grounded reasoning across non-English languages remains underexplored. This paper examines the reasoning and self-assessment abilities of LLMs across seven major Indian languages-Bengali, Gujarati, Hindi, Kannada, Malayalam, Tamil, and Telugu. We introduce a multilingual riddle dataset combining traditional riddles with contex… ▽ More The extent to which large language models (LLMs) can perform culturally grounded reasoning across non-English languages remains underexplored. This paper examines the reasoning and self-assessment abilities of LLMs across seven major Indian languages-Bengali, Gujarati, Hindi, Kannada, Malayalam, Tamil, and Telugu. We introduce a multilingual riddle dataset combining traditional riddles with context-reconstructed variants and evaluate five LLMs-Gemini 2.5 Pro, Gemini 2.5 Flash, Mistral-Saba, LLaMA 4 Scout, and LLaMA 4 Maverick-under seven prompting strategies. In the first stage, we assess riddle-solving performance and find that while Gemini 2.5 Pro performs best overall, few-shot methods yield only marginal gains, and accuracy varies notably across languages. In the second stage, we conduct a self-evaluation experiment to measure reasoning consistency. The results reveal a key finding: a model's initial accuracy is inversely correlated with its ability to identify its own mistakes. Top-performing models such as Gemini 2.5 Pro are overconfident (4.34% True Negative Rate), whereas lower-performing models like LLaMA 4 Scout are substantially more self-aware (42.09% True Negative Rate). These results point to clear gaps in multilingual reasoning and highlight the need for models that not only reason effectively but also recognize their own limitations. △ Less

Submitted 4 November, 2025; v1 submitted 2 November, 2025; originally announced November 2025.

Bare-Metal RISC-V + NVDLA SoC for Efficient Deep Learning Inference

Authors: Vineet Kumar, Ajay Kumar M, Yike Li, Shreejith Shanker, Deepu John

Abstract: This paper presents a novel System-on-Chip (SoC) architecture for accelerating complex deep learning models for edge computing applications through a combination of hardware and software optimisations. The hardware architecture tightly couples the open-source NVIDIA Deep Learning Accelerator (NVDLA) to a 32-bit, 4-stage pipelined RISC-V core from Codasip called uRISC_V. To offload the model accele… ▽ More This paper presents a novel System-on-Chip (SoC) architecture for accelerating complex deep learning models for edge computing applications through a combination of hardware and software optimisations. The hardware architecture tightly couples the open-source NVIDIA Deep Learning Accelerator (NVDLA) to a 32-bit, 4-stage pipelined RISC-V core from Codasip called uRISC_V. To offload the model acceleration in software, our toolflow generates bare-metal application code (in assembly), overcoming complex OS overheads of previous works that have explored similar architectures. This tightly coupled architecture and bare-metal flow leads to improvements in execution speed and storage efficiency, making it suitable for edge computing solutions. We evaluate the architecture on AMD's ZCU102 FPGA board using NVDLA-small configuration and test the flow using LeNet-5, ResNet-18 and ResNet-50 models. Our results show that these models can perform inference in 4.8 ms, 16.2 ms and 1.1 s respectively, at a system clock frequency of 100 MHz. △ Less

Submitted 18 November, 2025; v1 submitted 22 August, 2025; originally announced August 2025.

Comments: Accepted paper in 2025 IEEE 38th International System-on-Chip Conference (SOCC)

Fracture Detection and Localisation in Wrist and Hand Radiographs using Detection Transformer Variants

Authors: Aditya Bagri, Vasanthakumar Venugopal, Anandakumar D, Revathi Ezhumalai, Kalyan Sivasailam, Bargava Subramanian, VarshiniPriya, Meenakumari K S, Abi M, Renita S

Abstract: Background: Accurate diagnosis of wrist and hand fractures using radiographs is essential in emergency care, but manual interpretation is slow and prone to errors. Transformer-based models show promise in improving medical image analysis, but their application to extremity fractures is limited. This study addresses this gap by applying object detection transformers to wrist and hand X-rays. Meth… ▽ More Background: Accurate diagnosis of wrist and hand fractures using radiographs is essential in emergency care, but manual interpretation is slow and prone to errors. Transformer-based models show promise in improving medical image analysis, but their application to extremity fractures is limited. This study addresses this gap by applying object detection transformers to wrist and hand X-rays. Methods: We fine-tuned the RT-DETR and Co-DETR models, pre-trained on COCO, using over 26,000 annotated X-rays from a proprietary clinical dataset. Each image was labeled for fracture presence with bounding boxes. A ResNet-50 classifier was trained on cropped regions to refine abnormality classification. Supervised contrastive learning was used to enhance embedding quality. Performance was evaluated using AP@50, precision, and recall metrics, with additional testing on real-world X-rays. Results: RT-DETR showed moderate results (AP@50 = 0.39), while Co-DETR outperformed it with an AP@50 of 0.615 and faster convergence. The integrated pipeline achieved 83.1% accuracy, 85.1% precision, and 96.4% recall on real-world X-rays, demonstrating strong generalization across 13 fracture types. Visual inspection confirmed accurate localization. Conclusion: Our Co-DETR-based pipeline demonstrated high accuracy and clinical relevance in wrist and hand fracture detection, offering reliable localization and differentiation of fracture types. It is scalable, efficient, and suitable for real-time deployment in hospital workflows, improving diagnostic speed and reliability in musculoskeletal radiology. △ Less

Submitted 19 August, 2025; originally announced August 2025.

Comments: 18 pages, 21 figures

MSC Class: 68T45 ACM Class: I.2.10

MARVEL: An End-to-End Framework for Generating Model-Class Aware Custom RISC-V Extensions for Lightweight AI

Authors: Ajay Kumar M, Cian O'Mahoney, Pedro Kreutz Werle, Shreejith Shanker, Dimitrios S. Nikolopoulos, Bo Ji, Hans Vandierendonck, Deepu John

Abstract: Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Existing accelerator-generation tools, such as AMD's FINN, do not adequately address extreme resource limitations faced by IoT endpoints operating in bare-metal environments without an operating system (OS). To overcome th… ▽ More Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Existing accelerator-generation tools, such as AMD's FINN, do not adequately address extreme resource limitations faced by IoT endpoints operating in bare-metal environments without an operating system (OS). To overcome these constraints, we propose MARVEL-an automated, end-to-end framework that generates custom RISC-V ISA extensions tailored to specific DNN model classes, with a primary focus on convolutional neural networks (CNNs). The proposed method profiles high-level DNN representations in Python and generates an ISA-extended RISC-V core with associated compiler tools for efficient deployment. The flow leverages (1) Apache TVM for translating high-level Python-based DNN models into optimized C code, (2) Synopsys ASIP Designer for identifying compute-intensive kernels, modeling, and generating a custom RISC-V and (3) Xilinx Vivado for FPGA implementation. Beyond a model class specific RISC-V, our approach produces an optimized bare-metal C implementation, eliminating the need for an OS or extensive software dependencies. Unlike conventional deployment pipelines relying on TensorFlow/PyTorch runtimes, our solution enables seamless execution in highly resource-constrained environments. We evaluated the flow on popular DNN models such as LeNet-5*, MobileNetV1, ResNet50, VGG16, MobileNetV2 and DenseNet121 using the Synopsys trv32p3 RISC-V core as a baseline. Results show a 2x speedup in inference and upto 2x reduction in energy per inference at a 28.23% area overhead when implemented on an AMD Zynq UltraScale+ ZCU104 FPGA platform. △ Less

Submitted 3 August, 2025; originally announced August 2025.

Comments: To be published in IEEE Open Journal of Circuits and Systems

The Amazon Nova Family of Models: Technical Report and Model Card

Authors: Amazon AGI, Aaron Langford, Aayush Shah, Abhanshu Gupta, Abhimanyu Bhatter, Abhinav Goyal, Abhinav Mathur, Abhinav Mohanty, Abhishek Kumar, Abhishek Sethi, Abi Komma, Abner Pena, Achin Jain, Adam Kunysz, Adam Opyrchal, Adarsh Singh, Aditya Rawal, Adok Achar Budihal Prasad, Adrià de Gispert, Agnika Kumar, Aishwarya Aryamane, Ajay Nair, Akilan M, Akshaya Iyengar, Akshaya Vishnu Kudlu Shanbhogue , et al. (761 additional authors not shown)

Abstract: We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents… ▽ More We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents and text. Amazon Nova Micro is a text-only model that delivers our lowest-latency responses at very low cost. Amazon Nova Canvas is an image generation model that creates professional grade images with rich customization controls. Amazon Nova Reel is a video generation model offering high-quality outputs, customization, and motion control. Our models were built responsibly and with a commitment to customer trust, security, and reliability. We report benchmarking results for core capabilities, agentic performance, long context, functional adaptation, runtime performance, and human evaluation. △ Less

Submitted 17 March, 2025; originally announced June 2025.

Comments: 48 pages, 10 figures

Report number: 20250317

Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases

Authors: K. A. Muthukumar, Dhruva Nandi, Priya Ranjan, Krithika Ramachandran, Shiny PJ, Anirban Ghosh, Ashwini M, Aiswaryah Radhakrishnan, V. E. Dhandapani, Rajiv Janardhanan

Abstract: Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular net… ▽ More Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover's Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT's spectral insights and EMD's capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large. △ Less

Submitted 31 March, 2025; originally announced April 2025.

Comments: EMD, Fundus image, CNN, CVD prediction

Journal ref: Sci Rep 15, 4390 (2025)

ViKANformer: Embedding Kolmogorov Arnold Networks in Vision Transformers for Pattern-Based Learning

Authors: Shreyas S, Akshath M

Abstract: Vision Transformers (ViTs) have significantly advanced image classification by applying self-attention on patch embeddings. However, the standard MLP blocks in each Transformer layer may not capture complex nonlinear dependencies optimally. In this paper, we propose ViKANformer, a Vision Transformer where we replace the MLP sub-layers with Kolmogorov-Arnold Network (KAN) expansions, including Vani… ▽ More Vision Transformers (ViTs) have significantly advanced image classification by applying self-attention on patch embeddings. However, the standard MLP blocks in each Transformer layer may not capture complex nonlinear dependencies optimally. In this paper, we propose ViKANformer, a Vision Transformer where we replace the MLP sub-layers with Kolmogorov-Arnold Network (KAN) expansions, including Vanilla KAN, Efficient-KAN, Fast-KAN, SineKAN, and FourierKAN, while also examining a Flash Attention variant. By leveraging the Kolmogorov-Arnold theorem, which guarantees that multivariate continuous functions can be expressed via sums of univariate continuous functions, we aim to boost representational power. Experimental results on MNIST demonstrate that SineKAN, Fast-KAN, and a well-tuned Vanilla KAN can achieve over 97% accuracy, albeit with increased training overhead. This trade-off highlights that KAN expansions may be beneficial if computational cost is acceptable. We detail the expansions, present training/test accuracy and F1/ROC metrics, and provide pseudocode and hyperparameters for reproducibility. Finally, we compare ViKANformer to a simple MLP and a small CNN baseline on MNIST, illustrating the efficiency of Transformer-based methods even on a small-scale dataset. △ Less

Submitted 2 March, 2025; originally announced March 2025.

Comments: This paper represents ongoing research and may be subject to revisions, refinements, and additional experiments in future updates

Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning

Authors: Nandakishor M, Anjali M

Abstract: Creating personalized and adaptable conversational AI remains a key challenge. This paper introduces a Continuous Learning Conversational AI (CLCA) approach, implemented using A2C reinforcement learning, to move beyond static Large Language Models (LLMs). We use simulated sales dialogues, generated by LLMs, to train an A2C agent. This agent learns to optimize conversation strategies for personaliz… ▽ More Creating personalized and adaptable conversational AI remains a key challenge. This paper introduces a Continuous Learning Conversational AI (CLCA) approach, implemented using A2C reinforcement learning, to move beyond static Large Language Models (LLMs). We use simulated sales dialogues, generated by LLMs, to train an A2C agent. This agent learns to optimize conversation strategies for personalization, focusing on engagement and delivering value. Our system architecture integrates reinforcement learning with LLMs for both data creation and response selection. This method offers a practical way to build personalized AI companions that evolve through continuous learning, advancing beyond traditional static LLM techniques. △ Less

Submitted 18 February, 2025; originally announced February 2025.

High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2

Authors: Nandakishor M, Anjali M

Abstract: Electrocardiogram (ECG) interpretation is a cornerstone of cardiac diagnostics. This paper explores a practical approach to enhance ECG image interpretation using the multimodal LLaMA 3.2 model. We used a parameter-efficient fine-tuning strategy, Low-Rank Adaptation (LoRA), specifically designed to boost the model's ability to understand ECG images and achieve better outcomes across a wide range o… ▽ More Electrocardiogram (ECG) interpretation is a cornerstone of cardiac diagnostics. This paper explores a practical approach to enhance ECG image interpretation using the multimodal LLaMA 3.2 model. We used a parameter-efficient fine-tuning strategy, Low-Rank Adaptation (LoRA), specifically designed to boost the model's ability to understand ECG images and achieve better outcomes across a wide range of cardiac conditions. Our method is tailored for ECG analysis and leverages ECGInstruct, a large-scale instruction dataset with 1 Million samples. This dataset is a rich collection of synthesized ECG images, generated from raw ECG data from trusted open-source repositories like MIMIC-IV ECG and PTB-XL. Each ECG image in ECGInstruct comes with expert-written questions and detailed answers, covering diverse ECG interpretation scenarios, including complex cardiac conditions like Myocardial Infarction and Conduction Disturbances. Our fine-tuning approach efficiently adapts the LLaMA 3.2 model (built upon LLaMA 3) by integrating low-rank adaptation techniques, focusing on efficiency by updating only a small set of parameters, specifically ignoring the `lm_head` and `embed_tokens` layers. This paper details the model setup, our efficient fine-tuning method, and implementation specifics. We provide a thorough evaluation through extensive experiments, demonstrating the effectiveness of our method across various ECG interpretation tasks. The results convincingly show that our parameter-efficient LoRA fine-tuning achieves excellent performance in ECG image interpretation, significantly outperforming baseline models and reaching accuracy comparable to or exceeding traditional CNN-based methods in identifying a wide range of cardiac abnormalities, including over 70 conditions from the PTB-XL dataset. △ Less

Submitted 30 January, 2025; originally announced January 2025.

A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Information

Authors: Anuprabha M, Krishna Gurugubelli, V Kesavaraj, Anil Kumar Vuppala

Abstract: Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing cross-attention mechanism, our method learns the acoustic and linguistic similarities between speech… ▽ More Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing cross-attention mechanism, our method learns the acoustic and linguistic similarities between speech and text representations. This approach assesses specifically the pronunciation deviations across different severity levels, thereby enhancing the accuracy of dysarthric detection and severity assessment. All the experiments have been performed using UA-Speech dysarthric database. Improved accuracies of 99.53% and 93.20% in detection, and 98.12% and 51.97% for severity assessment have been achieved when speaker-dependent and speaker-independent, unseen and seen words settings are used. These findings suggest that by integrating text information, which provides a reference linguistic knowledge, a more robust framework has been developed for dysarthric detection and assessment, thereby potentially leading to more effective diagnoses. △ Less

Submitted 26 April, 2025; v1 submitted 22 December, 2024; originally announced December 2024.

Comments: Submitted to ICASSP 2025

Report number: 10889515

Journal ref: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5

Simultaneous Communication and Tracking using Fused Bistatic Measurements

Authors: Avinash M, Srikrishna Bhashyam

Abstract: In this paper, we propose a bistatic sensing-assisted beam tracking method for simultaneous communication and tracking of user vehicles navigating arbitrary-shaped road trajectories. Prior work on simultaneous communication and tracking assumes a colocated radar receiver at the transmitter for sensing measurements using the reflected Integrated Sensing and Communication (ISAC) signals in the mmWav… ▽ More In this paper, we propose a bistatic sensing-assisted beam tracking method for simultaneous communication and tracking of user vehicles navigating arbitrary-shaped road trajectories. Prior work on simultaneous communication and tracking assumes a colocated radar receiver at the transmitter for sensing measurements using the reflected Integrated Sensing and Communication (ISAC) signals in the mmWave band. Full isolation between transmitter and receiver is required here to avoid self-interference. We consider the bistatic setting where the sensing receivers are not colocated and can be realized in practice using traditional half-duplex transmit or receive nodes. First, we process the echoes reflected from the vehicle at multiple multi-antenna nodes at various locations, facilitating estimation of the vehicle's current position. Then, we propose selection criteria for the estimates and a maximum likelihood (ML) fusion scheme to fuse these selected estimates based on the estimated error covariance matrices of these measurements. This fusion scheme is important in bistatic and multistatic settings as the localization error depends significantly on the geometry of the transmitter, target, and receiver locations. Finally, we predict the vehicle's next location using a simple kinematic equation-based model. Through extensive simulation, we study the average spectral efficiency of communication with a moving user using the proposed simultaneous communication and tracking scheme. The proposed fusion-based scheme achieves almost the same average spectral efficiency as an ideal scheme that knows the exact trajectory. We also show that the proposed scheme can be easily extended to systems with Hybrid Digital-Analog architectures and performs similarly even in these systems. △ Less

Submitted 20 November, 2024; originally announced November 2024.

IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery

Authors: Dincy R. Arikkat, Abhinav M., Navya Binu, Parvathi M., Navya Biju, K. S. Arunima, Vinod P., Rafidha Rehiman K. A., Mauro Conti

Abstract: In the rapidly evolving landscape of cyber security, intelligent chatbots are gaining prominence. Artificial Intelligence, Machine Learning, and Natural Language Processing empower these chatbots to handle user inquiries and deliver threat intelligence. This helps cyber security knowledge readily available to both professionals and the public. Traditional rule-based chatbots often lack flexibility… ▽ More In the rapidly evolving landscape of cyber security, intelligent chatbots are gaining prominence. Artificial Intelligence, Machine Learning, and Natural Language Processing empower these chatbots to handle user inquiries and deliver threat intelligence. This helps cyber security knowledge readily available to both professionals and the public. Traditional rule-based chatbots often lack flexibility and struggle to adapt to user interactions. In contrast, Large Language Model-based chatbots offer contextually relevant information across multiple domains and adapt to evolving conversational contexts. In this work, we develop IntellBot, an advanced cyber security Chatbot built on top of cutting-edge technologies like Large Language Models and Langchain alongside a Retrieval-Augmented Generation model to deliver superior capabilities. This chatbot gathers information from diverse data sources to create a comprehensive knowledge base covering known vulnerabilities, recent cyber attacks, and emerging threats. It delivers tailored responses, serving as a primary hub for cyber security insights. By providing instant access to relevant information and resources, this IntellBot enhances threat intelligence, incident response, and overall security posture, saving time and empowering users with knowledge of cyber security best practices. Moreover, we analyzed the performance of our copilot using a two-stage evaluation strategy. We achieved BERT score above 0.8 by indirect approach and a cosine similarity score ranging from 0.8 to 1, which affirms the accuracy of our copilot. Additionally, we utilized RAGAS to evaluate the RAG model, and all evaluation metrics consistently produced scores above 0.77, highlighting the efficacy of our system. △ Less

Submitted 8 November, 2024; originally announced November 2024.

Aerodynamic Study of Leading-Edge Protuberance to Improve the Performance of NACA 0009 Blade

Authors: Chaitanya Kumar Konda, Vidyashankar. S, Ulavish. V. S, Sachin. A. M, Mahesh. K. Varpe

Abstract: Symmetric NACA airfoils tend to undergo abrupt stall characteristics at higher angle of attacks. The abrupt stall has deteriorating effect on lift as well as the efficiency of the airfoils. Abruptness in stall restricts the airfoil to operate only at lower angle of attacks. So, in order to improve the efficiency of airfoils at higher angle of attacks and make it suitable for operation over higher… ▽ More Symmetric NACA airfoils tend to undergo abrupt stall characteristics at higher angle of attacks. The abrupt stall has deteriorating effect on lift as well as the efficiency of the airfoils. Abruptness in stall restricts the airfoil to operate only at lower angle of attacks. So, in order to improve the efficiency of airfoils at higher angle of attacks and make it suitable for operation over higher range of angle of attacks, there are many flow control techniques. One such technique is addition of leading-edge protuberance. Leading-edge protuberances are the leading-edge modification of the wing. Leading-edge of the wing is modified with sinusoidal structural modification. This modification has two parameters i.e., Pitch and Amplitude. Many configurations of the protuberances can be obtained by changing the Pitch to Amplitude ratio of the protuberance. In the present work, the Reynolds number is 50k for NACA 0009. The Pitch to Amplitude ratio is varied from PAR1 to PAR27. PAR6 is found to be the better case which has higher lift and efficiency in the post-stall angle of attacks. At the deep stalling AOA of the baseline, i.e., at 13.6o, PAR6 is found to have the highest increase in lift and efficiency compared to the other post stalling AOAs with it having around 39.6% more lift and 27.3% more efficiency compared to the baseline. △ Less

Submitted 29 October, 2024; originally announced October 2024.

Comments: 12 Pages, 16 figures

Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics

Authors: Abanoub M. Girgis, Hyowoon Seo, Mehdi Bennis

Abstract: This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is… ▽ More This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules. These models form the proposed logical Koopman AE framework that reduces communication costs while improving state prediction accuracy and control performance, showing a 91.65% reduction in communication samples and significant performance gains in simulation. △ Less

Submitted 5 December, 2025; v1 submitted 3 October, 2024; originally announced October 2024.

Machine Translation with Large Language Models: Decoder Only vs. Encoder-Decoder

Authors: Abhinav P. M., SujayKumar Reddy M, Oswald Christopher

Abstract: This project, titled "Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder," aims to develop a multilingual machine translation (MT) model. Focused on Indian regional languages, especially Telugu, Tamil, and Malayalam, the model seeks to enable accurate and contextually appropriate translations across diverse language pairs. By comparing Decoder-only and Encoder-Decoder… ▽ More This project, titled "Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder," aims to develop a multilingual machine translation (MT) model. Focused on Indian regional languages, especially Telugu, Tamil, and Malayalam, the model seeks to enable accurate and contextually appropriate translations across diverse language pairs. By comparing Decoder-only and Encoder-Decoder architectures, the project aims to optimize translation quality and efficiency, advancing cross-linguistic communication tools.The primary objective is to develop a model capable of delivering high-quality translations that are accurate and contextually appropriate. By leveraging large language models, specifically comparing the effectiveness of Decoder-only and Encoder-Decoder architectures, the project seeks to optimize translation performance and efficiency across multilingual contexts. Through rigorous experimentation and analysis, this project aims to advance the field of machine translation, contributing valuable insights into the effectiveness of different model architectures and paving the way for enhanced cross-linguistic communication tools. △ Less

Submitted 11 September, 2024; originally announced September 2024.

Early Detection of Coronary Heart Disease Using Hybrid Quantum Machine Learning Approach

Authors: Mehroush Banday, Sherin Zafar, Parul Agarwal, M Afshar Alam, Abubeker K M

Abstract: Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various d… ▽ More Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. A quantum leap in the healthcare industry will increase processing power and optimise multiple models. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilizing an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method's robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. The marked rise in heart disease and death rates impacts worldwide human health and the global economy. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilizes techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional machine learning algorithms and to minimize computational expenses. The proposed method has been developed in the Raspberry Pi 5 Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher. △ Less

Submitted 1 October, 2024; v1 submitted 17 September, 2024; originally announced September 2024.

Comments: I found a mistake in methodology presentation. Also I have observed more precised results with new dataset. So my research guide ask me to modify the current version

KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer

Authors: Aness Al-Qawlaq, Ajay Kumar M, Deepu John

Abstract: This paper explores the adaptation of Transformerbased models for edge devices through the quantisation and hardware acceleration of the ARM Keyword Transformer (KWT) model on a RISC-V platform. The model was targeted to run on 64kB RAM in bare-metal C using a custom-developed edge AI library. KWT-1 was retrained to be 369 times smaller, with only a 10% loss in accuracy through reducing output cla… ▽ More This paper explores the adaptation of Transformerbased models for edge devices through the quantisation and hardware acceleration of the ARM Keyword Transformer (KWT) model on a RISC-V platform. The model was targeted to run on 64kB RAM in bare-metal C using a custom-developed edge AI library. KWT-1 was retrained to be 369 times smaller, with only a 10% loss in accuracy through reducing output classes from 35 to 2. The retraining and quantisation reduced model size from 2.42 MB to 1.65 kB. The integration of custom RISC-V instructions that accelerated GELU and SoftMax operations enabled a 5x speedup and thus ~5x power reduction in inference, with inference clock cycle counts decreasing from 26 million to 5.5 million clock cycles while incurring a small area overhead of approximately 29%. The results demonstrate a viable method for porting and accelerating Transformer-based models in low-power IoT devices. △ Less

Submitted 19 November, 2025; v1 submitted 22 July, 2024; originally announced July 2024.

Comments: 6 pages, 7 figures, published in the IEEE SOCC 2024 conference

A Channel Attention-Driven Hybrid CNN Framework for Paddy Leaf Disease Detection

Authors: Pandiyaraju V, Shravan Venkatraman, Abeshek A, Pavan Kumar S, Aravintakshan S A, Senthil Kumar A M, Kannan A

Abstract: Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early and accurate disease identification is important in agriculture to avoid crop loss and improve cultivation. In this research, we propose a novel hybrid deep learning (DL) classifier designed by extending the Squeeze-and… ▽ More Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early and accurate disease identification is important in agriculture to avoid crop loss and improve cultivation. In this research, we propose a novel hybrid deep learning (DL) classifier designed by extending the Squeeze-and-Excitation network architecture with a channel attention mechanism and the Swish ReLU activation function. The channel attention mechanism in our proposed model identifies the most important feature channels required for classification during feature extraction and selection. The dying ReLU problem is mitigated by utilizing the Swish ReLU activation function, and the Squeeze-andExcitation blocks improve information propagation and cross-channel interaction. Upon evaluation, our model achieved a high F1-score of 99.76% and an accuracy of 99.74%, surpassing the performance of existing models. These outcomes demonstrate the potential of state-of-the-art DL techniques in agriculture, contributing to the advancement of more efficient and reliable disease detection systems. △ Less

Submitted 16 July, 2024; originally announced July 2024.

Comments: 17 pages, 4 tables, 10 figures

ACM Class: F.2.2; I.2.7

Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models

Authors: Manas Jhalani, Annervaz K M, Pushpak Bhattacharyya

Abstract: In the realm of multimodal tasks, Visual Question Answering (VQA) plays a crucial role by addressing natural language questions grounded in visual content. Knowledge-Based Visual Question Answering (KBVQA) advances this concept by adding external knowledge along with images to respond to questions. We introduce an approach for KBVQA, augmenting the existing vision-language transformer encoder-deco… ▽ More In the realm of multimodal tasks, Visual Question Answering (VQA) plays a crucial role by addressing natural language questions grounded in visual content. Knowledge-Based Visual Question Answering (KBVQA) advances this concept by adding external knowledge along with images to respond to questions. We introduce an approach for KBVQA, augmenting the existing vision-language transformer encoder-decoder (OFA) model. Our main contribution involves enhancing questions by incorporating relevant external knowledge extracted from knowledge graphs, using a dynamic triple extraction method. We supply a flexible number of triples from the knowledge graph as context, tailored to meet the requirements for answering the question. Our model, enriched with knowledge, demonstrates an average improvement of 4.75\% in Exact Match Score over the state-of-the-art on three different KBVQA datasets. Through experiments and analysis, we demonstrate that furnishing variable triples for each question improves the reasoning capabilities of the language model in contrast to supplying a fixed number of triples. This is illustrated even for recent large language models. Additionally, we highlight the model's generalization capability by showcasing its SOTA-beating performance on a small dataset, achieved through straightforward fine-tuning. △ Less

Submitted 14 June, 2024; originally announced June 2024.

Comments: 16 pages, 12 figures

Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks

Authors: Abanoub M. Girgis, Alvaro Valcarce, Mehdi Bennis

Abstract: In remote control systems, transmitting large data volumes (e.g., images, video frames) from wireless sensors to remote controllers is challenging when uplink capacity is limited (e.g., RedCap devices or massive wireless sensor networks). Furthermore, controllers often need only information-rich representations of the original data. To address this, we propose a semantic-driven predictive control… ▽ More In remote control systems, transmitting large data volumes (e.g., images, video frames) from wireless sensors to remote controllers is challenging when uplink capacity is limited (e.g., RedCap devices or massive wireless sensor networks). Furthermore, controllers often need only information-rich representations of the original data. To address this, we propose a semantic-driven predictive control combined with a channel-aware scheduling to enhance control performance for multiple devices under limited network capacity. At its core, the proposed framework, coined Time-Series Joint Embedding Predictive Architecture (TS-JEPA), encodes high-dimensional sensory data into low-dimensional semantic embeddings at the sensor, reducing communication overhead. Furthermore, TS-JEPA enables predictive inference by predicting future embeddings from current ones and predicted commands, which are directly used by a semantic actor model to compute control commands within the embedding space, eliminating the need to reconstruct raw data. To further enhance reliability and communication efficiency, a channel-aware scheduling is integrated to dynamically prioritize device transmissions based on channel conditions and age of information (AoI). Simulations on inverted cart-pole systems show that the proposed framework significantly outperforms conventional control baselines in communication efficiency, control cost, and predictive accuracy. It enables robust and scalable control under limited network capacity compared to traditional scheduling schemes. △ Less

Submitted 2 July, 2025; v1 submitted 7 June, 2024; originally announced June 2024.

End-to-End User-Defined Keyword Spotting using Shifted Delta Coefficients

Authors: Kesavaraj V, Anuprabha M, Anil Kumar Vuppala

Abstract: Identifying user-defined keywords is crucial for personalizing interactions with smart devices. Previous approaches of user-defined keyword spotting (UDKWS) have relied on short-term spectral features such as mel frequency cepstral coefficients (MFCC) to detect the spoken keyword. However, these features may face challenges in accurately identifying closely related pronunciation of audio-text pair… ▽ More Identifying user-defined keywords is crucial for personalizing interactions with smart devices. Previous approaches of user-defined keyword spotting (UDKWS) have relied on short-term spectral features such as mel frequency cepstral coefficients (MFCC) to detect the spoken keyword. However, these features may face challenges in accurately identifying closely related pronunciation of audio-text pairs, due to their limited capability in capturing the temporal dynamics of the speech signal. To address this challenge, we propose to use shifted delta coefficients (SDC) which help in capturing pronunciation variability (transition between connecting phonemes) by incorporating long-term temporal information. The performance of the SDC feature is compared with various baseline features across four different datasets using a cross-attention based end-to-end system. Additionally, various configurations of SDC are explored to find the suitable temporal context for the UDKWS task. The experimental results reveal that the SDC feature outperforms the MFCC baseline feature, exhibiting an improvement of 8.32% in area under the curve (AUC) and 8.69% in terms of equal error rate (EER) on the challenging Libriphrase-hard dataset. Moreover, the proposed approach demonstrated superior performance when compared to state-of-the-art UDKWS techniques. △ Less

Submitted 23 May, 2024; originally announced May 2024.

Towards Unsupervised Question Answering System with Multi-level Summarization for Legal Text

Authors: M Manvith Prabhu, Haricharana Srinivasa, Anand Kumar M

Abstract: This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-level fusion of Legal-Bert embeddings using… ▽ More This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-level fusion of Legal-Bert embeddings using ensemble features, including CNN, GRU, and LSTM. To address the lengthy nature of Legal explanation in the dataset, we introduce T5-based segment-wise summarization, which successfully retained crucial information, enhancing the model's performance. Our unsupervised system witnessed a 20-point increase in macro F1-score on the development set and a 10-point increase on the test set, which is promising given its uncomplicated architecture. △ Less

Submitted 1 July, 2024; v1 submitted 19 March, 2024; originally announced March 2024.

Comments: 6 pages, 2 figures

Journal ref: In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval 2024), pages 193 to 199, Mexico City, Mexico. Association for Computational Linguistics

Density and Affinity Dependent Social Segregation and Arbitrage Equilibrium in a Multi-class Schelling Game

Authors: Venkat Venkatasubramanian, Jessica Shi, Leo Goldman, Arun Sankar E. M., Abhishek Sivaram

Abstract: Contrary to the widely believed hypothesis that larger, denser cities promote socioeconomic mixing, a recent study (Nilforoshan et al. 2023) reports the opposite behavior, i.e. more segregation. Here, we present a game-theoretic model that predicts such a density-dependent segregation outcome in both one- and two-class systems. The model provides key insights into the analytical conditions that le… ▽ More Contrary to the widely believed hypothesis that larger, denser cities promote socioeconomic mixing, a recent study (Nilforoshan et al. 2023) reports the opposite behavior, i.e. more segregation. Here, we present a game-theoretic model that predicts such a density-dependent segregation outcome in both one- and two-class systems. The model provides key insights into the analytical conditions that lead to such behavior. Furthermore, the arbitrage equilibrium outcome implies the equality of effective utilities among all agents. This could be interpreted as all agents being equally "happy" in their respective environments in our ideal society. We believe that our model contributes towards a deeper mathematical understanding of social dynamics and behavior, which is important as we strive to develop more harmonious societies. △ Less

Submitted 6 March, 2024; originally announced March 2024.

Comments: arXiv admin note: text overlap with arXiv:2312.05765

Airavata: Introducing Hindi Instruction-tuned LLM

Authors: Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain, Aswanth Kumar M, Mohammed Safi Ur Rahman Khan, Diptesh Kanojia, Ratish Puduppully, Mitesh M. Khapra, Raj Dabre, Rudra Murthy, Anoop Kunchukuttan

Abstract: We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additional… ▽ More We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at https://ai4bharat.github.io/airavata. △ Less

Submitted 26 February, 2024; v1 submitted 26 January, 2024; originally announced January 2024.

Comments: Work in progress

Smart safety watch for elderly people and pregnant women

Authors: Balachandra D S, Maithreyee M S, Saipavan B M, Shashank S, P Devaki, Ms. Ashwini M

Abstract: Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the Internet of Things (IoT) has made human-computer interaction through sensor fusion an acknowledged and potent approach for tackling the issue of fall detection. Ev… ▽ More Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the Internet of Things (IoT) has made human-computer interaction through sensor fusion an acknowledged and potent approach for tackling the issue of fall detection. Even IoT-enabled systems can deliver economical health monitoring solutions tailored to pregnant women within their daily environments. Recent research indicates that these remote health monitoring setups have the potential to enhance the well-being of both the mother and the infant throughout the pregnancy and postpartum phases. One more emerging advancement is the integration of 'panic buttons,' which are gaining popularity due to the escalating emphasis on safety. These buttons instantly transmit the user's real-time location to pre-designated emergency contacts when activated. Our solution focuses on the above three challenges we see every day. Fall detection for the elderly helps the elderly in case they fall and have nobody around for help. Sleep pattern sensing is helpful for pregnant women based on the SPO2 sensors integrated within our device. It is also bundled with heart rate monitoring. Our third solution focuses on a panic situation; upon pressing the determined buttons, a panic alert would be sent to the emergency contacts listed. The device also comes with a mobile app developed using Flutter that takes care of all the heavy processing rather than the device itself. △ Less

Submitted 3 December, 2023; originally announced December 2023.

Analysis of system capacity and spectral efficiency of fixed-grid network

Authors: Adarsha M, S. Malathi, Santosh Kumar

Abstract: In this article, the performance of a fixed grid network is examined for various modulation formats to estimate the system's capacity and spectral efficiency. The optical In-phase Quadrature Modulator structure is used to build a fixed grid network modulation, and the homodyne detection approach is used for the receiver. Data multiplexing is accomplished using the Polarization Division Multiplexed… ▽ More In this article, the performance of a fixed grid network is examined for various modulation formats to estimate the system's capacity and spectral efficiency. The optical In-phase Quadrature Modulator structure is used to build a fixed grid network modulation, and the homodyne detection approach is used for the receiver. Data multiplexing is accomplished using the Polarization Division Multiplexed technology. 100 Gbps, 150 Gbps, and 200 Gbps data rates are transmitted under these circumstances utilizing various modulation formats. Various pre-processing and signal recovery steps are explained by using modern digital signal processing systems. The achieved spectrum efficiencies for PM-QPSK, PM-8 QAM, and PM-16 QAM, respectively, were 2, 3, and 4 bits/s/Hz. Different modulation like PM-QPSK, PM-8-QAM, and PM-16-QAM each has system capacities of 8-9, 12-13.5, and 16-18 Tbps and it reaches transmission distances of 3000, 1300, and 700 kilometers with acceptable Bit Error Rate less than equal to 2*10-3 respectively. Peak optical power for received signal detection and full width at half maximum is noted for the different modulations under a fixed grind network. △ Less

Submitted 30 September, 2023; originally announced October 2023.

Journal ref: International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.5, September 2023

Immersive Virtual Reality Platform for Robot-Assisted Antenatal Ultrasound Scanning

Authors: Shyam A, Aparna Purayath, Keerthivasan S, Akash S M, Aswathaman Govindaraju, Manojkumar Lakshmanan, Mohanasankar Sivaprakasam

Abstract: Maternal health remains a pervasive challenge in developing and underdeveloped countries. Inadequate access to basic antenatal Ultrasound (US) examinations, limited resources such as primary health services and infrastructure, and lack of skilled healthcare professionals are the major concerns. To improve the quality of maternal care, robot-assisted antenatal US systems with teleoperable and auton… ▽ More Maternal health remains a pervasive challenge in developing and underdeveloped countries. Inadequate access to basic antenatal Ultrasound (US) examinations, limited resources such as primary health services and infrastructure, and lack of skilled healthcare professionals are the major concerns. To improve the quality of maternal care, robot-assisted antenatal US systems with teleoperable and autonomous capabilities were introduced. However, the existing teleoperation systems rely on standard video stream-based approaches that are constrained by limited immersion and scene awareness. Also, there is no prior work on autonomous antenatal robotic US systems that automate standardized scanning protocols. To that end, this paper introduces a novel Virtual Reality (VR) platform for robotic antenatal ultrasound, which enables sonologists to control a robotic arm over a wired network. The effectiveness of the system is enhanced by providing a reconstructed 3D view of the environment and immersing the user in a VR space. Also, the system facilitates a better understanding of the anatomical surfaces to perform pragmatic scans using 3D models. Further, the proposed robotic system also has autonomous capabilities; under the supervision of the sonologist, it can perform the standard six-step approach for obstetric US scanning recommended by the ISUOG. Using a 23-week fetal phantom, the proposed system was demonstrated to technology and academia experts at MEDICA 2022 as a part of the KUKA Innovation Award. The positive feedback from them supports the feasibility of the system. It also gave an insight into the improvisations to be carried out to make it a clinically viable system. △ Less

Submitted 7 September, 2023; originally announced September 2023.

Comments: The paper was accepted and presented at IEEE ROMAN 2023

On Rotation Distance of Rank Bounded Trees

Authors: Anoop S. K. M., Jayalal Sarma

Abstract: Computing the rotation distance between two binary trees with $n$ internal nodes efficiently (in $poly(n)$ time) is a long standing open question in the study of height balancing in tree data structures. In this paper, we initiate the study of this problem bounding the rank of the trees given at the input (defined by Ehrenfeucht and Haussler (1989) in the context of decision trees). We define the… ▽ More Computing the rotation distance between two binary trees with $n$ internal nodes efficiently (in $poly(n)$ time) is a long standing open question in the study of height balancing in tree data structures. In this paper, we initiate the study of this problem bounding the rank of the trees given at the input (defined by Ehrenfeucht and Haussler (1989) in the context of decision trees). We define the rank-bounded rotation distance between two given binary trees $T_1$ and $T_2$ (with $n$ internal nodes) of rank at most $r$, denoted by $d_r(T_1,T_2)$, as the length of the shortest sequence of rotations that transforms $T_1$ to $T_2$ with the restriction that the intermediate trees must be of rank at most $r$. We show that the rotation distance problem reduces in polynomial time to the rank bounded rotation distance problem. This motivates the study of the problem in the combinatorial and algorithmic frontiers. Observing that trees with rank $1$ coincide exactly with skew trees (binary trees where every internal node has at least one leaf as a child), we show the following results in this frontier : We present an $O(n^2)$ time algorithm for computing $d_1(T_1,T_2)$. That is, when the given trees are skew trees (we call this variant as skew rotation distance problem) - where the intermediate trees are restricted to be skew as well. In particular, our techniques imply that for any two skew trees $d(T_1,T_2) \le n^2$. We show the following upper bound : for any two trees $T_1$ and $T_2$ of rank at most $r_1$ and $r_2$ respectively, we have that: $d_r(T_1,T_2) \le n^2 (1+(2n+1)(r_1+r_2-2))$ where $r = max\{r_1,r_2\}$. This bound is asymptotically tight for $r=1$. En route our proof of the above theorems, we associate binary trees to permutations and bivariate polynomials, and prove several characterizations in the case of skew trees. △ Less

Submitted 10 May, 2024; v1 submitted 8 April, 2023; originally announced April 2023.

Comments: 28 pages, 2 figures, Abstract shortened to meet arxiv requirements, accepted journal version

Journal ref: Fundamenta Informaticae, Volume 191, Issue 2 (July 8, 2024) fi:11200

BriFiSeg: a deep learning-based method for semantic and instance segmentation of nuclei in brightfield images

Authors: Gendarme Mathieu, Lambert Annika M., El Debs Bachir

Abstract: Generally, microscopy image analysis in biology relies on the segmentation of individual nuclei, using a dedicated stained image, to identify individual cells. However stained nuclei have drawbacks like the need for sample preparation, and specific equipment on the microscope but most importantly, and as it is in most cases, the nuclear stain is not relevant to the biological questions of interest… ▽ More Generally, microscopy image analysis in biology relies on the segmentation of individual nuclei, using a dedicated stained image, to identify individual cells. However stained nuclei have drawbacks like the need for sample preparation, and specific equipment on the microscope but most importantly, and as it is in most cases, the nuclear stain is not relevant to the biological questions of interest but is solely used for the segmentation task. In this study, we used non-stained brightfield images for nuclei segmentation with the advantage that they can be acquired on any microscope from both live or fixed samples and do not necessitate specific sample preparation. Nuclei semantic segmentation from brightfield images was obtained, on four distinct cell lines with U-Net-based architectures. We tested systematically deep pre-trained encoders to identify the best performing in combination with the different neural network architectures used. Additionally, two distinct and effective strategies were employed for instance segmentation, followed by thorough instance evaluation. We obtained effective semantic and instance segmentation of nuclei in brightfield images from standard test sets as well as from very diverse biological contexts triggered upon treatment with various small molecule inhibitor. The code used in this study was made public to allow further use by the community. △ Less

Submitted 6 November, 2022; originally announced November 2022.

Learning Classifiers for Imbalanced and Overlapping Data

Authors: Shivaditya Shivganesh, Nitin Narayanan N, Pranav Murali, Ajaykumar M

Abstract: This study is about inducing classifiers using data that is imbalanced, with a minority class being under-represented in relation to the majority classes. The first section of this research focuses on the main characteristics of data that generate this problem. Following a study of previous, relevant research, a variety of artificial, imbalanced data sets influenced by important elements were crea… ▽ More This study is about inducing classifiers using data that is imbalanced, with a minority class being under-represented in relation to the majority classes. The first section of this research focuses on the main characteristics of data that generate this problem. Following a study of previous, relevant research, a variety of artificial, imbalanced data sets influenced by important elements were created. These data sets were used to create decision trees and rule-based classifiers. The second section of this research looks into how to improve classifiers by pre-processing data with resampling approaches. The results of the following trials are compared to the performance of distinct pre-processing re-sampling methods: two variants of random over-sampling and focused under-sampling NCR. This paper further optimises class imbalance with a new method called Sparsity. The data is made more sparse from its class centers, hence making it more homogenous. △ Less

Submitted 22 October, 2022; originally announced October 2022.

Predictive Closed-Loop Remote Control over Wireless Two-Way Split Koopman Autoencoder

Authors: Abanoub M. Girgis, Hyowoon Seo, Jihong Park, Mehdi Bennis, Jinho Choi

Abstract: Real-time remote control over wireless is an important-yet-challenging application in 5G and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultra-reliable and low-latency communication (URLLC) links but also predicting future states, which may consume enormous communication resources and struggle with a short predictio… ▽ More Real-time remote control over wireless is an important-yet-challenging application in 5G and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultra-reliable and low-latency communication (URLLC) links but also predicting future states, which may consume enormous communication resources and struggle with a short prediction time horizon. To fill this void, in this article we propose a novel two-way Koopman autoencoder (AE) approach wherein: 1) a sensing Koopman AE learns to understand the temporal state dynamics and predicts missing packets from a sensor to its remote controller; and 2) a controlling Koopman AE learns to understand the temporal action dynamics and predicts missing packets from the controller to an actuator co-located with the sensor. Specifically, each Koopman AE aims to learn the Koopman operator in the hidden layers while the encoder of the AE aims to project the non-linear dynamics onto a lifted subspace, which is reverted into the original non-linear dynamics by the decoder of the AE. The Koopman operator describes the linearized temporal dynamics, enabling long-term future prediction and coping with missing packets and closed-form optimal control in the lifted subspace. Simulation results corroborate that the proposed approach achieves a 38x lower mean squared control error at 0 dBm signal-to-noise ratio (SNR) than the non-predictive baseline. △ Less

Submitted 14 September, 2022; originally announced September 2022.

Representation Learning in Continuous-Time Dynamic Signed Networks

Authors: Kartik Sharma, Mohit Raghavendra, Yeon Chang Lee, Anand Kumar M, Srijan Kumar

Abstract: Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i.e., link signs and signed weights) in the future. However,… ▽ More Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i.e., link signs and signed weights) in the future. However, existing works have modeled either (static) signed networks or dynamic (unsigned) networks but not dynamic signed networks. Since both sign and dynamics inform the graph structure in different ways, it is non-trivial to model how to combine the two features. In this work, we propose a new Graph Neural Network (GNN)-based approach to model dynamic signed networks, named SEMBA: Signed link's Evolution using Memory modules and Balanced Aggregation. Here, the idea is to incorporate the signs of temporal interactions using separate modules guided by balance theory and to evolve the embeddings from a higher-order neighborhood. Experiments on 4 real-world datasets and 4 different tasks demonstrate that SEMBA consistently and significantly outperforms the baselines by up to $80\%$ on the tasks of predicting signs of future links while matching the state-of-the-art performance on predicting the existence of these links in the future. We find that this improvement is due specifically to the superior performance of SEMBA on the minority negative class. △ Less

Submitted 5 February, 2023; v1 submitted 7 July, 2022; originally announced July 2022.

Interference-Aware Accurate Signal Recovery in sub-1 GHz UHF Band Reuse-1 Cellular OFDMA Downlinks

Authors: Abhay Mohan M V, Giridhar K

Abstract: Reuse-1 systems operating in the sub-1 GHz UHF band are limited by substantial co-channel interference (CCI). In such orthogonal frequency division multiple access (OFDMA) cellular systems, the inter-sector or inter-tower interference (ITI) makes accurate signal recovery quite challenging as sub-1 GHz bands only support single-input single-output (SISO) links. Interference-aware receiver algorithm… ▽ More Reuse-1 systems operating in the sub-1 GHz UHF band are limited by substantial co-channel interference (CCI). In such orthogonal frequency division multiple access (OFDMA) cellular systems, the inter-sector or inter-tower interference (ITI) makes accurate signal recovery quite challenging as sub-1 GHz bands only support single-input single-output (SISO) links. Interference-aware receiver algorithms are essential to mitigate the ITI in such low-frequency bands. Such algorithms enable ubiquitous mobile broadband access over the entire homeland, say with >95% geographical coverage with quality of service guarantees. One element of the interference-aware signal recovery is the least-squares-based joint channel estimation scheme that uses non-orthogonal pilot subcarriers. This estimator is then compared with a variant that uses orthogonal pilot subcarriers to bring out the advantage of this joint estimator. It is shown that the proposed joint estimator requires fewer pilots to be well-determined when compared to its under-determined orthogonal counterpart. Moreover, it is easy to implement and does not require any knowledge of channel statistics. This work also derives a compensation factor needed for the interference-aware detector in the presence of inter-carrier interference (ICI) originating from multiple transmitters. Simulation results show that the proposed joint channel estimator outperforms traditional estimators at moderate to high frequency selectivity. The proposed compensation factor to the joint detector is found to be essential for recovering the transmitted signal in the absence of phase-tracking pilots. △ Less

Submitted 15 November, 2022; v1 submitted 1 August, 2021; originally announced August 2021.

Comments: in IEEE Open Journal of the Communications Society, 2022

Journal ref: IEEE Open Journal of the Communications Society, 2022

Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights

Authors: Vivek Khetan, Annervaz K M, Erin Wetherley, Elena Eneva, Shubhashis Sengupta, Andrew E. Fano

Abstract: The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules and regulations, failure to identify changes can be costly. In contrast to expert analysis or the development of domain-specific ontology and taxonomies, we use a task-based approach for fulfilling specific information n… ▽ More The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules and regulations, failure to identify changes can be costly. In contrast to expert analysis or the development of domain-specific ontology and taxonomies, we use a task-based approach for fulfilling specific information needs within a new domain. Specifically, we propose to extract task-based information from incoming instance data. A pipeline constructed of state of the art NLP technologies, including a bi-LSTM-CRF model for entity extraction, attention-based deep Semantic Role Labeling, and an automated verb-based relationship extractor, is used to automatically extract an instance level semantic structure. Each instance is then combined with a larger, domain-specific knowledge graph to produce new and timely insights. Preliminary results, validated manually, show the methodology to be effective for extracting specific information to complete end use-cases. △ Less

Submitted 19 April, 2021; v1 submitted 18 April, 2021; originally announced April 2021.

ACM Class: I.2.7

Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction

Authors: Abanoub M. Girgis, Hyowoon Seo, Jihong Park, Mehdi Bennis, Jinho Choi

Abstract: Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state se… ▽ More Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension, but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power. △ Less

Submitted 16 April, 2021; originally announced April 2021.

Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling

Authors: Abanoub M. Girgis, Jihong Park, Mehdi Bennis, Mérouane Debbah

Abstract: This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of actuators that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only a fraction of actuators is controlled, while the rest of the actuato… ▽ More This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of actuators that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only a fraction of actuators is controlled, while the rest of the actuators are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control $2$x more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is $18$x larger than a round-robin scheduling baseline. △ Less

Submitted 27 January, 2021; originally announced January 2021.

Neural Abstractive Text Summarizer for Telugu Language

Authors: Mohan Bharath B, Aravindh Gowtham B, Akhil M

Abstract: Abstractive Text Summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming for humans to summarize manually large documents of text. Much of work in abstractive text summarization is being done in English and almost no significant work has been reported… ▽ More Abstractive Text Summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming for humans to summarize manually large documents of text. Much of work in abstractive text summarization is being done in English and almost no significant work has been reported in Telugu abstractive text summarization. So, we would like to propose an abstractive text summarization approach for Telugu language using Deep learning. In this paper we are proposing an abstractive text summarization Deep learning model for Telugu language. The proposed architecture is based on encoder-decoder sequential models with attention mechanism. We have applied this model on manually created dataset to generate a one sentence summary of the source text and have got good results measured qualitatively. △ Less

Submitted 18 January, 2021; originally announced January 2021.

Comments: 11 pages, 2 figures. Presented the paper at Third International Conference on Soft Computing and Signal Processing (ICSCSP 2020) and is currently in production. It will soon be published in springer Advances in Intelligent Systems and Computing (AISC) series

Sarcasm Detection in Tweets with BERT and GloVe Embeddings

Authors: Akshay Khatri, Pranav P, Anand Kumar M

Abstract: Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actua… ▽ More Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response. △ Less

Submitted 20 June, 2020; originally announced June 2020.

Comments: 5 pages Submitted to ACL 2020 conference

Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback

Authors: Anumeha Agrawal, Rosa Anil George, Selvan Sunitha Ravi, Sowmya Kamath S, Anand Kumar M

Abstract: Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee's suitability for the position - their professional qualifications, interperson… ▽ More Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee's suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Therefore, candidates need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee's facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results. △ Less

Submitted 16 June, 2020; v1 submitted 14 June, 2020; originally announced June 2020.

Comments: 9 pages, ACL 2020