Abstract

This paper presents an Internet of Things (IoT) enabled system that utilizes machine learning (ML) techniques to estimate the water flow in pipelines based on multi-modal sensor data. Traditional methods of water flow monitoring, mainly digital water flow meters, are prohibitively expensive, making largescale deployments challenging. To address this, an alternative has been proposed that leverages the correlation between water flow rate, motor current consumption, and pipeline pressure. Current consumption data of water motors has been integrated with pipeline pressure data to develop ML-based solutions that estimate the value of water flow without directly measuring it. The proposed approach enables digital flow monitoring at a fraction of the cost of conventional digital flow meters while offering high time granularity and accuracy, making it a scalable and practical solution for smart water management in diverse settings. The system’s robustness has been demonstrated by testing the model against erasures in the data.

Abstract

CubeSats have become a popular platform for low-cost space missions, supporting application ranging from Earth observation to space exploration. Despite their growing adoption, CubeSats face significant challenges in supporting in-orbit software updates with strong authentication and integrity guarantees due to severe constraints on computation, power, and communication bandwidth. Existing approaches either impose unsustainable overhead (e.g., public key cryptography) or focus solely on ground station-to-CubeSat delivery. Prior work such as CSUM offers an efficient hash chain based protocol for broadcast authentication from ground stations. However, it does not address the dissemination of update within a CubeSat cluster, which limits scalability and increase transmission redundancy. In this paper, we propose an enhancement to the CSUM protocol by replacing its custom hash concatenation mechanism with a standardized HMAC construction to achieve stronger resistance against cryptographic attacks, with minimal trade-off in efficiency. Building on this enhancement, we introduce CSUM-G, an extension of CSUM that enables authenticated software update propagation across a CubeSat cluster using inter-satellite links and shared secret cluster keys. In our implementation, CSUM-G achieves 100% update success with only 0.13-0.20 average retries per update, and propagates updates in as little as 0.04 seconds for 6 CubeSats and 3.68 seconds for 600 nodes.

Abstract

The problem of recovering from qubit erasures has recently gained attention as erasures occur in many physical systems such as photonic systems, trapped ions, superconducting qubits and circuit quantum electrodynamics. While several linear-time decoders for error correction are known, their errorcorrecting capability is limited to half the minimum distance of the code, whereas erasure correction allows one to go beyond this limit. As in the classical case, stopping sets pose a major challenge in designing efficient erasure decoders for quantum LDPC codes. In this paper, we show through simulation, that an attractive alternative here, is the use of quantum expander codes in conjunction with the peeling decoder that has linear complexity. We also discuss additional techniques including small-set-flip decoding, that can be applied following the peeling operation, to improve decoding performance and their associated complexity.

Abstract

Dual discriminator generative adversarial networks (D2 GANs) were introduced to mitigate the problem of mode collapse in generative adversarial networks. In D2 GANs, two discriminators are employed alongside a generator: one discriminator rewards high scores for samples from the true data distribution, while the other favors samples from the generator. In this work, we first introduce {dual discriminator α-GANs (D2 α-GANs)}, which combines the strengths of dual discriminators with the flexibility of a tunable loss function, α-loss. We further generalize this approach to arbitrary functions defined on positive reals, leading to a broader class of models we refer to as generalized dual discriminator generative adversarial networks. For each of these proposed models, we provide theoretical analysis and show that the associated min-max optimization reduces to the minimization of a linear combination of an f-divergence and a {reverse} f-divergence. This generalizes the known simplification for D2-GANs, where the objective reduces to a linear combination of the KL-divergence and the reverse KL-divergence. Finally, we perform experiments on 2D synthetic data and use multiple performance metrics to capture various advantages of our GANs.

Abstract

We present LayForge, a two-track lay sum- mary generation system developed for the Bio- LaySumm2025 shared task(Xiao et al., 2025) . Task 1.1 addresses the lay summarization using only the internal content of the article, while Task 1.2 augments this process with do- main knowledge such as biomedical definitions and concept explanations. BioLaySumm em- ploys a modular architecture that leverages large language models (LLMs), a BioBERT- based named entity recognizer (NER), and the UMLS(Bodenreider, 2004) knowledge base to create readable, informative, and faithful lay summaries. Our system shows strong perfor- mance on both tasks when evaluated on the PLOS and elife subset(Goldsack et al., 2022), particularly in readability and factuality metrics. The architecture illustrates how modularity and domain adaptation can be effectively combined for accessible biomedical communication.

Abstract

This paper presents the development of a real-time object detection system using Frequency Modulated Continuous Wave(FMCW) millimeter-wave radar signals and the PYNQ-ZU FPGA Board, which is widely used in (advanced driving assistance system)ADAS and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3D point cloud images into 2D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2D image using the VGG 16 model. We then assessed four machine learning models for object detection and found that the Support Vector Machine (SVM) model and Logistic Regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, Top-View Filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

Abstract

Robotic inspection of transmission lines presents significant challenges due to the complexity of navigating along the wires. Existing systems often rely on either flight modes for visual inspection or articulated crawling mechanisms for contact-based inspection. However, these approaches face limitations in effectively bypassing in-line obstacles or pylons, which are common in transmission line environments. This letter presents WireFlie, a novel hybrid robotic system that integrates rolling and flight modes to overcome these challenges. The system consists of a pair of underactuated arms mounted on a drone platform, designed for secure, collision-free locking and detaching, enabling seamless transitions between locomotion modes. WireFlie supports both single-arm and dual-arm rolling, allowing it to bypass in-line obstacles such as Stockbridge dampers, dual spacers, and sleeves, and to overcome larger obstacles like pylons using flight. Additionally, we propose a high-level controller for autonomous locking, detaching, and obstacle avoidance. Experiments are conducted on a custom-made setup that closely resembles a transmission wire. We evaluate both the design and control aspects of our system, with results including kinematic analysis, wire detection, autonomous locking and the corresponding trajectory, and obstacle detection and avoidance strategies. This research contributes to the field of robotic infrastructure inspection by merging aerial and wire-based locomotion, providing efficient and autonomous monitoring of power lines.

Abstract

Emotion detection in text plays a very crucial role in NLP applications such as sentiment anal- ysis and feedback analysis. This study tackles two tasks: multi-label emotion detection, where the goal is to classify text based on six emotions (joy, sadness, fear, anger, surprise, and disgust) in a multilingual setting, and emotion intensity prediction, which assigns an ordinal intensity score to each of the above-given emotions. Using the BRIGHTER dataset, a multilingual corpus spanning 28 languages, the paper ad- dresses issues like class imbalances by treating each emotion as an independent binary classifi- cation problem. The paper first explores strate- gies such as static embeddings such as GloVe with logistic regression classifiers on top of it. To capture contextual nuances more effectively, we fine-tune transformer based models, such as BERT and RoBERTa. Our approach demon- strates the benefits of fine-tuning for improved emotion prediction, while also highlighting the challenges of multilingual and multi-label clas- sification.

Abstract

Machine Translation (MT) is an essential tool for communication among people across different cultures, yet Named Entity (NE) translation remains a major challenge due to its rarity in occurrence and ambiguity. Traditional approaches, like using lexicons or parallel corpora, often fail to generalize to unseen entities and, hence, do not perform well. To address this, we create a silver dataset using the Google Translate API and fine-tune the facebook/nllb200-distilled-600M model with LoRA (LowRank Adaptation) to enhance translation accuracy while also maintaining efficient memory use. Evaluated with metrics such as BLEU, COMET, and M-ETA, our results show that fine-tuning a specialized MT model improves NE translation without having to rely on largescale general-purpose models.

Abstract

Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive applications effectively. Graph neural networks (GNNs) for temporal graphs are one such category of specialized models. For the first time, our approach integrates a reject option strategy within the framework of GNNs for continuous time dynamic graphs (CTDGs). This allows the model to strategically abstain from making predictions when the uncertainty is high and confidence is low, thus minimizing the risk of critical misclassification and enhancing the results and reliability. We propose a coverage-based abstention prediction model to implement the reject option that maximizes prediction within a specified coverage. It improves the prediction score for link prediction and node classification tasks. Temporal GNNs deal with extremely skewed datasets for the next state prediction or node classification task. In the case of class imbalance, our method can be further tuned to provide a higher weight to the minority class. Exhaustive experiments are presented on four datasets for dynamic link prediction and two datasets for dynamic node classification tasks. This demonstrates the effectiveness of our approach in improving the reliability and area under the curve (AUC)/average precision (AP) scores for predictions in dynamic graph scenarios. The results highlight our model’s ability to efficiently handle the trade-offs between prediction confidence and coverage, making it a dependable solution for applications requiring high precision in dynamic and uncertain environments.