Abstract

Existing Machine Translation (MT) research often suggests a single, fixed set of hyperparameters for word segmentation models, textbf{symmetric Byte Pair Encoding} (BPE), which applies the same number of merge operations (NMO) to train tokenizers for both source and target languages. However, we demonstrate that this uniform approach doesn't guarantee optimal MT performance across different language pairs and data sizes. This work investigates BPE segmentation recipes across various data volumes and language pairs to evaluate MT system performance. We find that utilizing textbf{asymmetric BPE}—where the source and target languages have different NMOs—significantly improves results over the symmetric approach, especially in low-resource settings (50K, 100K, and 500K sentence pairs). Specifically, asymmetric BPE yield statistically significant (p<0.05) average gains of 5.32, 4.46, and 0.7 CHRF++ on English-Hindi in low-resource setups (50K, 100K, and 500K sentence pairs, respectively). We validated this trend across six additional language pairs (English Telugu, Shona, Norwegian, Kyrgyz, Hausa, and Inuktitut), observing statistically significant improvement in 10 out of 12 systems compared to symmetric BPE. Our findings indicate a high NMO for the source (4K to 32K) and a low NMO for the target (0.5K to 2K) provides optimal results, particularly benefiting low-resource MT.

Abstract

The emerging concept of Internet of Things (IoT)-based Healthcare Industry 5.0 emphasizes the integration of human intelligence with cutting-edge technologies, such as artificial intelligence (AI), blockchain, IoT, big data analytics (BDA), and machine learning (ML) models to revolutionize healthcare delivery. However, alongside the immense potential and opportunities of Healthcare 5.0, the rapid expansion of sensitive medical data within the cloud-based healthcare systems also brings significant challenges related to data security, privacy, and resource requirements. Since most healthcare infrastructures depend on semi-trusted centralized cloud storage, it then becomes crucial to store medical records in encrypted form in order to ensure confidentiality and privacy of the data. At the same time, these systems must provide seamless and efficient search capabilities for authorized medical personnel in the healthcare system. To address these concerns and enable cost-effective, secure access and data management, we propose a novel authenticated certificateless verifiable searchable public key encryption scheme (ACLV-SPKE) that emerges as a robust and promising solution for securing healthcare data. The proposed framework not only resists diverse adversarial attacks but also ensures efficiency in terms of communication and computation costs while offering improved functionality over recent state-of-the-art solutions presented in the literature. The proposed scheme effectively resists both inside and outside keyword guessing attacks (KGAs), achieving strong security guarantees such as ciphertext indistinguishability (CT-IND) and trapdoor indistinguishability (TP-IND), which are proved in the random oracle models. In addition, we applied BDA on a real healthcare dataset to evaluate the performance metrics, and the outcomes are presented in this article.

Abstract

We present a critical discourse analysis of the 2024 U.S. presidential debates, examining Donald Trump's rhetorical strategies in his interactions with Joe Biden and Kamala Harris. We introduce a novel annotation framework, BEADS (Bias Enriched Annotation for Dialogue Structure), which systematically extends the DAMSL framework to capture bias driven and adversarial discourse features in political communication. BEADS includes a domain and language agnostic set of tags that model ideological framing, emotional appeals, and confrontational tactics. Our methodology compares detailed human annotation with zero shot ChatGPT assisted tagging on verified transcripts from the Trump and Biden (19,219 words) and Trump and Harris (18,123 words) debates. Our analysis shows that Trump consistently dominated in key categories: Challenge and Adversarial Exchanges, Selective Emphasis, Appeal to Fear, Political Bias, and Perceived Dismissiveness. These findings underscore his use of emotionally charged and adversarial rhetoric to control the narrative and influence audience perception. In this work, we establish BEADS as a scalable and reproducible framework for critical discourse analysis across languages, domains, and political contexts.

Abstract

Time-Sensitive Networking (TSN) is crucial for ensuring deterministic communication in real-time applications. In TSN, Network Traversal Time (NTT) is a critical metric that quantifies the timeliness of received data. Maintaining low NTT is essential for ensuring accurate system responses and to prevent outdated information from affecting real-time operations. However, the presence of non-zero arrival jitter negatively impacts NTT and poses challenges in providing predictable performance. When jitter increases, packets may arrive inconsistently, causing some to be delayed while others may arrive in bursts. To address this, we analyze the NTT bounds for time-critical flows, influenced by arrival jitter. We propose an analytical framework to estimate best and worst-case ST slots for data transmission, considering interference from higher priority flows and establishing data reachability along the network path across the switches. To validate our analysis, we conducted experiments using both a synthetic task set and an automotive use case. We also analyzed the impact of flow parameters on NTT determination, which, in some scenarios, led to pessimistic bound estimations.

Abstract

This paper presents a real-time, low-latency communication and energy- efficient framework for IoT-enabled micromobility systems, specifically targeting gated environments such as residential campuses, educational institutions and industrial zones. As a core application platform, an IoT-enabled Segway is deployed and integrated with the proposed framework, serving as a practical micro-mobility solution within gated communities. The proposed system enables both Vehicle-to-Vehicle (V2V) communication using ESP-NOW, and Vehicle-to-Infrastructureto-Vehicle (V2I2V) communication using Wi-Fi-based UDP, implemented using ESP32-S3 and NodeMCU microcontrollers integrated with IMU, ToF, Ultrasonic, and GPS sensors. To evaluate performance, end-to-end latency is measured across varying distances under both Line-of-Sight (LoS) and Non-Lineof-Sight (NLoS) conditions. Along with the latency measurement, power profiling system is also implemented using the INA226 sensor, comparing the energy consumption of ESP-NOW and 4G-based communication. To extend this analysis, Simulation of Urban MObility (SUMO) simulation was performed. Results demonstrate the effectiveness of the proposed framework in supporting reliable micro-mobility communication, offering a practical foundation for intelligent transport in localized smart environments. This work introduces a novel, energy-efficient, and infrastructure-independent V2X communication solution tailored for smart gated communities.

Abstract

The rapid evolution of vehicular edge computing (VEC) has made task offloading to roadside units (RSUs) essential for addressing the computational demands of modern vehicular applications. However, efficiently partitioning and scheduling tasks across RSUs in dynamic environments remains challenging. This paper introduces a novel algorithm, HAPTA (Horizontal, Adaptive Learning-based, Partitioned, Timeslot-based Algorithm), a time-slot based task partitioning and offloading approach using the Upper Confidence Bound (UCB) algorithm, to adaptively allocate tasks to RSUs. We consider task offloading horizontally across RSU nodes. Unlike static or heuristic-based scheduling methods, the proposed HAPTA framework dynamically selects RSUs with optimal resource availability while considering vehicular mobility and task deadlines. The framework supports splitting tasks into subtasks, enabling efficient resource utilization and meeting stringent latency constraints. Experimental evaluations on real-world vehicular datasets demonstrate that the HAPTA approach outperforms previously introduced heuristic-based methods, achieving higher task completion rates and lower latency, particularly in high-density scenarios.

Abstract

Automated Guided Vehicles (AGVs) are widely used for material handling across various industries, such as warehouses, manufacturing plants, and automated container terminals. As AGVs are battery-powered, it is necessary to schedule AGV's tasks considering energy-related aspects. That is, understanding how an AGV's energy is consumed or recharged in a given operational environment is crucial to optimizing both operational efficiency and energy consumption. This article presents a comprehensive analysis of energy-aware AGV operations, emphasizing three critical areas: the impact of the operational environment, energy consumption modeling, and energy-aware decision-making for scheduling and path planning. This article examines the layout structures and energy supply strategies in two different environments: automated container terminals and workshops, to understand the environmental impact on AGV energy efficiency. A comprehensive review of energy consumption models provides insight into the physical characteristics of AGVs' energy consumption. Finally, we provide variations of task scheduling and path planning problems that explicitly take into account such energy aspects and introduce how literature tackles to solve those problems.

Abstract

Efficient traffic signal control is critical for reducing urban congestion, yet traditional rule-based and machine learning approaches fail to adapt to dynamic conditions. Reinforcement Learning (RL) provides a data-driven alternative, and this study evaluates classical exploration strategies Epsilon-Greedy, Thompson Sampling, Upper Confidence Bound (UCB), and Softmax alongside hybrid variants including Softmax with Temperature Annealing (SMX-TA), Softmax with Entropy Regularization (SMX-ER), and their combinations with UCB and Thompson Sampling. Our framework is based on value-based RL (Double Deep Q-Networks), chosen for scalability and stability compared to on-policy methods such as PPO and A2C, and implemented with parallel simulation in SUMO and CityFlow across synthetic grids ( 1 × 1 , 4 × 4 , 6 × 6 ) and real-world datasets (Hyderabad, New York). Benchmarking against classical baselines (FixedTime, MaxPressure) and recent RL models (FRAP, CoLight, GCN) shows that the proposed SMX-ER+UCB consistently yields the lowest average travel times and robust adaptability across traffic conditions. An ablation study confirms the complementary benefits of entropy regularization and UCB, while integration into scalable controllers such as CoLight demonstrates network-level generalization. These results highlight hybrid exploration as an effective and practical strategy for real-time, city-wide traffic optimization. Such large-scale optimization requires high-performance computing (HPC) to support parallel simulations, GPU-accelerated training, and multi-agent coordination, underscoring the necessity of HPC frameworks for intelligent transportation systems.

Abstract

Psychomotor changes, while crucial indicators of depres- sion, remain underrepresented in clinical observations. We examined the relationship between depression and psychomotor behavior by analyzing head-tracking data related to yaw movements made during exploration of 360° emotional videos, alongside valence and arousal ratings on 9-point Likert scale. Symptoms of depres- sion were recorded using the Patient Health Question- naire (PHQ-9). While subjective ratings for valence and arousal showed no differences across depression groups, the head-tracking data revealed novel results. Individu- als with moderate to severe depression exhibited signif- icantly lower scanning speed and standard deviation in yaw movement compared to minimal to mild depression. Although preliminary, these results underscore the im- portance of psychomotor measures in diagnosis, risk as- sessment, and monitoring in psychiatric care, alongside subjective evaluations.

Abstract

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a significant global health concern, particularly in settings where access to trained clinicians and advanced diagnostic tools is limited. This work presents an edge-based AI platform for real-time classification of respiratory conditions using lung sound analysis. The system captures respiratory audio via a microphone and processes it locally on a PYNQ-ZU FPGA platform using a lightweight Convolutional Neural Network (CNN) trained on Mel spectrogram representations. Evaluated on the publicly available ICBHI2017 dataset, the model achieved an accuracy of 96%, with a sensitivity of 97.06% and specificity of 94.12%. These results demonstrate the potential of portable, AI-driven auscultation tools to provide accessible and consistent respiratory diagnostics in resource-constrained environments.