Ponce, Hiram
Main Affiliation
Preferred name
Ponce, Hiram
Official Name
Ponce Espinosa, Hiram Eredín
ORCID
0000-0002-6559-7501
Researcher ID
K-7593-2019
Scopus Author ID
54911890000
171 results
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Item type:Publication, Assessing AI-Generated Legal Reasoning: A Benchmark for Legal Text Quality from Literature Review(Springer Nature Switzerland, 2025); The adoption of Large Language Models in law has sparked debate over how best to evaluate AI-generated legal reasoning. Existing benchmarks focus on surface-level accuracy, overlooking deeper dimensions such as argumentative coherence, practical usability, and alignment with jurisprudential values. This paper provides a comprehensive framework that integrates insights from formalism, interpretivism, realism, and argumentation theory to assess legal AI outputs. We first explore the philosophical foundations of legal reasoning, drawing on MacCormick’s concepts of internal and external justification and Perelman’s notions of audience-centered persuasion to highlight the rhetorical and moral dimensions essential for evaluation. Next, we examine structured approaches to evaluation from related fields before showing why existing benchmarks (e.g., LexGLUE, LegalBench, LegalAgentBench) only partially capture the subtleties of legal reasoning. We also contrast common law and civil law traditions to illustrate how a one-size-fits-all approach neglects the distinct roles of precedent versus codified statutes. Building on these theoretical and comparative insights, we propose a three-stage evaluation methodology that begins with automated screening for factual consistency, proceeds to expert-led rubric assessment across five dimensions (Accuracy, Reasoning, Clarity, Usefulness, and Safety), and concludes with iterative refinement through reliability checks. This structured approach, validated through a pilot study, aims to strike a balance between scalability and nuance, equipping researchers and practitioners with a robust tool for assessing AI-generated legal texts. Unifying theoretical rigor, domain-specific practicality, and cross-jurisdictional adaptability, this framework lays a solid foundation for legal AI benchmarks and paves the way for safer, more transparent deployment of AI in law. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design and Prototyping of a Bio-inspired Robotic Tail(Springer Nature Switzerland, 2025); Ramírez, AlexiaBio-inspired robotics, which involves observing and replicating the behavior of natural systems, has driven significant innovation in the technical development of robotic components. A primary objective within this field is to emulate the sophisticated internal functions of animals to solve complex engineering challenges, such as control and stability. While the integration of bio-mimetic tails in robotic systems is not new, with applications ranging from running to walking robots, existing designs are often limited in their functionality. Currently, most robotic tails primarily offer stability within static or limited dynamic contexts. The objective of this work is to design a preliminary robotic tail inspired by the chameleon’s tail, using hard and soft materials. Design and prototyping are discussed in the paper. We anticipate that this robotic tail can be used in the context of dynamic stability of quadruped robots. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deploying Real-Time Speech Recognition on ESP32 Using TinyML and Edge Impulse(Springer Nature Switzerland, 2025); ; The emergence of Tiny Machine Learning (TinyML) has enabled real-time on-device inference on ultra-low-power microcontrollers, eliminating reliance on cloud computing while significantly reducing latency, power consumption, and bandwidth requirements. This study explores the deployment of a TinyML-based speech recognition system on an ESP32 microcontroller, leveraging Edge Impulse for model development, Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, and TensorFlow Lite for Microcontrollers (TFLM) for efficient inference. The model was trained on a curated subset of the Google Speech Commands Dataset, incorporating background noise augmentation to enhance robustness in real-world environments. Using Edge Impulse’s EON Compiler, the model was fully quantized and optimized, achieving a 37% reduction in RAM usage and 27% in ROM. The final model attained 87.14% accuracy on testing data and 97.1% average classification confidence during real-time inference, with excellent noise rejection (99.6%) and latency of 266 ms. Compared to state-of-the-art systems deployed on more powerful platforms, the proposed approach achieves competitive accuracy while maintaining real-time inference and minimal resource consumption on ultra-low-power hardware. This makes it particularly suitable for battery-powered IoT, robotics, and embedded automation applications where connectivity and energy efficiency are critical. By balancing performance and efficiency, this research highlights the viability of deploying speech recognition systems on constrained microcontrollers. Future work will explore advanced architectures and enhanced feature extraction strategies to further improve recognition accuracy, especially for short or phonetically similar commands. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of an Underwater Mechatronics System for Detecting Dissolved Solids in Water(IEEE, 2024) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modular IoT Hydroponics System(MDPI AG, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preface : Machine Learning Methods in Biomedical Field Computer-Aided Diagnostics, Healthcare and Biology Applications(Springer Science and Business Media Deutschland GmbH, 2026); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Novel Ethical Design Framework Applied to Image Classification Challenges in the Fashion Industry(Springer Nature Switzerland, 2025); As artificial intelligence (AI) continues to play a pivotal role in image classification applications, the ethical implications of these technologies become increasingly significant. This paper explores the intersection of AI and ethics in the context of image classification, specifically focusing on the application of ethical design principles through a framework for a use of case in the fashion industry involving bags images and social media. This work delves into the integration of a comprehensive ethical framework around all the design process. The case study involves the development and implementation of a neural network tailored for bag image classification, leveraging transfer learning techniques. Through a meticulous examination of the ethical dimensions inherent in image classification, the study aims to establish a foundation for responsible and transparent AI practices. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Metaheuristic Strategy in Automatic Robotics Navigation for Patient Care in Hospitals(Springer Nature Switzerland, 2025); ; This chapter presents a novel approach for developing a global automatic navigation strategy for a mobile robot for patient care into health centers and hospitals. The proposal uses a metaheuristic strategy through the Grey Wolf Optimizer algorithm to find the shortest route between the robot’s starting and patient destination points, while also integrating knowledge of the environment acquired through vision. The global navigation tracking is achieved through the use of Fuzzy Logic Rules to control the robot’s wheel velocity according to its current position and orientation. The proposed strategy was implemented in a virtual robotics environment demonstrating that the approach successfully generated optimal paths for various environments with a minimal number of control points and in a relatively short amount of time. In addition, a simulated hospital room with common furniture and tasks was used to evaluate the performance of the global navigation strategy, demonstrating that the robot could successfully generate a global route and navigate within this environment. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Orthosis Control Based on Electromyographic Signals and Machine Learning(Springer Nature Switzerland, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Causal Artificial Intelligence in Legal Language Processing: A Systematic Review(MDPI, 2025); Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law’s narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis. ©The authors ©Entropy ©MDPI.