Emerging Technologies

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Showing new listings for Tuesday, 10 June 2025

Cross submissions (showing 10 of 10 entries)

[1] arXiv:2506.06381 (cross-list from cs.RO) [pdf, html, other]

Title: CPS-Guard: Framework for Dependability Assurance of AI- and LLM-Based Cyber-Physical Systems

Trisanth Srinivasan, Santosh Patapati, Himani Musku, Idhant Gode, Aditya Arora, Samvit Bhattacharya, Abubakr Nazriev, Sanika Hirave, Zaryab Kanjiani, Srinjoy Ghose, Srinidhi Shetty

Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)

Cyber-Physical Systems (CPS) increasingly depend on advanced AI techniques to operate in critical applications. However, traditional verification and validation methods often struggle to handle the unpredictable and dynamic nature of AI components. In this paper, we introduce CPS-Guard, a novel framework that employs multi-role orchestration to automate the iterative assurance process for AI-powered CPS. By assigning specialized roles (e.g., safety monitoring, security assessment, fault injection, and recovery planning) to dedicated agents within a simulated environment, CPS-Guard continuously evaluates and refines AI behavior against a range of dependability requirements. We demonstrate the framework through a case study involving an autonomous vehicle navigating an intersection with an AI-based planner. Our results show that CPS-Guard effectively detects vulnerabilities, manages performance impacts, and supports adaptive recovery strategies, thereby offering a structured and extensible solution for rigorous V&V in safety- and security-critical systems.

[2] arXiv:2506.06459 (cross-list from cs.LG) [pdf, html, other]

Title: Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control

Ruitao Chen, Mozhang Guo, Jinge Li

Subjects: Machine Learning (cs.LG); Emerging Technologies (cs.ET); Robotics (cs.RO); Systems and Control (eess.SY)

Automated driving (AD) has substantially improved vehicle safety and driving comfort, but their impact on passenger well-being, particularly infant sleep, is not sufficiently studied. Sudden acceleration, abrupt braking, and sharp maneuvers can disrupt infant sleep, compromising both passenger comfort and parental convenience. To solve this problem, this paper explores the integration of reinforcement learning (RL) within AD to personalize driving behavior and optimally balance occupant comfort and travel efficiency. In particular, we propose an intelligent cruise control framework that adapts to varying driving conditions to enhance infant sleep quality by effectively synergizing wearable sensing and vehicle data. Long short-term memory (LSTM) and transformer-based neural networks are integrated with RL to model the relationship between driving behavior and infant sleep quality under diverse traffic and road conditions. Based on the sleep quality indicators from the wearable sensors, driving action data from vehicle controllers, and map data from map applications, the model dynamically computes the optimal driving aggressiveness level, which is subsequently translated into specific AD control strategies, e.g., the magnitude and frequency of acceleration, lane change, and overtaking. Simulation results demonstrate that the proposed solution significantly improves infant sleep quality compared to baseline methods, while preserving desirable travel efficiency.

[3] arXiv:2506.06469 (cross-list from cs.NI) [pdf, html, other]

Title: Steps towards an Ecology for the Internet

Anil Madhavapeddy, Sam Reynolds, Alec P. Christie, David A. Coomes, Michael W. Dales, Patrick Ferris, Ryan Gibb, Hamed Haddadi, Sadiq Jaffer, Josh Millar, Cyrus Omar, William J. Sutherland, Jon Crowcroft

Comments: To appear in the sixth decennial Aarhus conference: Computing X Crisis, Aug 2025

Subjects: Networking and Internet Architecture (cs.NI); Emerging Technologies (cs.ET)

The Internet has grown from a humble set of protocols for end-to-end connectivity into a critical global system with no builtin "immune system". In the next decade the Internet will likely grow to a trillion nodes and need protection from threats ranging from floods of fake generative data to AI-driven malware. Unfortunately, growing centralisation has lead to the breakdown of mutualism across the network, with surveillance capitalism now the dominant business model. We take lessons from from biological systems towards evolving a more resilient Internet that can integrate adaptation mechanisms into its fabric. We also contribute ideas for how the Internet might incorporate digital immune systems, including how software stacks might mutate to encourage more architectural diversity. We strongly advocate for the Internet to "re-decentralise" towards incentivising more mutualistic forms of communication.

[4] arXiv:2506.06580 (cross-list from cs.AI) [pdf, other]

Title: AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture

Xiaoran Liu, Istvan David

Subjects: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Software Engineering (cs.SE); Systems and Control (eess.SY)

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.

[5] arXiv:2506.06590 (cross-list from cs.CC) [pdf, html, other]

Title: Robust predicate and function computation in continuous chemical reaction networks

Kim Calabrese, David Doty, Mina Latifi

Subjects: Computational Complexity (cs.CC); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET)

We initiate the study of rate-constant-independent computation of Boolean predicates and numerical functions in the continuous model of chemical reaction networks (CRNs), which model the amount of a chemical species as a nonnegative, real-valued *concentration*. Real-valued numerical functions have previously been studied, finding that exactly the continuous, piecewise rational linear functions $f: \mathbb{R}_{> 0}^k \to \mathbb{R}_{> 0}$ can be computed *stably*, a.k.a., *rate-independently*, meaning that the CRN gets the answer correct no matter the rate at which reactions occur.
We show that, contrary to functions, continuous CRNs are severely limited in the Boolean predicates they can stably decide, reporting an answer based only on which inputs are 0 or positive.
This limitation motivates a slightly relaxed notion of rate-independent computation in CRNs that we call *robust computation*. The standard mass-action rate model is used, in which each reaction is assigned a rate equal to the product of its reactant concentrations and its rate constant. The computation is correct in this model if it converges to the correct output for any positive choice of rate constants. This adversary is weaker than the stable computation adversary, the latter being able to run reactions at non-mass-action rates.
We show that CRNs can robustly decide every finite Boolean combination of *threshold predicates*: those predicates defined by taking a rational weighted sum of the inputs $\mathbf{x} \in \mathbb{R}^k_{\ge 0}$ and comparing to a constant, answering the question ``Is $\sum_{i=1}^k w_i \cdot \mathbf{x}(i) > h$?'', for rational weights $w_i$ and real threshold $h$. Turning to function computation, we show that CRNs can robustly compute any piecewise affine function with rational coefficients, where threshold predicates determine which affine piece to evaluate for a given input.

[6] arXiv:2506.07135 (cross-list from cs.SE) [pdf, html, other]

Title: Taxonomy of migration scenarios for Qiskit refactoring using LLMs

José Manuel Suárez, Luís Mariano Bibbó, Joaquín Bogado, Alejandro Fernandez

Comments: Accepted for publication in ASQC JAIIO 54 (this https URL)

Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)

As quantum computing advances, quantum programming libraries' heterogeneity and steady evolution create new challenges for software developers. Frequent updates in software libraries break working code that needs to be refactored, thus adding complexity to an already complex landscape. These refactoring challenges are, in many cases, fundamentally different from those known in classical software engineering due to the nature of quantum computing software. This study addresses these challenges by developing a taxonomy of quantum circuit's refactoring problems, providing a structured framework to analyze and compare different refactoring approaches. Large Language Models (LLMs) have proven valuable tools for classic software development, yet their value in quantum software engineering remains unexplored. This study uses LLMs to categorize refactoring needs in migration scenarios between different Qiskit versions. Qiskit documentation and release notes were scrutinized to create an initial taxonomy of refactoring required for migrating between Qiskit releases. Two taxonomies were produced: one by expert developers and one by an LLM. These taxonomies were compared, analyzing differences and similarities, and were integrated into a unified taxonomy that reflects the findings of both methods. By systematically categorizing refactoring challenges in Qiskit, the unified taxonomy is a foundation for future research on AI-assisted migration while enabling a more rigorous evaluation of automated refactoring techniques. Additionally, this work contributes to quantum software engineering (QSE) by enhancing software development workflows, improving language compatibility, and promoting best practices in quantum programming.

[7] arXiv:2506.07436 (cross-list from cs.CV) [pdf, other]

Title: Prompt to Protection: A Comparative Study of Multimodal LLMs in Construction Hazard Recognition

Nishi Chaudhary, S M Jamil Uddin, Sathvik Sharath Chandra, Anto Ovid, Alex Albert

Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)

The recent emergence of multimodal large language models (LLMs) has introduced new opportunities for improving visual hazard recognition on construction sites. Unlike traditional computer vision models that rely on domain-specific training and extensive datasets, modern LLMs can interpret and describe complex visual scenes using simple natural language prompts. However, despite growing interest in their applications, there has been limited investigation into how different LLMs perform in safety-critical visual tasks within the construction domain. To address this gap, this study conducts a comparative evaluation of five state-of-the-art LLMs: Claude-3 Opus, GPT-4.5, GPT-4o, GPT-o3, and Gemini 2.0 Pro, to assess their ability to identify potential hazards from real-world construction images. Each model was tested under three prompting strategies: zero-shot, few-shot, and chain-of-thought (CoT). Zero-shot prompting involved minimal instruction, few-shot incorporated basic safety context and a hazard source mnemonic, and CoT provided step-by-step reasoning examples to scaffold model thinking. Quantitative analysis was performed using precision, recall, and F1-score metrics across all conditions. Results reveal that prompting strategy significantly influenced performance, with CoT prompting consistently producing higher accuracy across models. Additionally, LLM performance varied under different conditions, with GPT-4.5 and GPT-o3 outperforming others in most settings. The findings also demonstrate the critical role of prompt design in enhancing the accuracy and consistency of multimodal LLMs for construction safety applications. This study offers actionable insights into the integration of prompt engineering and LLMs for practical hazard recognition, contributing to the development of more reliable AI-assisted safety systems.

[8] arXiv:2506.07714 (cross-list from cs.CR) [pdf, html, other]

Title: Profiling Electric Vehicles via Early Charging Voltage Patterns

Francesco Marchiori, Denis Donadel, Alessandro Brighente, Mauro Conti

Comments: Accepted to be presented at the AI&CPSS Workshop in conjunction with ARES 2025

Subjects: Cryptography and Security (cs.CR); Emerging Technologies (cs.ET); Machine Learning (cs.LG)

Electric Vehicles (EVs) are rapidly gaining adoption as a sustainable alternative to fuel-powered vehicles, making secure charging infrastructure essential. Despite traditional authentication protocols, recent results showed that attackers may steal energy through tailored relay attacks. One countermeasure is leveraging the EV's fingerprint on the current exchanged during charging. However, existing methods focus on the final charging stage, allowing malicious actors to consume substantial energy before being detected and repudiated. This underscores the need for earlier and more effective authentication methods to prevent unauthorized charging. Meanwhile, profiling raises privacy concerns, as uniquely identifying EVs through charging patterns could enable user tracking.
In this paper, we propose a framework for uniquely identifying EVs using physical measurements from the early charging stages. We hypothesize that voltage behavior early in the process exhibits similar characteristics to current behavior in later stages. By extracting features from early voltage measurements, we demonstrate the feasibility of EV profiling. Our approach improves existing methods by enabling faster and more reliable vehicle identification. We test our solution on a dataset of 7408 usable charges from 49 EVs, achieving up to 0.86 accuracy. Feature importance analysis shows that near-optimal performance is possible with just 10 key features, improving efficiency alongside our lightweight models. This research lays the foundation for a novel authentication factor while exposing potential privacy risks from unauthorized access to charging data.

[9] arXiv:2506.07743 (cross-list from math.NA) [pdf, html, other]

Title: Quantum-Enhanced Spectral Solution of the Poisson Equation

G. Intoccia, U. Chirico, G. Pepe, S. Cuomo

Subjects: Numerical Analysis (math.NA); Emerging Technologies (cs.ET)

We present a hybrid numerical-quantum method for solving the Poisson equation under homogeneous Dirichlet boundary conditions, leveraging the Quantum Fourier Transform (QFT) to enhance computational efficiency and reduce time and space complexity. This approach bypasses the integration-heavy calculations of classical methods, which have to deal with high computational costs for large number of points. The proposed method estimates the coefficients of the series expansion of the solution directly within the quantum framework. Numerical experiments validate its effectiveness and reveal significant improvements in terms of time and space complexity and solution accuracy, demonstrating the capability of quantum-assisted techniques to contribute in solving partial differential equations (PDEs). Despite the inherent challenges of quantum implementation, the present work serves as a starting point for future researches aimed at refining and expanding quantum numerical methods.

[10] arXiv:2506.07810 (cross-list from quant-ph) [pdf, html, other]

Title: A weighted quantum ensemble of homogeneous quantum classifiers

Emiliano Tolotti, Enrico Blanzieri, Davide Pastorello

Comments: 21 pages, 4 figures

Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG)

Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifiers with indexing registers for data encoding. This approach leverages instance-based quantum classifiers, enabling feature and training point subsampling through superposition and controlled unitaries, and allowing for a quantum-parallel execution of diverse internal classifiers with different data compositions in superposition. The method integrates a learning process involving circuit execution and classical weight optimization, for a trained ensemble execution with weights encoded in the circuit at test-time. Empirical evaluation demonstrate the effectiveness of the proposed method, offering insights into its performance.

Replacement submissions (showing 2 of 2 entries)

[11] arXiv:2505.13453 (replaced) [pdf, other]

Title: Pel, A Programming Language for Orchestrating AI Agents

Behnam Mohammadi

Comments: 1. Updated author email address (I graduated so I added my alumni email). 2. Changed mono-font color to blue for better readability

Subjects: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)

The proliferation of Large Language Models (LLMs) has opened new frontiers in computing, yet controlling and orchestrating their capabilities beyond simple text generation remains a challenge. Current methods, such as function/tool calling and direct code generation, suffer from limitations in expressiveness, scalability, cost, security, and the ability to enforce fine-grained control. This paper introduces Pel, a novel programming language specifically designed to bridge this gap. Inspired by the strengths of Lisp, Elixir, Gleam, and Haskell, Pel provides a syntactically simple, homoiconic, and semantically rich platform for LLMs to express complex actions, control flow, and inter-agent communication safely and efficiently. Pel's design emphasizes a minimal, easily modifiable grammar suitable for constrained LLM generation, eliminating the need for complex sandboxing by enabling capability control at the syntax level. Key features include a powerful piping mechanism for linear composition, first-class closures enabling easy partial application and functional patterns, built-in support for natural language conditions evaluated by LLMs, and an advanced Read-Eval-Print-Loop (REPeL) with Common Lisp-style restarts and LLM-powered helper agents for automated error correction. Furthermore, Pel incorporates automatic parallelization of independent operations via static dependency analysis, crucial for performant agentic systems. We argue that Pel offers a more robust, secure, and expressive paradigm for LLM orchestration, paving the way for more sophisticated and reliable AI agentic frameworks.

[12] arXiv:2506.05588 (replaced) [pdf, html, other]

Title: Preprocessing Methods for Memristive Reservoir Computing for Image Recognition

Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky

Comments: 6 pages, 8 figures

Subjects: Neural and Evolutionary Computing (cs.NE); Hardware Architecture (cs.AR); Emerging Technologies (cs.ET)

Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite growing interest, a comprehensive evaluation that quantifies the impact of these factors is still lacking. This paper systematically compares various preprocessing methods for memristive RC systems, assessing their effects on accuracy and energy consumption. We also propose a parity-based preprocessing method that improves accuracy by 2-6% while requiring only a modest increase in device count compared to other methods. Our findings highlight the importance of informed preprocessing strategies to improve the efficiency and scalability of memristive RC systems.