Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-intensive. To tackle the aforementioned issues, we present DrPlanner, the first framework designed to automatically diagnose and repair motion planners using large language models. Initially, we generate a structured description of the planner and its planned trajectories from both natural and programming languages. Leveraging the profound capabilities of large language models in addressing reasoning challenges, our framework returns repaired planners with detailed diagnostic descriptions. Furthermore, the framework advances iteratively with continuous feedback from the evaluation of the repaired outcomes. Our approach is validated using search-based motion planners; experimental results highlight the need of demonstrations in the prompt and the ability of our framework in identifying and rectifying elusive issues effectively.
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Undefined behavior in C often causes devastating security vulnerabilities. One practical mitigation is compartmentalization, which allows developers to structure large programs into mutually distrustful compartments with clearly specified privileges and interactions. In this paper we introduce SECOMP, a compiler for compartmentalized C code that comes with machine-checked proofs guaranteeing that the scope of undefined behavior is restricted to the compartments that encounter it and become dynamically compromised. These guarantees are formalized as the preservation of safety properties against adversarial contexts, a secure compilation criterion similar to full abstraction, and this is the first time such a strong criterion is proven for a mainstream programming language. To achieve this we extend the languages of the CompCert verified C compiler with isolated compartments that can only interact via procedure calls and returns, as specified by cross-compartment interfaces. We adapt the passes and optimizations of CompCert as well as their correctness proofs to this compartment-aware setting. We then use compiler correctness as an ingredient in a larger secure compilation proof that involves several proof engineering novelties, needed to scale formally secure compilation up to a C compiler.
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate this limitation, we present Holodeck, a system that generates 3D environments to match a user-supplied prompt fully automatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and museums, adjust the designs for styles, and can capture the semantics of complex queries such as "apartment for a researcher with a cat" and "office of a professor who is a fan of Star Wars". Holodeck leverages a large language model (i.e., GPT-4) for common sense knowledge about what the scene might look like and uses a large collection of 3D assets from Objaverse to populate the scene with diverse objects. To address the challenge of positioning objects correctly, we prompt GPT-4 to generate spatial relational constraints between objects and then optimize the layout to satisfy those constraints. Our large-scale human evaluation shows that annotators prefer Holodeck over manually designed procedural baselines in residential scenes and that Holodeck can produce high-quality outputs for diverse scene types. We also demonstrate an exciting application of Holodeck in Embodied AI, training agents to navigate in novel scenes like music rooms and daycares without human-constructed data, which is a significant step forward in developing general-purpose embodied agents.
The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance (Pd.M.) becomes increasingly pivotal. This paper presents a comprehensive survey of the current state of Pd.M. in the computing continuum, with a focus on the combination of scalable AI technologies. Recognizing the limitations of traditional maintenance practices in the face of increasingly complex and heterogenous computing continuum systems, the study explores how AI, especially machine learning and neural networks, is being used to enhance Pd.M. strategies. The survey encompasses a thorough review of existing literature, highlighting key advancements, methodologies, and case studies in the field. It critically examines the role of AI in improving prediction accuracy for system failures and in optimizing maintenance schedules, thereby contributing to reduced downtime and enhanced system longevity. By synthesizing findings from the latest advancements in the field, the article provides insights into the effectiveness and challenges of implementing AI-driven predictive maintenance. It underscores the evolution of maintenance practices in response to technological advancements and the growing complexity of computing continuum systems. The conclusions drawn from this survey are instrumental for practitioners and researchers in understanding the current landscape and future directions of Pd.M. in distributed systems. It emphasizes the need for continued research and development in this area, pointing towards a trend of more intelligent, efficient, and cost-effective maintenance solutions in the era of AI.
The customization of services in Fifth-generation (5G) and Beyond 5G (B5G) networks relies heavily on network slicing, which creates multiple virtual networks on a shared physical infrastructure, tailored to meet specific requirements of distinct applications, using Software Defined Networking (SDN) and Network Function Virtualization (NFV). It is imperative to ensure that network services meet the performance and reliability requirements of various applications and users, thus, service assurance is one of the critical components in network slicing. One of the key functionalities of network slicing is the ability to scale Virtualized Network Functions (VNFs) in response to changing resource demand and to meet Customer Service Level agreements (SLAs). In this paper, we introduce a proactive closed-loop algorithm for end-to-end network orchestration, designed to provide service assurance in 5G and B5G networks. We focus on dynamically scaling resources to meet key performance indicators (KPIs) specific to each network slice and operate in parallel across multiple slices, making it scalable and capable of managing completely automatically real-time service assurance. Through our experiments, we demonstrate that the proposed algorithm effectively fulfills service assurance requirements for different network slice types, thereby minimizing network resource utilization and reducing the over-provisioning of spare resources.
Recent advancements in Large Language Models (LLMs) have revolutionized the AI field but also pose potential safety and ethical risks. Deciphering LLMs' embedded values becomes crucial for assessing and mitigating their risks. Despite extensive investigation into LLMs' values, previous studies heavily rely on human-oriented value systems in social sciences. Then, a natural question arises: Do LLMs possess unique values beyond those of humans? Delving into it, this work proposes a novel framework, ValueLex, to reconstruct LLMs' unique value system from scratch, leveraging psychological methodologies from human personality/value research. Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs, synthesizing a taxonomy that culminates in a comprehensive value framework via factor analysis and semantic clustering. We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system. Based on this system, we further develop tailored projective tests to evaluate and analyze the value inclinations of LLMs across different model sizes, training methods, and data sources. Our framework fosters an interdisciplinary paradigm of understanding LLMs, paving the way for future AI alignment and regulation.
The escalating prevalence of diabetes globally underscores the need for diabetes management. Recent research highlights the growing focus on digital biomarkers in diabetes management, with innovations in computational frameworks and noninvasive monitoring techniques using personalized glucose metrics. However, they predominantly focus on insulin dosing and specific glucose values, or with limited attention given to overall glycemic control. This leaves a gap in expanding the scope of digital biomarkers for overall glycemic control in diabetes management. To address such a research gap, we propose GluMarker -- an end-to-end framework for modeling digital biomarkers using broader factors sources to predict glycemic control. Through the assessment and refinement of various machine learning baselines, GluMarker achieves state-of-the-art on Anderson's dataset in predicting next-day glycemic control. Moreover, our research identifies key digital biomarkers for the next day's glycemic control prediction. These identified biomarkers are instrumental in illuminating the daily factors that influence glycemic management, offering vital insights for diabetes care.
When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems
Many research papers have recently focused on behavioral-based driver authentication systems in vehicles. Pushed by Artificial Intelligence (AI) advancements, these works propose powerful models to identify drivers through their unique biometric behavior. However, practitioners have not yet shown any interest in the topic. Indeed, several limitations and oversights make implementing the state-of-the-art impractical, such as the computational resources required for training and the management of false positives. Furthermore, while being proposed as security measures, researchers neglect possible attacks on these systems that can make them counterproductive. Driven by the significant gap between research and practical application, this paper seeks to connect these two domains. We develop two lightweight behavioral-based driver authentication systems based on Machine Learning (ML) and Deep Learning (DL) architectures designed for our constrained environments. We formalize a realistic system and threat model reflecting a real-world vehicle's network for their implementation. When evaluated on real driving data, our models outclass the state-of-the-art with an accuracy of up to 0.999 in identification and authentication. Motivated by the inherent vulnerabilities of ML and DL models, we are the first to propose GAN-CAN, a class of novel evasion attacks, showing how attackers can still exploit these systems with a perfect attack success rate (up to 1.000). Our attacks are effective under different assumptions on the attacker's knowledge and allow stealing a vehicle in less than 22 minutes. Finally, we formalize requirements for deploying driver authentication systems securely and avoiding attacks such as GAN-CAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
Underwater datacenters (UDCs) hold promise as next-generation data storage due to their energy efficiency and environmental sustainability benefits. While the natural cooling properties of water save power, the isolated aquatic environment and long-range sound propagation in water create unique vulnerabilities which differ from those of on-land data centers. Our research discovers the unique vulnerabilities of fault-tolerant storage devices, resource allocation software, and distributed file systems to acoustic injection attacks in UDCs. With a realistic testbed approximating UDC server operations, we empirically characterize the capabilities of acoustic injection underwater and find that an attacker can reduce fault-tolerant RAID 5 storage system throughput by 17% up to 100%. Our closed-water analyses reveal that attackers can (i) cause unresponsiveness and automatic node removal in a distributed filesystem with only 2.4 minutes of sustained acoustic injection, (ii) induce a distributed database's latency to increase by up to 92.7% to reduce system reliability, and (iii) induce load-balance managers to redirect up to 74% of resources to a target server to cause overload or force resource colocation. Furthermore, we perform open-water experiments in a lake and find that an attacker can cause controlled throughput degradation at a maximum allowable distance of 6.35 m using a commercial speaker. We also investigate and discuss the effectiveness of standard defenses against acoustic injection attacks. Finally, we formulate a novel machine learning-based detection system that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our dataset of profiled hard disk drives under 30-second FIO benchmark execution. With this work, we aim to help manufacturers proactively protect UDCs against acoustic injection attacks and ensure the security of subsea computing infrastructures.
The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. When new software is deployed however, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior. Therefore, a concept is developed mirroring the existing cloud system into a simulation. To collect the necessary data, an observability platform is presented, capturing telemetry and architecture information. Subsequently, a simulation environment is designed that converts the architecture into a simulation model and simulates its dynamic workload by utilizing captured communication data. The proposed concept is evaluated in a real-world application scenario for electric vehicle charging: Vehicles can apply for an unoccupied charging station at a cloud service backend, the latter which manages all incoming requests and performs the assignment. Benchmarks are conducted by comparing the collected telemetry data with the simulated results under different loads and injected faults. The results show that regular cloud behavior is mirrored well by the simulation and that misbehavior due to fault injection is well visible, indicating that simulations are a promising data source for anomaly detection in connected vehicle cloud environments during operation.
Australia is a leading AI nation with strong allies and partnerships. Australia has prioritised robotics, AI, and autonomous systems to develop sovereign capability for the military. Australia commits to Article 36 reviews of all new means and methods of warfare to ensure weapons and weapons systems are operated within acceptable systems of control. Additionally, Australia has undergone significant reviews of the risks of AI to human rights and within intelligence organisations and has committed to producing ethics guidelines and frameworks in Security and Defence. Australia is committed to OECD's values-based principles for the responsible stewardship of trustworthy AI as well as adopting a set of National AI ethics principles. While Australia has not adopted an AI governance framework specifically for Defence; Defence Science has published 'A Method for Ethical AI in Defence' (MEAID) technical report which includes a framework and pragmatic tools for managing ethical and legal risks for military applications of AI.
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