Logs are crucial for analyzing large-scale software systems, offering insights into system health, performance, security threats, potential bugs, etc. However, their chaotic nature$\unicode{x2013}$characterized by sheer volume, lack of standards, and variability$\unicode{x2013}$makes manual analysis complex. The use of clustering algorithms can assist by grouping logs into a smaller set of templates, but lose the temporal and relational context in doing so. On the contrary, Large Language Models (LLMs) can provide meaningful explanations but struggle with processing large collections efficiently. Moreover, representation techniques for both approaches are typically limited to either plain text or traditional charting, especially when dealing with large-scale systems. In this paper, we combine clustering and LLM summarization with event detection and Multidimensional Scaling through the use of Time Curves to produce a holistic pipeline that enables efficient and automatic summarization of vast collections of software system logs. The core of our approach is the proposal of a semimetric distance that effectively measures similarity between events, thus enabling a meaningful representation. We show that our method can explain the main events of logs collected from different applications without prior knowledge. We also show how the approach can be used to detect general trends as well as outliers in parallel and distributed systems by overlapping multiple projections. As a result, we expect a significant reduction of the time required to analyze and resolve system-wide issues, identify performance bottlenecks and security risks, debug applications, etc.
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The design of soft robots is still commonly driven by manual trial-and-error approaches, requiring the manufacturing of multiple physical prototypes, which in the end, is time-consuming and requires significant expertise. To reduce the number of manual interventions in this process, topology optimization can be used to assist the design process. The design is then guided by simulations and numerous prototypes can be tested in simulation rather than being evaluated through laborious experiments. To implement this simulation-driven design process, the possible design space of a slender soft pneumatic actuator is generalized to the design of the circular cross-section. We perform a black-box topology optimization using genetic algorithms to obtain a cross-sectional design of a soft pneumatic actuator that is capable of reaching a target workspace defined by the end-effector positions at different pressure values. This design method is evaluated for three different case studies and target workspaces, which were either randomly generated or specified by the operator of the design assistant. The black-box topology optimization based on genetic algorithms proves to be capable of finding good designs under given plausible target workspaces. We considered a simplified simulation model to verify the efficacy of the employed method. An experimental validation has not yet been performed. It can be concluded that the employed black-box topology optimization can assist in the design process for slender soft pneumatic actuators. It supports at searching for possible design prototypes that reach points specified by corresponding actuation pressures. This helps reduce the trial-and-error driven iterative manual design process and enables the operator to focus on prototypes that already offer a good viable solution.
Vulnerability detection is crucial for maintaining software security, and recent research has explored the use of Language Models (LMs) for this task. While LMs have shown promising results, their performance has been inconsistent across datasets, particularly when generalizing to unseen code. Moreover, most studies have focused on the C/C++ programming language, with limited attention given to other popular languages. This paper addresses this gap by investigating the effectiveness of LMs for vulnerability detection in JavaScript, Java, Python, PHP, and Go, in addition to C/C++ for comparison. We utilize the CVEFixes dataset to create a diverse collection of language-specific vulnerabilities and preprocess the data to ensure quality and integrity. We fine-tune and evaluate state-of-the-art LMs across the selected languages and find that the performance of vulnerability detection varies significantly. JavaScript exhibits the best performance, with considerably better and more practical detection capabilities compared to C/C++. We also examine the relationship between code complexity and detection performance across the six languages and find only a weak correlation between code complexity metrics and the models' F1 scores.
In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we introduce novel bounds and derive confidence intervals for the Average Potential Outcome (APO) - a standard metric for evaluating continuous-valued treatment or exposure effects. We demonstrate that these bounds are sharp under a continuous sensitivity model, in the sense that they give the smallest possible interval under this model, and propose a doubly robust version of our estimators. In a comparative analysis with the method of Jesson et al. (2022) (arXiv:2204.10022), using both simulated and real datasets, we show that our approach not only yields sharper bounds but also achieves good coverage of the true APO, with significantly reduced computation times.
Artificial intelligence techniques are increasingly being applied to solve control problems, but often rely on black-box methods without transparent output generation. To improve the interpretability and transparency in control systems, models can be defined as white-box symbolic policies described by mathematical expressions. For better performance in partially observable and volatile environments, the symbolic policies are extended with memory represented by continuous-time latent variables, governed by differential equations. Genetic programming is used for optimisation, resulting in interpretable policies consisting of symbolic expressions. Our results show that symbolic policies with memory compare with black-box policies on a variety of control tasks. Furthermore, the benefit of the memory in symbolic policies is demonstrated on experiments where memory-less policies fall short. Overall, we present a method for evolving high-performing symbolic policies that offer interpretability and transparency, which lacks in black-box models.
In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed QuestSecure, a system that interprets diverse document formats and generates precise responses by integrating large language models (LLMs) with an advanced retrieval system. Our experiments show that QuestSecure significantly improves response accuracy and operational efficiency. By employing advanced NLP techniques and tailored retrieval mechanisms, the system consistently produces contextually relevant and semantically rich responses, reducing cognitive load on security teams and minimizing potential errors. This research offers promising avenues for automating complex security management tasks, enhancing organizational security processes.
Today's information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a pivotal role to share encryption keys and create digital signatures. However, quantum computers threaten the security of traditional public-key cryptosystems as they can tame computational problems underlying the schemes, i.e., discrete logarithm and integer factorization. The prospective arrival of capable-enough quantum computers already threatens today's secret communication in terms of their long-term secrecy when stored to be later decrypted. Therefore, researchers strive to develop and deploy alternative schemes. In this work, evaluate a key exchange protocol based on combining public-key schemes with physical-layer security, anticipating the prospect of quantum attacks. If powerful quantum attackers cannot immediately obtain private keys, legitimate parties have a window of short-term secrecy to perform a physical-layer jamming key exchange (JKE) to establish a long-term shared secret. Thereby, the protocol constraints the computation time available to the attacker to break the employed public-key cryptography. In this paper, we outline the protocol, discuss its security, and point out challenges to be resolved.
Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media platforms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and elevated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technologies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These advances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to collect and curate the dataset, and the dataset's characteristics at the current stage.
Hyperproperties are commonly used in computer security to define information-flow policies and other requirements that reason about the relationship between multiple computations. In this paper, we study a novel class of hyperproperties where the individual computation paths are chosen by the strategic choices of a coalition of agents in a multi-agent system. We introduce HyperATL*, an extension of computation tree logic with path variables and strategy quantifiers. Our logic can express strategic hyperproperties, such as that the scheduler in a concurrent system has a strategy to avoid information leakage. HyperATL* is particularly useful to specify asynchronous hyperproperties, i.e., hyperproperties where the speed of the execution on the different computation paths depends on the choices of the scheduler. Unlike other recent logics for the specification of asynchronous hyperproperties, our logic is the first to admit decidable model checking for the full logic. We present a model checking algorithm for HyperATL* based on alternating automata, and show that our algorithm is asymptotically optimal by providing a matching lower bound. We have implemented a prototype model checker for a fragment of HyperATL*, able to check various security properties on small programs.
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with high carried object probability and strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.
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