Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities

Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly. Existing methods commonly extract log sequence features using classical machine learning techniques to identify whether a new sequence is an anomaly or not. However, these classical approaches often require trade-offs between efficiency and accuracy. The advent of quantum machine learning (QML) offers a promising alternative. By transforming parts of classical machine learning computations into parameterized quantum circuits (PQCs), QML can significantly reduce the number of trainable parameters while maintaining accuracy comparable to classical counterparts. In this work, we introduce a unified framework, \ourframework{}, for evaluating QML models in the context of LogAD. This framework incorporates diverse log data, integrated QML models, and comprehensive evaluation metrics. State-of-the-art methods such as DeepLog, LogAnomaly, and LogRobust, along with their quantum-transformed counterparts, are included in our framework.Beyond standard metrics like F1 score, precision, and recall, our evaluation extends to factors critical to QML performance, such as specificity, the number of circuits, circuit design, and quantum state encoding. Using \ourframework{}, we conduct extensive experiments to assess the performance of these models and their quantum counterparts, uncovering valuable insights and paving the way for future research in QML model selection and design for LogAD.

Privacy Protection Framework against Unauthorized Sensing in the 5.8 GHz ISM Band

Unauthorized sensing activities pose an increasing threat to individual privacy, particularly in the industrial, scientific, and medical (ISM) band where regulatory frameworks remain limited. This paper presents a novel signal process methodology to monitor and counter unauthorized sensing activities. Specifically, we model the pedestrian trajectories as a random process. Then, we leverage the Cram\'er-Rao bound (CRB) to evaluate sensing performance and model it as sampling error of such a random process. Through simulation, we verify the accuracy of monitoring unauthorized sensing activities in urban scenarios, and validate the effectiveness of corresponding mitigation strategies.

Towards Zero-Shot Annotation of the Built Environment with Vision-Language Models (Vision Paper)

Equitable urban transportation applications require high-fidelity digital representations of the built environment: not just streets and sidewalks, but bike lanes, marked and unmarked crossings, curb ramps and cuts, obstructions, traffic signals, signage, street markings, potholes, and more. Direct inspections and manual annotations are prohibitively expensive at scale. Conventional machine learning methods require substantial annotated training data for adequate performance. In this paper, we consider vision language models as a mechanism for annotating diverse urban features from satellite images, reducing the dependence on human annotation to produce large training sets. While these models have achieved impressive results in describing common objects in images captured from a human perspective, their training sets are less likely to include strong signals for esoteric features in the built environment, and their performance in these settings is therefore unclear. We demonstrate proof-of-concept combining a state-of-the-art vision language model and variants of a prompting strategy that asks the model to consider segmented elements independently of the original image. Experiments on two urban features -- stop lines and raised tables -- show that while direct zero-shot prompting correctly annotates nearly zero images, the pre-segmentation strategies can annotate images with near 40% intersection-over-union accuracy. We describe how these results inform a new research agenda in automatic annotation of the built environment to improve equity, accessibility, and safety at broad scale and in diverse environments.

Exploring 6G Potential for Industrial Digital Twinning and Swarm Intelligence in Obstacle-Rich

With the advent of 6G technology, the demand for efficient and intelligent systems in industrial applications has surged, driving the need for advanced solutions in target localization. Utilizing swarm robots to locate unknown targets involves navigating increasingly complex environments. Digital Twinning (DT) offers a robust solution by creating a virtual replica of the physical world, which enhances the swarm's navigation capabilities. Our framework leverages DT and integrates Swarm Intelligence to store physical map information in the cloud, enabling robots to efficiently locate unknown targets. The simulation results demonstrate that the DT framework, augmented by Swarm Intelligence, significantly improves target location efficiency in obstacle-rich environments compared to traditional methods. This research underscores the potential of combining DT and Swarm Intelligence to advance the field of robotic navigation and target localization in complex industrial settings.

Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings

The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guarded, closed-weight models in terms of performance, domain adaptation, cost, and generalization. Centering under-resourced yet risk-intolerant settings in government, research, and healthcare, we see for-profit closed-weight models as incompatible with requirements for transparency, privacy, adaptability, and standards of evidence. Yet the performance penalty in using open-weight models, especially in low-data and low-resource settings, is unclear. We assess the feasibility of using smaller, open-weight models to replace GPT-4-Turbo in zero-shot, few-shot, and fine-tuned regimes, assuming access to only a single, low-cost GPU. We assess value-sensitive issues around bias, privacy, and abstention on three additional tasks relevant to those topics. We find that with relatively low effort, very low absolute monetary cost, and relatively little data for fine-tuning, small open-weight models can achieve competitive performance in domain-adapted tasks without sacrificing generality. We then run experiments considering practical issues in bias, privacy, and hallucination risk, finding that open models offer several benefits over closed models. We intend this work as a case study in understanding the opportunity cost of reproducibility and transparency over for-profit state-of-the-art zero shot performance, finding this cost to be marginal under realistic settings.

A Robust UAV-Based Approach for Power-Modulated Jammer Localization Using DoA

Unmanned aerial vehicles (UAVs) are well-suited to localize jammers, particularly when jammers are at non-terrestrial locations, where conventional detection methods face challenges. In this work we propose a novel localization method, sample pruning gradient descend (SPGD), which offers robust performance against multiple power-modulated jammers with low computational complexity.

Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation

Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation.

3D Cooperative Localization in UAV Systems: CRLB Analysis and Security Solutions

This paper presents a robust and secure framework for achieving accurate and reliable cooperative localization in multiple unmanned aerial vehicle (UAV) systems. The Cramer-Rao low bound (CRLB) for the three-dimensional (3D) cooperative localization network is derived, with particular attention given to the non-uniform spatial distribution of anchor nodes. Challenges of mobility and security threats are addressed, corresponding solutions are brought forth and numerically assessed . The proposed solution incorporates two key components: the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly Detection (TAD). The MAGD adapts the gradient descent algorithm to handle the configuration changes in cooperative localization systems, ensuring accurate localization in dynamic scenarios. The TAD cooperates with reputation propagation (RP) scheme to detect and mitigate potential attacks by identifying malicious data, enhancing the security and resilience of the cooperative localization.

Breaking of brightness consistency in optical flow with a lightweight CNN network

Sparse optical flow is widely used in various computer vision tasks, however assuming brightness consistency limits its performance in High Dynamic Range (HDR) environments. In this work, a lightweight network is used to extract illumination robust convolutional features and corners with strong invariance. Modifying the typical brightness consistency of the optical flow method to the convolutional feature consistency yields the light-robust hybrid optical flow method. The proposed network runs at 190 FPS on a commercial CPU because it uses only four convolutional layers to extract feature maps and score maps simultaneously. Since the shallow network is difficult to train directly, a deep network is designed to compute the reliability map that helps it. An end-to-end unsupervised training mode is used for both networks. To validate the proposed method, we compare corner repeatability and matching performance with origin optical flow under dynamic illumination. In addition, a more accurate visual inertial system is constructed by replacing the optical flow method in VINS-Mono. In a public HDR dataset, it reduces translation errors by 93\%. The code is publicly available at https://github.com/linyicheng1/LET-NET.

Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology

An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal treatment). To make our predictions, we explore temporal, visual and multi-modal models that combine satellite sensing and deep learning. We find that all of our neural-based approaches improve over conventional buffelgrass green-up models, and discuss how neural model deployment promises significant resource savings.