Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This paper proposes an agentic workflow to automate and scale the red-teaming process of LLMs through the Composition-of-Principles (CoP) framework, where human users provide a set of red-teaming principles as instructions to an AI agent to automatically orchestrate effective red-teaming strategies and generate jailbreak prompts. Distinct from existing red-teaming methods, our CoP framework provides a unified and extensible framework to encompass and orchestrate human-provided red-teaming principles to enable the automated discovery of new red-teaming strategies. When tested against leading LLMs, CoP reveals unprecedented safety risks by finding novel jailbreak prompts and improving the best-known single-turn attack success rate by up to 19.0 times.

ABSTRACT This paper presents a novel approach to pollution assessment by investigating support vector machines (SVM) with an uncertainty option to overcome the limitations of traditional kriging. While kriging is a major tool for geostatistical modelling, allowing to estimate the distribution of contaminants in a region from a small set of samples, it does not allow to extract also the uncertainty map. An uncertainty map is of great interest, as it allows to identify regions of high uncertainty where one should sample in order to reduce high level of uncertainties. In this paper, we propose two variants of the SVM with an uncertainty option, each using a different hinge loss to improve the accuracy and efficiency. These losses allow to estimate different levels of contaminations, as well as uncertainty, such as the three levels: positive, uncertain and negative, namely for pollution estimation: high‐pollution, uncertain and low‐pollution. In addition to the exploration of SVM variants, we propose an innovative active sample selection strategy based on the uncertainty criterion. This strategy is designed to systematically reduce uncertainties in pollution assessment, thus providing adaptability to dynamic environmental changes. An incremental SVM with an uncertainty option is introduced to further optimise the sample selection process. Furthermore, the decision‐making process is refined through the introduction of a novel three‐hinge loss. The corresponding optimization problem and its resolution allow for a more nuanced contamination assessment with multiple levels of estimation, providing a valuable tool for characterising contamination levels with increased granularity. Extensive experiments on synthetic and real data validate the proposed methodology. Synthetic data simulations assess the quality of the approach, while real data from a two‐dimensional porosity measurement demonstrate practical applicability. This research contributes to the advancement of pollution assessment methodologies, providing an adaptable solution for environmental monitoring.

Abstract To promptly detect abnormal events in surveillance videos, this article designs a video anomaly detection method based on multiple instance learning. Generally, abnormal events occur less frequently compared to normal events. Traditional video surveillance relies on manual operation to monitor scenes and detect abnormal events by watching surveillance videos. However, watching surveillance footage is a labor‐intensive task, and prolonged observation can lead to visual fatigue and lack of concentration, which in turn results in missed detections and false positives [1]. Therefore, it is crucial to develop intelligent algorithms for video anomaly detection. The method can detect whether segments of a video contain abnormal events. First, the I3D network is used as a feature extractor to capture spatiotemporal features from the input video. Then, the spatiotemporal information is processed and input into a segment‐level anomaly detector based on multiple instance learning for detection. The authors treat abnormal videos as positive bags and normal videos as negative bags, and automatically learn a deep anomaly ranking model that can predict abnormal segments. Finally, the results of the training were tested and analyzed, demonstrating that the model is capable of detecting abnormal traffic segments.

Information technology and personalized demands are driving the transportation system from passive transportation to active service provision. Autonomous transportation systems (ATSs) are resultantly emerging. A strongly related system architecture can effectively support the planning, construction, and operation of ATS. The current research on the relationship between architectures focuses on the definition of elements and the description of interfaces; however, insufficient research has examined the mapping relationship of the architectural elements. Therefore, to construct a set of architecture systems with explicit mapping relationships, we proposed an information flow (IF) extraction method based on data flow (DF) text to realize automated mapping between logical DF and physical IF. First, to better describe the ATS, we deconstructed the system with service as the basic unit, introduced the structure and constituent elements of the logical and physical architecture, and summarized the mapping principles of the architectural elements. Next, we extracted the DF set based on flow attributes and implemented the vectorization of the DF text. Then, we obtained the similarity of the DFs through text semantic analysis and completed the identification of similar DFs. Finally, to realize the independent construction of IF, we designed a graph-based DF aggregation method to complete the automated mapping of architectural elements. Experimental results showed that the construction method of IF proposed in this article met the requirements of ATS architecture and could effectively support the design and construction of the ATS architecture system.

Cellular signaling data (CSD) have attracted unprecedented attention due to their large size, long observation period, and high followability. Before applying CSD, a series of data processing steps are indispensable; among those steps, staying point recognition is the basis for recognizing individual travel states and thus the influence of further application of CSD. Previous work indicates that the existing staying point recognition algorithms have two common aspects. One is the requirement of a fixed spatiotemporal threshold to analyze the user’s travel characteristics. The other is the insufficiency of accuracy assessment, which indicates that further studies are expected owing to the lack of ground truth data in CSD. In this work, a “spatiotemporal window”-based algorithm is proposed to recognize individual staying and moving states. First, an iterative-learning-based model is designed to cluster individual trajectory points without predefined spatiotemporal thresholds. Then, rules to distinguish the staying or moving cluster are made from individual travel characteristics. Moreover, verification work is carried out by collecting volunteers’ ground truth data using our developed smartphone application, which achieves an accuracy of 91.3%. Finally, the results demonstrate the effectiveness and robustness of the algorithm through the performance of comparison and sensitivity analyses.