Understanding the underlying argumentative flow in analytic argumentative writing is essential for discourse comprehension, especially in complex argumentative discourse such as think-tank commentary.However, existing structure modeling approaches often rely on surface-level topic segmentation, failing to capture the author's rhetorical intent and reasoning process.To address this limitation, we propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question (reflecting the author's intent) and a set of attentional foci (highlighting analytical pathways).To assess its effectiveness, we introduce an argument reconstruction task in which the modeled discourse structure guides both evidence retrieval and argument generation.We construct a high-quality dataset comprising 600 authoritative Chinese think-tank articles for experimental analysis.To quantitatively evaluate performance, we propose two novel metrics: (1) Claim Coverage, measuring the proportion of original claims preserved or similarly expressed in reconstructions, and (2) Evidence Coverage, assessing the completeness of retrieved supporting evidence.Experimental results show that our framework uncovers the author's argumentative logic more effectively and offers better structural guidance for reconstruction, yielding up to a 10% gain in claim coverage and outperforming strong baselines across both curated and LLM-based metrics.

Given a specific class space, open set recognition (OSR) requires a model to not only accurately classify data samples belonging to that class space, but also to identify samples from unseen classes. Based on prototype learning, existing OSR methods seek to build a separable feature space in which the unseen classes are as far away as possible from the given classes. However, these methods may overly focus on either the unseen classes or the given classes, resulting in unstable performance and lack of robustness. To handle this challenge, a robust OSR learning framework, named dual prototype learning is proposed. During the training phase, a set of dual prototypes is created for each given class, where one prototype (intra-class prototype) is used to form a compact intra-class feature representation, and the other prototype (anti-class prototype) is used to form a space that does not belong to the class by pushing the anti-class prototype away from the intra-class prototype. In the inference phase, our method can achieve a more stable and robust decision-making process by simultaneously measuring the distance between the sample and the intra-class prototypes, as well as the distance between the sample and the anti-class prototypes. Qualitative quantitative experiments demonstrate that the proposed method outperforms existing methods in terms of classification performance and robustness.

The uncertainty of risk factors in infrastructure construction presents significant challenges to the management of construction progress. To effectively control construction progress while ensuring quality and reducing costs, we propose an adaptive scheduling method for infrastructure construction progress based on digital twin. First, we establish a digital twin model that reflects the construction progress by considering relevant progress information. Subsequently, taking into account the uncertainties associated with human factors, construction processes, and environmental factors, we develop a safety assessment method for power infrastructure and formulate a construction stoppage model. Then, we design an adaptive scheduling method that considers the uncertainties related to construction stoppage risks. Finally, the effectiveness and practicality of the proposed methods are validated through a comparative analysis of different cases.

Email worms propagate across networks by taking advantage of email relationships. Modeling the propagation of email worms can help predict their potential damages and develop countermeasures. We propose a novel analytical model on the propagation process of modern reinfection email worms. It relies on probabilistic analysis, and thus can provide a steady and reliable assessment on the propagation dynamics. Additionally, by introducing virtual users to represent the repetitious spreading process, the proposed model overcomes the computational challenge caused by reinfection processes. To demonstrate the benefits of our model, we conduct a series of experimental evaluation. The results show that our novel approach achieves a greater accuracy and is more suitable for modeling modern email worms than previous models.

There are the two common means for propagating worms: scanning vulnerable computers in the network and sending out malicious email attachments. Modeling the propagation of worms can help us understand how worms spread and devise effective defence strategies. Most traditional models simulate the overall scale of infected network in each time tick, making them invalid for examining deep inside the propagation procedure among individual nodes. For this reason, this paper proposes a novel probability matrix to model the propagation mechanism of the two main classes of worms (scanning and email worms) by concentrating on the propagation probability. The objective of this paper is to access the spreading and work out an effective scheme against the worms. In order to evaluate the effects of each major component in our probability model, we implement a series of experiments for both worms. From the results, the network administrators can make decision on how to reduce the number of vulnerable nodes to a certain threshold for scanning worms, and how to immunize the highly-connected node for preventing worm's propagation for email worms.

Each year, large amounts of money and labor are spent on patching the vulnerabilities in operating systems and various popular software to prevent exploitation by worms. Modeling the propagation process can help us to devise effective strategies against those worms’ spreading. This paper presents a microcosmic analysis of worm propagation procedures. Our proposed model is different from traditional methods and examines deep inside the propagation procedure among nodes in the network by concentrating on the propagation probability and time delay described by a complex matrix. Moreover, since the analysis gives a microcosmic insight into a worm's propagation, the proposed model can avoid errors that are usually concealed in the traditional macroscopic analytical models. The objectives of this paper are to address three practical aspects of preventing worm propagation: (i) where do we patch? (ii) how many nodes do we need to patch? (iii) when do we patch? We implement a series of experiments to evaluate the effects of each major component in our microcosmic model. Based on the results drawn from the experiments, for high-risk vulnerabilities, it is critical that networks reduce the number of vulnerable nodes to below 80%. We believe our microcosmic model can benefit the security industry by allowing them to save significant money in the deployment of their security patching schemes.

Stepping stone attacks are often used by network intruders to hide their identities. The Round Trip Times (RTT) between the send packets and corresponding echo packets for the connection chains of stepping stones are critical for detecting such attacks. In this paper, we propose a novel real-time RTT getting algorithm for stepping stones which is based on the estimation of the current RTT value. Our experiments show that it is far more precise than the previous real-time RTT getting algorithms. We also present the probability analysis which shows that our algorithm has a high matching rate and a high accurate rate.