Text watermarking aims to subtly embeds statistical signals into text by controlling the Large Language Model (LLM)’s sampling process, enabling watermark detectors to verify that the output was generated by the specified model. The robustness of these watermarking algorithms has become a key factor in evaluating their effectiveness. Current text watermarking algorithms embed watermarks in high-entropy tokens to ensure text quality. In this paper, we reveal that this seemingly benign design can be exploited by attackers, posing a significant risk to the robustness of the watermark. We introduce a generic efficient paraphrasing attack, the Self-Information Rewrite Attack (SIRA), which leverages the vulnerability by calculating the self-information of each token to identify potential pattern tokens and perform targeted attack. Our work exposes a widely prevalent vulnerability in current watermarking algorithms. The experimental results show SIRA achieves nearly 100% attack success rates on seven recent watermarking methods with only $0.88 per million tokens cost. Our approach does not require any access to the watermark algorithms or the watermarked LLM and can seamlessly transfer to any LLM as the attack model even mobile-level models. Our findings highlight the urgent need for more robust watermarking.

While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes - Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.

In computer-based learning environments (CBLEs), adopting effective self-regulated learning (SRL) strategies requires sophisticated coordination of multiple SRL processes. While various studies have proposed adaptive SRL scaffolds (i.e. real-time advice on adopting effective SRL processes) and embedded them in CBLEs to facilitate learners' effective use of SRL strategies, two key research gaps remain. First, there is a lack of research on SRL scaffolds that are based on continuous assessment of both learners' SRL processes and learning conditions (e.g., awareness of learning resources) to provide adaptive support. Second, current analytics-based scaffolding mechanisms lack the scalability needed to effectively address multiple learning conditions. Integration of analytics of SRL with generative artificial intelligence (GenAI) can provide scalable scaffolding for real-time SRL processes and evolving conditions. Yet, empirical studies implementing and evaluating effects of this integration remain scarce. To address these limitations, we conducted a randomized control trial, assigning participants to three groups (control, process only, and process with condition groups) to investigate the effects of using GenAI to turn insights from real-time analytics about students' SRL processes and conditions into adaptive scaffolds. The results demonstrate that integrating real-time analytics with GenAI in adaptive SRL scaffolds - addressing both SRL processes and dynamic conditions - promotes more metacognitive learning patterns compared to the control and process-only groups. In addition, the learners showed varying levels of compliance with analytics-based GenAI scaffolds, and this was also reflected in how the learners coordinated their SRL processes, particularly in the performance phase of SRL. This study contributes to the literature by designing, implementing, and evaluating the impact of adaptive scaffolds on learners' SRL processes using real-time analytics with GenAI.

Despite the remarkable capabilities of deep neural networks in image recognition, the dependence on activation functions remains a largely unexplored area and has yet to be eliminated. On the other hand, Polynomial Networks is a class of models that does not require activation functions, but have yet to perform on par with modern architectures. In this work, we aim close this gap and propose MONet, which relies *solely* on multilinear operators. The core layer of MONet, called Mu-Layer, captures multiplicative interactions of the elements of the input token. MONet captures high-degree interactions of the input elements and we demonstrate the efficacy of our approach on a series of image recognition and scientific computing benchmarks. The proposed model outperforms prior polynomial networks and performs on par with modern architectures. We believe that MONet can inspire further research on models that use entirely multilinear operations.

We propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.

Coding discourse data is critical to many learning analytics studies. To code their data, researchers may use manual techniques, automated techniques, or a combination thereof. Manual coding can be time-consuming and error prone; automated coding can be difficult to implement for non-technical users. Generative artificial intelligence (GAI) offers a user friendly alternative to automated discourse coding via prompting and APIs. We assessed the ability of GAI, specifically the GPT class of models, at automatically coding discourse in the context of a learning analytics study using a variety of prompting and training strategies. We found that fine-tuning approaches produced the best results; however, no results achieved standard thresholds for reliability in our field.

This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.

This article investigates the adaptive learning control for a class of switched strict-feedback nonlinear systems with external disturbances and input dead zone. To handle unknown nonlinearity and compound disturbances, a collaborative estimation learning strategy based on neural approximation and disturbance observation is proposed, and the adaptive neural switched control scheme is studied in a dynamic surface control framework. In the adaptive learning control design, to obtain the evaluation information of uncertain learning, the prediction error is constructed based on the composite learning scheme. Then, the prediction error and the compensated tracking error are applied to construct the adaptive laws of switched neural weights and switched disturbance observers. The system stability analysis is carried out through the Lyapunov approach, where the switching signal with average dwell time is considered. Through the simulation test, the effectiveness of the proposed adaptive learning controller is verified.

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

This paper addresses a terminal sliding mode strategy to control the attitude of quadrotor while achieving the finite time convergence. To deal with system uncertainty and time-varying disturbance, a hybrid controller using single-hidden layer feedforward network (SLFN) and disturbance observer (DOB) is proposed. Fast terminal sliding mode surface is designed to construct the sliding mode control. To improve learning speed, the updating law of SLFN weight utilizes the information of the fast terminal sliding mode. The effectiveness of the proposed controller is demonstrated with simulation example.