Recent advances in generative AI have introduced a new programming paradigm—vibe coding, a natural language–driven mode of AI collaboration. While promising for adults, little is known about how children engage with this approach, especially in block-based environments. To explore this gap, we conducted workshops with children of varying Scratch experience (n=41) and interviewed five Scratch teachers. Our study investigates how vibe coding impacts children’s programming learning and practice, and what challenges arise. Findings show that vibe coding has both positive and negative impacts across three key contexts of children’s programming experience: acquisition, application, and creation. Across the stages of vibe coding—goal articulation, information interpretation, and outcome evaluation—children encounter distinct challenges. By examining the mismatches between core assumptions of vibe coding and children’s needs, and analyzing its applicability across different contexts, we offer child-centered design implications for future vibe coding systems and GenAI tools.

Contrastive learning (CL) has demonstrated exceptional capability in extracting supervised signals and mitigating noise, increasingly attracting interest for its application in knowledge graph-enhanced (KG) recommendation. Nevertheless, existing approaches predominantly rely on a single augmentation strategy to construct contrastive views, often failing to effectively balance feature perturbation with semantic preservation. To overcome this limitation, we propose motif-guided multiview contrastive learning (MMCL), a unified framework for contrastive augmentation. MMCL leverages diverse view generation strategies across the user–item bipartite graph and the KG, perturbing graph structures while preserving essential semantics to the greatest extent possible. Specifically, for collaborative signals, we devise a data augmentation mechanism to model the interaction dynamics between users and items. For semantic information, we introduce a novel graph augmentation technique by constructing an interactive motif KG, enabling semantic contrastive learning within localized views. Furthermore, joint enhancement of graph and interaction data allows the model to capture global topological features of nodes. MMCL integrates contrastive learning at both local and global levels, effectively embedding local semantics and global topology into the learned representations. Extensive experiments conducted on three publicly available datasets reveal that MMCL outperforms advanced methods, particularly in scenarios with sparse interactions and noisy KG, while significantly alleviating popularity bias in recommendation.

Time-series clinical data are crucial for advancing healthcare analytics in areas like predictive modeling for patient outcomes, monitoring and managing chronic diseases, personalized treatment planning, and real-time alerting systems for critical care. Current studies focused on leveraging self-supervised learning methods to fully exploit the potential of such data without requiring a large number of labels. However, current methods use non-adaptive, hand-configured intensities, provide no mechanism for automatically discovering hard augmentations, and seldom integrate magnitude and temporal domains. To fill this gap, our study introduces robust self-supervised learning for unlabeled time-series data. Specifically, we propose a novel parameterized augmentor that emphasizes training on challenging samples to enhance learning effectiveness. Additionally, our temporal augmentation strategy is designed to add diversity to the temporal aspect of the data by time stretching and compression. Furthermore, alternating training framework is designed to integrate the augmentor with other model components for the min−max optimization, while using three unique loss functions to capture a broad spectrum of both global and predictive features effectively. Our experimental results demonstrate that this approach significantly outperforms existing methods, especially in scenarios with limited labeled data, and shows remarkable proficiency in cross-domain feature extraction.

| openaire: EC/HE/101141916/EU//Artificial User

Touchscreen typing requires coordinating the fingers and visual attention for button-pressing, proofreading, and error correction. Computational models need to account for the associated fast pace, coordination issues, and closed-loop nature of this control problem, which is further complicated by the immense variety of keyboards and users. The paper introduces CRTypist, which generates human-like typing behavior. Its key feature is a reformulation of the supervisory control problem, with the visual attention and motor system being controlled with reference to a working memory representation tracking the text typed thus far. Movement policy is assumed to asymptotically approach optimal performance in line with cognitive and design-related bounds. This flexible model works directly from pixels, without requiring hand-crafted feature engineering for keyboards. It aligns with human data in terms of movements and performance, covers individual differences, and can generalize to diverse keyboard designs. Though limited to skilled typists, the model generates useful estimates of the typing performance achievable under various conditions.

Current models for multi-intent detection and slot-filling have made some progress, but often overlook the potential of rich syntactic information and rely on window mechanisms that capture only local slot interactions. In this paper, we propose a Multi-grained Syntactic Graph Interaction Model (MSGIM) for joint multi-intent detection and slot filling. The model has two main core components: (1) Multi-grained syntax module. This module utilizes syntactic dependency types and syntactic dependency distances between words, enabling the model to learn syntactic structure and semantic information more comprehensively. (2) Syntactic graph interaction layer. Specifically, we construct a syntactic slot-aware layer and a syntactic intent-slot interaction layer based on dependency tree. These layers can capture more distant slot dependencies and the interactions between intents and slots. Experimental results show that our model achieves a significant performance improvement, with an overall accuracy improvement of 4.4% on the MixATIS dataset compared to the previous best model.

In this article, we present a work using a smartphone with an off-the-shelf WiFi router for human activity recognition with various scales. The router serves as a hotspot for transmitting WiFi packets. The smartphone is configured with customized firmware and developed software for capturing WiFi channel state information (CSI) data. We extract the features from the CSI data associated with specific human activities, and utilize the features to classify the activities using machine learning models. To evaluate the system performance, we test 20 types of human activities with different scales including seven small motions, four medium motions, and nine big motions. We recruit 60 participants and spend 140 hours for data collection at various experimental settings, and have 36 000 data points collected in total. Furthermore, for comparison, we adopt three distinct machine learning models, including convolutional neural networks (CNNs), decision tree, and long short-term memory. The results demonstrate that our system can predict these human activities with an overall accuracy of 97.25%. Specifically, our system achieves a mean accuracy of 97.57% for recognizing small-scale motions that are particularly useful for gesture recognition. We then consider the adaptability of the machine learning algorithms in classifying the motions, where CNN achieves the best predicting accuracy. As a result, our system enables human activity recognition in a more ubiquitous and mobile fashion that can potentially enhance a wide range of applications such as gesture control, sign language recognition, etc.

This paper focuses on the three-dimensional (3-D) source localization problem using one-dimensional (1-D) angle of arrival (AOA) and time difference of arrival (TDOA) measurements. A weighted least square (WLS) estimate is proposed by exploiting the pseudo-linearization technique for this novel scheme. This method provides a closed-form solution of the source position. Then, the Gauss-Newton method is applied to improve the accuracy further. The Cramer-Rao lower bound (CRLB) is derived to evaluate the performance. Simulation results show that the performance of the proposed localization algorithm is close to the CRLB in small measurement noise region.

Joint angle and delay estimation (JADE) has become a key technique for accurate localization in dense multipath indoor scenarios. This paper addresses the problem of JADE for multiple reflections of a multi-carrier signal impinging on multiple-antenna system. Inspired by the Minimum Variance Distortionless Response (MVDR), we propose an efficient spectral method for this problem. The proposed method is a high resolution estimator which only need two-dimensional search. Unlike the maximum likelihood JADE estimator, the number of paths is not required known a prior. Furthermore, the smoothing technique and diagonal loading technique are exploited to construct reasonable sample covariance matrix. Simulation results using the IEEE 802.11ac standard's setup parameters are provided to validate the proposed method's effectiveness. At a low or medium signal-to-noise ratio (SNR), the performance of the proposed method is superior to the MUSIC-JADE algorithm with path number estimation.