Predicting human mobility remains a fundamental challenge, especially when individuals deviate from routine patterns due to exploration, disruptions, or rare events. While sequential models like Transformers excel at capturing regular movement patterns, their performance often degrades under nonroutine scenarios involving rare or unfamiliar transitions. To address this, we propose ROAM (Routine-Oriented Adaptive Mobility Predictor), a novel framework that jointly models human mobility from both sequential and relational perspectives, enabling adaptive handling of both routine and nonroutine behaviors during prediction. ROAM combines a sequential encoder that captures historically frequent transitions with a complementary graph-based relational reasoning module that encodes both user-specific and group-level mobility structures. To dynamically integrate these views, we introduce a hierarchical confidence-aware gating mechanism that adaptively balances sequential and relational predictions based on their internal reliability. Extensive experiments on real-world mobility datasets show that ROAM consistently outperforms state-of-the-art baselines in next location prediction. Further analysis reveals that the combination of sequential and relational reasoning substantially improves robustness, particularly under out-of-routine scenarios.

Accurate dam displacement prediction is vital for optimizing maintenance and ensuring structural safety. Nevertheless, current models often struggle to effectively capture the complex relationships between structural responses and environmental variables, alongside the interactions between temporal dynamics and multivariate data, resulting in suboptimal predictive accuracy. Therefore, we propose a dual-branch interactive fusion network (DBIFN) for dam displacement prediction using parallel temporal representation and gated cross-attention. The dual-branch architecture, which parallelly integrates the enhanced Transformer (eTransformer) and long short-term memory (LSTM), is designed to optimize feature extraction and interaction modeling across multiple dimensions. Specifically, eTransformer is dedicated to extracting features from targeted displacement sequences, while LSTM effectively processes auxiliary environmental dynamics, enabling a comprehensive analysis of underlying patterns within monitoring data. To fully fuse the interpreted temporal features from dual-branch outputs, we introduce a new cross-attention module to utilize the multi-dimensional gated attention unit to efficiently encode them into semantic representations, followed by a Kolmogorov-Arnold network mapping for further representation enhancement. The effectiveness of the proposed model is validated using real-world monitoring datasets collected from a concrete dam project, with experiments conducted across multiple monitoring points. Results demonstrate that DBIFN achieves superior prediction accuracy compared to both single-branch and conventional baseline models. Across all monitoring points, the proposed model can effectively capture temporal variations, attaining an average coefficient of determination of over 0.95 on the test set and outperforming comparative models in most metrics. Furthermore, statistical significance testing confirms the reliability and reproducibility of the results, while computational efficiency is maintained within inference time constraints. These findings offer valuable insights into the practical application of DBIFN-based monitoring models and support informed decision-making.

Web has become increasingly oriented to multimedia content. Most information on the web is conveyed from images. Text localization in web image plays an important role in web image information extraction and retrieval. Current works on text localization in web images assume that text regions are in homogenous color and high contrast. Hence, the approaches may fail when text regions are in multi-color or imposed in complex background. In this paper, we propose a text extraction algorithm from web images based on the probabilistic candidate selection model. The model firstly segments text region candidates from input images using wavelet, Gaussian mixture model (GMM) and triangulation. The likelihood of a candidate region containing text is then learnt using a Bayesian probabilistic model from two features, namely, histogram of oriented gradient (HOG) and local binary pattern histogram Fourier feature (LBP-HF). Finally best candidate regions are integrated to form text regions. The algorithm is evaluated using 155 non-homogenous web images containing around 600 text regions. The results show that the proposed model is able to extract text regions from non-homogenous images effectively.

Graphical components information extraction is a crucial step in the chart recognition and understanding process. However, existing methods of information extraction from chart images either are type-dependent or rely on certain assumptions. In this paper, we present a general method to extract vectorized graphical information from scientific chart images. Our algorithm firstly constructs a data structure called directional single-connected chains (DSCC). It then employs ellipse-specific fitting and orthogonal diagonalization to calculate the curvatures of the chains and classify the chains into either straight lines or arcs. Finally we combine all straight lines and all arcs accordingly and use linear regression to compute their attributes. The DSCC has a good property in that it is less susceptible to noise. The experiment results show that our algorithm is efficient, robust and accurate.