Traffic engineering (TE) is becoming increasingly important in modern networks, as it can improve network performance by splitting traffic across paths. However, traditional TE solvers can be too slow for rapid changes, while recent machine learning (ML) solvers are fast but often fail to support dynamic network conditions, such as topology changes or link capacity changes. Moreover, they usually support only simple TE objectives that do not account for potential link overload, which makes them less practical. In this paper, we present KaeTE, an ML-based TE solver that supports dynamic network conditions and the throughput objective. The design of KaeTE leverages the convexity of the throughput objective. Specifically, KaeTE first makes the throughput objective strongly convex through regularization. KaeTE then adopts a learning-to-optimize (L2O)-inspired model to iteratively refine the dual variables. To ensure that the final TE solutions do not overload any link, KaeTE generates the final TE solutions and the corresponding throughput objective value through a constraint-aware loss function. Evaluations on both dynamic and static network conditions show that KaeTE consistently outperforms baselines in our evaluated settings.

Building outlines have many applications. However, owing to the diversity of buildings and the complexity of the surrounding environment, automatic extraction of building outlines from remote sensing data remains challenging. This paper presents a novel approach for extracting building outlines from digital surface models (DSM) and orthophotographs. The DSM provides initial contour lines, while the orthophotograph indicates where vegetation is obstructing the building outline. The approach introduces two key algorithms: Distance-Constrained Clustering (DCC), to cluster contour lines, and Gradient-based Optimal Contour Selection (G-OCS), to select building outlines. Vegetation information is used to recover obstructed building outlines and improve outline accuracy and completeness. Experimental results, using the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen benchmark dataset, demonstrate the method’s performance (quality metric: 85.0% for individual regions, 73.6% for individual objects, and 99.1% for objects >50 m). Validation using a dataset from Shandong Province (China) confirmed the method’s robustness and applicability for complex urban environments. The approach effectively handles challenges such as interference from vegetation and irregular building structures, outperforming techniques such as WHUZ, CNN/8F+, and HD-Net. This novel method automates building outline extraction and provides useful building information, with applications in urban planning, disaster management, and smart city development.

Traditional disk failure prediction approaches struggle to scale with data growth, as they treat data as a whole collection to obtain the global data view for preprocessing and training. Existing distributed machine learning and stream mining systems are designed to scale data processing, particularly for training. However, scaling disk failure prediction faces challenges in the scalability of preprocessing, including additional data movements from data collection to training, data inflation during preprocessing, and multiple-to-multiple data allocation. To address these challenges, we present SCALEDFP, a general framework for scaling disk failure prediction via multi-source stream mining based on three techniques: near-data preprocessing, random downsampling, and training data allocation. SCALEDFP scales disk failure prediction with the number of data sources. It achieves significant throughput gains of preprocessing and training with comparable prediction accuracy against a state-of-the-art disk failure prediction approach that collects data in a centralized place.

Federated Learning (FL) has been applied across diverse domains as a powerful technique but faces critical challenges in privacy protection. Secure aggregation and additive homomorphic encryption are two of the most commonly used cryptographic methods to protect model updates. To provide a strong privacy guarantee, both methods satisfy the additivity and require the integer form to encrypt model updates. However, they still suffer from a non-negligible overhead of the computation and communication. To mitigate such overhead, existing approaches of lossy compression (e.g., gradient compression) have been explored but exhibit the inapplicability of FL with additive cryptographic methods. In this paper, we propose FedSSA, a novel compression framework to reduce the overhead of additive cryptographic methods in FL based on two new techniques: (i) QSRHT Sketch, a sketch-based compression method that supports large compression ratios with a bounded error with the integer requirement, and (ii) periodic rehashing, which ensures the unbiasedness of QSRHT Sketch. Our evaluation shows that FEDSSA achieves a high compression ratio ($\times 160$) with a low model accuracy degradation (less than 5%). For additive homomorphic encryption, FEDSSA reduces the average computation time per round by up to 58.15 % compared to state-of-the-art compressor that support additive homomorphic encryption, with a low test accuracy drop (within 2.2 %).