Waterway transportation sustainably facilitates global trade through eco-efficient cargo movement, where accurate water level forecasting is critical for ensuring navigational safety and operational continuity. To develop a highly accurate prediction model, it is essential to consider the periodic characteristics of water level data, which often emerge in real-world datasets. This study introduces a novel reservoir state structure based on reservoir computing theory, the Self-attention Hierarchical Kernel Reservoir State Network (SHK-RSN). It employs three primary mechanisms. First, a hierarchical feature extraction method groups training data and extracts high-dimensional features from these groups using the kernel trick in a hierarchical manner. Second, a self-attention weight selection approach is introduced to replace the random weights in the Hierarchical Kernel Reservoir State Network (HK-RSN), improving the rationale for hidden neuron connections and enhancing the interpretability of weight selection. Third, a novel reservoir state structure is proposed to capture periodic information and extract temporal features across periods, enabling the model to capture richer temporal information and identify relationships among periods. Experiments are conducted on one artificial and five real-world time series datasets, with forecast performance evaluated over 1–7 steps. Our proposed model, SHK-RSN, is compared with models based on randomization, the kernel trick, and deep learning. The experimental results demonstrate that SHK-RSN exhibits superior forecasting ability relative to the baselines. It achieves the best Symmetric Mean Absolute Percentage Error (SMAPE) across all datasets in the 1–7 period average among baseline methods, demonstrating a relative improvement of 25.7% to 46.9% over the conventional Echo State Network.

Multi-access edge computing (MEC) is a promising technology that can enhance the computational capabilities of maritime Internet of Things (MIoT) systems. By deploying smart buoys equipped with MEC servers (MSs) and base stations (BSs) along fairway boundaries, the distributed computing cluster can significantly improve the timeliness and efficiency in processing latency-sensitive and resource-intensive MIoT applications. However, due to the limited computational resources and service coverage of these smart buoys, mobile MIoT users (MUs), e.g., vessels, may experience degraded access latency and service discontinuity. Resource allocation (RA) and service migration serve as effective techniques for integrating and coordinating the communication, computing, and energy resources of MIoTs. This paper investigates a joint RA and service migration (JRASM) optimization problem for smart-buoy-enabled maritime MEC networks to minimize the long-term task processing latency of MUs while ensuring service continuity. The JRASM optimization addresses the coupled challenges of resource competition, random task generation, and vessel mobility constraints, while integrating collision avoidance mechanisms. Subsequently, the mixed-integer nonlinear programming problem is reformulated as a Markov Decision Process (MDP) and solved by employing deep reinforcement learning (DRL) methods. A novel branching double deep Q network (Branching DDQN)-based JRASM algorithm is designed to decompose the hybrid discrete-continuous action space into independent subspaces, achieving enhanced learning efficiency while reducing parameters by 40%. Simulation results demonstrate that the proposed Branching DDQN-based JRASM algorithm achieves significantly superior performance in terms of latency reduction and task completion rate compared to other DRL-based JRASM baselines, particularly under high-workload scenarios.

Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism‐based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.

Some Hamiltonian-like properties of k-ary n-cube are explored. In this paper, we show that k-ary 2-cube is almost Hamiltonian-connected, bipanconnected, bipancyclic. And we prove that k-ary n-cube is almost Hamiltonian-connected when n \ge 3 , and Hamiltonianconnected if k is odd.