General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query–table mismatch. Recent LLM-based retrieval augmentation methods mitigate this issue by generating synthetic queries, yet they often rely on heuristic partial-table selection and seldom leverage these synthetic queries as supervision to improve the embedding model. We introduce CGPT, a training framework that enhances table retrieval through LLM-generated supervision. CGPT constructs semantically diverse partial tables by clustering table instances using K-means and sampling across clusters to broaden semantic coverage. An LLM then generates synthetic queries for these partial tables, which are used in hard-negative contrastive fine-tuning to refine the embedding model. Experiments across four public benchmarks (MimoTable, OTTQA, FetaQA, and E2E-WTQ) show that CGPT consistently outperforms the existing baselines with an average R@1 improvement of 16.54%. Under cross-domain evaluation, CGPT further demonstrates strong cross-domain generalization and remains effective even when using smaller LLMs for synthetic query generation. These results indicate that semantically guided partial-table construction, combined with contrastive training from LLM-generated supervision, provides an effective and scalable paradigm for large-scale table retrieval. Our code is available at https://github.com/yumeow0122/CGPT.

Table retrieval is the task of retrieving the most relevant tables from large-scale corpora given natural language queries. However, structural and semantic discrepancies between unstructured text and structured tables make embedding alignment particularly challenging. Recent methods such as QGpT attempt to enrich table semantics by generating synthetic queries, yet they still rely on coarse partial-table sampling and simple fusion strategies, which limit semantic diversity and hinder effective query–table alignment. We propose STAR (Semantic Table Representation), a lightweight framework that improves semantic table representation through semantic clustering and weighted fusion. STAR first applies header-aware K-means clustering to group semantically similar rows and selects representative centroid instances to construct a diverse partial table. It then generates cluster-specific synthetic queries to comprehensively cover the table's semantic space. Finally, STAR employs weighted fusion strategies to integrate table and query embeddings, enabling fine-grained semantic alignment. This design enables STAR to capture complementary information from structured and textual sources, improving the expressiveness of table representations. Experiments on five benchmarks show that STAR achieves consistently higher Recall than QGpT on all datasets, demonstrating the effectiveness of semantic clustering and weighted fusion for robust table representation. Our code is available at https://github.com/adsl135789/STAR.

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data has remained untouched. In this article, we introduce a badminton language to fully describe the process of the shot, and we propose a deep-learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency between the action sequence and the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. We also conducted case studies to show the ability to enhance players’ decision-making confidence and to provide advanced insights for coaching, which benefits the badminton analysis community and bridges the gap between the field of badminton and computer science.

In this paper, we explore the task of extracting prosecution elements (text description about prosecution elements) in an indictment. We approach the prosecution element extraction problem by formulating it as a reading comprehension task. Specifically, our idea is to train a reading comprehension model to extract a text span to indicate the statement of a crime element according to an asked question. By such a reformulation, we leverage the power of neural machine reading models to prosecution element extraction task. Experimental evaluation demonstrates the feasibility of the machine reading reformulation. We also make our code and data available on https://github.com/NCHU-NLP-Lab/Legal-Document-Question-Answering

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data is remained untouched. In this paper, we introduce a badminton language to fully describe the process of the shot and propose a deep learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency of the action sequence to the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. The source code is publicly available at https://github.com/wywyWang/Shot-Influence.

Mining data collected from industrial manufacturing process plays an important role for intelligent manufacturing in Industry 4.0. In this paper, we propose a deep convolutional model for predicting wafer fabrication quality in an intelligent integrated-circuit manufacturing application. The wafer fabrication quality prediction is motivated by the need for improving product line efficiency and reducing manufacturing cost by detecting potential defective work-in-process (WIP) wafers. This work considers the following two crucial data characteristics for wafer fabrication. First, our model is designed to learn spatial correlation between quality measurements on WIP wafers and fabrication results through an encoder-decoder neural network. Second, we leverage the fact that different products share the same raw manufacturing process to enable the knowledge transferring between prediction models of different products. Performance evaluation on real data sets is conducted to validate the strengths of our model on quality prediction, model interpretability, and feasibility of transferring knowledge.

In this paper, we investigate the problem of mining multivariate time series data generated from sensors mounted on manufacturing stations for early product quality prediction. In addition to accurate quality prediction, another crucial requirement for industrial production scenarios is model interpretability, i.e., to understand the significance of an individual time series with respect to the final quality. Aiming at the goals, this paper proposes a multi-task learning model with an encoder-decoder architecture augmented by the matrix factorization technique and the attention mechanism. Our model design brings two major advantages. First, by jointly considering the input multivariate time series reconstruction task and the quality prediction in a multi-task learning model, the performance of the quality prediction task is boosted. Second, by incorporating the matrix factorization technique, we enable the proposed model to pay/learn attentions on the component of the multivariate time series rather than on the time axis. With the attention on components, the correlation between a sensor reading and a final quality measure can be quantized to improve the model interpretability. Comprehensive performance evaluation on real data sets is conducted. The experimental results validate that strengths of the proposed model on quality prediction and model interpretability.

Understanding users is a key for many business applications. In this paper, we propose to pursue user preference understanding by their Wi-Fi logs collected from their mobile devices. As shown, Wi-Fi data are essentially of various information types and with noises. The challenges lie in how to refine relevant information from noisy Wi-Fi data. Aiming at the challenges, this paper proposes a data cleaning and information enrichment framework for enabling user preference understanding through Wi-Fi logs, and introduces a series of filters for cleaning, correcting, and refining Wi-Fi logs. A comprehensive experiment with real data collected from users is made to verify the effectiveness of the proposed techniques for cleaning noisy Wi-Fi data for user preference profiling. To the best of our knowledge, this work is the first attempt to study user behavior understanding by mining Wi-Fi logs.

Nowadays, mobile devices have become a ubiquitous medium supporting various forms of functionality and are widely accepted for commons. In this study, we investigate using Wi-Fi logs from a mobile device to discover user preferences. The core ideas are two folds. First, every Wi-Fi access point is with a network name, normally a human-readable string, called SSID (Service Set Identifier). Since SSIDs are often with semantics, from which we can infer the place where the user stayed. Second, a Wi-Fi log is produced when the user is near a Wi-Fi access point. A high frequency of a consecutively observed SSID implies a long stay duration at a place. To the best of our knowledge, our work is the first attempting to understand users from the collected Wi-Fi logs from mobile devices. However, Wi-Fi logs are essentially of various information types and with noises. How to assess the information types, eliminate irrelevant information, and clean up the noises within partial-informative SSIDs are therefore keys for profiling user preferences over Wi-Fi logs. In this paper, we propose a data cleaning and information enrichment framework for enabling the user preference understanding through collected Wi-Fi logs, and introduce a data clean framework for cleaning, correcting, and refining Wi-Fi logs. In addition, a comprehensive experiment with data collected from users is made to verify the effectiveness of the proposed techniques for cleaning noisy Wi-Fi data for user preferences profiling. The experiment results demonstrate the effectiveness of the proposed framework for profiling user preferences through Wi-Fi logs.

Sensor networks have received considerable attention in recent years, and are employed in many applications. In these applications, statistical aggregates such as Sum over the readings of a group of sensor nodes are often needed. One challenge for computing sensor data aggregates comes from the communication failures, which are common in sensor networks. To enhance the robustness of the aggregate computation, multipath-based aggregation is often used. However, the multipath-based aggregation suffers from the problem of overcounting sensor readings. The approaches using the multipath-based aggregation therefore need to incorporate techniques that avoid overcounting sensor readings. In this paper, we present a novel technique named scalable counting for efficiently avoiding the overcounting problem. We focus on having an (ε, δ) accuracy guarantee for computing an aggregate, which ensures that the error in computing the aggregate is within a factor of ε with probability (1 - δ). Our schemes using the scalable counting technique efficiently compute the aggregates under a given accuracy guarantee. We provide theoretical analyses that show the advantages of the scalable counting technique over previously proposed techniques. Furthermore, extensive experiments are made to validate the theoretical results and manifest the advantages of using the scalable counting technique for sensor data aggregation.

In this article we propose a method that can compress a small-world-like massive digraph (directed graph) into at most half size of the original representation represented by using adjacency list. This method also provides a fast decompression algorithm that works as quickly as adjacency list does. In this paper we deal with the problem of finding a compact representation of a graph from which the vertices adjacent to any specified vertex can be easily determined.