Jiong Yu

EAAI Journal 2026 Journal Article

A novel attention-based long short-term memory latency prediction model for stream processing applications

• Zheng Chu
• Dongwen Chen
• Xinfeng Zhang
• Baozhu Li
• Jiong Yu
• Xusheng Du
• Jian Zhou
• Weiyun Li

The proliferation of the Internet of Things has led to a significant increase in the number and types of smart devices, resulting in an exponential growth of streaming data volume and types. Consequently, many fields have adopted stream processing application (SPA) to handle real-time processing scenarios. Practitioners and scientists across various domains rely on latency prediction of these applications, which is essential for performance analysis and proactive optimization. However, predicting the latency of SPAs remains challenging due to their diverse types and internal complexity. To accurately predict the latency, this paper explores related work extensively and investigates the directed acyclic graph characteristics of such applications. Three kinds of features (i. e. , application features, data features, and system features) are identified and summarized as major factors influencing latency. Furthermore, we propose and implement a artificial intelligence-based real-time prediction framework in Apache Flink, a popular stream processing system, for application latency prediction. This framework collects three types of real-time metrics, constructs features, and utilizes an attention-based long short-term memory recurrent neural network model to accurately predict latency at run-time. Experimental results from six benchmarks show that the proposed model accurately predicts latency using the identified features. More importantly, our model outperforms the state-of-the-art model in terms of prediction error and accuracy due to the proposed real-time framework and attention mechanism. Furthermore, our model can achieve high prediction accuracy for newly developed applications in a short time.

AILAW Journal 2026 Journal Article

ALTER: a lightweight topic-aware representation legal case retrieval system

• Zhengying Wang
• Jiong Yu

Abstract Legal Case Retrieval not only involves the identification and selection of similar cases, but also encompasses an in-depth comparison and abstraction of their holdings, reasoning structures, and underlying legal principles, serving as a critical pillar of judicial digital transformation and the development of smart courts. With the rapid advancement of neural networks, deep learning, and pretrained language models, retrieval paradigms have gradually evolved from traditional keyword matching toward intelligent approaches such as semantics-based dense retrieval. Owing to the high level of domain specificity and structural complexity of legal documents, case-based retrieval has long remained a focal research topic in this field. To leverage the strengths of traditional methods in capturing global semantics while fully exploiting the contextual modeling capabilities of pretrained language models, this paper proposes ALTER, A Lightweight Topic-awarE Representation legal case retrieval system. Specifically, ALTER incorporates topic modeling to enhance the global semantic representations learned by pretrained language models, and introduces a Co-Attention layer to explicitly facilitate bidirectional interactions between the topic encoder and the text encoder. In addition, a multi-label classification task is employed to guide the text encoder to implicitly learn topical information, resulting in topic-aware text representations. During the online retrieval stage, only the text encoder is used to construct the index, enabling efficient and lightweight inference while achieving joint matching between text and topics. Experimental results on the Chinese LCR dataset LeCaRD and the English LCR dataset COLIEE23-24 demonstrate that ALTER consistently achieves state-of-the-art performance across multiple evaluation metrics.

EAAI Journal 2026 Journal Article

Corrigendum to “A novel attention-based long short-term memory latency prediction model for stream processing applications” [Eng. Appl. Artif. Intell. 163 (2026) 112890]

• Zheng Chu
• Dongwen Chen
• Xinfeng Zhang
• Baozhu Li
• Jiong Yu
• Xusheng Du
• Jian Zhou
• Weiyun Li

AAAI Conference 2024 Conference Paper

Improving Open-Domain Dialogue Response Generation with Multi-Source Multilingual Commonsense Knowledge

• Sixing Wu
• Jiong Yu
• Jiahao Chen
• Xiaofan Deng
• Wei Zhou

Knowledge-grounded Dialogue Response Generation (KRG) can facilitate informative and fidelity dialogues using external knowledge. Prior monolingual works can only use the knowledge of the corresponding native language. Thus, due to the prohibitive costs of collecting and constructing external knowledge bases, the limited scale of accessible external knowledge always constrains the ability of KRG, especially in low-resource language scenarios. To this end, we propose a new task, Multi-Source Multilingual Knowledge-Grounded Response Generation (MMKRG), which simultaneously uses multiple knowledge sources of different languages. We notice that simply combining knowledge of different languages is inefficient due to the Cross-Conflict issue and Cross-Repetition issue. Thus, we propose a novel approach MMK-BART, which uses a simple but elegant Estimate-Cluster-Penalize mechanism to overcome the mentioned issues and adopts the multilingual language model mBART as the backbone. Meanwhile, based on the recent multilingual corpus XDailyDialog, we propose an MMKRG dataset MMK-DailyDialog, which has been aligned to the large-scale multilingual commonsense knowledge base ConceptNet and supports four languages (English, Chinese, German, and Italian). Extensive experiments have verified the effectiveness of our dataset and approach in monolingual, cross-lingual, and multilingual scenarios.