Many real-world optimization problems are multimodal with multiple globally optimal solutions, yet their evaluations are often computationally expensive—defining expensive multimodal optimization problems. Surrogate-assisted evolutionary algorithms reduce costs but often fail to balance global optima discovery with solution precision under limited budgets. To address these challenges, this article proposes surrogate-assisted particle swarm algorithm (SAPSO). Its three key innovations are: first, a ring-topology global-local search framework that simultaneously refines known solutions and explores new regions; second, an archive selection strategy to eliminate redundant searches and prioritize unexplored promising regions; and finally, an improved multimodal detection strategy that identifies distinct optima using existing data without additional expensive evaluations. To validate its practical utility, SAPSO is first applied to optimize the parameters of a permanent magnet synchronous motor, yielding three reliable solutions for different operational needs. Furthermore, the extensive experiment is carried out on the CEC2013 test set. In terms of the global optimal solution indicators, SAPSO outperformed MaMPSO in 10 functions, DSCPSO-MMO in 19 functions, and MHS-QPSO in 11 functions. For the valley ratio, SAPSO achieved better results than MaMPSO in 12 functions, DSCPSO-MMO in 18 functions, and MHS-QPSO in 17 functions. These results demonstrate SAPSO’s ability to consistently discover multiple high-quality solutions.

Graph node anomaly detection has important applications in practical scenarios. Although many graph neural networks (GNNs) have been proposed, how to design tailored spectral filters for node anomaly detection to fully mine high-frequency signals in the graph is still a challenge. Most GNNs are equivalent to low-pass filters and mine multiorder signals through a series structure. The computational cost increases as the number of layers increases and further leads to an over-smoothing problem. They mainly focus on low-frequency signals and suppress high-frequency signals, thus smoothing the differences between abnormal and normal nodes, making them indistinguishable. Due to the difficulty in mining high-frequency signals, the poorly distinguishable feature representations learned by low-pass GNNs can even harm the performance of data augmentation. To solve the above challenges, in this article, we propose a or-gate mixup multiscale spectral GNN (MMGNN) from the spectral domain. Specifically, we design multiorder multiscale bandpass filters through the superposition of polynomial spectral filters and then decompose them into preprocessing parts and training parts to form a double-parallel structure, which can effectively mine high-frequency signals in the graph and reduce computational cost. Finally, we propose or-gate mixup to perform data augmentation in the spectral space to improve model generalization. Experimental results on four real-world datasets demonstrate the effectiveness of the proposed MMGNN against the state-of-the-art methods.

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks.

Zekang Li, Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Current state-of-the-art approaches for image captioning typically adopt an autoregressive manner, i.e., generating descriptions word by word, which suffers from slow decoding issue and becomes a bottleneck in real-time applications. Non-autoregressive image captioning with continuous iterative refinement, which eliminates the sequential dependence in a sentence generation, can achieve comparable performance to the autoregressive counterparts with a considerable acceleration. Nevertheless, based on a well-designed experiment, we empirically proved that iteration times can be effectively reduced when providing sufficient prior knowledge for the language decoder. Towards that end, we propose a novel two-stage framework, referred to as Semi-Autoregressive Image Captioning (SAIC), to make a better trade-off between performance and speed. The proposed SAIC model maintains autoregressive property in global but relieves it in local. Specifically, SAIC model first jumpily generates an intermittent sequence in an autoregressive manner, that is, it predicts the first word in every word group in order. Then, with the help of the partially deterministic prior information and image features, SAIC model non-autoregressively fills all the skipped words with one iteration. Experimental results on the MS COCO benchmark demonstrate that our SAIC model outperforms the preceding non-autoregressive image captioning models while obtaining a competitive inference speedup.

Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challenge (DSTC8). There are two challenges in this task: 1) making effective interaction among different modalities; 2) better understanding dialogues and generating informative responses. To tackle the challenges, we propose a universal multimodal transformer and introduce the multi-task learning method to learn joint representations among different modalities as well as generate informative and fluent responses by leveraging the pre-trained language model. Our method extends the natural language generation pre-trained model to multimodal dialogue generation task, which allows fine-tuning language models to capture information across both visual and textual modalities. Our system achieves the best performance in the objective evaluation in both DSTC7-AVSD and DSTC8-AVSD dataset and achieves an impressive 98.4% of the human performance based on human ratings in the DSTC8-AVSD challenge.

Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have limitations to efficiently exploit relevant context due to the lack of a powerful mechanism for modeling interactions between the slot and the dialogue history. Besides, existing methods usually ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots and eventually hurts overall performance. In this paper, we propose to enhance the DST through employing a contextual hierarchical attention network to not only discern relevant information at both word level and turn level but also learn contextual representations. We further propose an adaptive objective to alleviate the slot imbalance problem by dynamically adjust weights of different slots during training. Experimental results show that our approach reaches 52.68% and 58.55% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets respectively and achieves new state-of-the-art performance with considerable improvements (+1.24% and +5.98%).

Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do not exploit this kind of knowledge effectively enough. In this paper, we propose a novel Transformer-based architecture for multi-turn document grounded conversations. In particular, we devise an Incremental Transformer to encode multi-turn utterances along with knowledge in related documents. Motivated by the human cognitive process, we design a two-pass decoder (Deliberation Decoder) to improve context coherence and knowledge correctness. Our empirical study on a real-world Document Grounded Dataset proves that responses generated by our model significantly outperform competitive baselines on both context coherence and knowledge relevance.

Configuration tuning is vital to optimize the performance of database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to the diverse database instances and query workloads, which make the database administrator (DBA) incompetent. Although there are some studies on automatic DBMS configuration tuning, they have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, there are a large number of knobs that are in continuous space and have unseen dependencies, and they cannot recommend reasonable configurations in such high-dimensional continuous space. Lastly, in cloud environment, they can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL). CDBTune utilizes the deep deterministic policy gradient method to find the optimal configurations in high-dimensional continuous space. CDBTune adopts a try-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the difficulty of collecting massive high-quality samples. CDBTune adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves efficiency of online tuning. We conducted extensive experiments under 6 different workloads on real cloud databases to demonstrate the superiority of CDBTune. Experimental results showed that CDBTune had a good adaptability and significantly outperformed the state-of-the-art tuning tools and DBA experts.