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WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

Journal of Computer Science & Technology 2021年第4期36卷 741-761页

作者： Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China

Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are performed artificially(e.g.,by database administrators),but it is hard for them to achieve high performance through tuning in various types of systems and in various *** recent years,there have been some studies on tuning traditional database systems,but all these methods have some *** this article,we put forward a tuning system based on attention-based deep reinforcement learning named WATuning,which can adapt to the changes of workload characteristics and optimize the system performance efficiently and ***,we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of *** algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems,and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate ***,WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of ***,WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make *** experimental results show that the throughput and the latency of WATuning are improved by 52.6%and decreased by 31%,respectively,compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.

关键词： attention mechanism auto-tuning system reinforcement learning(RL) workload-aware

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PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

arXiv

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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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OmniSQL: Synthesizing High-quality Text-to-SQL data at Scale

arXiv

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arXiv 2025年

作者： Li, Haoyang Wu, Shang Zhang, Xiaokang Huang, Xinmei Zhang, Jing Jiang, Fuxin Wang, Shuai Zhang, Tieying Chen, Jianjun Shi, Rui Chen, Hong Li, Cuiping Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China ByteDance Inc China

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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Graph Neural Networks with Learnable and Optimal Polynomial Bases

arXiv

引用

arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

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Predicting ICU Pressure Injuries with Historical data: A Multivariate Time Series Approach

Predicting ICU Pressure Injuries with Historical Data: A Mul...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Lintai Cui Liuqi Jin Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Hospital-acquired pressure injury (HAPI) is a se-rious healthcare problem for intensive care unit (ICU) patients, which significantly affects their quality of life and prognosis, and increases hospitalization time and medical expenses. The current prediction models cannot accurately predict the pressure injury (PI) in ICU wards, as these models usually only predict based on the patient's current physical condition and electronic health record data, resulting in poor recall and precision, which affects the prediction performance. To solve this problem, we applied the multivariate time series data of I CU patients from admission to discharge and established a PI prediction model for ICU patients by utilizing the bidirectional long short-term memory neural network (Bi-LSTM) model. Experiments on the MIMIC-III (Medical Information Mart for Intensive Care) dataset show that the Bi-LSTM model has an F1 score of 0.24 and an AUC (Area Under Curve) value of 0.81, which are better than other models. This validates the effectiveness of the Bi-LSTM on the multivariate time series data to predict the occurrence of PI in ICU wards and assist nursing staff to allocate nursing resources more effectively.

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2412-2426页

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs

arXiv

引用

arXiv 2024年

作者： Ji, Shengwei Tian, Yujie Liu, Fei Li, Xinlu Wu, Le The School of Artificial Intelligence and Big Data Hefei University Hefei China The School of Computer Science and Information Engineering Hefei University of Technology Hefei China The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education China

Graph Convolutional Networks (GCNs) are widely used in graph-based applications, such as social networks and recommendation systems. Nevertheless, large-scale graphs or deep aggregation layers in full-batch GCNs consume significant GPU memory, causing out of memory (OOM) errors on mainstream GPUs (e.g., 29GB memory consumption on the Ogbn-products graph with 5 layers). The subgraph sampling methods reduce memory consumption to achieve lightweight GCNs by partitioning the graph into multiple subgraphs and sequentially training GCNs on each subgraph. However, these methods yield gaps among subgraphs, i.e., GCNs can only be trained based on subgraphs instead of global graph information, which reduces the accuracy of GCNs. In this paper, we propose PromptGCN, a novel prompt-based lightweight GCN model to bridge the gaps among subgraphs. First, the learnable prompt embeddings are designed to obtain global information. Then, the prompts are attached into each subgraph to transfer the global information among subgraphs. Extensive experimental results on seven large-scale graphs demonstrate that PromptGCN exhibits superior performance compared to baselines. Notably, PromptGCN improves the accuracy of subgraph sampling methods by up to 5.48% on the Flickr dataset. Overall, PromptGCN can be easily combined with any subgraph sampling method to obtain a lightweight GCN model with higher accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphics processing unit

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

引用

41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

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China Communications 2021年第2期18卷 259-270页

作者： Jinhua Pan Lusheng Wang Hai Lin Zhiheng Zha Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

To support dramatically increased traffic loads,communication networks become *** cell association(CA)schemes are timeconsuming,forcing researchers to seek fast *** paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is *** the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until *** the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user *** demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional ***,performance metrics,such as capacity and fairness,can be guaranteed.

关键词： ultra-dense cellular networks(UDCN) cell association(CA) deep Q-learning proportional fairness Q-learning

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