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IEEE International Conference on Big Data and cloud computing (Bdcloud)

作者： Hangyu Cheng Guosheng Kang Jianxun Liu Yiping Wen Buqing Cao Zhen Wang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process models. However, BPMN cannot support the intuitive modeling for some typical tasks in complex business scenarios of the Industrial Internet context, increasing the communication cost between modelers and business personnel. The current solutions try to extend the attributes of BPMN elements with annotations, but a large number of annotations makes business process models complex and cumbersome. This paper proposes to extend new business process modeling elements for Industrial Internet application scenarios based on BPMN extension mechanism, generating an extended business process modeling language, namely BPMN++. And an extended process modeling tool is implemented, which is availab.e online. A case study over a real-world Industrial Internet process validates the usefulness of this extension mechanism and the well understandability of business process models.

关键词： Industries Process modeling Annotations Internet Personnel Task analysis Optimization

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Efficient and Scalab.e Implicit Graph Neural Networks with Virtual Equilibrium

Efficient and Scalable Implicit Graph Neural Networks with V...

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IEEE International Conference on Big Data

作者： Qi Chen Yifei Wang Yisen Wang Jianlong Chang Qi Tian Jiansheng Yang Zhouchen Lin School of Mathematical Sciences Peking University China Key Lab. of Machine Perception (MoE) School of Intelligence Science and Technology Peking University China Huawei Cloud & AI Shenzhen China

ISBN: (纸本)9781665480468

On large-scale graphs, many graph neural networks are problematic in capturing long-range dependencies due to the oversmoothing problem. Recently, Graph Equilibrium Models (GEQs) arise as a promising solution to this issue. Their output is the equilibrium of a fixed-point equation, which can be seen as the result of iterating a GNN layer for infinite times, so that they inherently have global receptive fields. However, to find the equilibrium, GEQs require running costly full-batch root-finding algorithms from scratch during each model update, which leads to severe efficiency and scalab.lity issues that prevent them from scaling to large graphs. To address these limitations, we propose VEQ, an efficient learning method to scale GEQs to large graphs. Instead of initializing the equilibrium from scratch in full-batch training, VEQ uses the latest equilibrium of in-batch nodes and their 1-hop neighbors (dubbed Virtual Equilibrium) to accelerate and calibrate the root-finding process in mini-batch training. With virtual equilibrium as an informative prior, VEQ is able to reach the equilibrium in fewer steps while still capturing global dependencies. Theoretically, we provide convergence analysis for the forward and backward pass of VEQ. Empirically, VEQ significantly outperforms existing GEQs by a large margin (more than 1.5%) on all benchmark datasets, with much less training time and memory. Also, VEQ achieves competitive and even superior performance to many highly engineered explicit GNNs on large-scale benchmark datasets like ogbn-arxiv and ogbn-products. VEQ shows that after we resolve the efficiency and scalab.lity issues, GEQs are indeed favorable on large graphs due to their advantage of capturing long-range dependencies.

关键词： Training Learning systems Scalab.lity Memory management Big Data Benchmark testing Mathematical models

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Adaptive feature selection for no-reference image quality assessment by mitigating semantic noise sensitivity 24

Adaptive feature selection for no-reference image quality as...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xudong Li Timin Gao Runze Hu Yan Zhang Shengchuan Zhang Xiawu Zheng Jingyuan Zheng Yunhang Shen Ke Li Yutao Liu Pingyang Dai Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China School of Information and Electronics Beijing Institute of Technology Beijing China School of Medicine Xiamen University Tencent Youtu Lab. School of Computer Science and Technology Ocean University of China

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model's generalization ability. Extensive experiments conducted on eight standard IQA datasets have demonstrated the effectiveness of our proposed QFM-IQM.

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Federated Linear Bandit Learning via Over-the-air Computation

arXiv

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arXiv 2023年

作者： Wang, Jiali Jiang, Yuning Liu, Xin Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Automatic Control Laboratory EPFL Switzerland ShanghaiTech University China

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings. © 2023, CC BY-NC-SA.

关键词： Fading channels

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have availab.e resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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SupportPlay: A Multi-Agent Role-Playing System for Personalized and Sustained Multimodal Emotional Support Conversation 25

SupportPlay: A Multi-Agent Role-Playing System for Personali...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Geng Tu Bingbing Wang Erik Cambria Wenjie Li Ruifeng Xu Harbin Institute of Technology Shenzhen China College of Computing & Data Science NTU Singapore Singapore Singapore The Hong Kong Polytechnic University Hong Kong China Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China and GD Prov. Key Lab. of Novel Security Intelligence Technologies Shenzhen China

ISBN: (纸本)9798400713316

In this demo, we present SupportPlay, a multi-agent role-playing system for emotional support conversation (ESC) that addresses the limitations of existing methods in providing personalized, sustained, and multimodal support. SupportPlay generates potential seeker profiles from existing ESC datasets and employs GPT-powered agents to play various seeker roles, enabling the supporter to learn personalized memories for each seeker. Subsequently, the supporter can induce general memory from these memories for real user interactions while learning the user's personalized memory in a similar manner. Through continuous memory management-including retrieval, storage, reflection, and forgetting-SupportPlay delivers tailored emotional support across interactions. By integrating text, speech, and video, SupportPlay creates immersive emotional support experiences.

关键词： emotional support conversation

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Integrating global context contrast and local sensitivity for blind image quality assessment 24

Integrating global context contrast and local sensitivity fo...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xudong Li Runze Hu Jingyuan Zheng Yan Zhang Shengchuan Zhang Xiawu Zheng Ke Li Yunhang Shen Yutao Liu Pingyang Dai Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China School of Information and Electronics Beijing Institute of Technology Beijing China School of Medicine Xiamen University. Tencent Youtu Lab. School of Computer Science and Technology Ocean University of China

Blind Image Quality Assessment (BIQA) mirrors subjective made by human observers. Generally, humans favor comparing relative qualities over predicting absolute qualities directly. However, current BIQA models focus on mining the "local" context, i.e., the relationship between information among individual images and the absolute quality of the image, ignoring the "global" context of the relative quality contrast among different images in the training data. In this paper, we present the Perceptual Context and Sensitivity BIQA (CSIQA), a novel contrastive learning paradigm that seamlessly integrates "global" and "local" perspectives into the BIQA. Specifically, the CSIQA comprises two primary components: 1) A Quality Context Contrastive Learning module, which is equipped with different contrastive learning strategies to effectively capture potential quality correlations in the global context of the dataset. 2) A Quality-aware Mask Attention Module, which employs the random mask to ensure the consistency with visual local sensitivity, thereby improving the model's perception of local distortions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods.

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Big Data Reference Architectures, a systematic literature review 31

Big Data Reference Architectures, a systematic literature re...

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31st Australasian Conference on Information Systems, ACIS 2019

作者： Ataei, Pouya Litchfield, Alan School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Service and Cloud Computing Research Lab. Auckland University of Technology Auckland New Zealand

Today, we live in a world that produces data at an unprecedented rate. The significant amount of data has raised lots of attention and many strive to harness the power of this new material. In the same direction, academics and practitioners have considered means through which they can incorporate data-driven functions and explore patterns that were otherwise unknown. This has led to a concept called Big Data. Big Data is a field that deals with data sets that are too large and complex for traditional approaches to handle. Technical matters are fundamentally critical, but what is even more necessary, is an architecture that supports the orchestration of Big Data systems;an image of the system providing with clear understanding of different elements and their interdependencies. Reference architectures aid in defining the body of system and its key components, relationships, behaviors, patterns and limitations. This study provides an in-depth review of Big Data Reference Architectures by applying a systematic literature review. The study demonstrates a synthesis of high-quality research to offer indications of new trends. The study contributes to the body of knowledge on the principles of Reference Architectures, the current state of Big Data Reference Architectures, and their limitations. © 2020 authors.

关键词： Big data

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Modeling Multilevel Business Process Monitoring via BPMN Extension

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Kang, Guosheng Wang, Zhen Cheng, Hangyu Liu, Jianxun Wen, Yiping Peng, Jun School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Computer Science Fudan University Shanghai China

Business process monitoring involves real-time supervision of a series of activities carried out by an organization to achieve specific objectives. Process event monitoring points (PEMP) are used to pinpoint specific locations within a process model where expected events are anticipated to occur. However, in general business process modeling languages, such as business process model and notation (BPMN), there is a lack of explicit modeling for PEMPs. This article proposes a method for modeling pairwise event monitoring points in business process models to track the execution status of specific activities or process segments via BPMN extension. Specifically, process monitoring points are designed by expanding the modeling element of sequence flow. Moreover, the business process model is decomposed using the refined process structure tree (RPST) to verify the soundness of the designed pairwise monitoring points. The proposed method allows flexible monitoring at the process segment level. And the process monitoring data could be used for a clear understanding of business progress, especially useful in heterogeneous business process model to support decision-making. Through case study from real-world business processes, the effectiveness and usefulness of the proposed process monitoring modeling method is validated. © 2025 John Wiley & Sons Ltd.

关键词： Electronic trading

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Combining Bilstm and Self-Attention Mechanism for Code Search

SSRN

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SSRN 2022年

作者： Cao, Ben Liu, Jianxun School of Computer Science and Technology Hunan University of Science and Technology Hunan411100 China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan411100 China

With the wide application of deep learning in code search, especially the proposed code search model based on attention mechanism, the accuracy of code search has been greatly improved. However, the attention mechanism only captures the attention weight relationship between two words in the code fragment, without considering the contextual semantic relationship that exists between words in the code fragment, which can help improve the accuracy of code search. To address this problem, this paper proposes a model that combining BiLSTM and self-attention mechanisms for code search(CBLSAM-CS). The model first captures the contextual relationship of each word in the code fragment by a recurrent neural network, and then uses the self-attentive mechanism to extract deep-level features of the sequence. In order to verify the effectiveness of the proposed model, the paper conducts an experimental comparison with three other baseline models, on the basis of a public dataset containing 18 million code *** experimental results show that the proposed model in this paper achieves 92.4% and 93.5% in MRR value and NDCG metrics, respectively, which are better than the baseline ***, it shows that the CBLSAM-CS model proposed in this paper can effectively improve the accuracy and efficiency of code search. © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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