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Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application

作者： Jiawei Sun Jia Hao Xiaoning Zhang School of Mechanical Engineering Beijing Institute of Technology China School of Mechanical Engineering Beijing Institute of Technology China Yangtze Delta Region Academy Beijing Institute of Technology China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application?Ministry of Industry and Information Technology Beijing Institute of Technology China and Tianjin Key Laboratory of Space Environment Simulation Technology China

ISBN: (纸本)9798400709449

Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problems in the production process may directly affect the safety of people's lives and property. Thus, this paper proposes a production-quality early warning method for firearms components based on cloud-edge collaboration. Real-time early warning and rapid processing for abnormal conditions in the production process are realized based on cloud data analysis and edge intelligent prediction, thereby improving production efficiency and quality. The cloud edge collaboration framework is developed for cloud training of the long short-term memory models and real-time sample edge acquisition, enhancing the adaptability of quality early warning algorithms under specific conditions and the real-time quality of early warning for firearms components.

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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Global Green Digital Economy: Challenges and Development: Preface for ITQM 2022

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Procedia Computer Science 2022年第C期214卷 1-3页

作者： Shi, Yong Li, Jianping Kou, Gang Berg, Daniel Tien, James College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Engineering University of Miami Coral GablesFL33124 United States

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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NSPG-Miner: Mining Repetitive Negative Sequential Patterns

arXiv

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arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

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Holo-Mol: An Explainable Hybrid Deep Learning Framework for Predicting Reactivity of Hydroxyl Radical to Water Contaminants Based on Holographic Fused Molecular Representations

SSRN

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SSRN 2023年

作者： Mao, Dianhui Liu, Junling Li, Xuebo Zuo, Min Yan, Wenjing Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University Beijing100048 China

The reaction rate constants of hydroxyl radical (OH•) in the aqueous phase are crucial physicochemical parameters for assessing the persistence of organic compounds in water environments. However, the continuous influx of newly emerging organic compounds into water creates both experimental and theoretical challenges in understanding the reactivity of their degradation processes. Recently, deep learning (DL) methods have proven their ability to predict chemical properties. This study presented Holo-Mol, a DL model for predicting the OH• rate constants in the aqueous phase. Holo-Mol utilized a fused learning approach based on holographic molecular representations, which provides comprehensive molecular information from three dimensions. Also, the 2D/3D graph attention mechanism and fusion gate were utilized to enhance molecular feature extraction. The model was evaluated on a dataset of 1374 organic compounds and resulted in a high goodness-of-fit that outperforms previous methods. This research further used interpretability methods to analyze the effects of combining representations from different dimensions based on accurate predictions of the model. It demonstrated that different dimensions of representations can provide different patterns of "knowledge" to the model, assisting in its learning process in molecular property prediction. Finally, based on interpretability techniques, this study identified key substructures susceptible to attack by OH•. In summary, the results demonstrated the potential of Holo-Mol in expanding the application of advanced oxidative technologies for treating contaminants in water. Additionally, it offers novel mechanistic insights into the reaction mechanism of OH•. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Unveiling Funded Research: Impact and Dual Innovation

SSRN

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SSRN 2023年

作者： Yang, Alex Jie School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China International Joint Informatics Laboratory Nanjing University Nanjing210023 China

In the relentless pursuit of scientific advancement, comprehending the profound impact and innovation nature inherent in funded research projects assumes paramount significance. To illuminate this matter, I delve into the realm of research supported by the esteemed National Institutes of Health (NIH) and the distinguished National Science Foundation (NSF). Our evaluative framework encompasses a spectrum of metrics, including the 5-year citation, normalized citation, and citation to patents, as markers of research impact. Moreover, I embrace ex-ante innovation (Novelty) and ex-post innovation (Disruption) as dual indispensable yardsticks for evaluating the innovative nature of research projects. Novelty denotes the manifestation of atypical combinations of existing knowledge, while Disruption signifies the extent of paradigm-shifting potential and the ability to exert a disruptive influence on future research endeavors. First, the analysis reveals that funded research projects manifest a conspicuously heightened impact in comparison to their non-funded counterparts. Second, I uncover a noteworthy finding, namely that funded research demonstrates significantly higher levels of ex-ante innovation (Novelty). However, in a surprising twist, the investigation also uncovers that funded research exhibits significantly lower levels of ex-post innovation (Disruption). Additionally, I undertake a meticulous scrutiny of the robustness of the research findings by scrutinizing patterns across disparate years and fields. This comprehensive analysis further unveils inconspicuous heterogeneity, thus substantiating the multifaceted nature of funded research and the vast potential for its innovation across various disciplines and temporal dimensions. © 2023, The Authors. All rights reserved.

关键词： Patents and inventions

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GKG-LLM: A Unified Framework for Generalized knowledge Graph Construction

arXiv

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arXiv 2025年

作者： Zhang, Jian Wei, Bifan Qi, Shihao Zhu, Haiping Liu, Jun Lin, Qika School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China National University of Singapore Singapore

The construction of Generalized knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typically construct these types of graph separately, overlooking holistic insights and potential unification that could be beneficial in computing resources and usage perspectives. However, a key challenge in developing a unified framework for GKG is obstacles arising from task-specific differences. In this study, we propose a unified framework for constructing generalized knowledge graphs to address this challenge. First, we collect data from 15 sub-tasks in 29 datasets across the three types of graphs, categorizing them into in-sample, counter-task, and out-of-distribution (OOD) data. Then, we propose a three-stage curriculum learning fine-tuning framework, by iteratively injecting knowledge from the three types of graphs into the Large Language Models. Extensive experiments show that our proposed model improves the construction of all three graph types across in-domain, OOD and counter-task data. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution

VRFormer: 360-Degree Video Streaming with FoV Combined Predi...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Zhihao Zhang Haipeng Du Shouqin Huang Weizhan Zhang Qinghua Zheng Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University School of Continuing Education Xi'an Jiaotong University

360-degree video has shown great potential to the mainstream since its immersive experience. However, 360-degree video streaming requires ultrahigh bandwidth and low latency, which limit the improvement of user quality of experience (QoE). Currently, methods combining field of view (FoV) prediction and adaptive video streaming provide an effective method for addressing the above issues. However, existing FoV prediction methods based on recurrent neural networks (RNN) cannot capture long-range dependency from input to output. Current deep reinforcement learning (DRL)-based adaptive strategies fail to estimate the future bandwidth with high accuracy and fully explore the capability of VR devices. To ameliorate these limitations, we design a DRL-based 360-degree video streaming method named VRFormer with FoV combined prediction and super resolution (SR). First, we adopt a content-aware transformer-based encoder-decoder network to make the long-term FoV prediction. It combines the user's head movement history, eye-tracking history, and user attention extracted from a convolutional neural network (CNN)-based network. Second, we introduce a DNN-based SR network running on VR devices to reconstruct high-definition video content. Finally, we apply a DRL-based network to adaptively allocate rates for future tiles and dynamically control video content reconstruction. Experiments have verified that the proposed method can effectively improve the quality of experience (QoE) of the user's viewing experience compared to the state-of-the-art methods.

关键词： Recurrent neural networks Superresolution Bandwidth Reinforcement learning Prediction methods Streaming media Transformers

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Dynamic Graph Pattern Matching in Medical knowledge Graphs 4

Dynamic Graph Pattern Matching in Medical Knowledge Graphs

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4th International Conference on data Intelligence and Security, ICDIS 2022

作者： Du, Xun Tao, Zhenchao Li, Lei School of Computer Science and Information Engineering Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459693

As the study of graph pattern matching (GPM) attracts more and more scholars, the research of GPM in the medical field begins to emerge, from protein structure analysis to breast cancer classification diagnosis, and GPM is more and more widely adopted. However, in medical knowledge graphs (MKG), when the pattern graph dynamically changes, to obtain the matching results of the changed pattern graph, the traditional method has to carry out global matching instead of local matching, which causes a waste of resources and takes a lot of time. To quickly return the matching results of the dynamic pattern graph in MKGs, in this paper, we introduce a caching mechanism to cache the previous matching results and propose a new longest common path matching (M-LCPM) algorithm based on multithreading. The algorithm only performs partial matching on the changed pattern graph and can quickly obtain the matching result. Considering that the M-LCPM algorithm cannot get matching results when the pattern graph changes completely and there may be missing matching subgraphs when the M-LCPM algorithm performs matching, we optimize the M-LCPM algorithm and propose the M-LCPM-Enhance algorithm. Experimental results on two data sets show that our dynamic algorithm is more efficient than existing algorithms. © 2022 IEEE.

关键词： graph pattern matching longest common path medical knowledge graphs

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