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2020 Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Track at BPM, BPM-D 2020

作者： Stertz, Florian Winter, Karolin Rinderle-Ma, Stefanie Research Group Workow Systems and Technology Faculty of Computer Science Data Science@Uni Vienna University of Vienna Vienna Austria

Instance spanning constraints (ISC) realize controls on the execution of multiple process instances of one or several process types. Examples include synchronization at critical resources and authorization across several instances. ISC leave marks in process execution logs, e.g., process tasks having the same time stamp. As ISC are not always known and might change over time, process execution logs provide a valuable re- source for mining ISC. Algorithms for ISC discovery have been proposed in previous work. SVIPEX implements these algorithms and provides a novel visualization for discovered ISC through a lightweight web service. This way ISC mining becomes accessible. SVIPEX maturity is demon- strated based on case studies from manufacturing and higher education. Copyright © 2020 for this paper by its authors.

关键词： Web services

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RegMiner: Taming the complexity of regulatory documents for digitalized compliance management

RegMiner: Taming the complexity of regulatory documents for ...

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2020 Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Track at BPM, BPM-D 2020

作者： Winter, Karolin Gall, Manuel Rinderle-Ma, Stefanie Research Group Workow Systems and Technology Faculty of Computer Science Data Science@Uni Vienna University of Vienna Vienna Austria

Business process compliance has become a crucial aspect for companies due to severe fines that can be imposed if constraints and rules emerging from regulatory documents are violated. Regulatory docu- ments are often written in natural language and analyzing them is mainly done manually since only limited tool support is available. Therefore, we present RegMiner, a web service for discovering and visualizing con- straints from regulatory documents. By employing NLP and data min- ing techniques, compliance constraints can be automatically extracted, grouped, and visualized leading to a separation of relevant and non- relevant document parts and insights into, e.g., duties of stakeholders. A case study based on a current document from the European parliament regarding the financial domain demonstrates RegMiner's maturity. Copyright © 2020 for this paper by its authors.

关键词： Web services

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Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid model

Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid m...

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第十四届中国卫星导航年会

作者： Dengao Li Xinfang Zhang Jumin Zhao Fanming Wu Ran Feng Jinhua Zhao Danyang Shi College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content(TEC)in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network(LSTM)and the fast training speed of gated recurrent unit network(GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour(HD)are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error(RMSE)of the prediction results of this BLSLSTM-GRU model is decreased by 23.07%,17.48% and 9.46%compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy.

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory data mining Cybernetics Convergence

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MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

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computers in Biology and Medicine 2024年 179卷 108847-108847页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Ejiyi, Makuachukwu Bennedith Ukwuoma, Chiagoziem Ejiyi, Thomas Ugochukwu Muoka, Gladys Wavinya Gyarteng, Emmanuel S.A. Bamisile, Olusola O. Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Chengdu China

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis. © 2024 Elsevier Ltd

关键词： Mixers (machinery)

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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第十四届中国卫星导航年会

作者： Fanming Wu Dengao Li Jinhua Zhao Ran Feng Danyang Shi Xinfang Zhang Jumin Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

There is a growing interest to use Global Navigation Satellite System(GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is *** sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile,combining GNSS-PWV time domain features(PWV value, PWV increment,PWV decrement, rate of PWV increment, rate of PWV decrement) and colocated meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong’s HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models.

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making data visualization Prediction algorithms Real-time systems Planning

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Cross-Modality Translation with Generative Adversarial networks to Unveil Alzheimer’s Disease Biomarkers

Cross-Modality Translation with Generative Adversarial Netwo...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Reihaneh Hassanzadeh Anees Abrol Hamid Reza Hassanzadeh Vince D. Calhoun School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA Courtesy Faculty Appointment College of Pharmacy University of Florida

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the application of generative models to disorder-specific datasets and their ability to preserve diagnostic patterns remain relatively unexplored. Hence, in this study, we investigated the use of a generative adversarial network (GAN) in the context of Alzheimer’s disease (AD) to generate functional network connectivity (FNC) and T1-weighted structural magnetic resonance imaging data from each other. We employed a cycle-GAN to synthesize data in an unpaired data transition and enhanced the transition by integrating weak supervision in cases where paired data were available. Our findings revealed that our model could offer remarkable capability, achieving a structural similarity index measure (SSIM) of 0.89 ± 0.003 for T1s and a correlation of 0.71 ± 0.004 for FNCs. Moreover, our qualitative analysis revealed similar patterns between generated and actual data when comparing AD to cognitively normal (CN) individuals. In particular, we observed significantly increased functional connectivity in cerebellar-sensory motor and cerebellar-visual networks and reduced connectivity in cerebellar-subcortical, auditory-sensory motor, sensory motor-visual, and cerebellar-cognitive control networks. Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer’s patients.

关键词： Neuroimaging Biological system modeling Magnetic resonance imaging Generative adversarial networks Motors Particle measurements data models Indexes Alzheimer's disease Engineering in medicine and biology

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GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning

arXiv

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arXiv 2022年

作者： Kamar, Doğay Üre, Nazım Kemal Ünal, Gözde Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Artificial Intelligence and Data Science Research Center Istanbul Technical University Istanbul Turkey

In this study, we address the problem of efficient exploration in reinforcement learning. Most common exploration approaches depend on random action selection, however these approaches do not work well in environments with sparse or no rewards. We propose Generative Adversarial network-based Intrinsic Reward Module that learns the distribution of the observed states and sends an intrinsic reward that is computed as high for states that are out of distribution, in order to lead agent to unexplored states. We evaluate our approach in Super Mario Bros for a no reward setting and in Montezuma's Revenge for a sparse reward setting and show that our approach is indeed capable of exploring efficiently. We discuss a few weaknesses and conclude by discussing future *** Codes 68T20 (Primary) 68T05, 68T07 (Secondary) © 2022, CC BY.

关键词： Generative adversarial networks

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