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Nutmeg: a MIP and CP Hybrid Solver Using Branch-and-Check

Operations Research Forum 2020年第3期1卷 1-27页

作者： Lam, Edward Gange, Graeme Stuckey, Peter J. Van Hentenryck, Pascal Dekker, Jip J. Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Melbourne VIC Australia H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA United States

This paper describes the implementation of Nutmeg, a solver that hybridizes mixed integer linear programming and constraint programming using the branch-and-cut style of logic-based Benders decomposition known as branch-and-check. Given a high-level constraint programming model, Nutmeg automatically derives a mixed integer programming master problem that omits global constraints with weak linear relaxations, and a constraint programming subproblem identical to the original model. At every node in the branch-and-bound search tree, the linear relaxation computes dual bounds and proposes solutions, which are checked for feasibility of the omitted constraints in the constraint programming subproblem. In the case of infeasibility, conflict analysis generates Benders cuts, which are appended to the linear relaxation to cut off the candidate solution. Experimental results show that Nutmeg’s automatic decomposition outperforms pure constraint programming and pure mixed integer programming on problems known to have successful implementations of logic-based Benders decomposition, but performs poorly on general problems, which lack specific decomposable structure. Nonetheless, Nutmeg outperforms the standalone approaches on one problem with no known decomposable structure, providing preliminary indications that a hand-tailored decomposition for this problem could be worthwhile. On the whole, Nutmeg serves as a valuable tool for novice modelers to try hybrid solving and for expert modelers to quickly compare different logic-based Benders decompositions of their problems. © 2020, Springer Nature Switzerland AG.

关键词： Branch-and-check Conflict analysis Constraint programming Dual bound Hybridization Lazy clause generation Linear constraints Logic-based Benders decomposition Mixed integer programming Nogood

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of Computer science & technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images

Assessing Parkinson's Disease Severity Using Channel Attenti...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Fatemeh Fadaie Hamed Aghapanah Alireza Sedighifard Hadi Niknam Shirvan Department of Computer Engineering AI and Robotics University of Isfahan Isfahan Iran Department of Bioelectrics and Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Artificial Intelligence Iran University of Science and Technology Isfahan Iran Faculty of New Sciences and Technologies University of Tehran Isfahan Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively.

关键词： Adaptation models Solid modeling Attention mechanisms Accuracy Parkinson's disease Graph convolutional networks Tracking Transfer learning Cameras data models

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Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System 25

Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV ...

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25th International Computer science and engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Maitrichit, Nagorn Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Thailand has faced unrest in recent years, as have other countries around the world. The continuation of present trends means a tendency for an increase in both crimes against people and property. Nowadays, CCTV technology is widely used as surveillance and monitoring tools to help keep people safe. However, most of them still rely primarily on police personnel to inspect the displays. A weapon detection system can reduce the screen-reading workload of police officers with a limited workforce. The integration of weapon detection with CCTV cameras has a role to play in solving the problem. To develop the weapon detection system, the datasets used in this research were collected from 2 public datasets: ARMAS Weapon detection dataset and IMFDB Weapon detection system. The object detection method was used from TensorFlow Object Detection API using 1) SSD MobileNet-V1, 2) EfficientDet-D0 and 3) Faster R-CNN Inception Resnet-V2. For all experimental results, the object detection model is the Faster R-CNN Inception V2 using dataset 1, ARMAS Weapon detection dataset, with the highest mAP of 0.540 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.793 and 0.627, respectively. © 2021 IEEE.

关键词： Object detection

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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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Language Person Search with Pair-Based Weighting Loss 1

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zhang, Peng Ouyang, Deqiang Jiang, Chunlin Shao, Jie Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China

ISBN: (数字)9783030678326

ISBN: (纸本)9783030678319

Language person search, which means retrieving specific person images with natural language description, is becoming a research hotspot in the area of person re-identification. Compared with person re-identification which belongs to image retrieval task, language person search poses challenges due to heterogeneous semantic gap between different modal data of image and text. To solve this problem, most existing methods employ softmax-based classification loss in order to embed the visual and textual features into a common latent space. However, pair-based loss, as a successful approach of metric learning, is hardly mentioned in this task. Recently, pair-based weighting loss for deep metric learning has shown great potential in improving the performance of many retrieval-related tasks. In this paper, to better correlate person image with given language description, we introduce pair-based weighting loss which encourages model to assign appropriate weights to different image-text pairs. We have conducted extensive experiments on the dataset CUHK-PEDES and the experimental results validated the effectiveness of our proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games

arXiv

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arXiv 2024年

作者： Yuwono, Steve Hussain, Ahmar Kamal Schwung, Dorothea Schwung, Andreas Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Lübecker Ring 2 Soest59494 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Artificial Intelligence and Data Science in Automation Technology Hochschule Düsseldorf University of Applied Sciences Münsterstraße 156 Düsseldorf40476 Germany

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders' decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial-parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5-13% while satisfying the production demand, which significantly improves potential (global objective) values. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Classified Recognition Method for Analyzing User Sentiments Using Social Media Posts

Classified Recognition Method for Analyzing User Sentiments ...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： Ramesh S K. Vishnupriya A. Anandaraj Satheesh N P Titus Richard Dixit Dutt Bohra Department of Computing Technologies College of Engineering & Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Department of Artificial Intelligence and Data science Karpagam college of engineering Coimbatore Tamilnadu India Department of CSE Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of AI&DS Bannari Amman Institute of Technology Erode Tamilnadu India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of CSE IIT Jodhpur Rajasthan India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Social media texts and posts are analyzed through modern computing and artificial intelligence systems to detect user sentiments. The severity of the words and their related activities are analyzed to detect the user sentiments. In this paper, a Classified Sentiment Recognition Method (CSRM) is introduced. The proposed method utilizes deep learning to identify and classify sentiments using their associated words. Using the associated labels, deep learning trains the context and occurrence of the texts/ posts to detect precise sentiments. Thus the learning steps are induced with label, context, and occurrence-based training to improve the sentiment detection and type classification. This proposed method is verified using recognition accuracy, classification rate, and recognition error.

关键词： Deep learning Training Sentiment analysis Accuracy Social networking (online) Employment Market research

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

Optical Character Recognition (OCR) in Handwritten Character...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： P L Lekshmy S. Velmurugan Indra Kumari S. Kayalvili B. Teja Sree P. Karthik Kumar Department of Computer Science and Engineering LBS Institute of Technology for Women Kerala India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India Department of Machine Learning Data Research Applied AI Korea National University of Science and Technology (UST) Daejeon South Korea Department of Artificial Intelligence Kongu Engineering College (Autonomous) Erode Tamilnadu India Department of Information Technology S.R.K.R. Engineering College Chinaamiram Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate.

关键词： Training Image segmentation Image recognition Codes Optical character recognition Neural networks Streaming media

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Bridging Horizons in Diabetes Prediction: A Comparative Exploration of Machine Learning and Deep Learning Approaches in Pima Indian Women

Bridging Horizons in Diabetes Prediction: A Comparative Expl...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： L. Chandra Sekhar Reddy Monikadevi Gottipalli P. Sravanthi J. Rajanikanth Ganesh Yalamarthi Neelima Gurrapu Department of CSE (Data science) CMR College of Engineering & Technology Hyderabad India Department of CSE Sir C R Reddy College of Engineering Eluru India Department of CSE CMR College of Engineering & Technology Hyderabad India Department of CSE S R K R Engineering College Bhimavaram India Department of AI&ML SIR C R Ramalinga Reddy Polytechnic College Eluru India Department of computer science and Artificial intelligence SR University Warangal India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This study has demonstrated that diabetes prediction models will require powerful machine learning and anomaly detection methodologies in practice. Researchers are exploring novel outlier identification algorithms to enhance predictions. This study has addressed various concerns outlined in prior research. According to this study, diagnostic records, when combined with powerful machine learning such as Adaboost and XGBoost, can offer a precise forecast of the likelihood of diabetes. RNNs also improved deep learning, according to experts. Experts use IQR for the straightened subspace to detect outliers. The stand appears to have a highly positive impact on prediction models. IQR-boosted RNNs outperform XGBoost and AdaBoost in terms of prediction and efficiency. This suggests that combining outlier detection with advanced machine learning could potentially improve diabetes diagnosis and care. The identification of IQR outliers, using RNNs cultivated through iteration with a different line of machine learning, may improve diabetes prediction outcomes. RNN produces better results, with 93% accuracy. The study demonstrated that enhanced machine learning and outlier detection led to improvements in diabetes prediction algorithms.

关键词： Deep learning Machine learning algorithms Neural networks Predictive models Prediction algorithms Diabetes Public healthcare

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