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PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation

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arXiv 2021年

作者： Keser, Reyhan Kevser Ayanzadeh, Aydin Aghdam, Omid Abdollahi Kilcioglu, Caglar Toreyin, Behcet Ugur Ure, Nazim Kemal Research Group Informatics Institute Istanbul Technical University Turkey Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Arcelik Research and Development Turkey Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Turkey

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher-student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets. © 2021, CC BY-NC-ND.

关键词： Distillation

A DDoS Attack Detection System for Industry 5.0 using Digital Twins and Machine Learning

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A DDoS Attack Detection System for Industry 5.0 using Digita...

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Akshat Gaurav Brij B. Gupta Kwok Tai Chui Varsha Arya Elhadj Benkhelifa Ronin Institute Montclair NJ USA Department of Computer Science and Information Engineering International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taichung Taiwan Lebanese American University Beirut Lebanon Hong Kong Metropolitan University (HKMU) Asia University Taichung Taiwan Staffordshire University UK

Industry 5.0 is a new paradigm that seeks to improve production via the use of cutting-edge technologies like the Internet of Things (IoT) and cloud computing. Distributed denial of service (DDoS) assaults, for example, may severely harm industrial processes and disrupt the whole supply chain, but this integration of technology also presents significant security issues. Therefore, it is crucial in the age of Industry 5.0 to create reliable DDoS attack detection systems. In this research, we offer a DDoS attack detection system for the Industry 5.0 environment that makes use of Digital Twins (DTs) and Machine Learning (ML). Our method takes use of the strengths of DTs to model the production flow virtually, which can then be used to track the system’s behaviour and spot any hiccups. For further analysis of the DTs’ output data in order to spot possible DDoS assaults, we use explicable ML algorithms like the decision tree classifier.

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Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

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arXiv 2022年

作者： Wang, Hongyi Hu, Hongjie Chen, Qingqing Li, Yinhao Iwamoto, Yutaro Han, Xian-Hua Chen, Yen-Wei Tong, Ruofeng Lin, Lanfen The College of Computer Science and Technology Zhejiang University Hangzhou310063 China The Department of Radiology Sir Run Run Shaw Hospital Hangzhou310016 China The Research Center for Healthcare Data Science Zhejiang Laboratory Hangzhou311100 China The College of Information Science and Engineering Ritsumeikan University Kusatsu5250058 Japan The Artificial Intelligence Research Center Yamaguchi University Yamaguchi7538511 Japan The DUT-RU Co-Research Center of Advanced ICT for Active Life Research Center for Healthcare Data Science Zhejiang Laboratory Hangzhou311100 China

High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and dimensionality in contrast to currently limited GPU memory. Therefore, most existing 3D image segmentation methods use patch-based models, which have low inference efficiency and ignore global contextual information. To address these problems, we propose a super-resolution (SR) based patch-free 3D image segmentation framework that can realize HR segmentation from a global-wise low-resolution (LR) input. The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input. To furthermore balance the information loss with the LR input, we propose a High-Frequency Guidance Module (HGM), and design an efficient selective cropping algorithm to crop an HR patch from the original image as restoration guidance for it. In addition, we also propose a Task-Fusion Module (TFM) to exploit the inter connections between segmentation and SR task, realizing joint optimization of the two tasks. When predicting, only the main segmentation task is needed, while other modules can be removed for acceleration. The experimental results on two different datasets show that our framework has a four times higher inference speed compared to traditional patch-based methods, while its performance also surpasses other patch-based and patch-free models. The code will be made available upon acceptance. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Unsupervised Single Valued Neutrosophic Sets Approach for Cl...

3rd International Conference on Machine Learning and data engineering, ICMLDE 2024

作者： Byeon, Haewon Kumar, Pramod Khan, Ihtiram Raza Alghayadh, Faisal Yousef Rusho, Maher Ali Soni, Mukesh Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Bhadana Haryana Kablana124104 India Computer Science Department Jamia Hamdard Delhi India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder BoulderCO United States Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune India

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method's capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Integrated circuit layout

Sentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Miningsentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Mining

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SSRN

SSRN 2023年

作者： Tebyani, Sahand Jazayeri, Kian Ahadi, Seyedeh Aridis Çelebi, Erbuğ Jahandar, Farzad Department of Management Information Systems School of Applied Sciences Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Department of Engineering Artificial Intelligence Application and Research Center Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Khoury College of Computer Science Northeastern University United States Cyprus International University Cyprus

Recurrent Neural Networks (RNNs) have long had a dominant position in the fields of sentiment analysis and Natural Language Processing (NLP). This research gives a thorough comparative analysis of two unique models for opinion mining: Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). Notably, these models have a GloVe-based tokenizer installed to improve their verbal comprehension. We use the Amazon reviews dataset, concentrating on mobile devices and accessories, a rich source of varied user opinions, to assess their effectiveness. We compare their performance to that of the powerful large language model DistilBERT in addition to bespoke models. Our study goes into the complex technique of sentiment analysis to unearth subtle insights. Performance assessments cover a wide range of measures and factors and provide a comprehensive understanding of the models' capabilities. We also contrast how well these models extract highly relevant terms, demonstrating how well they can extract insightful information from user-generated content. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

MassSpecGym: A benchmark for the discovery and identification of molecules

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arXiv 2024年

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of Computing Science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Germany Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag: Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym – the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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engineering applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen 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 Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

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arXiv 2022年

作者： Dang, Yuchen Chen, Ziqi Li, Heng Shu, Hai Center for Data Science New York University New YorkNY United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Province Shenzhen China Department of Biostatistics School of Global Public Health New York University New YorkNY United States

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://***/yd1008/ts_ensemble_sunspot. Copyright © 2022, The Authors. All rights reserved.

关键词： Solar energy

FishEye8K: A Benchmark and dataset for Fisheye Camera Object Detection

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arXiv 2023年

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University United Arab Emirates College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan University at Albany State University of New York NY United States Department of Computer Science National Yang Ming Chiao Tung University Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

With the advance of ai, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The Fish-Eye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection