检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Huang, Tiantian Xu, Denan Zhang, Hang Bai, Ou Aravelli, Aparna Zhou, Xiaoxiao Han, Bin School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430070 China Human Cyber-Physical Systems Laboratory The Department of Electrical and Computer Engineering Florida International University Florida33174 United States Applied Research Center Florida International University Florida33174 United States Guangdong Intelligent Robotics Institute Dongguan523808 China

Complete compliance of soft robots is a trending research topic. One significant aspect is the control element. In the current work, a flexible electro-rheological (ER) valve development is chosen due to the advantage of its dynamic power density, convenient electronic control mode and no mechanical moving parts. These properties enable the ER valve to be miniaturized and easily integrated into a fluid-driven soft robot. However, the integration effect of the existing flexible ER valve configuration in soft robots is not ideal. In this study, a novel concentric semi-cylindrical flexible giant ER (GER) valve with a dual flowing direction is proposed for a soft robot to enable a larger deformation bending motion with multiple motion modes. The resulting valve has an overall volume of 50 mm 3 , a mass of 0.88 g, and a maximum controlling pressure of 84 kPa with a pressure change rate of 13.9, which is suitable for most soft robot applications. The pressure difference calculation method for the concentric semi-cylindrical GER valve is discussed. The effects of size, voltages, and flow rates on the valve’s static and dynamic performances, as well as the characteristics of the two pressure regulation modes, are investigated through simulations and experiments. The seamless integration and flexibility of the proposed valve are demonstrated by controlling a typical network actuator with six motion modes. Finally, a soft robot composed of two actuators is fabricated and realized the grasping of multiple everyday objects using different motion modes. © 2022, The Authors. All rights reserved.

关键词： Nanofluidics

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Neural piecewise-constant delay differential equations

arXiv

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arXiv 2022年

作者： Zhu, Qunxi Shen, Yifei Li, Dongsheng Lin, Wei Research Institute of Intelligent Complex Systems Fudan University China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Microsoft Research Asia China School of Mathematical Sciences State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Shanghai Center for Mathematical Sciences Center for Computational Systems Biology LCNBI and Research Institute of Intelligent Complex Systems Fudan University China

Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neural network, called the Neural Piecewise-Constant Delay Differential Equations (PCDDEs). Here, unlike the recently proposed framework of the Neural Delay Differential Equations (DDEs), we transform the single delay into the piecewiseconstant delay(s). The Neural PCDDEs with such a transformation, on one hand, inherit the strength of universal approximating capability in Neural DDEs. On the other hand, the Neural PCDDEs, leveraging the contributions of the information from the multiple previous time steps, further promote the modeling capability without augmenting the network dimension. With such a promotion, we show that the Neural PCDDEs do outperform the several existing continuousdepth neural frameworks on the one-dimensional piecewiseconstant delay population dynamics and real-world datasets, including MNIST, CIFAR10, and *** Codes 34Kxx, 93Cxx, 92B2 © 2022, CC BY.

关键词： Ordinary differential equations

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Personnel selection under Intuitionistic Fuzzy Multi-Criteria Decision Analysis evaluation

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Procedia computer science 2024年 246卷 3840-3850页

作者： Izabela Auguściak Jakub Więckowski Wojciech Sałabun University of WSB Merito ul. Śniadeckich 3 70-224 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland

Decision-making involves various aspects of daily life, encompassing routine tasks, complex business strategies, and resource management. With problems often including multiple criteria, detailed analysis becomes imperative to ensure a reliable assessment. Consequently, diverse tools have emerged to aid decision-makers in making well-informed choices, with Multi-Criteria Decision Analysis (MCDA) methods standing out for structured evaluations and adaptable processes. MCDA methods accommodate both crisp and fuzzy data, handling uncertainties inherent in decision problems. However, the abundance of methods poses a challenge in selecting the most suitable approach. Comparative analyses underscore the variability in outcomes, emphasizing the importance of employing multiple techniques for robust decision-making. Within resource management, MCDA methods offer versatile solutions, integrating expert knowledge and fuzzy logic to enhance reliability. As technological advancements introduce novel tools, empirical validation becomes crucial. Fuzzy ranking emerges as a promising approach, offering comprehensive insights into decision stability and robustness. This study applies selected MCDA methods within an Intuitionistic Fuzzy (IF) environment to personnel selection, analyzing multiple criteria with the participation of decision-makers. Through Monte Carlo simulation and fuzzy ranking, the study validates the efficacy of fuzzy ranking in practical multi-criteria decision-making scenarios, offering insights into decision robustness and stability. The proposed research provides a comprehensive approach to personnel selection, producing reliable and robust recommendations under changing input conditions.

关键词： Human resource management personnel selection decision-making intuitionistic fuzzy sets

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Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology

arXiv

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arXiv 2023年

作者： Li, Yiqing Noack, Bernd R. Wang, Tianyu Cornejo Maceda, Guy Y. Pickering, Ethan Shaqarin, Tamir Tyliszczak, Artur School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China Department of Mechanical Engineering Tafila Technical University Tafila66110 Jordan Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology Czestochowa42-201 Poland

We optimize the jet mixing using large eddy simulations (LES) at a Reynolds number of 3000. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning, and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius (Req) derived from the streamwise velocity in a plane located 8 diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. The plant benefits from a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius Req with a 7-armed blooming jet after around 570 evaluations. The control input requires 2% momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double helix solution. The learning is accelerated and converges to another better optimum by including surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing. © 2023, CC BY-NC-ND.

关键词： Flow patterns

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Zero-Knowledge Proof-based Practical Federated Learning on Blockchain

arXiv

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Li, Meng Liu, Jiamou Zhu, Liehuang Russello, Giovanni Asghar, Muhammad Rizwan The School of Computer Science The University of Auckland Auckland New Zealand The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Institute of Information Technology Beijing Institute of Technology Fujian351100 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China The Department of Computer Science University of Surrey The School of Computer Science The University of Auckland Auckland New Zealand

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-Knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series

arXiv

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arXiv 2023年

作者： Kuznetsova, Rita Pace, Alizée Burger, Manuel Yèche, Hugo Rätsch, Gunnar Department of Computer Science ETH Zürich Switzerland ETH AI Center ETH Zürich Switzerland Max Planck Institute for Intelligent Systems Tübingen Germany Medical Informatics Unit Zürich University Hospital Zürich Switzerland Swiss Institute of Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland

Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care Unit (ICU), the state-of-the-art remains to tackle sequence classification in a tabular manner with tree-based methods. Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features. Given the similar level of feature heterogeneity exhibited by ICU time-series and motivated by these findings, we explore these novel methods’ impact on clinical sequence modeling tasks. By jointly using such advances in deep learning for tabular data, our primary objective is to underscore the importance of step-wise embeddings in time-series modeling, which remain unexplored in machine learning methods for clinical data. On a variety of clinically relevant tasks from two large-scale ICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of state-of-the-art methods for tabular time-series as time-step embedding models, showing overall performance improvement. In particular, we evidence the importance of feature grouping in clinical time-series, with significant performance gains when considering features within predefined semantic groups in the step-wise embedding module. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion and Intensity of Emotion Classes from Meme Images using Single and Multi Modalities 1

HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion ...

引用

1st Workshop on Multimodal Fact-Checking and Hate Speech Detection, DE-FACTIFY 2022

作者： Nguyen, Thanh Tin Pham, Nhat Truong Nguyen, Ngoc Duy Nguyen, Hai Nguyen, Long H. Kim, Yong-Guk Human Computer Interaction Lab Department of Computer Engineering Sejong University Seoul Korea Republic of Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia Khoury College of Computer Sciences Northeastern University Boston United States Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

Nowadays, memes found on internet are overwhelming. Although they are innocuous and sometimes entertaining, there exist memes that contain sarcasm, offensive, or motivational feelings. In this study, several approaches are proposed to solve the multiple modality problem in analysing the given meme dataset. The imbalance issue has been addressed by using a new Auto Augmentation method and the uncorrelation issue has been mitigated by adopting deep Canonical Correlation Analysis to find the most correlated projections of visual and textual feature embedding. In addition, both stacked attention and multi-hop attention network are employed to efficiently generate aggregated features. As a result, our team, i.e. HCILab, achieved a weighted F1 score of 0.4995 for sentiment analysis, 0.7414 for emotion classification, and 0.5301 for scale/intensity of emotion classes on the leaderboard. This results are obtained by using concatenation between image and text model and our code can be found at https://***/JMRa8. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Sentiment analysis

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Volatility forecasting using Deep Learning and sentiment analysis

arXiv

引用

arXiv 2022年

作者： Ncume, Vuyo van Zyl, T.L. Paskaramoorthy, A. Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Statistical Sciences University of Cape Town Cape Town South Africa

Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day’s volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment for volatility forecasting appears to be market specific. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning

arXiv

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arXiv 2023年

作者： Li, Heng Liu, Haofeng Fu, Huazhu Xu, Yanwu Shu, Hai Niu, Ke Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Singapore School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Department of Biostatistics School of Global Public Health New York University New YorkNY United States Computer School Beijing Information Science and Technology University Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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