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RETRACTED ARTICLE: Energy-efficient and sustainable communication in optical networks for eliminating path reservation criteria and providing guaranteed packet transmission between nodes

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Neural Computing and Applications 2023年第10期37卷 7453-7453页

作者： Asha, P. Kalaavathi, B. Shantha Kumari, K. Malarvizhi, K. Kishore Kumar, A. Sobitha Ahila, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering JCT College of Engineering and Technology Coimbatore India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

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ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce NAS Space for Heterogeneous Pipeline of CNNs

ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Huijuan Duan Danghui Wang Xingting Zhao Kuizhi Mei Jihe Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

Efficiently deploying neural network models on heterogeneous edge systems faces unique challenges. Two major issues are underutilized pipelines caused by hardware heterogeneity and explosive growth of architecture search space. To address these challenges, we propose ExpoNAS, a framework enabling fast deployment-oriented neural architecture search on heterogeneous platforms. ExpoNAS evaluates model partitioning and deployability before search to meet accuracy and latency constraints. However, repeated partitioning leads to exponential growth in architecture search space. We first observe a strong correlation between operational exposure and accuracy improvement, and then we propose an exposure-based reduction technique that excludes the operations of low exposure. By removing these non-critical operations, the exposure-based pruning successfully reduces architecture search time. Our experiments on popular datasets show that the ExpoNAS pipelined design improves execution efficiency by a factor of 2 compared to traditional batch processing. Without compromising accuracy, ExpoNAS is able to reduce the architecture search time by 47.5% for discovering high-accuracy networks, compared to the SOTA NAS strategies.

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Bitcoin price forecasting: A comparative study between statistical and machine learning methods 2

Bitcoin price forecasting: A comparative study between stati...

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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Waheeb, Waddah Shah, Habib Jabreel, Mohammed Puig, Domenec School of Computing Asia Pacific University of Technology Innovation Kuala Lumpur Bukit Jalil57000 Malaysia College of Computer Science King Khalid University Department of Computer Science Abha62529 Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona43007 Spain

ISBN: (纸本)9781728154671

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods. © 2020 IEEE.

关键词： Bitcoin

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Bayesian deep multi-instance learning for student performance prediction based on campus big data

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Neurocomputing 2025年 647卷

作者： Huang, Jiayang Yang, Keyi Wang, Quan Yang, Pengfei Ruan, Ziling Wang, Jiaxi Zhang, Zhi-Qiang School of Computer Science and Technology Xidian University Xi'an710126 China The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an710126 China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds Leeds United Kingdom

Problem: Predicting student performance using campus big data and deep learning methods has emerged as a promising alternative to traditional psychological assessments, which are often delayed and subjective. However, existing deep learning approaches face significant challenges, including the sparsity of campus big data and susceptibility to overfitting. Objective: To address these issues, this study aims to develop a robust and effective framework for predicting student performance by overcoming data sparsity and overfitting problems. Method: We propose a novel Bayesian Deep Multi-Instance Learning (Bay-DeepMIL) framework. The method integrates multi-instance learning (MIL) to handle data sparsity and incorporates a Bayesian framework to treat network parameters as probabilistic distributions, enhancing robustness and mitigating overfitting. Additionally, a Bayesian multi-head attention mechanism is introduced to dynamically assign importance to different instances, improving the extraction of key information from multi-instance data. Results: Extensive experiments demonstrate that Bay-DeepMIL outperforms state-of-the-art methods in prediction accuracy. The framework also provides uncertainty estimates for each prediction, offering valuable confidence measures and decision support for educational stakeholders. Conclusion: The proposed Bay-DeepMIL framework not only advances the technical capabilities of predictive models but also provides practical tools for enhancing educational decision-making. This study underscores the effectiveness of integrating Bayesian inference with multi-instance learning to address core challenges such as data sparsity and model overfitting in student performance prediction. © 2025 Elsevier B.V.

关键词： Students

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AdaDiffGrad: An Adaptive Batch Size Implementation Technique for DiffGrad Optimization Method. 14

AdaDiffGrad: An Adaptive Batch Size Implementation Technique...

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14th International Conference on Innovations in Information technology, IIT 2020

作者： Khan, Wajid Ali, Sara Muhammad, U.S. Khan Jawad, Muhammad Ali, Mazhar Nawaz, Raheel COMSATS University Department of Computer Science Abbottabad Pakistan National University of Sciences and Technology Department of Robotics Islamabad Pakistan COMSATS University Department of Computer Science Islamabad Campus Pakistan COMSATS University Department of Electrical Engineering Lahore Campus Pakistan Manchester Metropolitan University School of Computing Mathematics and Digital Technology United Kingdom

ISBN: (纸本)9781728181844

Stochastic Gradient Descent is a major contributor to the success of the deep neural networks. The gradient provides basic knowledge about the function direction and its rate of change. However, SGD changes the step size equally for all parameters irrespective of their gradient behavior. Recently, several efforts have been made to improve the SGD method, such as AdaGrad, RMSprop, Adam, and diffGrad. The diffGrad is an appropriate and enhanced technique that uses fraction constant based on previous gradient information for gradient calculation. This fraction constant decreases the momentum resulting in slow convergence towards an optimal solution. This paper addresses the slow convergence problem of the diffGrad algorithm and proposed a new adaDiffGrad algorithm. In adaDiffGrad an adoptive batch size is implemented for the diffGrad to overcome the problem of slow convergence. The proposed model is experimented for image categorization and classification over CIFAR10, CIFAR100, and FakeImage dataset. The results are compared with the state of art models, such as Adam, AdaGrad, DiffGrad, RMSprop, and, SGD. The results show that adaDiffGrad outperforms other optimizers and improves the accuracy of the diffGrad. © 2020 IEEE.

关键词： Image classification

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GraspAda: Deep Grasp Adaptation through Domain Transfer

GraspAda: Deep Grasp Adaptation through Domain Transfer

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yiting Chen Junnan Jiang Ruiqi Lei Yasemin Bekiroglu Fei Chen Miao Li Institute of Technological Sciences Wuhan University Wuhan China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electrical Engineering Chalmers University of Technology Goteborg Sweden Department of Computer Science University College London UK Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong China

Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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The Noise Covariances of Linear Gaussian Systems with Unknown Inputs Are Not Uniquely Identifiable Using Autocovariance Least-squares

arXiv

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arXiv 2022年

作者： Kong, He Sukkarieh, Salah Arnold, Travis J. Chen, Tianshi Zheng, Wei Xing The Department of Mechanical and Energy Engineering South University of Science and Technology Shenzhen518055 China The Australian Centre for Field Robotics University of Sydney NSW2006 Australia The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China The School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Existing works in optimal filtering for linear Gaussian systems with arbitrary unknown inputs assume perfect knowledge of the noise covariances in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the noise covariances of linear Gaussian systems with arbitrary unknown inputs. This paper considers the above identifiability question using the correlation-based autocovariance least-squares (ALS) approach. In particular, for the ALS framework, we prove that (i) the process noise covariance Q and the measurement noise covariance R cannot be uniquely jointly identified;(ii) neither Q nor R is uniquely identifiable, when the other is known. This not only helps us to have a better understanding of the applicability of existing filtering frameworks under unknown inputs (since almost all of them require perfect knowledge of the noise covariances) but also calls for further investigation of alternative and more viable noise covariance methods under unknown inputs. Especially, it remains to be explored whether the noise covariances are uniquely identifiable using other correlation-based methods. We are also interested to use regularization for noise covariance estimation under unknown inputs, and investigate the relevant property guarantees for the covariance estimates. The above topics are the main subject of our current and future work. © 2022, CC BY.

关键词： Kalman filters

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To smile or not to smile: The effect of mismatched emotional expressions in a Human-Robot cooperative task

To smile or not to smile: The effect of mismatched emotional...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ilaria Torre Anna Deichler Matthew Nicholson Rachel McDonnell Naomi Harte Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Division of Speech Music and Hearing KTH Royal Institute of Technology Stockholm Sweden ADAPT Research Centre Trinity College Dublin Dublin Ireland School of Computer Science and Statistics Trinity College Dublin Dublin Ireland School of Electronic Electrical Engineering Trinity College Dublin Dublin Ireland

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Emotional expressivity is essential for successful Human-Robot Interaction. However, robots often have different levels of expressivity in their face and voice. Here we ask whether this modality mismatch influences human behaviour and perception of the robot. Participants played a cooperative task with a robot that displayed matched and mismatched smiling expressions in the face and voice. Emotional expressivity did not influence acceptance of robot’s recommendations or subjective evaluations of the robot. However, we found that the robot had overall a higher social influence than a virtual character, and was evaluated more positively.

关键词： Task analysis Robots Faces

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