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Enhancing NR-IQA Model Robustness through Simple Image Compression Techniques

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Yujia Yang, Chenxi Yu, Zhaofei Huang, Tiejun Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing School of Computer Science Beijing China Peking University School of Mathematical Sciences Beijing China China Institution for Artificial Intelligence Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing China

No-Reference Image Quality Assessment (NR-IQA) plays a crucial role in various real-world applications by predicting image quality scores without the need for reference images. Despite the impressive performance of deep learning-based NR-IQA models, they remain vulnerable to adversarial attacks, which introduce imperceptible perturbations to input images, causing significant changes in predicted scores. In this study, we explore the use of simple JPEG compression techniques, as well as their combination with norm regularization training, to defend against these adversarial attacks. Our results demonstrate that image compression is an effective method to enhance model robustness, and it can further improve the robustness of NR-IQA models when combined with appropriate training strategies. Since excessive compression may reduce performance on clean images, it is essential to strike a balance. This work provides valuable insights into designing effective image compression methods for NR-IQA models. © 1991-2012 IEEE.

关键词： Image compression

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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation 5

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Faculty of Applied Science Department of Applied Statistics Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798331505998

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields. © 2024 IEEE.

关键词： Spatio-temporal data

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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Extraction of Gene Regulatory Relation Using BioBERT

Extraction of Gene Regulatory Relation Using BioBERT

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Essien, Clement He, Fei Hannink, Mark Popescu, Mihail Xu, Dong University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States Northeast Normal University School of Information Science and Technology Changchun China University of Missouri Department of Biochemistry Bond Life Sciences Center ColumbiaMO United States University of Missouri Department of Health Management and Informatics ColumbiaMO United States University of Missouri Department of Electrical Engineering and Computer Science Bond Life Sciences Center ColumbiaMO United States

ISBN: (纸本)9781665468190

Relation Extraction (RE) is a critical task typically carried out after Named Entity recognition for identifying gene-gene association from scientific publication. Current state-of the-art tools have limited capacity as most of them only extract entity relations from abstract texts. The retrieved gene-gene relations typically do not cover gene regulatory relations. There still exists a lot of room for improvement. In this work, we propose GeREx, a transformer based RE tool for identifying gene regulatory relations from full texts by incorporating labeling from pathway figures. GeREx achieved an F1-Score of 83.45% when evaluated on an independent test dataset. © 2022 IEEE.

关键词： Genes

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Large Scale Deterministic Creation of Single Photon Emitters in Silicon Nitride Nanopillars

Large Scale Deterministic Creation of Single Photon Emitters...

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CLEO: QELS_Fundamental science, QELS 2022

作者： Peana, Samuel Yesilyurt, Omer Mkhitaryan, Vahagn Senichev, Alexander Martin, Zachariah O. Lagutchev, Alexei S. Boltasseva, Alexandra Shalaev, Vladimir M. Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center National Quantum Information Science Research Center The U.S. Department of Energy Oak RidgeTN37931 United States

We demonstrated large scale deterministic creation of single photon emitters in annealed silicon nitride on silicon oxide pillars. The estimated single photon emitter yield is approximately 50% with a lateral accuracy... 详细信息

ISBN: (纸本)9781557528209

关键词： Silicon nitride

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Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

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science China(Information sciences) 2022年第5期65卷 250-260页

作者： Hai JIN Jiang XIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies,has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency,auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.

关键词： blockchain distributed systems internet of value challenges development

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A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion Sensors 47

A Hybrid Residual CNN with Channel Attention Mechanism for C...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350365597

Recognizing individuals continuously using wearable devices has several applications in personalized services and security. This paper presents a hybrid deep network architecture called SE-ResBiLSTM that integrates a squeeze-and-excitation (SE) mechanism with residual bidirectional LSTM layers. The goal is to enhance the accuracy of person identification using data from wearable motion sensors. The SE blocks facilitate channel-wise feature refinement, highlighting helpful information. Residual connections enable the training of deeper LSTM architectures. The proposed method's accuracy in identifying individuals across increasing periods between training and testing is evaluated using the SP-SW HAR dataset, which contains activity data collected from wristwatch sensors. By leveraging representation learning and exhibiting resilience against sensor noise and temporal fluctuations, SE-ResBiLSTM improves recognition accuracy compared to regular and convolutional LSTM networks. According to the trial data, SE-ResBiLSTM achieved a maximum identification accuracy of 98.71 %. The experimental results demonstrate that this approach significantly boosts the performance of user identification using wristwatch sensors. © 2024 IEEE.

关键词： Wearable sensors

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Algorithmic Techniques for Necessary and Possible Winners

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ACM/IMS Transactions on Data science 2021年第3期2卷 1–23页

作者： Chakraborty, Vishal Delemazure, Theo Kimelfeld, Benny Kolaitis, Phokion G. Relia, Kunal Stoyanovich, Julia Computer Science and Engineering Department Jack Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Ecole Normale Superieure 45 Rue d'Ulm Paris75005 France Henry and Marilyn Taub Faculty of Computer Science Technion Haifa3200003 Israel Computer Science and Engineering Department Jack Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Computer Science and Engineering Department Tandon School of Engineering New York University 370 Jay Street BrooklynNY11201 United States Center for Data Science New York University 60 5th Avenue New YorkNY10011 United States

We investigate the practical aspects of computing the necessary and possible winners in elections over incomplete voter preferences. In the case of the necessary winners, we show how to implement and accelerate the polynomial-time algorithm of Xia and Conitzer. In the case of the possible winners, where the problem is NP-hard, we give a natural reduction to Integer Linear Programming (ILP) for all positional scoring rules and implement it in a leading commercial optimization solver. Further, we devise optimization techniques to minimize the number of ILP executions and, oftentimes, avoid them altogether. We conduct a thorough experimental study that includes the construction of a rich benchmark of election data based on real and synthetic data. Our findings suggest that, the worst-case intractability of the possible winners notwithstanding, the algorithmic techniques presented here scale well and can be used to compute the possible winners in realistic scenarios. © 2021 Association for Computing Machinery.

关键词： Integer programming

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A Different Approach Design for Higher-Order Highly Selective Wideband Bandpass Filters Using Cascaded Building Blocks

A Different Approach Design for Higher-Order Highly Selectiv...

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Asia-Pacific Conference on Microwave

作者： Adnan Nadeem Nosherwan Shoaib Symeon Nikolaou Dimitra Psychogiou Photos Vryonides Department of Electrical Engineering Frederick University and Frederick Research Center Nicosia Cyprus School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan School of Engineering University College Cork Tyndall National Institute Cork Ireland

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

This paper presents a novel design approach for creating higher-order wideband bandpass filters (BPFs). The technique builds on a conventional filter topology, featuring shunt short-circuited stubs connected by transmission lines. In this method, the traditional $\lambda_{\mathrm{g}}/4$ interconnecting lines and $\lambda_{\mathrm{g}}/4$ short-circuited stubs are replaced by a building block comprising $\lambda_{\mathrm{g}}/8$ lines separated by $\lambda_{\mathrm{g}}/8$ short-circuited coupled-line sections. To enhance selectivity, $)_{\vee \mathrm{g}}/2$ open-and short-circuited coupled-line segments are used at the filter's input and output. The paper provides design guidelines and formulas through ABCD analysis for practical filter implementations. Additionally, a design example is demonstrated, along with its practical validation through the manufacturing and testing of a fifth-order microstrip-type BPF.

关键词： Band-pass filters Microwave measurement Power transmission lines Prototypes Transmission line measurements Reflection Topology Microwave filters Wideband Testing

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Monolithic Integration of Quantum Emitters with Silicon Nitride Photonic Platform

Monolithic Integration of Quantum Emitters with Silicon Nitr...

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CLEO: QELS_Fundamental science, QELS 2022

作者： Senichev, Alexander Peana, Samuel Martin, Zachariah O. Yesilyurt, Omer Sychev, Demid Lagutchev, Alexei S. Boltasseva, Alexandra Shalaev, Vladimir M. Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center The U.S. Department of Energy Oak RidgeTN37931 United States

Silicon nitride has great potential for integrated quantum photonics. We demonstrate monolithic integration of intrinsic quantum emitters in SiN with waveguides which show a room-temperature off-chip count rate of ~10... 详细信息

ISBN: (纸本)9781557528209

关键词： Silicon nitride

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