检索结果-内蒙古大学图书馆

12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Light extraction improvement via ITO p-electrodes for InGaN red micro-LEDs emitting at 640 nm

Optics Continuum

引用

Optics Continuum 2025年第5期4卷 1040-1050页

作者： Cesur Altinkaya Mohammed A Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia cesur.altinkaya@kaust.edu.sa Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia daisuke.iida@kaust.edu.sa kazuhiro.ohkawa@kaust.edu.sa

We present light extraction efficiency (LEE) improvement for InGaN red micro-light emitting diodes (micro-LEDs) of various sizes operating at low current densities. We compared the characteristics of micro-LEDs with indium tin oxide (ITO) transparent p-electrodes with conventional opaque metal p-electrodes. 50 µm × 50 µm micro-LEDs with ITO p-electrodes achieved a peak on-wafer external quantum efficiency (EQE) of 2.54% with an emission wavelength of 640 nm at a current density as low as 0.4 A/cm 2 . This represents a 1.18-fold improvement in peak EQE compared to devices with metal p-electrodes. Light ray tracing simulation confirmed that the ITO p-electrodes exhibit 1.18 times higher light escape than metal-based micro-LEDs, validating the role of enhanced light extraction. These findings provide valuable insights for advancing high-definition display and VR applications.

关键词： Atomic layer deposition Indium tin oxide Light extraction Opacity Quantum efficiency Ray tracing

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks 3

A Hybrid Learning Approach for Automatic Data Labelling and ...

引用

3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Kanwal, Rimsha Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Computer Science Islamabad Pakistan College of Engineering Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798350322125

Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Stable Discontinuous Galerkin Time-Domain Method for Simulation of Thin Conductive Layers

A Stable Discontinuous Galerkin Time-Domain Method for Simul...

引用

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Özakin, M. Burak Chen, Liang Ahmed, Shehab Bagci, Hakan Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia Ali I. Al-Naimi Petroleum Engineering Research Center Thuwal23955 Saudi Arabia

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A conductive layer that is much thinner than the skin depth is often incorporated into electromagnetic solvers as a resistive boundary condition (RBC) to avoid fine meshes (and consequently small time steps for time-domain methods). When the discontinuous Galerkin time-domain (DGTD) method, which relies on an explicit Runge-Kutta (RK) scheme for time integration, is used in a simulation involving an RBC surface with high conductivity, the solution becomes unstable. In this work, to circumvent this bottleneck, a locally-implicit time marching scheme is developed. The proposed method uses implicit Euler backward and explicit Euler forward schemes to discretize the time derivatives in local DG equations associated with the elements that "touch"and do not "touch"the RBC surface, respectively. This yields a locally-implicit Euler (LIME) time marching scheme that maintains its stability in the presence of a highly conductive layer. Indeed, numerical results demonstrate that LIME-DGTD is more stable than RK-DGTD for a transmission problem involving a thin metal sheet. © 2022 IEEE.

关键词： Lime

来源：评论

学校读者我要写书评

暂无评论

The Impact of Middle and High School Students Participation in the Innovation Nation Competition on Their Education 24

The Impact of Middle and High School Students Participation ...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Mustafa, Dheya Zriqat, Samah Mustafa, Intisar Ghazi Althebyan, Qutaibah College of Engineering The Hashemite University Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Dept. of Information Technology and Computer Science Irbid Jordan College of Education Qatar University Dept. of Educational Sciences Doha Qatar Al Ain University Cybersecurity Program Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350384307

The Innovative Nation competition is a Jordanian initiative that uses workshops, training camps, and competitions to improve students' general and soft skills, problem-solving, creativity, and invention. The curriculum is divided into three parts, each of which has the potential to increase interest in science, Technology, engineering, and Mathematics (STEM). The goal of this research is to determine how much the STEM competition has influenced students' education and decision to pursue jobs in science and technology. Personal interviews with students and mentors were employed in this study to assess their skill sets, prospective career paths, and interests. The collected data was reviewed, and it was found that participation in the Innovation Nation competition increased students' interest in STEM vocations. © 2023 IEEE.

关键词： Artificial intelligence education Innovation non-curricular learning PictoBlox STEM

来源：评论

学校读者我要写书评

暂无评论

Edge Intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm 23

Edge Intelligence-Based UAV Human Target Recognition with Im...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

来源：评论

学校读者我要写书评

暂无评论

A Clustering-Based Color Reordering Method for Reversible Data Hiding in Palette Images

引用

IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

引用

CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

关键词： Photonic devices

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

引用

5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improve the Performance of Pretrained Language Model in BioASQ 10b Phase B

NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improv...

引用

2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Ting, Hao-Hsuan Zhang, Yu Han, Jen-Chieh Tsai, Richard Tzong-Han Interdisciplinary Program of Electrical Engineering and Computer Science National Central University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan IoX Center National Taiwan University Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taiwan

This paper presents our system for the BioASQ10b Phase B task. For ideal answers, we used the fine-tuned BioBERT model on the MNLI dataset to construct sentence embeddings and combined it with BERTScore to select sentences from the provided Snippets as answers. For the exact answers, we also used the BioBERT model and used the snippet scores generated from the ideal answer selection model to predict the exact answers for factoid and list questions. The exact answers of our fifth test batch ranked second place. In addition, the ideal answers we submitted achieved first place in the ROUGE score in all test batches from batch second to fifth. © 2022 Copyright for this paper by its authors.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：