检索结果-内蒙古大学图书馆

IEEE International Conference on Communications (ICC)

作者： Chun-An Yang Shih-Chieh Chen Yi-Hsuan Peng Yu-Wen Chen Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Computer Systems Technology New York City College of Technology Brooklyn New York USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Dockless electric bike (E-bike) sharing has become a new urban modality of green transportation to offer convenient services. Typically, the service provider arranges a truck starting from the depot to visit multiple parking locations to replace low-energy batteries. However, visiting many parking locations may cause a considerable tour cost. One efficient way is to aggregate low-energy E-bikes together. Some incentive mechanisms are thus adopted to encourage E-bike users to move their bikes to suitable parking locations, but leading to an incentive cost. The service provider would like to balance the tour cost of the truck and the incentive cost of E-bike users. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. The simulation results with the real dataset show that our algorithm outperforms the other baselines.

关键词： Costs Shared transport Simulation Aggregates Approximation algorithms Batteries Green transportation

来源：评论

学校读者我要写书评

暂无评论

Near-Optimal Content Service Algorithm with Procurement and Placement in Edge Networks

Near-Optimal Content Service Algorithm with Procurement and ...

引用

IEEE Conference on Global Communications (GLOBECOM)

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines.

关键词： Procurement Costs Simulation Streaming media Approximation algorithms Real-time systems Telecommunications Servers User preference Optimization

来源：评论

学校读者我要写书评

暂无评论

Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN)

Fingerprint generation and authentication though Adaptive co...

引用

International Multi-Topic ICT Conference (IMTIC)

作者： Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Alina Baber Maria Andleeb Siddiqui Dept. of Computer Science & Information Technology NED University of Engineering and Technology Karachi Pakistan Dept of Software Engineering Sir Syed University of Engineering and Technology karachi Pakistan

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

引用

Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

引用

International Seminar on Application for Technology of information and Communication (ISemantic)

作者： Alvin Steven Suharnawi Farrikh Alzami Filmada Ocky Saputra Rama Aria Megantara Pulung Nurtantio Andono Dept. of Information Systems Universitas Dian Nuswantoro Indonesia Dept. of Information Engineering Universitas Dian Nuswantoro Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools &automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function

CDFMR: A Distributed Statistical Analysis of Stock Market Da...

引用

IEEE Cloud Summit

作者： Devendra Dahiphale Abhijeet Wadkar Karuna Pande Joshi Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County USA Dept. of Information Systems University of Maryland Baltimore County USA

The stock market generates massive data daily on top of a deluge of historical data. Investors and traders look to stock market data analysis for assurance in their investments, a prime indicator of our global economy. This has led to immense popularity in the topic, and consequently, much research has been done on stock market predictions and future trends. However, due to the relatively slow electronic trading systems and order processing times, the velocity of data, the variety of data, and social factors, there is a need for gaining speed, control, and continuity in data processing (real-time stream processing) considering the amount of data that is being produced daily. Unfortunately, processing this massive amount of data on a single node is inefficient, time-consuming, and unsuitable for real-time processing. Recently, there have been many advancements in Big Data processing technologies such as Hadoop, Cloud MapReduce, and HBase. This paper proposes a MapReduce algorithm for statistical stock market analysis with a Cumulative Distribution Function (CDF). We also highlight the challenges we faced during this work and their solutions. We further showcase how our algorithm is spanned across multiple functions, which are run using multiple MapReduce jobs in a cascaded fashion.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LEARNING EFFICIENT POSITIONAL ENCODINGS WITH GRAPH NEURAL NETWORKS

arXiv

引用

arXiv 2025年

作者： Kanatsoulis, Charilaos I. Choi, Evelyn Jegelka, Stephanie Leskovec, Jure Ribeiro, Alejandro Dept. of Computer Science Stanford University United States School of Computation Information and Technology Technical University of Munich Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we identify four key properties that graph PEs should satisfy: stability, expressive power, scalability, and genericness. We find that existing eigenvector-based PE methods often fall short of jointly satisfying these criteria. To address this gap, we introduce PEARL, a novel framework of learnable PEs for graphs. Our primary insight is that message-passing GNNs function as nonlinear mappings of eigenvectors, enabling the design of GNN architectures for generating powerful and efficient PEs. A crucial challenge lies in initializing node attributes in a manner that is both expressive and permutation equivariant. We tackle this by initializing GNNs with random node inputs or standard basis vectors, thereby unlocking the expressive power of message-passing operations, while employing statistical pooling functions to maintain permutation equivariance. Our analysis demonstrates that PEARL approximates equivariant functions of eigenvectors with linear complexity, while rigorously establishing its stability and high expressive power. Experimental evaluations show that PEARL outperforms lightweight versions of eigenvector-based PEs and achieves comparable performance to full eigenvector-based PEs, but with one or two orders of magnitude lower complexity. Our code is available at https://***/ehejin/Pearl-PE. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

引用

26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

来源：评论

学校读者我要写书评

暂无评论

Utilization of Online Collaborative Tools in software engineering: An Empirical Study on Review Meetings 6

Utilization of Online Collaborative Tools in Software Engine...

引用

6th International Conference on computer science and engineering, UBMK 2021

作者： Akman, Ibrahim Turhan, Cigdem Hacaloglu, Tuna Dept. of Computer Engineering Atilim University Ankara Turkey Dept of Software Engineering Atilim University Ankara Turkey Dept of Information Systems Engineering Atilim University Ankara Turkey

ISBN: (纸本)9781665429085

software development involves a significant amount of team effort where collaboration and communication of the team members are crucial. The team meetings are core activities in all stages of the software development process. Even though these meetings often are conducted face-to-face (F2F) with a lackof technology utilization, changing global conditions such as the COVID-19 pandemic require other solutions urgently without interrupting the software development schedule. For this purpose, online collaborative tools provide new opportunities for software teams to work together avoiding waste in time and resources and the relevant literature is immature. This study aims to assess the factors affecting the integration of online collaborative tools to SE practices with a special reference to review meetings. For this purpose, a sample of 73 SE sophomore and junior students who are future software professionals participated in experimental review meetings based on predefined scenarios. The findings indicate that the utilization of OCT's has positive effects on the participants' actual performance and improves the interaction between team members compared to F2F meetings. © 2021 IEEE

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Automated Weed Species Classification in Rice Cultivation Using Deep Learning

Automated Weed Species Classification in Rice Cultivation Us...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hamdadur Rahman Mahbub Alam Khan Shakil Khan Zishad Hossain Limon Md Ismail Hossain Siddiqui Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib Sheikh Shemanto Afridi Shafiur Rahman Dept. of Management of Information System International American University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Weeds in rice fields compete for essential resources such as water, nutrients, and light, leading to reduced crop yields, increased production costs, and environmental concerns. Traditional methods of weed detection are often labor-intensive, subjective, and inefficient, highlighting the need for automated approaches in precision agriculture. This study introduces an ensemble model designed for robust and accurate classification of rice weeds, addressing the shortcomings of conventional methods and enhancing detection scalability. The proposed ensemble model combines four deep learning architectures: EfficientNet-V2, SqueezeNet, InceptionResNet-v2, and DenseNet-201. By leveraging the complementary strengths of these models, we aim to improve performance. The study utilizes the Rice Field Weed Detection Dataset, which contains 3,632 high-resolution images that have been augmented to a total of 21,792 samples. Advanced preprocessing techniques such as resizing, normalization, and augmentation are applied to ensure effective feature extraction. Our model achieved a remarkable classification accuracy of 99.51%, significantly surpassing individual models and other state-of-the-art approaches. In addition, we developed a web-based application for real-time weed detection, providing a user-friendly platform for farmers and agricultural professionals to implement timely weed management strategies. This system supports sustainable farming by reducing the need for excessive herbicide use and promoting environmentally friendly practices. This research highlights the potential of ensemble deep learning for precise agricultural weed detection. Future work will focus on integrating diverse datasets and developing mobile applications to enhance accessibility and scalability.

关键词： Deep learning Accuracy Scalability Transfer learning Feature extraction Real-time systems Mobile applications Crop yield Water resources Farming

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：