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2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

作者： Yangjing, Long Yizhen, Huang Department of Mathematics Shanghai Jiaotong University 800 Dongchuan Road Minhang District Shanghai 200240 China Department of Computer Science and Engineering Shanghai Jiaotong University 800 Dongchuan Road Minhang District Shanghai 200240 China Digital Media and Data Reconstruction Lab. Shanghai Jiaotong University Graphics Team of the Wireless Platform Engineering Cellular and Handheld Group Intel Asia-Pacific Research and Development Ltd.

ISBN: (纸本)1424406560

Demosaicking is one of the important tasks in the image-processing pipeline in digital cameras using a single electronic sensor overlaid with a Color Filter Array. We quantitatively shows that, demosaicking algorithms perform better in low-gradient flat areas than in high-gradient steep areas. Based on this, an adaptive scheme is proposed that uses more complex neural networks to tackle steep areas in larger sizes of neighborhoods. And interpolation is edge-directed with different networks for different chosen directions. Thus networks are specialized in learning and depicting non-linear spatial inter-pixel correlations at respective gradients and directions. Its performance surpasses Go's neural-network method greatly. Compared with 2 recent state-of-the-art methods, our method provides an excellent trade-off between computational expense and PSNR, and well preserves image edge information. As an extension, we compare the performance of these algorithms with and without Lukac's postprocessing. © 2006 IEEE.

关键词： Adaptive systems

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Super-resolution using neural networks based on the optimal recovery theory

Super-resolution using neural networks based on the optimal ...

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2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

作者： Huang, Yizhen Long, Yangjing Department of Computer Science and Engineering Shanghai Jiaotong University 800 Dongchuan Road Minhang District Shanghai 200240 China Department of Mathematics Shanghai Jiaotong University 800 Dongchuan Road Minhang District Shanghai 200240 China Graphics Team of the Wireless Platform Engineering Cellular and Handheld Group Intel Asia-Pacific Research and Development Ltd. Digital Media and Data Reconstruction Lab. Shanghai Jiaotong University

ISBN: (纸本)1424406560

An optimal recovery based neural-network Super Resolution algorithm is developed. The proposed method is computationally less expensive and outputs images with high subjective quality, compared with previous neural-network or optimal recovery algorithms. It is evaluated on classical SR test images with both generic and specialized training sets, and compared with other state-of-the-art methods. Results show that our algorithm is among the state-of-the-art, both in quality and efficiency. © 2006 IEEE.

关键词： Image quality

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Collaborative authoring of Walden's paths

Collaborative authoring of Walden's paths

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2nd International Conference on Theory and Practice of Digital Libraries, TPDL 2012

作者： Li, Yuanling Bogen II, Paul Logasa Pogue, Daniel Furuta, Richard Shipman, Frank Center for the Study of Digital Libraries Department of Computer Science and Engineering Texas A and M University College Station TX United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge United States User Experience and Interaction Design Team Production Enhancement Halliburton Energy Services Houston TX United States

ISBN: (纸本)9783642332890

This paper presents a prototype of an authoring tool to allow users to collaboratively build, annotate, manage, share and reuse collections of distributed resources from the World Wide Web. This extends on the Walden's Path project's work to help educators bring resources found on the World Wide Web into a linear contextualized structure. The introduction of collaborative authoring feature fosters collaborative learning activities through social interaction among participants, where participants can coauthor paths in groups. Besides, the prototype supports path sharing, branching and reusing;specifically, individual participant can contribute to the group with private collections of knowledge resources;paths completed by group can be shared among group members, such that participants can tailor, extend, reorder and/or replace nodes to have sub versions of shared paths for different information needs. © 2012 Springer-Verlag.

关键词： World Wide Web

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Cash Flow Forecasting Based on Wavelet Transform and Neural Networks

Cash Flow Forecasting Based on Wavelet Transform and Neural ...

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International Conference on computer Science and engineering (UBMK)

作者： Zümra Söylen Farida Mammadova Mandana Fasounaki Ayşe İrem Özmen Gökhan İnce Department of Computer Engineering Istanbul Technical University İstanbul Türkiye R&D Department MDP Group İstanbul Türkiye Department of Computer Engineering & AI and Data Science Application and Research Center Istanbul Technical University İstanbul Türkiye

Cash flow forecasting is a critical task for businesses and financial institutions to ensure effective financial planning and decision-making. However, limited data availability poses a significant challenge when developing accurate and robust cash flow prediction models. In this paper, we investigate the performance of various forecasting methods and propose an approach based on wavelet transform for improving the forecasting accuracy. We demonstrate the effectiveness of the proposed approach with the best combination of wavelet functions and methods for forecasting future values in a univariate time series. We investigate the impact of wavelet transform on forecasting techniques based on open-source datasets. Our methodology includes data collection, preprocessing, feature engineering, model selection, and experimentation using different performance evaluation metrics.

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Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation

arXiv

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arXiv 2024年

作者： Islam, Md. Farhadul Zabeen, Sarah Manab, Meem Arafat Mahin, Mohammad Rakibul Hasan Mondal, Joyanta Jyoti Reza, Md. Tanzim Hasan, Md Zahidul Haque, Munima Sadeque, Farig Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer and Information Sciences University of Delaware United States School of Law and Government Dublin City University Ireland Gina Cody School of Engineering and Computer Science Concordia University Canada

The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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IBB Traffic Graph data: Benchmarking and Road Traffic Prediction Model

arXiv

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arXiv 2024年

作者： Olug, Eren Kaya, Kiymet Tugay, Resul Oguducu, Sule Gunduz Istanbul Technical University Department of Artificial Intelligence and Data Engineering Istanbul Turkey Istanbul Technical University Department of Computer Engineering Istanbul Turkey ITU AI Research and Application Center Istanbul Turkey BTS Group Istanbul Turkey Gazi University Department of Computer Engineering Ankara Turkey

Road traffic congestion prediction is a crucial component of intelligent transportation systems, since it enables proactive traffic management, enhances suburban experience, reduces environmental impact, and improves overall safety and efficiency. Although there are several public datasets, especially for metropolitan areas, these datasets may not be applicable to practical scenarios due to insufficiency in the scale of data (i.e. number of sensors and road links) and several external factors like different characteristics of the target area such as urban, highways and the data collection location. To address this, this paper introduces a novel IBB Traffic graph dataset as an alternative benchmark dataset to mitigate these limitations and enrich the literature with new geographical characteristics. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. Moreover, we propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering, node embedding with GLEE to represent interrelated relationships within the traffic network, and traffic prediction with ExtraTrees. The results indicate that the proposed model consistently outperforms the baseline models, demonstrating an average accuracy improvement of 4%. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph embeddings

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Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disorder

arXiv

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arXiv 2024年

作者： Islam, Md. Farhadul Manab, Meem Arafat Mondal, Joyanta Jyoti Zabeen, Sarah Rahman, Fardin Bin Hasan, Md Zahidul Sadeque, Farig Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh School of Law and Government Dublin City University Dublin Ireland Department of Computer Science College of Arts and Sciences University of Alabama at Birmingham United States Gina Cody School of Engineering and Computer Science Concordia University Canada

Autism Spectrum Disorder (ASD) is a complicated neurological condition which is challenging to diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with maintaining attention spans and have less focused vision. The eye-tracking technology has drawn special attention in the context of ASD since anomalies in gaze have long been acknowledged as a defining feature of autism in general. Deep Learning (DL) approaches coupled with eye-tracking sensors are exploiting additional capabilities to advance the diagnostic and its applications. By learning intricate nonlinear input-output relations, DL can accurately recognize the various gaze and eye-tracking patterns and adjust to the data. Convolutions alone are insufficient to capture the important spatial information in gaze patterns or eye tracking. The dynamic kernel-based process known as involutions can improve the efficiency of classifying gaze patterns or eye tracking data. In this paper, we utilise two different image-processing operations to see how these processes learn eye-tracking patterns. Since these patterns are primarily based on spatial information, we use involution with convolution making it a hybrid, which adds location-specific capability to a deep learning model. Our proposed model is implemented in a simple yet effective approach, which makes it easier for applying in real life. We investigate the reasons why our approach works well for classifying eye-tracking patterns. For comparative analysis, we experiment with two separate datasets as well as a combined version of both. The results show that IC with three involution layers outperforms the previous approaches. © 2024, CC BY-NC-SA.

关键词： Eye tracking

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IBB Traffic Graph data: Benchmarking and Road Traffic Prediction Model

IBB Traffic Graph Data: Benchmarking and Road Traffic Predic...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Eren Olug Kiymet Kaya Resul Tugay Sule Gunduz Oguducu Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey ITU AI Research and Application Center Istanbul Turkey BTS Group Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Computer Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

关键词： Representation learning Computational modeling Roads Urban areas Predictive models Sensor phenomena and characterization Benchmark testing Road traffic data models Safety

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C2MS: Dynamic Monitoring and Management of Cloud Infrastructures

C2MS: Dynamic Monitoring and Management of Cloud Infrastruct...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Gary A. McGilvary Josep Rius Íñigo Goiri Francesc Solsona Adam Barker Malcolm Atkinson Edinburgh Data-Intensive Research Group The University of Edinburgh ICG Software Industrial Polygon Torrefarrera Spain Dept. of Computer Science Rutgers University Dept. of Computer Science and Industrial Engineering Univrsity of Lleida School of Computer Science University of St Andrews

Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration, an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS), a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.

关键词： Servers Monitoring Temperature measurement Clouds Temperature sensors Educational institutions

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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