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A design Framework for Preserving Cultural Heritage with AR Technology—An Innovative Exploration on Ethnic Motifs and Its Symbolism of the Naga Tribes 9th

A Design Framework for Preserving Cultural Heritage with AR ...

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9th International Conference on Research into design, ICoRD 2023

作者： Roy, Susmita Singh, Pankaj Pratap Padun, Abhijit Dept. of Multimedia Communication and Design CIT Kokrajhar Assam BTAD Kokrajhar India Dept. of Computer Science and Engineering CIT Kokrajhar Assam BTAD Kokrajhar India

ISBN: (纸本)9789819902927

The strong sense of immersion and interaction of advanced technology have an immense contribution towards cultural preservation in this digital era. Presently, the augmented technology is preferable and very popular from the aspect of the cultural heritage domain to revive and preserve cultural assets. This article presents a design-based framework to revive the original essence and significance of ethnic motifs and symbolism embedded in the Naga woven fabrics with Augmented Reality (AR) technology. This design framework proposes a smartphone-based application to decodes and interpret the meaning and symbolism of the motifs & patterns from Naga traditional woven fabric. This framework uses AR technology to scan and extract motifs & patterns from the traditional woven fabrics, thereby displaying the story on the visual screen. The information related to the motifs and patterns is delivered with authentic interpretations of the Naga ancient core. This application further explores the possibility of implementing it for cultural education which may create a unique identity of the hand woven fabrics of Naga tribes on digital platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Augmented reality

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PROS 2.0: A Plug-In for Routability Optimization and Routed Wirelength Estimation Using Deep Learning

PROS 2.0: A Plug-In for Routability Optimization and Routed ...

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作者： Chen, Jingsong Kuang, Jian Zhao, Guowei Huang, Dennis J.-H. Young, Evangeline F. Y. The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Cadence Design Systems Digital & Signoff Group San JoseCA95134 United States

Recently, the topic of how to utilize prior knowledge obtained by machine-learning (ML) techniques during the EDA flow has been widely studied. In this article, we study this topic and propose a practical plug-in named PROS for both routability optimization and routed wirelength estimation which can be applied in the state-of-the-art commercial EDA tool or an academic EDA flow with negligible runtime overhead. PROS consists of three parts: 1) an effective fully convolutional network (FCN)-based predictor that only utilizes the data from placement result to forecast global routing (GR) congestion;2) a parameter optimizer that can reasonably adjust GR cost parameters based on the prediction result to generate a better GR solution for detailed routing (DR);and 3) a convolutional neural network (CNN)-based wirelength estimator which can report accurate routed wirelength at the placement stage by using the predicted GR congestion. Experiments show that on the industrial benchmark suite in the advanced technology node, PROS can achieve high accuracy of GR congestion prediction and significantly reduce design rule checking (DRC) violations by 11.65% on average, and on the DAC-2012 benchmark suite, PROS can achieve a very low error rate (1.82%) for wirelength estimation which greatly outperforms that of FLUTE (21.52%) by 19.70%. © 1982-2012 IEEE.

关键词： Deep learning

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Investigating Youtube's Educational Videos: Patterns, User Conduct and Suggestion Techniques

Investigating Youtube's Educational Videos: Patterns, User C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sundar, Koushik Eugene Berna, I. Vijayakumar, R. Ambeth Kumar, V.D. Vijay, K. Citibank NA Jersey CityNJ United States Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Design Chennai India Mizoram University Department of Computer Engineering Mizoram Aizawl India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331522988

This research voyage drew us far into the world of instructional videos on YouTube, a platform that promotes learning and discovery. Our goal was to identify patterns and trends in instructional films so that we might improve the user experience by suggesting better content. We conducted an analysis of data that we directly gathered from YouTube using certain technologies. The varied lifespans of trending videos across nations were one fascinating discovery. Certain videos were more well-known for a longer period of time than others. We were captivated by the cultural diversity found in video lifespans. We also looked at likes, dislikes, views, and comments to find robust user involvement worldwide. Through engagement data, different countries demonstrated their preferences for expressing oneself. It was discovered that a video's popularity was greatly influenced by the timing of the content's release. Content producers can stay relevant by keeping in mind this correlation. Sentiment analysis also uncovered emotional overtones connected to video tags, offering perceptions into what appeals to viewers. We also identified the optimal time, based on user involvement and behavior, to watch instructional videos. Ultimately, we used recommendation algorithms to tailor the YouTube learning experience by fusing content-based and collaborative filtering. All things considered, clarifies the complex world of instructional content and offers advice to users and producers on this dynamic platform. Our research will continue to influence the direction of more interesting instructional content as YouTube develops. © 2024 IEEE.

关键词： Video analysis

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Plant Disease Detection and Classification Using Transfer Learning

Plant Disease Detection and Classification Using Transfer Le...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karthigaiveni, S. Madhubala, C. Avinash, G. Saranya, E. Rohini, P. Yogapriya, G. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Avadi Tamilnadu Chennai India School of Environment and Architecture Design Srm Institute of Science and Technology Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science Engineering Chennai India Saranathan College of Engineering Department of Computer Science and Engineering Tamilnadu Trichy India Chettinad School of Architecture Chettinad Academy of Research and Education Tamilnadu Chennai India

ISBN: (纸本)9798331543617

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Matrix full rank,Controllability and Observability:Revisit Basic Concepts

Matrix full rank,Controllability and Observability:Revisit B...

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第43届中国控制会议

作者： Hongchenyu Yang Tai-Cheng Yang Chen Peng Engang Tian Yang Song Department of Engineering and Design University of Sussex School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In control theory using the state-space method,it is often assumed that a matrix is of full rank to further mathematical development. Few people stop to ask two questions:(1) How far is the 'distance' between the given full-rank matrix and a non-full-rank matrix?,and(2) How does this 'distance' affect control? In robust system analysis and design,in addition to verifying that a system is stable,one also measures how far it is from an unstable system. The simplest stability measurement for a single-input-single-output(SISO) linear system is the gain margin and phase margin. Similarly,beyond a 'Yes' or 'No' answer,for a full-rank matrix in control,we should ask the above two questions. Pursuing such questions leads to a lot of interesting and useful results. Some of them are well-documented in the literature;some are yet to be fully explored. This paper illustrates some existing research and proposes some future studies. In general,we can review all control lemmas and theorems based on matrix full-rank conditions to explore further studies. The study can also extend to non-linear systems. The basic nonlinear system controllability(observability) tests are based on if the relevant matrices,constructed using Lie Bracket(Lie Derivative),are of full rank. Furthermore,similar studies can be on another important matrix property:positiveness,and on more challenging research into the degrees of controllability and observability for networked control systems.

关键词： Review Basic Concepts Explore the Possibilities of Further Studies

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Community Opinion Maximization in Social Networks

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Liu, Yilu Zhang, Qingfu Wang, Zhenkun Department of Computer Science City University of Hong Kong Hong Kong China School of System Design and Intelligent Manufacturing and the Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Maximizing the influence of opinions is an emerging research topic in social networks. Although the community is a key structure of social networks, little effort has been made to investigate how to maximize the influence of opinions on all communities. This paper proposes a systematic approach to address this issue. First, we construct a multi-faceted opinion evolution (MFOE) model with three critical influence factors, namely, individuals, neighbors, and communities, to describe the opinion evolution process in social networks. The convergence analysis confirms its ability to reveal the influence of opinions. Then, we define the overall community opinion to measure the influence of opinions on all communities and employ it as the objective function to formulate an optimization problem called community opinion maximization (COM). We show that the COM problem is NP-hard. To optimize this problem, a memetic algorithm with three problem-specific schemes is developed and termed MACOM. Extensive experimental studies on real-world social networks demonstrate the plausibility of the MFOE model and the effectiveness of MACOM. IEEE

关键词： Optimization

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WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks 38

WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xia, Jun Yue, Zhihao Zhou, Yingbo Ling, Zhiwei Shi, Yiyu Wei, Xian Chen, Mingsong MoE Eng. Research Center of SW/HW Co-design Tech. and App. East China Normal University China Department of Computer Science and Engineering University of Notre Dame United States

Due to the increasing popularity of Artificial Intelligence (AI), more and more backdoor attacks are designed to mislead Deep Neural Network (DNN) predictions by manipulating training samples or processes. Although backdoor attacks have been investigated in various scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily identified by existing backdoor detection algorithms. To overcome this weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains high-frequency image features through Discrete Wavelet Transform (DWT) to generate highly stealthy backdoor triggers. By introducing an asymmetric frequency obfuscation method, our approach adds an adaptive residual to the training and inference stages to improve the impact of triggers, thus further enhancing the effectiveness of WaveAttack. Comprehensive experimental results show that, WaveAttack can not only achieve higher effectiveness than state-of-the-art backdoor attack methods, but also outperform them in the fidelity of images (i.e., by up to 28.27% improvement in PSNR, 1.61% improvement in SSIM, and 70.59% reduction in IS). Our code is available at https://***/BililiCode/WaveAttack. © 2024 Neural information processing systems foundation. All rights reserved.

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures 5

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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User-Centric Crowdsourcing Approach to Improve Urban Accessibility Data Collection

User-Centric Crowdsourcing Approach to Improve Urban Accessi...

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2024 IEEE MIT Undergraduate Research Technology Conference, URTC 2024

作者： Ortiz, Tyler Tang, Vicky Sutton, Karla The City College of New York Cuny New York Grove School of Engineering Computer Science New York United States School of Constructed Environments Parsons School of Design New York New York United States Lab School for Collaborative Studies New York Department of Computer Science New York United States

ISBN: (纸本)9798331531003

While crowdsourcing is an effective method for collecting large-scale datasets, it often faces challenges related to data quality and quantity. This study employs crowdsourcing to collect comprehensive urban accessibility data, focusing on storefront accessibility information in a large city. This task presents significant challenges in terms of data completeness and quality. To address these issues, we propose a user-centric, request-driven crowdsourcing approach that gathers accessibility data based on requests from individuals who are blind or have low vision (BLV). This approach strengthens the user-volunteer feedback loop, increasing volunteer engagement and enhancing user experience. It allows BLV individuals to effectively utilize the collected storefront accessibility data for independent travel. A preliminary user study confirms the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Crowdsourcing

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Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective 24

Exploring Fungal Morphology Simulation and Dynamic Light Con...

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2024 SIGGRAPH Asia 2024 Art Papers, SA 2024

作者： Wang, Kexin He, Ivy Li, Jinke Asadipour, Ali Sun, Yitong Brown University School of Engineering Providence United States Rhode Island School of Design Providence United States Brown University Department of Computer Science Providence United States Brown University Department of Molecular Microbiology and Immunology Providence United States Royal College of Art Computer Science Research Centre London United Kingdom

ISBN: (纸本)9798400711336

Fungal simulation and control are considered crucial techniques in Bio-Art creation. However, coding algorithms for reliable fungal simulations have posed significant challenges for artists. This study equates fungal morphology simulation to a two-dimensional graphic time-series generation problem. We propose a zero-coding, neural network-driven cellular automaton. Fungal spread patterns are learned through an image segmentation model and a time-series prediction model, which then supervise the training of neural network cells, enabling them to replicate real-world spreading behaviors. We further implemented dynamic containment of fungal boundaries with lasers. Synchronized with the automaton, the fungus successfully spreads into pre-designed complex shapes in reality. © 2024 Copyright held by the owner/author(s).

关键词： Image segmentation

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