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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Kumar Laxmikant Arthi A V Vinodhini B Natarajan R Bhuvaneswari Prabu Selvam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of Electronics and Communication Engineering University College of Engineering Ariyalur India School of Computer Science and Engineering VIT University Chennai India

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains.

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Analog Magnetic Sensor-Robotic System for Steel Structure Inspection

Analog Magnetic Sensor-Robotic System for Steel Structure In...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Pham, Anh Quyen La, Duc La, Hung Manh AIR-VIET Corporation Viet Nam Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Lab Department of Computer Science and Engineering RenoNV89557 United States

ISBN: (纸本)9798350312072

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk © 2024 IEEE.

关键词： Eddy current testing

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Data Bias Recognition in Museum Settings: Framework Development and Contributing Factors 25

Data Bias Recognition in Museum Settings: Framework Developm...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Quinto Lima, Stella Buraglia, Gabriela Kam-Kwai, Wong Roberts, Jessica School of Interactive Computing Georgia Institute of Technology AtlantaGA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Interactive Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798400713941

Critical thinking skills are increasingly important for comprehending our data-rich society. While museums provide data for discussion, visitors may not naturally question data in such displays due to the inherent authority of a museum. To investigate what factors can help visitors recognize bias in data, we interviewed visitors after they interacted with an augmented reality data map in an interactive data exhibition. Here, we present a qualitative analysis of fifteen semi-structured interviews with visitors who engaged with mapped data from the citizen science platform iNaturalist. The study revealed that 47% of participants were able to recognize bias, and familiarity was found to be a significant factor in this ability. We propose a three-layer framework to understand the cognitive processes of bias recognition in informal learning settings and apply this framework to our data to inform future work for designing displays to promote critical engagement with data in free-choice learning contexts. © 2025 Copyright held by the owner/author(s).

关键词： Human computer interaction

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement 24

PixelFade: Privacy-preserving Person Re-identification with ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Delong Peng, Yi-Xing Wu, Xiao-Ming Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798400706868

Online person re-identification services face privacy breaches from potential data leakage and recovery attacks, exposing cloud-stored images to malicious attackers and triggering public concern. The privacy protection of pedestrian images is crucial. Previous privacy-preserving person re-identification methods are unable to resist recovery attacks and compromise accuracy. In this paper, we propose an iterative method (PixelFade) to optimize pedestrian images into noise-like images to resist recovery attacks. We first give an in-depth study of protected images from previous privacy methods, which reveal that the chaos of protected images can disrupt the learning of recovery models. Accordingly, Specifically, we propose Noise-guided Objective Function with the feature constraints of a specific authorization model, optimizing pedestrian images to normal-distributed noise images while preserving their original identity information as per the authorization model. To solve the above non-convex optimization problem, we propose a heuristic optimization algorithm that alternately performs the Constraint Operation and the Partial Replacement Operation. This strategy not only safeguards that original pixels are replaced with noises to protect privacy, but also guides the images towards an improved optimization direction to effectively preserve discriminative features. Extensive experiments demonstrate that our PixelFade outperforms previous methods in resisting recovery attacks and Re-ID performance. © 2024 ACM.

关键词： Differential privacy

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Socio-technical Approaches to Solid Waste Management in Rural India: A Case Study of Smart Pipe Composting in Raichur District, Karnataka 8th

Socio-technical Approaches to Solid Waste Management in Rura...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Chethan, R.K. Jambha, Aniketh V. Pathania, Chirag Siddhartha, Mutyala Sai Sri Sanjay Arifuzzaman Darshan, K. Cornet, Souresh Jayaprakash, Sajithkumar K. Department of Commerce and Management School of Arts Humanities and Commerce Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Artificial Intelligence Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka Mysuru India Department of Visual Communication School of Arts Humanities and Commerce Amrita Vishwa Vidyapeetham Mysuru India Amrita School for Sustainable Futures Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

Rural areas in developing countries are plagued with complex, intricate challenges. In India, rural communities are seriously lagging regarding Sanitation and Waste management facilities. The majority of communities lack the resources to address those issues. Government programs have partly been able to address those issues, but much still needs to be done to solve the challenges completely. This study aimed to understand better the challenges associated with Solid Waste Management in rural India. It specifically explores how technology can help to complete the last mile of sustainability through a user-centered and participatory problem-solving approach. Taking the case of a community in Raichur district, Karnataka, a smart pipe composting design is proposed to address the Solid Waste Management challenges in the community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Waste management

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An Investigation of GCN-based Human Action Recognition Using Skeletal Features 27

An Investigation of GCN-based Human Action Recognition Using...

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27th International Conference on Automation and computing, ICAC 2022

作者： Dai, Chuan Wei, Yajuan Xu, Zhijie Chen, Minsi Liu, Ying Fan, Jiulun University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Xi'an University of Posts and Telecommunications School of Computer Science Xi'an China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Human action recognition is one of the most challenging and attractive areas in the field of computer vision. Conventional research on human action recognition has mainly focused on data modality of video or optical flow. However, the human skeletal feature has much stronger expressive power of motion dynamics, which is not sensitive to illumination and scene variation. Owing to the advantages of deep learning approaches on skeleton data in recent years, many pilot approaches have been proposed, which are merited by their significant performance enhancements on both baseline and large-scale datasets. This research investigates these models and their breakthroughs, especially focusing on the graph convolution network (GCN) and skeleton-based data techniques. The report work mainly covers the following aspects: comparing RNN, CNN and GCN-based approaches from the perspective of their operational logics;a detailed review of the best referred models in recent years;a development framework of skeletal feature-based human action recognition framework is proposed with preliminary assessments using benchmarking datasets;and finally, the envisaged future directions for skeletal feature-based human action recognition study are discussed. © 2022 IEEE.

关键词： Deep learning

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Loc4Plan: Locating Before Planning for Outdoor Vision and Language Navigation 24

Loc4Plan: Locating Before Planning for Outdoor Vision and La...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tian, Huilin Meng, Jingke Zheng, Wei-Shi Li, Yuan-Ming Yan, Junkai Zhang, Yunong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798400706868

Vision and Language Navigation (VLN) is a challenging task that requires agents to understand instructions and navigate to the destination in a visual environment. One of the key challenges in outdoor VLN is keeping track of which part of the instruction was completed. To alleviate this problem, previous works mainly focus on grounding the natural language to the visual input, but neglecting the crucial role of the agent's spatial position information in the grounding process. In this work, we first explore the substantial effect of spatial position locating on the grounding of outdoor VLN, drawing inspiration from human navigation. In real-world navigation scenarios, before planning a path to the destination, humans typically need to figure out their current location. This observation underscores the pivotal role of spatial localization in the navigation process. In this work, we introduce a novel framework, Locating before Planning (Loc4Plan), designed to incorporate spatial perception for action planning in outdoor VLN tasks. The main idea behind Loc4Plan is to perform the spatial localization before planning a decision action based on corresponding guidance, which comprises a block-aware spatial locating (BAL) module and a spatial-aware action planning (SAP) module. Specifically, to help the agent perceive its spatial location in the environment, we propose to learn a position predictor that measures how far the agent is from the next intersection for reflecting its position, which is achieved by the BAL module. After this locating process, we propose the PSA module to associate visual observations After the locating process, we propose the SAP module to incorporate spatial information to ground the corresponding guidance and enhance the precision of action planning. Extensive experiments on the Touchdown and map2seq datasets show that the proposed Loc4Plan outperforms the SOTA methods. © 2024 ACM.

关键词： Visual languages

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Guest Editorial Special Section on Generative AI and Large Language Models Enhanced 6G Wireless Communication and Sensing

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 0-2页

作者： Jiacheng Wang Geng Sun Dusit Niyato Hina Tabassum Gabriel-Miro Muntean Nelson Fonseca College of Computing and Data Science Nanyang Technological University Jurong West Singapore College of Computer Science and Technology Jilin University Changchun China Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto ON Canada School of Electronic Engineering Dublin City University Dublin 9 Ireland Institute of Computing State University of Campinas Campinas Brazil

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Unveiling IoT security in reality: a firmware-centric journey 24

Unveiling IoT security in reality: a firmware-centric journe...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Nicolas Nino Ruibo Lu Wei Zhou Kyu Hyung Lee Ziming Zhao Le Guan School of Computing University of Georgia School of Cyber Science and Engineering Huazhong University of Science and Technology Khoury College of Computer Sciences Northeastern University

ISBN: (纸本)9781939133441

To study the security properties of the Internet of Things (IoT), firmware analysis is crucial. In the past, many works have been focused on analyzing Linux-based firmware. Less known is the security landscape of MCU-based IoT devices, an essential portion of the IoT ecosystem. Existing works on MCU firmware analysis either leverage the companion mobile apps to infer the security properties of the firmware (thus unable to collect low-level properties) or rely on small-scale firmware datasets collected in ad-hoc ways (thus cannot be generalized). To fill this gap, we create a large dataset of MCU firmware for real IoT devices. Our approach statically analyzes how MCU firmware is distributed and then captures the firmware. To reliably recognize the firmware, we develop a firmware signature database, which can match the footprints left in the firmware compilation and packing process. In total, we obtained 8,432 confirmed firmware images (3,692 unique) covering at least 11 chip vendors across 7 known architectures and 2 proprietary architectures. We also conducted a series of static analyses to assess the security properties of this dataset. The result reveals three disconcerting facts: 1) the lack of firmware protection, 2) the existence of N-day vulnerabilities, and 3) the rare adoption of security mitigation.

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