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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Suneel, Sajja Veeramanikandan, P. Anil Kumar, N. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Institute of Aeronautical Engineering Telangana Dundigal Hyderabad India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9789819751457

The food processing sector is undergoing a rapid transformation, primarily driven by the infusion of groundbreaking technologies like the Internet of Things (IoT). This study aims to explore the creation and incorporation of an IoT-centric monitoring and control mechanism within food processing to amplify its quality, safety, and efficiency. By melding sensor technologies, instantaneous data interpretation, and cloud tech, we crafted a sophisticated monitoring framework specifically for the diverse demands of food processing. The results highlighted substantial enhancements in live tracking, product traceability, and foresight-based upkeep, resulting in reduced waste and streamlined production timelines. Moreover, the system's versatile nature allows its use across various food processing domains, ranging from milk products to baked goods. This technological integration not only denotes financial gains for the sector but also suggests a transformative change in the industry's perspective toward ensuring quality, safety, and efficiency in our increasingly interconnected era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Processed foods

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On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

On the (Limited) Generalization of MasterFace Attacks and It...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Philipp Terhörst Florian Bierbaum Marco Huber Naser Damer Florian Kirchbuchner Kiran Raja Arjan Kuijper Norwegian University of Science and Technology Gjvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany

ISBN: (纸本)9781665463959

A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolled subjects, MasterFace attacks represent a potential security risk for widely-used face recognition systems. Previous works proposed methods for generating such images and demonstrated that these attacks can strongly compromise face recognition. However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data. This makes it hard to state the generalizability of these attacks. In this work, we comprehensively analyse the generalizability of MasterFace attacks in empirical and theoretical investigations. The empirical investigations include the use of six state-of-the-art face recognition models, cross-dataset and cross-model evaluation protocols, and utilizing testing datasets of significantly higher size and variance. The results indicate a low generalizability when MasterFaces are training on a different face recognition model than the one used for testing. In these cases, the attack performance is similar to zero-effort imposter attacks. In the theoretical investigations, we define and estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. The current trend of increasing the fairness and generalizability in face recognition indicates that the vulnerability of future systems might further decrease. Future works might analyse the utility of MasterFaces for understanding and enhancing the robustness of face recognition models.

关键词： Training Analytical models Protocols Face recognition Sociology Market research Robustness

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CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images

arXiv

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arXiv 2024年

作者： Jin, Yuan Ma, Gege Chen, Geng Lyu, Tianling Egger, Jan Lyu, Junhui Zhang, Shaoting Zhu, Wentao Zhejiang Lab 311121 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China The Zhejiang University School of Medicine Sir Run Run Shaw Hospital Hangzhou310016 China Shanghai Artificial Intelligence Laboratory Shanghai200120 China

The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagnosis. However, the majority of existing models rely solely on single-modality image input, leading to limited diagnostic accuracy. To this end, we propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images, i.e., CT and pathological images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets as well as independent CT image sets, and consequently optimize the pathology-related feature extractions from CT images. This adaptive learning approach enhances the flexibility in processing multi-dimensional and multi-modality datasets and results in performance elevating in the model testing phase. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training, and thereby helps to explore the "gold standard" pathological information from the corresponding CT scans. To evaluate the effectiveness, adaptability, and generalization ability of our model, we conducted extensive experiments on a large-scale multi-center dataset and compared our model with a series of state-of-the-art classification models. The experimental results demonstrated the superiority of our model for lung cancer subtype classification, showcasing significant improvements in accuracy metrics such as ACC, AUC, and F1-score. Copyright © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

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TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications

Neuroscience Informatics

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Neuroscience Informatics 2025年第2期5卷 100209-100209页

作者： Gupta, Suneet Gupta, Praveen Lal, Bechoo Deka, Aniruddha Sarma, Hirakjyoti Gupta, Sheifali School of Computer Science and Engineering Galgotias University Greater Noida India Dept of Computer Science and Engineering KLEF-KL University Vijayawada Campus Andhra Pradesh India Computer Science and Engineering Faculty of Engineering Assam down town University Assam India GUIST Gauhati University Assistant Professor India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

Accurate and non-invasive sleep staging is essential for evaluating sleep quality and diagnosing neurological and sleep disorders. Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. By enhancing discriminative features and suppressing irrelevant ones, the model generates a robust multimodal representation of sleep information. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network captures temporal dependencies in sleep stage transitions, improving classification accuracy. The effectiveness of MFDL is validated on the publicly available Sleep-EDF dataset, achieving 94.1% accuracy, 88.2% Kappa coefficient, and 81.9% MF1 score. Notably, the recall rates for the challenging N1 and REM sleep stages are significantly enhanced to 64.6% and 93.5%, respectively. These results highlight the potential of MFDL in enhancing tFUS-based neuromodulation by providing precise, data-driven sleep state monitoring, paving the way for advanced non-invasive brain stimulation technologies in next-gen clinical applications. © 2025 The Authors

关键词： Bi-LSTM Discriminative features EEG Next-gen clinical applications Non-invasive brain signal Sleep staging Transcranial FUS

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Application of Neural Networks and Graphical Representations for Musical Genre Classification 19th

Application of Neural Networks and Graphical Representations...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Modrzejewski, Mateusz Szachewicz, Jakub Rokita, Przemyslaw Division of Computer Graphics Institute of Computer Science The Faculty of Electronics and Information Technology Warsaw University of Technology Nowowiejska 15/19 Warsaw00-665 Poland

ISBN: (纸本)9783030614003

In this paper we have presented a method for musical genre classification using neural networks. We have used two algorithms (CNN and PRCNN) and two graphical representations: chromograms and spectrograms. We have used a large dataset of music divided into eight genres, with certain overlapping musical features. Key, style-defining elements and the overall character of specific genres are represented in our proposed visual representation and recognized by the networks. We show that the networks have learned to distinguish between genres upon features observable by a human listener and compare the metrics for the network models. Results of the conducted experiments are described and discussed, along with our conclusions and comparison with similar solutions. © 2020, Springer Nature Switzerland AG.

关键词： Music

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Research of Virtual Simulation Experiment Course Resource Intelligent Information System Construction 2

Research of Virtual Simulation Experiment Course Resource In...

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2nd IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2022

作者： Wang, Yangping Dang, Jianwu Yong, Jiu Wang, Wenrun Yue, Biao Jin, Fangrui Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University College of Intelligence and Computing Tianjin University Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China College of Electronic and Information Engineering Lanzhou Jiaotong University Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China

ISBN: (纸本)9781665442763

In order to enable the construction of virtual simulation experiment resources in colleges and universities to keep up with the development of professional technology, this paper proposes to build a 'diversified virtual simulation experiment teaching resource construction system' through exploration and research. With the help of virtual reality, AR, panoramic roaming, video and other technologies, it provides a unified development framework, development documents and case templates for professional teachers to independently develop virtual simulation experiment teaching resources in line with undergraduate teaching, and helps teachers quickly build virtual simulation experiment resources in accordance with unified standards. It effectively solves the problems that it is difficult for college teachers to independently develop virtual simulation courseware and the technology is not unified. © 2022 IEEE.

关键词： Virtual reality

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Profit aware Resource Allocation in Fog Computing: A Stackelberg Game Approach

Profit aware Resource Allocation in Fog Computing: A Stackel...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Akshita Doad Nitin Gupta Mohammad S. Obaidat Kuldeep Singh Jadon Piyush Rawat Kuie–Fang Hsiao Department of Computer Science and Engineering National Institute of Technology Hamirpur HP India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China The Amity University Noida UP India University of Texas-Permain Basin Odessa Tx USA

Recently, Internet of Things (IoT) based smart systems have led to more latency-sensitive and bandwidth hungry IoT applications. Fog computing as an extension of Cloud computing fulfills those requirements more effectively. However, a key challenge for the fog computing is multiple resources allocation between heterogeneous Data Service Operators (DSOs) and End Users (EUs). The DSOs want to increase their revenues, however, due to lack of efficient resource allocation mechanisms, they often end up compromising Quality of Service (QoS) and violating Service Level Agreements (SLAs). It is very important for them to make a balance among user's expectations, expenses of providers and penalties for not providing services within the deadline. Hence, a profit-aware resource allocation policy using Stackelberg game is proposed in the work where DSO acts as leaders and EU as followers. The DSOs being the leaders, set the prices first, send it to the followers, and then the followers can compute their best demand for resources accordingly. Next, Stackelberg equilibrium for each type of resource is obtained to find the best resource demand strategy for an EU. The simulation results demonstrate the superiority of the proposed algorithm.

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Multilabel segmentation and analysis of skeletal muscle and adipose tissue in routine abdominal CT scans

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computers in Biology and Medicine 2025年 186卷 109622-109622页

作者： Kreher, Robert Hille, Georg Preim, Bernhard Hinnerichs, Mattes Borggrefe, Jan Surov, Alexey Saalfeld, Sylvia Department of Simulation and Graphics Faculty of Computer Science University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Radiology and Nuclear Medicine University Hospital Magdeburg Germany Department of Radiology Neuroradiology and Nuclear Medicine Johannes Wesling University Hospital Minden Germany Department of Computational Medicine Ilmenau University of Technology Germany

Purpose: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segmentation of the intermuscular fat. Method: Based on a self-created data set of 130 patients, an extended Unet structure was trained and evaluated with the help of Dice score, IoU and Pixel Accuracy. In addition, the interobserver variability for the decision between ground truth and post-processed segmentation was calculated to illustrate the relevance in everyday clinical practice. Results: On average, the presented approach achieved 91.0±0.065% DSC for the muscle tissues and 88.9±0.062% DSC for the adipose tissues. It was shown that by post-processing the intermuscular fat tissue, physicians prefer the result of the algorithm presented in the paper to their segmentation by 91.51%. Conclusion: The algorithm provided more precise segmentations of muscles and adipose tissue, demonstrating high-quality performance in segmenting muscle tissue. In qualitative evaluations, physicians preferred the algorithm's segmentation over expert segmentations, with the preference quantified as 91.51%. This indicates that, based on their assessment, the algorithm's results were significantly favored. This qualitative feedback supports the algorithm's use in subsequent analyses of patient fitness, leveraging the detailed information it provides. © 2025 The Authors

关键词： Muscle

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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