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TechRxiv 2022年

作者： Verma, Monu Vipparthi, Santosh Kumar Singh, Girdhari Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur302017 India Mehta Family School of Data Science and Artificial Intelligence IIT Guwahati781039 India

Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain higher performance for MER. Also, rich survey articles on MER are available by summarizing the datasets, experimental settings, conventional and deep learning methods. In contrast, these studies lack the ability to convey the impact of network design paradigms and experimental setting strategies for DL based MER. Therefore, this paper aims to provide a deep insight into the DL-based MER frameworks with a perspective on promises in network model designing, experimental strategies, challenges, and research needs. Also, the detailed categorization of available MER frameworks is presented in various aspects of model design and technical characteristics. Moreover, an empirical analysis of the experimental and validation protocols adopted by MER methods is presented. The challenges mentioned earlier and network design strategies may assist the affective computing research community in forge ahead in MER research. Finally, we point out the future directions, research needs and draw our conclusions. © 2022, CC BY.

关键词： Face recognition

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Design and Implementation of Robust Multicast Trees for Lossy Wireless Ad-Hoc Networks

Design and Implementation of Robust Multicast Trees for Loss...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M. Preetha Jagmeet Sohal B.R Sampangi Rama Reddy Nakul S. Sharma S. Hemelatha Manpreet Singh Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune INDIA Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper affords a layout and implementation of a sturdy multicast tree technique for loss wireless advert-hoc networks. The proposed technique seeks to provide dependable delivery of statistics in dynamic environments where hyperlinks revel in excessive degrees of packet loss. This method is based on a mixture of spanning tree clustering, hyperlink-country routing, and acknowledgements-primarily based recuperation mechanisms, using re-transmissions and adaptive retransmission periods to catch up on packet loss. The usage of these combined techniques permits for minimal deterioration of verbal exchange performance because of packet losses, whilst retaining a green network layout. Simulations had been conducted to evaluate the overall performance of the proposed method in assessment to previous works. The effects showed that the proposed method outperformed the other techniques in terms of multicast message transport fee. The proposed approach is also proven to reduce overhead site visitors because of retransmissions while as compared to previous works. The proposed method affords a suitable and efficient solution for reliable multicasting in loss wireless ad-hoc networks.

关键词： Wireless communication Uncertainty Layout Packet loss Throughput Routing Ad hoc networks Robustness Hypertext systems Wireless fidelity

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Image Recognition Based on Enhanced-Conformer

Image Recognition Based on Enhanced-Conformer

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Runlin Gong Ke Qi Yicong Zhou Wenbin Chen Jingdong Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Department of Computer and Information Science University of Macau Macau China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou China

ISBN: (数字)9781665471978

ISBN: (纸本)9781665471985

Convolutional Neural Networks (CNNs) has always dominated visual recognition tasks, and it is difficult to link distant information in images due to the size limitation of each convolution filter. Vision Transformer (ViT) can capture features at a distance in an image, but lacks the details of local features. Conformer combines the advantages of both using Convolutional Neural Networks (CNNs) and Attention mechanisms in parallel, but it does not take into account the relationship between different samples. Therefore, we propose a new attention calculation method, Extra-Attention, which can effectively learn intra-sample and inter-sample relationships. In order to combine the advantages of CNNs and Attention, in our work, we proposed a new network Enhanced-Conformer (ENC) based on Conformer, in which the attention mechanism adopts a more efficient computational module Inside And Outside Transformer (IAOT), which contains three parallel Attention: Extra-Attention, Self-Attention, External-Attention. Enhanced-Conformer (ENC) can fuse local features, global features and external features at the same time. We conduct experiments on the commonly used image recognition datasets, the recognition accuracies reach 93.29%, 68.70 % and 57.63% on Tiny- ImageNet, CIFAR-10 and CIFAR-100, respectively.

关键词： Electrical engineering Visualization Image recognition Fuses Convolution Transformers Feature extraction

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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Analysis of cloud services using OWASP security design

Analysis of cloud services using OWASP security design

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Elumalaivasan Poongavanam Munirathinam. T Kayalvizhi. V Sankar Murugesan Department of Data Science and Business System SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Sona College of Technology Salem Tamil Nadu India School of Computer Science and Engineering and Information Science Presidency University Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

Cloud computing is extremely popular model among enterprises. It provides more availability of cloud resources when needed with high scalability and extremely cost-effective model. Security is one of the least understood concepts to provide and access for OWASP applications in cloud computing. Many enterprises especially banking clients are risk-averse and have less cloud adoption rate. In this study, security research in the area of cloud security is reviewed and AWS (Amazon Web Service) is the top provider of cloud computing. Since, we have also demonstrated how it works in the security exploration. This paper's major goal is to make cloud computing security is a key activity and prevent unauthorized access to the apps used in the real world.

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Advanced Dual Module Weapon Detection for Public Safety and Surveillance System

Advanced Dual Module Weapon Detection for Public Safety and ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Manikanta Sirigineedi M. Sowmiya HemavathI. U V.C. Bharathi Sayali S Karmode R. Sathya Department of Information Technology Vishnu Institute of Technology Vishnupur Bhimavaram Andhra Pradesh India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology AVADI School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Information Technology Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Navi Mumbai Maharashtra India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.

关键词： YOLO Weapons Surveillance Real-time systems Public security Classification algorithms Security Convolutional neural networks Feeds Expert systems

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SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection

arXiv

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arXiv 2023年

作者： Ye, Jianan Hu, Yijie Yang, Xi Wang, Qiu-Feng Huang, Chao Huang, Kaizhu School of Advanced Technology Xi'an Jiaotong-Liverpool University China Department of Electrical Engineering and Electronics University of Liverpool United Kingdom Department of Computer Science University of Liverpool United Kingdom Data Science Research Center Duke Kunshan University China

Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models. Recent wisdom of augmentation methods focuses on generating random pseudo instances that may lead to a mixture of augmented instances with seen anomalies, or out of the typical range of anomalies. To address this issue, we propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies which tend to stay in the plausible range of anomalies. Furthermore, we deploy a two-head learning strategy consisting of normal and anomaly learning heads, to learn the anomaly score of each sample. Theoretical analyses show that this mechanism offers a more tractable and tighter lower bound of the data log-likelihood. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample, facilitating the learning of discriminative representations of anomaly instances. Extensive experiments conducted on six real-world anomaly detection datasets demonstrate the superiority of our method to competing methods under various settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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A Novel Ensemble Deep Learning Framework for Breast Cancer Prediction

A Novel Ensemble Deep Learning Framework for Breast Cancer P...

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

作者： Pundru Chandra Shaker Reddy S Nithyapriya Nipun Sharma S Maheswari B Jayaram Tejaswini Katale School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Artificial Intelligence and Data Science Bannari Amman Institute of Technology Anna University Chennai Tamil Nadu India Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Mathematics Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Computer Science and Business System RMK Engineering College Kavaraipettai Tamil Nadu India Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India

Because of its high incidence and high mortality rate, breast cancer requires reliable means of early detection and diagnosis to improve survival rates for its patients. With advantages including increased accuracy and efficiency, deep learning (DL) and machine learning (ML) methods are quickly becoming indispensable in the fight against breast cancer. However, there is a need for additional study because existing approaches have scalability and performance constraints. Utilizing a pre-trained ResNet50V2 and ensemble-based ML approaches, we offer a hybrid approach to reliable breast cancer diagnosis in this study. Learning and extracting latent trends from complex breast-cancer(BC) images is made possible by DL's incorporation, with ML strategies contributing interpretability and generalizability. We performed wide experiments utilizing a openly accessible Invasive-Ductal-Carcinoma(IDC) dataset based on breast histopathology images of varying sample sizes. The resilience and great performance of our hybrid model are supported by the results of our extensive experiments. When compared to state-of-the-art models, our 95% accuracy rate, 94.5 precision, 94.2 recall, and 94.6 F1 score are all improvements. We also found that the best ML model to use with the ResNet50V2 architecture is the Light Boosting Classifier (LGB). This study's findings provide substantial contributions to the field of breast cancer diagnosis thanks to its novel approach, in-depth performance analysis, and trustworthy evaluation. In addition, it may help doctors make better decisions, provide better care, and boost results for people with breast cancer.

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Multikernel:Operating system solution to generalized functional safety

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Security and Safety 2023年第2期2卷 30-43页

作者： Yijing Song Huasheng Dai Jinhu Jiang Weihua Zhang School of Computer Science Fudan UniversityShanghai 200433China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Institute of Big Data Fudan UniversityShanghai 200433China Parallel Processing Institute Fudan UniversityShanghai 200433China

With the trend of digitalization,intelligence,and networking sweeping the world,functional safety and cyber security are increasingly intertwined and overlapped,evolving into the issue of generalized functional *** system reliability technology and network defense technology cannot provide quantifiable design implementation theories and *** the cornerstone of software systems,operating systems in particular are in need of efficient safety *** DHR architecture is a mature and comprehensive solution,and it is necessary to implement an OS-level DHR architecture,for which the multi-kernel operating system is a good *** multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy,heterogeneity,and dynamism on the kernel,so it has the generalized robustness of the DHR *** article analyzes the significance and requirements of OS-level DHR architecture,and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

关键词： Metaverse New infrastructure Security base Highly trusted network Trusted identiry Trusted circulation of data

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Single Frame Lidar-Camera Calibration Using Registration of 3D Planes

Single Frame Lidar-Camera Calibration Using Registration of ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Ashutosh Singandhupe Hung Manh La Quang Phuc Ha Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA School of Electrical and Data Engineering University of Technology Sydney Australia

ISBN: (纸本)9781665472616

This work focuses on finding the extrinsic parameters (rotation and translation) between Lidar and an RGB camera sensor. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The plane coefficients extracted from the sensor’s data are used to construct a well-structured set of 3D points. These 3D points are then ’aligned,’ which gives the relative transformation between the two sensors. We use our proposed Correntropy Similarity Matrix Iterative Closest Point (CoSMICP) Algorithm to estimate the relative transformation. This work uses a single frame of the point cloud data acquired from the Lidar sensor and a single frame from the calibrated camera data to perform this operation. From the camera image, we use the projection of the calibration target’s corner points to compute the 3D points, and along the process, we calculate the 3D plane equation using the corner points. We evaluate our approach on a simulated dataset with complex environment settings, making use of the freedom to assess under multiple configurations. Through the obtained results, we verify our method under various configurations.

关键词： Point cloud compression Three-dimensional displays Laser radar Transmission line matrix methods Robot vision systems Cameras Real-time systems

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