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21st international conference on Image Analysis and Processing, ICIAP 2022

21st International Conference on Image Analysis and Processi...

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21st international conference on Image Analysis and Processing, ICIAP 2022

ISBN: (纸本)9783031064326

The proceedings contain 168 papers. The special focus in this conference is on Image Analysis and Processing. The topics include: Leveraging Road Area Semantic Segmentation with Auxiliary steering Task;Pulmonary-Restricted COVID-19 Informative Visual Screening Using Chest X-ray Images from Portable Devices;unsupervised Multi-camera Domain Adaptation for Object Detection in Cultural Sites;automatic Classification of Fresco Fragments: A Machine and Deep learning study;The AIRES-CH Project: Artificial Intelligence for Digital REstoration of Cultural Heritages Using Nuclear Imaging and Multidimensional Adversarial Neural Networks;Grad 2 VAE: An Explainable Variational Autoencoder Model Based on Online Attentions Preserving Curvatures of Representations;pruning in the Face of Adversaries;Towards Latent Space Optimization of GANs Using Meta-learning;DMSANet: Dual Multi Scale Attention Network;enhanced data-Recalibration: Utilizing Validation data to Mitigate Instance-Dependent Noise in Classification;a Comparison of Deep learning Methods for Inebriation Recognition in Humans;contrastive Supervised Distillation for Continual Representation learning;multiple Input Branches Shift Graph Convolutional Network with DropEdge for Skeleton-Based Action Recognition;Robust Object Detection with Multi-input Multi-output Faster R-CNN;don’t Wait Until the Accident Happens: Few-Shot Classification Framework for Car Accident Inspection in a Real World;CVGAN: Image Generation with Capsule Vector-VAE;Avalanche RL: A Continual Reinforcement learning Library;towards an Efficient Facial Image Compression with Neural Networks;consistency Regularization for Unsupervised Domain Adaptation in Semantic Segmentation;medicinal Boxes Recognition on a Deep Transfer learning Augmented Reality Mobile Application;towards Efficient and data Agnostic Image Classification Training Pipeline for Embedded Systems;an intelligent Scanning Vehicle for Waste Collection Monitoring;avoiding Shortcuts in Unpaired Ima

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Mask R-CNN Network for Back Recognition on Adam's Bending Images 21

Mask R-CNN Network for Back Recognition on Adam's Bending Im...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuxin, Pu Hongbin, Cai Jipeng, Fan Xin, Zheng School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Chengdu Chengdian Goldisc HealthData Technology Co. Ltd. China School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China

ISBN: (纸本)9798331519254

Back recognition is critical for adolescent idiopathic scoliosis screening. Existing methods mainly rely on manual work which is time-consuming and laborious and may introduce errors and mismatching impacting the accuracy of subsequent screening outcomes. In this work, we propose to utilize the Mask R-CNN network as the framework for auto-extraction of the back region on Adam's bending images. However, due to the inherent limit receptive fields, initial Mask R-CNN can hardly learn long-range dependencies which are crucial for object detection and segmentation. To this end, we introduce the explicit visual center module into Mask R-CNN. In the EVC module, a lightweight MLP is devised to extract global long-range dependencies. To compensate for MLP's lack of ability to capture features of local corner regions, a learnable visual center is incorporated. By aggregating global long-range dependencies and discriminative feature representation of local key regions, our Mask R-CNN can achieve better performance on Adam's bending images. Moreover, we collect and annotate an Adam's bending image dataset for back recognition. Experimental results on our annotated dataset demonstrate that our improved Mask R-CNN considerably improves the average precision in detection and segmentation of Mask R-CNN from 78.6% to 83.6% and 85.8% to 87.0%, respectively. © 2024 IEEE.

关键词： Object detection

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21st international conference on Image Analysis and Processing, ICIAP 2022

21st International Conference on Image Analysis and Processi...

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21st international conference on Image Analysis and Processing, ICIAP 2022

ISBN: (纸本)9783031064296

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OWETC: Open world encrypted traffic classification based on semi-supervised class incremental learning 21

OWETC: Open world encrypted traffic classification based on ...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhao, Meng Di, Zhu Xia, Zhe Tian, Jing Xiang, Jianwen Jun, Lin China Electronic Product Reliability and Environmental Testing Research Institute Guangdong China Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

To address the dynamic changes in network environments and effectively tackle the open-world traffic classification challenge, we introduce a novel approach. Conventional traffic classification methods are constrained by their reliance on closed traffic sets and offline training scenarios, limiting their ability to recognize a fixed number of traffic classes and identify unknown traffic types. Our solution involves a traffic classification method founded on a combination of semi-supervised class-incremental learning and open-set recognition. To mitigate the issue of catastrophic forgetting in class-incremental learning, we leverage a substantial amount of external unlabeled traffic data. Specifically, we employ a class anchor clustering loss classifier with a discriminator to select external unlabeled traffic data as replay samples. This approach effectively mitigates the problem of catastrophic forgetting in class-incremental learning. Furthermore, the class anchor clustering loss is employed for incremental model training, enabling the model to identify unknown traffic data. We validate the performance of the model on encrypted traffic classical datasets, and the experimental results show that our method significantly outperforms traffic classification models based on other incremental learning methods. In particular, OWETC has a 3.28% performance improvement in average accuracy over multiple incremental sessions compared to the best baseline. Additionally, our enhanced open-set sampler displays a notable 17% reduction in false alarm rates with only a minor 1% accuracy degradation, surpassing the capabilities of existing open-set recognition methods. © 2023 IEEE.

关键词： Classification (of information)

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Self-Supervised Source Code Annotation from Related Research Papers 21

Self-Supervised Source Code Annotation from Related Research...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Haritz, Pierre Pfahler, Lukas Liebig, Thomas Kotthaus, Helena TU Dortmund Dept Comp Sci Chair Artificial Intelligence Dortmund Germany ML2R Dortmund Germany

ISBN: (纸本)9781665424271

Language analysis of scientific documents and analysis of source code have been done independently in the past. This work presents a network architecture and a self-supervised training approach to find alignments between published computer science research papers and their corresponding public source code by learning a representation of encodings from transformers, from which source code can be enriched with helpful information. We present our ideas, findings and plans for upcoming research.

关键词： natural language processing code understanding transformers self-supervised learning

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Analysis of data Transforming Technology for Malware Detection 21

Analysis of Data Transforming Technology for Malware Detecti...

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21st ACIS international Winter conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter)

作者： Han, Sung-Hwa Soongsil Univ ITPM Seoul 06987 South Korea

ISBN: (纸本)9781665418430

As AI technology advances and its use increases, efforts to incorporate machine learning for malware detection are increasing. However, for malware learning, a standardized data set is required. Because malware is unstructured data, it cannot be directly learned. In order to solve this problem, many studies have attempted to convert unstructured data into structured data. In this study, the features and limitations of each were analyzed by investigating and analyzing the method of converting unstructured data proposed in each study into structured data. As a result, most of the data conversion techniques suggest conversion mechanisms, but the scope of each technique has not been determined. The resulting data set is not suitable for use as training data because it has infinite properties.

关键词： Malware Machine learning GBM Detection Rate Malicious Script

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21st international conference on Image Analysis and Processing, ICIAP 2022

21st International Conference on Image Analysis and Processi...

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21st international conference on Image Analysis and Processing, ICIAP 2022

ISBN: (纸本)9783031064265

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EMIF-Bench: A Benchmark for Embodied Multi-Modal Instruction Following 21

EMIF-Bench: A Benchmark for Embodied Multi-Modal Instruction...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yixin, Qin Peng, Yin Zhiqi, Yu The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

In daily life, combining textual and visual instructions in an interleaved manner is typical and natural to boost communication efficiency and, more importantly, aid children's cognitive learning in both language and visual realms. However, in the field of AI and robotics, there is currently a lack of developed evaluation frameworks for evaluating embodied agents that understand visual-language instructions and break instructions down into a sequence of precise plans for performing the desired tasks with the environment. To facilitate this research, we build the first, to our knowledge, a new challenging benchmark, namely Embodied Multi-modal Instruction Following (EMIF-Bench). Our EMIF-Bench has several appealing properties: 1) Interleaved manner: Both instruction and its corresponding plans consist of both textual and visual information. 2) Rich instructions: It comprises 917 visual language instructions with corresponding sequences of plans, including seven types, from Cooking to Leisure, and involving 137 objects. 3) Diverse environments. We arranged 772 environments which involve 165 object classes and 1,009 corresponding prefabs. Powered by this challenging dataset and to highlight the task challenges, we further provide a baseline model, a multi-modal task planner, to evaluate its performance within the aspect of reasoning and generalization. The dataset and evaluation results are publicly available, and we hope EMIF-Bench can serve as a benchmark platform that inspires future research in embodied agents. All the code and data for EMIF-Bench is available at: https://***/qyx1121/EMIF-Bench. © 2024 IEEE.

关键词： Visual languages

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A new multiple instance algorithm using structural information 21

A new multiple instance algorithm using structural informati...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Zhu, Xiaoyan Wang, Ting Wang, Jiayin Xu, Ying Liu, Yuqian Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781665423984

Multiple instance learning (MIL) is semisupervised learning that predicts the label of a bag with a wide diversity of instances. It has many applications and thus attracts increasingly more attention. In this paper, we propose a new MIL algorithm using the structural information of a bag to predict its label. In the proposed method, a bag is transformed into a graph, and spectral clustering is employed to divide the graph into several subgraphs. Then, the graph Fourier transform is utilized to extract the features of the subgraphs. Finally, an end-to-end neural network is used to predict the label of a bag with the extracted features. An empirical study with 25 datasets was conducted to validate the effectiveness of the proposed method. The experimental results show that the proposed method performs better than the 6 baseline methods on most datasets.

关键词： Multiple instance learning spectral cluster graph Fourier transform neural network

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Air Quality Hotspot Monitoring with Trajectories of IoT in Smart City Implementation 3

Air Quality Hotspot Monitoring with Trajectories of IoT in S...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Bhangale, Sonal C. Varade, Harshal P. Thorat, Sandip R. Roy, Dharmendra Kumar William, P. Verma, Apurv Sanjivani K.B.P. Polytechnic Department of Mechatronics Kopargaon India Sanjivani College of Engineering Savitribai Phule Pune University Department of Mechanical Engineering Pune India Hyderabad Institute of Technology and Management Hyderabad Medchal India Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India School of Engineering & It Mats University Raipur India

ISBN: (纸本)9798350322842

In the era of 21st century development in industrialization and timely updating technology increases the people needs. Transportation application especially private transport have also increased. This cause emission of greenhouse gases like CO, CO2, NH3, NO, PM2.5 etc. leads to air pollution. Air pollution is serious environmental challenge in our daily lives. Lowering air quality adversely impacts on public health and environment. Measuring and monitoring air quality with the help of deploying IoT based sensors network at particular regions. In this study, AQMS is implemented for detecting air pollution hotspot and identifying source of trajectories. Real-time air quality monitoring system helps to monitor sudden change in the atmosphere. Machine learning techniques analyze the collected data which helps us to predict climate change. This model reduces complexity and improves efficiency and feasibility and can provide more reliable and accurate decisions regarding smart city development, industrial and transportation management, public health, the environment. © 2023 IEEE.

关键词： Python

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