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Anomaly Detection with Graph Attention network for Multimodal IoT Data Monitoring 29

Anomaly Detection with Graph Attention Network for Multimoda...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Li, Haoyu Gao, Yi Dong, Wei Zhejiang University College of Computer Science China

ISBN: (纸本)9798350330717

The popularity of the wireless network and the embedded system has promoted the widespread application of the Internet of Things (IoT) monitoring system which detects anomalies for discovering emergency events to avoid losses by deploying various sensors. In the face of anomaly detection algorithms for multi-dimensional data in the IoT monitoring system, the currently popular method is to implement a model based on the graph neural network (GNN). Graph attention network (GAT), a mutant of GNN, is widely used because of the attention mechanism in capturing multi-dimensional nonlinear features between nodes. However, the GAT's weakness in processing complex temporal features leads to the fact that anomalies with distinctive temporal features are hard to detect. In this paper, we propose Time-Series Graph Attention network (TSGAT), an innovative GAT-based model for anomaly detection. Given the simplified attention mechanism of the GAT and its weakness in extracting temporal pattern features, we embed the time-series layer to enhance the ability to process time-series data. In addition, we designed the loss function and anomaly scoring mechanism based on data reconstruction features, multidimensional features, and temporal features. TSGAT achieves the improvement of 7%-8% F1 score on two classical public datasets for IoT monitoring: SWaT and WADI, compared with the state-of-the-art GNN-based anomaly detection model. © 2023 IEEE.

关键词： anomaly detection GAT multimodal IoT data

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Deep Learning Based Pneumonia Infection Classification in Chest X-ray Images Using Convolutional neural network Model

Deep Learning Based Pneumonia Infection Classification in Ch...

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International conference on distributed Computing and Optimization Tech-niques (ICDCOT)

作者： Nayak, Jyoti Sahu, Devbrat Raipur Inst Technol Dept Comp Sci & Engn Chhatouna 492001 Chhattisgarh India

ISBN: (纸本)9789811922817;9789811922800

Pneumonia infection is a life threating entity which is caused by bacteria, viruses and fungi inflames the lungs making it difficult to breath. To recognize the infection, various stages are employed which includes lung examination through X-ray images. However, the structure of non-uniform infection represents vague details and may lead to poor detection causing improper treatment. Various image processing techniques had been applied in this domain but their performance were restricted due to the varying feature representation of lung infection. In this direction, a convolutional neural network model has been developed to classify between normal and pneumonia infected X-ray lung images. The performance of the model has been thoroughly examined and validated through the standard performance measures. The model achieved the classification accuracy of 99.06%, F1-score of 99.01% and sensitivity of 99.29%. These standard scores describe the model is potentially strong and outperforms with the state-of-the-art methods in this domain.

关键词： Computer-added diagnosis Convolutional neural network Deep learning Lung X-ray images Medical imaging Pneumonia infection

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Memory Efficient Deep neural network Training 27th

Memory Efficient Deep Neural Network Training

引用

27th International European conference on Parallel and distributed Computing (Euro-Par)

作者： Shilova, Alena Inria Bordeaux Sud Ouest Bordeaux France Univ Bordeaux Bordeaux France

ISBN: (纸本)9783031061561;9783031061554

Recently Artificial Intelligence (AI) has demonstrated a huge progress in solving complex problems such as image classification, text generation, translation... Its success is due to a development of hardware and algorithms making possible the emergence of Deep neural networks (DNNs). Such DNNs are composed of a number of operations from numerical linear algebra, whose order is defined with a Directed Acyclic Graph (DAG). These DNNs operate at the limit of the computational resources, and to go to deeper and more complex neural networks it is necessary either to design more powerful computational resources or optimize their usage. In our work, we address in particular the problem of lowering memory usage during the training of DNNs.

关键词： neural networks Rematerialization Offloading Model parallelism

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Design and Implementation of Lung Abnormality Detection Through Sparse Projection Features Enabled distributed Convolution network 2

Design and Implementation of Lung Abnormality Detection Thro...

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2nd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Ravikumar, K.V. Kumaran, U. Sree, B Teja Reddy, B Prashanth Kumar Rethaswi, Boligala Lavanya, M.G. Mother Theresa Institute of Engineering and Technology Department of Computer Science and Engineering Chittoor District Andhra Pradesh Palamaner517408 India

ISBN: (纸本)9798350397635

Due to various factors in recent days the lung infection has become a common disease in humans. The lung abnormality needs to be detected to avoid life threatening problems. Various researches are undergone to implement the early detection of lung abnormality. The evaluation of various techniques such as machine learning Deep learning are helpful to analyze the lung images in a more accurate way. The accurate localization and segmentation of lung abnormality become an interesting area of research. The presented system is focused on creating a robust architecture that considers sparse projection (SP) features enabled distributed convolution neural networks for making the classification of lung abnormality. Various intensity levels of lung images are considered as an important factor for differentiating the normal lung image and abnormal lung image. The proposed system considered axial CT are PET images for analysis. During the pre-processing stage the lung intensity pixel intensity is collected and morphological segmentation is applied to extract the land abnormality tissue from the background image. These inputs are fetched into ensemble distributed feature mapped convolutional neural networks (DFM-CNN) to make the classification effective. The performance evaluation of the model is developed by calculating the accuracy error rate and prediction time of the lung abnormality. © 2023 IEEE.

关键词： Convolution

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core network Control Plane Traffic 29

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Wang, Min Li, Peng Cheng, Zhang Liu, Wenmao Nie, Lei Bao, Haizhou Liu, Qin Zhang, Kai Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Nsfocus Technologies Group Co. Ltd China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350330717

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance. © 2023 IEEE.

关键词： 5G core network anomaly detection control plane graph neural network normalizing flows

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Application of Data Encryption in Chinese Named Entity Recognition 1

引用

32nd International conference on Artificial neural networks (ICANN)

作者： Dong, Jikun Long, Kaifang Yu, Hui Xu, Weizhi Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Shandong Normal Univ Sch Business Jinan Peoples R China Shandong Prov Key Lab Novel Distributed Comp Soft Jinan 250358 Peoples R China

ISBN: (数字)9783031441981

ISBN: (纸本)9783031441974;9783031441981

Recently, with the continuous development of deep learning, there has been a significant improvement in the performance of named entity recognition tasks. However, privacy and confidentiality concerns in specific fields, such as biomedical and military, limit the availability of data for training deep neural networks. To address the issues of data leakage and the disclosure of sensitive data in these domains, we propose an encryption learning framework. For the first time, we employ multiple encryption algorithms to encrypt the training data in the named entity recognition task, training the deep neural network with the encrypted data. Our experiments, conducted on six Chinese datasets, including three self-constructed datasets, demonstrate that the encryption method achieves satisfactory results. In fact, the performance of some models trained with encrypted data even surpasses that of the unencrypted method, highlighting the effectiveness of the introduced encryption method and partially resolving the problem of data leakage.

关键词： Chinese named entity recognition Data encryption Privacy protection Natural language processing Deep neural networks

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International conference on Data Science, Computation and Security, IDSCS 2023

International Conference on Data Science, Computation and Se...

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International conference on Data Science, Computation and Security, IDSCS 2023

ISBN: (纸本)9789819709748

The proceedings contain 49 papers. The special focus in this conference is on Data Science, Computation and Security. The topics include: OGIA: Ontology Integration and Generation Using Archaeology as a Domain;Data: A Key to HR Analytics for Talent Management;an Early Lumpy Skin Disease Detection System Using Machine Learning;extracting network Structures from Corporate Organization Charts Using Heuristic Image processing;generating Equations for Mathematical Word Problems Using Multi-head Attention Transformer;autonomous System Enabling Node and Edge Detection, Path Optimization, and Effective Color-Coded Box Management in Diverse Robotic Environments;GLANCE—Guided Language Through Autoregression Establishing Natural and Classifier-Free Editing;method for Design of Magnetic Field Active Silencing System Based on Robust Meta Model;Ontology Integration for Cultural Landscape Management Using ML and Assistive Artificial Intelligence;Early Phase Detection of Bacterial Blight in Pomegranate Using GAN Versus Ensemble Learning;classification of Diseased Leaves in Plants Using Convolutional neural networks;brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation;spectrum and Energy of the Mobius Function Graph of Finite Cyclic Groups;analysis of Multinomial Classification for Legal Document Categorization;pioneering Image Analysis with Hybrid Convolutional neural networks and Generative Adversarial networks for Enhanced Visual Perception;enhancing Medical Decision Support Systems with the Two-Parameter Logistic Regression Model;BI-RADS Score Prediction Using AI for Breast Cancer Screening;modeling and Analysis of the Lead-Lag network of Economic Indicators;predictive Maintenance Model for Industrial Equipment;taming the Complexity of distributed Lag Models: A Practical Approach to Multicollinearity, Outliers, and Auto-Correlation in Finance;algorithm of Robust Control for Multi-stand Rolling Mill Strip Based on Stochastic Multi-swarm Multi

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distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks 24th

Distributed Incentive Algorithm for Fine-Grained Offloading...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Wu, Junhong Wu, Yalan Huang, Jiale Wu, Jigang School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Integrated Circuits Guangdong University of Technology Guangzhou China

ISBN: (纸本)9789819615445

In vehicular ad hoc networks, few existing works on task offloading focus on co-offloading at intra-vehicle level and inter-vehicle level for deep neural network (DNN) inference. Moreover, they ignore the decentralized environment and selfishness of vehicles. This seriously limits the improvement of quality of services for DNN inference. To fill this gap, we formulate a fine-grained offloading problem considering two-level offloading and vehicle selfishness. To solve the problem, we propose a distributed algorithm based on coalition game to encourage vehicles to share their resources. Simulation results show that, the proposed algorithm outperforms the state-of-the-arts in terms of system payoff and running time of algorithms for most cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consensus algorithm

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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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： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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HyperSack: distributed Hyperparameter Optimization for Deep Learning using Resource-Aware Scheduling on Heterogeneous GPU Systems 31

HyperSack: Distributed Hyperparameter Optimization for Deep ...

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31st Annual IEEE International conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Alnaasan, Nawras Ramesh, Bharath Yao, Jinghan Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. Department of Computer Science and Engineering The Ohio State University ColumbusOH United States

ISBN: (纸本)9798331509095

Hyperparameter Optimization (HPO) can unlock the full potential of Deep Learning (DL) models;however, it is considered one of the most compute-intensive tasks in the DL domain due to multi-dimensional search spaces and complex neural network architectures. A common method for accelerating HPO workloads is parallelizing training jobs on multiple computing devices, such as modern GPUs in High-Performance Computing (HPC) environments. Nonetheless, existing HPO parallelization strategies underutilize powerful GPU devices, like the NVIDIA A100 and H100, especially for training lightweight Deep neural networks. Resource-sharing mechanisms can improve GPU utilization;nonetheless, naïve adaptations for HPO workloads lead to poor performance. Therefore, we propose HyperSack—a distributed HPO framework for dynamic and resource-aware scheduling on heterogeneous GPU-based HPC systems with resource elasticity and fault tolerance. HyperSack reduces the execution time of HPO workloads by orchestrating the placement of DL training jobs on GPU devices with different computational capabilities. It supports different hardware architectures, HPO workloads, and scheduling policies. Our evaluations on vision and language models HPO workloads show up to 2.8x performance improvement in execution time on A100 GPUs, 4.0x on H100 GPUs, and 3.9x on a combination of 12 A100 and 4 H100 GPUs using HyperSack over standard HPO parallelization methods. © 2024 IEEE.

关键词： Graphics processing unit

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