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DGE-GSIM: A multi-task dual graph embedding learning for graph similarity computation 2022

DGE-GSIM: A multi-task dual graph embedding learning for gra...

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6th international conference on machine learning and soft computing (icmlsc)

作者： Tan, Wenhui Cao, Peng Jin, Zhiyong Luo, Futao Wen, Guangqi Li, Weiping Northeastern Univ Comp Sci & Engn Key Lab Intelligent Comp Med Image Minist Educ Shenyang Peoples R China Northeastern Univ Comp Sci & Engn Shenyang Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9781450387477

Graph similarity estimation is a challenging task due to the complex graph structure. To achieve an exact similarity estimation for input graphs, two critical factors are how to learn an appropriate graph embedding and how to compute the similarity between a pair of graphs. Graph neural networks (GNN) generalize convolutional neural networks (CNN) to graph data for learning graph embeddings. However, the GNN models ignore the edge information when earning graph embeddings. To solve it, we propose a graph similarity estimation framework by taking into consideration the edge feature learning during the graph embedding learning. We name our approach as DGE-GSIM, i.e., Dual Graph Embedding for Graph SIMilarity learning. More specifically, we firstly construct an edge graph with node-edge switching, and then we propose a multi-task learning framework for learning a dual graph embedding with simultaneously considering the node and edge features with GNN. Finally, with the obtained dual graph embedding, we develop a three-way graph similarity computation from the perspectives of the graph-graph interaction, node-node interaction and node-graph interaction. It can model a comprehensive similarity for input graphs. We conduct extensive experiments on a very popular graph similarity/distance metric, GED, based on Linux dataset to demonstrate the effectiveness of the proposed method.

关键词： Graph similarity Graph convolution networks Multi-task learning Attention

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Federated learning based Object Detection using Dampened Harmonic Optimization 23

Federated Learning based Object Detection using Dampened Har...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Jain, Deepak Kumar Khanna, Akshit Gera, Bhavya Sangwan, Dhiraj Chongqing University of Posts and Telecommunications Chongqing China Birla Institute of Technology and Science Rajasthan Pilani India CSIR-CEERI Rajasthan Pilani India

ISBN: (纸本)9781450398633

Object Detection is the task of detecting and localizing objects of importance in visual media. The rapid increase in the number of powerful end devices such as mobiles and surveillance cameras have to lead to increasing in both resources and media generation. Object Detection has now become possible on end devices, but certain challenges need to be tackled to fully utilize the resources. Federated learning is one such framework that leverages the end device resources to build machine learning models while preserving data privacy. We model the federated learning framework for object detection on real-world heterogeneous datasets using a novel dampened harmonic optimizer to enhance local learning on the end device and hence reducing the communication cost during the learning process. We provide comparison of the commonly used FedAvg with our FedHarm optimization on multiple object detection models and datasets to demonstrate the merits of our proposition. Our method FedHarm with its dampened updates allows for greater local computation which reduces the overall communication rounds between edge devices and cloud and better handles heterogeneity in real-world datasets. FedHarm leads to faster convergence by 41% on average over FedAvg which is supported by extensive experiments. © 2023 ACM.

关键词： Object detection

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Post Processing Selection of Automatic Item Generation in Testing to Ensure Human-Like Quality with machine learning 23

Post Processing Selection of Automatic Item Generation in Te...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Duvvuri, Venkata Lee, Gahyoung Hsu, Yuwei Makwana, Asha Morgan, Chris College of Professional Studies Northeastern University United States AITestBanks Inc. United States

ISBN: (纸本)9781450398633

Automatic Item Generation (AIG) is increasingly used to process large amounts of information and scale the demand for computerized testing. Recent work in Artificial Intelligence for AIG (aka Natural Question Generation-NQG), states that even newer AIG techniques are short in syntactic, semantic, and contextual relevance when evaluated qualitatively on small datasets. We confirm this deficiency quantitatively over large datasets. Additionally, we find that human evaluation by Subject Matter Experts (SMEs) conservatively rejects at least g1/49% portion of AI test questions in our experiment over large diverse dataset topics. Here we present an analytical study of these differences, and this motivates our two-phased post-processing AI daisy chain machine learning (ML) architecture for selection and editing of AI generated questions using current techniques. Finally, we identify and propose the first selection step in the daisy chain using ML with 97+% accuracy, and provide analytical guidance for development of the second editing step with a measured lower bound on a BLEU score improvement of 2.4+%. © 2023 ACM.

关键词： Semantics

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A Traffic Sign Recognition Method Based on YOLOv5 Deep learning Algorithm 23

A Traffic Sign Recognition Method Based on YOLOv5 Deep Learn...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Tang, Yinqing Yu, Benguo Wang, Anran Liu, Fengning Hainan Medical University China

ISBN: (纸本)9781450398633

Aiming at the problems of low accuracy and slow recognition efficiency of the traditional traffic sign recognition algorithm in complex environment, a deep learning traffic sign recognition method based on YOLOv5 is proposed. Firstly, the Chinese traffic sign data set TT100K is randomly divided into training set and test set. Convolutional neural network YOLOv4 and convolutional neural network YOLOv5 are used to train respectively on the training set, so as to build the prediction model of traffic signs. Then the trained model is validated on the test set. Through the evaluation of the experimental, it is found that compared with YOLOv4 model, YOLOv5 model has higher recognition accuracy and faster recognition speed. © 2023 ACM.

关键词： Traffic signs

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A Hybrid Scheme of Reducing Read Latency in NAND Flash by Integrating Duplications and soft Decoding 2022

A Hybrid Scheme of Reducing Read Latency in NAND Flash by In...

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6th international conference on machine learning and soft computing (icmlsc)

作者： Zhang, Yaofang Li, Peixuan Xie, Ping Normal Univ Coll Comp Qinghai Xining 810016 Peoples R China State Key Lab Tibetan Intelligent Informat Proc & Xining 810016 Peoples R China Key Lab Internet Things Qinghai Prov Xining 810016 Peoples R China Acad Plateau Sci & Sustainabil Xining 810016 Peoples R China

ISBN: (纸本)9781450387477

The continuous development of the technology scale has made the storage density of NAND flash memory larger and larger, and its reliability has become lower and lower. Therefore, reliability has become an issue of concern to everyone. In order to alleviate reliability problems caused by technological expansion, LDPC codes are widely used in flash memory to provide high error correction capabilities. However, the decoding methods of LDPC codes are divided into hard decoding and soft decoding. The LDPC code is directly used and as the quantization level of soft decoding increases, the delay caused to the Solid-State Drive is also increasing. Taking into account the characteristics of the quantization level of LDPC soft decoding, this paper proposes a new method IDSD, which combines duplications and LDPC soft decoding. This scheme is mainly for hot data. While reading the data, it needs to be based on the quantization level of the record. Use the duplications or soft decoding method to correct the erroneous data. When the quantization level of LDPC soft decoding reaches one, it needs to read the data in the duplications at this time. This method reduces the reading of the Solid-State Drive. Take the delay and improve the average response time. Experimental results show that compared with the original scheme, the average response delay time of the IDSD scheme is increased by 5%-30%, and compared with the traditional refresh scheme, it is increased by 1%-30%.

关键词： LDPC duplications NAND flash quantization level soft decoding

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A real time video object tracking method for fish 2022

A real time video object tracking method for fish

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6th international conference on machine learning and soft computing (icmlsc)

作者： Gong, Longqin Hu, Zhuhua Zhou, Xiaoyi Hainan Univ Sch Comp Sci & Technol Haikou Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou Peoples R China Hainan Univ Sch Cyberspace Secur Sch Cryptol Haikou Peoples R China

ISBN: (纸本)9781450387477

Fish behavior is an important indicator of water quality in smart aquaculture. The change of fish behavior can timely and effectively reflect the change of water quality. However, for the fish behavior tracking, existing tracking methods still face challenges in tracking accuracy, tracking efficiency and self-adaptive tracking. In this paper, by optimizing Yolov4-tiny detector and Sort tracker, a real-time fish behavior tracking scheme based on tracking-by-detection structure is proposed. Firstly, in the detection stage, the RFB (Receptive Field Block) module is added in Yolov4-tiny network. The new detector can integrate the features of different scales and expand the receptive field, which can greatly improve the detection accuracy of large object. Secondly, in the matching stage, according to the characteristics of fish behavior, we optimize the Sort (Simple Online and Realtime Tracking) algorithm by removing the Kalman filter module (named Simple-Sort). Finally, a new fish behavior data set with detection box annotation is constructed and published. The experimental results show that compared with Sort algorithm, the proposed tracking scheme improves the MOTA and MOTP indicators by 0.8% and 3.5% respectively for the large scale object. For the other scale object, the MOTA and MOTP indicators increase by 1.5% and 2.1%, respectively.

关键词： Fish behavior tracking Smart mariculture object tracking YOLO deep learning

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Research on Online Consumer Demand Ranking and Content Prediction Based on Kano Model 24

Research on Online Consumer Demand Ranking and Content Predi...

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8th international conference on machine learning and soft computing, icmlsc 2024

作者： Zhao, Xiaogang Liu, Yuerui Shen, Hai Dang, Yiwei Hou, Jun Pu, Zitong Comparison of Chinese and Foreign Enterprises Strategy Institute Xi'an International Studies University China Business School Xi'an International Studies University China

ISBN: (纸本)9798400716546

Aiming at the shortcomings of high cost, low efficiency and coarse granularity of traditional consumer demand sequencing methods in practical applications, this paper proposes an online consumer demand ranking and content prediction method based on Kano model. First, this method combines SnowNLP sentiment analysis and consumer attention to classify the demand attributes. Then, the probability of topic words under different demands is obtained by LDA topic model. Finally, the ARIMA model is introduced to predict the probability of topic words and sentiment values under different demands in the future, which can accurately and efficiently recognize the rapidly changing consumer demands. Example calculations show that this method can classify and sort consumer demands in a more fine-grained way, which provides a reference for consumer demand prediction research of enterprises and solves the problem of priority level of enterprise marketing resource allocation. © 2024 ACM.

关键词： Sentiment analysis

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A Novel Deep learning Approach to the Statistical Downscaling of Temperatures for Monitoring Climate Change 2022

A Novel Deep Learning Approach to the Statistical Downscalin...

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6th international conference on machine learning and soft computing (icmlsc)

作者： Gerges, Firas Boufadel, Michel C. Bou-Zeid, Elie Nassif, Hani Wang, Jason T. L. New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA New Jersey Inst Technol Dept Civil & Environm Engn Ctr Nat Resources Newark NJ 07102 USA Princeton Univ Dept Civil & Environm Engn Princeton NJ 08544 USA Rutgers Univ New Brunswick Dept Civil & Environm Engn Piscataway NJ 08854 USA

ISBN: (纸本)9781450387477

General Circulation Models (GCMs) allow for the simulation of several climate variables through the year 2100. GCM simulations, however, are too coarse to monitor climate change at a local scale in a local region. Hence, one needs to perform spatial downscaling for these simulations, where statistical downscaling is often used. Statistical downscaling is performed by utilizing the largescale GCM outputs to forecast a local-scale field (e.g., temperature). In this paper, we develop a new deep learning approach, named AIG-TRANSFORMER, which employs a novel attention-based input grouping (AIG) neural network followed by a transformer, for the statistical downscaling of the weekly averages of maximum (Tmax) and minimum (Tmin) temperatures using GCM-simulated climatic fields (climate variables). We formulate the downscaling problem as a multivariate time series forecasting task, with multiple GCMsimulated climatic fields as input features. We employ an attention mechanism within the AIG network to give selective importance to the input features while reducing the size of the input fed to the transformer. To test AIG-TRANSFORMER, we perform the statistical downscaling over the Hackensack-Passaic Watershed, in northeast New Jersey. We compare our new deep learning approach against several existing machine learning methods including random forests, support vector regression and long short-term memory networks. Experimental results show that AIG-TRANSFORMER outperforms the existing methods for downscaling both the maximum and minimum temperatures, with a Nash-Sutcliffe Efficiency coefficient of 0.84 for Tmax, and 0.85 for Tmin. We further apply AIG-TRANSFORMER to produce long-term projections over the 20 years period from 2030 to 2049, and report the annual means for maximum and minimum temperatures.

关键词： Statistical Downscaling Climate Change Deep learning Transformer

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Abnormal Behavior Recognition of Underwater Fish Body Based on C3D Model 2022

Abnormal Behavior Recognition of Underwater Fish Body Based ...

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6th international conference on machine learning and soft computing (icmlsc)

作者： Hu, Zhuhua Li, Xianghui Xie, Xinyu Zhao, Yaochi Hainan Univ Sch Informat & Commun Engn Haikou Peoples R China Hainan Univ Sch Cyberspace Secur Sch Cryptol Haikou Peoples R China

ISBN: (纸本)9781450387477

The behavior of fish is the direct embodiment of fish life. It is of great significance for the management of mariculture to recognize the abnormal behavior of fish. Traditional abnormal behavior monitoring uses manual monitoring, which will cost a lot of man-power and material resources. With the development of science and technology and the progress of the times, deep learning methods have made great progress in the field of video behavior recognition, which provides an opportunity for abnormal behavior recognition and detection. Firstly, in this paper, a two-category video dataset of fish behavior is constructed. Dataset consisting of 200 videos, including 100 videos of abnormal fish behavior and 100 videos of normal fish behavior. Among them, 150 videos are used for training the network model, and 50 videos are used for testing the model performance. Secondly, C3D deep network model is used to conduct an abnormal behavior recognition experiment on the self-made fish behavior dataset in a complex environment. Meanwhile, the softmax cross-entropy loss and the weight attenuation L2 regularization are used to calculate the total loss. For the experimental results of video classification, this paper makes a more intuitive visualization. ROC curve and t-SNE dimensionality reduction algorithm are used to analyze and evaluate the experimental results. The experiment results show that the C3D model we used has a better recognition effect on fish abnormal behavior and can be used to recognize fish abnormal behavior in actual aquaculture video surveillance.

关键词： deep learning convolutional neural network fish abnormal behavior recognition C3D model

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Edge computing Ransomware Detection in IoT using machine learning

Edge Computing Ransomware Detection in IoT using Machine Lea...

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international conference on computing, Networking and Communications (ICNC)

作者： Radhakrishna, Tejesh Majd, Nahid Ebrahimi Calif State Univ San Marcos Dept Comp Sci & Informat Syst San Marcos CA 92096 USA

ISBN: (纸本)9798350371000;9798350370997

The resurgence of ransomware has emerged as a pressing security threat in computer networks and Internet connected machines and IoT devices. To address this challenge, accurate ransomware detectors are required to automatically detect and block the malicious traffic. Most ransomware detectors only detect whether the traffic is benign or ransomware. However, detecting the family of ransomware would be greatly useful to promptly eliminate or mitigate its destructive effects. To tackle this issue, we propose machine learning models that accurately detect each ransomware family. Our models aim to detect the ransomware network traffic and thwart it at the network edge before it enters the network. Considering that ransomwares directly work with the memory dump and file system, the information extracted from the operating system's functions on the memory dump is very useful to detect a ransomware attack. However, that information could be collected only when the ransomware has already infected the device and is actively disrupting the file system. In our research, we propose a framework that blocks the ransomware at the network edge. This restricts our research to using a dataset that extracts network traffic features with no access to the device's operating system's functionalities. An edge computing intrusion detection system is also beneficial for resource contained network devices, such as IoT, which have limited computational resources and cannot dynamically analyze the network traffic and run a strong intrusion detection system. We worked on CICAndMal2017 dataset and proposed a feature selection-based framework along with different machine learning models. We also applied a data augmentation technique to the training set to strengthen the data used to build our models. We extensively studied our proposed framework. Our experimental results demonstrated that chi-square feature selection with Random Forest and XGBoost models surpass other models and the state of the a

关键词： Ransomware Detection Network security Feature Selection machine learning

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