2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)最新文献

A New Debleeding Algorithm for Image colorization 一种新的图像着色去噪算法

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135513

Lianrong Chen, Naidan Mei, Lianjie Wang, Jianhong Li

引用次数: 0

Graph Convolutional Neural Networks based Marked Point Process 基于标记点处理的图卷积神经网络

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135515

Yongzhe Chang, Xinhang Xiao, Yang Yu

{"title":"Graph Convolutional Neural Networks based Marked Point Process","authors":"Yongzhe Chang, Xinhang Xiao, Yang Yu","doi":"10.1109/ICCECE58074.2023.10135515","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135515","url":null,"abstract":"A point process is a collection of positional variables in a given space, while a marked point process (MPP) is basically a point process that measures some additional feature or value at each point. Existing applications such as financial market models and hospitalization models have demonstrated that MPPS are suitable for modeling random events with meaningful values. A big challenge is to study the relationship between different types of events, or how different types of markers affect each other. While Hawkes procedures are an effective and efficient way to model point processes with internal influences, inferential methods of intensity functions can be expensive, especially for multi-dimensional processes. Graph convolutional Network (GCN) is a powerful neural network designed to directly process graphs. A GCN-based model is presented to study potential patterns in the marking process and to predict the location of future events without estimating the Hawkes intensity function. A real data set of water pipe leakage and rupture records over the last 50 years was used in the experiment, modeled as a marker point process, where leakage and rupture are two marker forms. Experimental results on synthetic data sets and real water pipe data sets show that the proposed model is superior to recent state-of-the-art methods.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117354156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Capacity-based identification of key nodes in urban road networks 基于容量的城市道路网络关键节点识别

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135287

Guimin Gong, Wen-hong Lv, Ge Gao, Qi Wang

引用次数: 0

FastIoTBot: Identifying IoT Bots by Fast Detecting Anomalous Domain Queries with Long Short-Term Memory Networks FastIoTBot:通过长短期记忆网络快速检测异常域查询来识别物联网机器人

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135366

Ruyu Li, Lihua Yin, Yuanfei Zhang, Kexiang Qian, Xi Luo

{"title":"FastIoTBot: Identifying IoT Bots by Fast Detecting Anomalous Domain Queries with Long Short-Term Memory Networks","authors":"Ruyu Li, Lihua Yin, Yuanfei Zhang, Kexiang Qian, Xi Luo","doi":"10.1109/ICCECE58074.2023.10135366","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135366","url":null,"abstract":"Along with the progression in technology, Internet of Things (IoT) has been dramatically developed in recent ten years. It connects physical world and digital world, which makes people's life more convenient. However, IoT devices have bring great vulnerability to Internet security since they usually under weak protection, which makes them easy to be exploited by criminals to launch multiple attacks. In fact, IoT devices have been a crucial part of botnets that launch horrible Distributed Denial of Service (DDoS) with explosive traffic. Unfortunately, traditional detection works have limited effectiveness face IoT botnets because of the restricted resources of IoT devices and unprecedented huge scale of IoT botnets. To mitigate the threat of IoT botnets, in this paper, we propose a lightweight system, named FastIoTBot, to discover compromised IoT devices in a fast way. FastIoTBot can distinguish compromised IoT devices instantly and prevent potential malicious behaviors by examining domain query activities. Specifically, FastIoTBot monitors the DNS query for a device and generates its NXDOMAIN query sequence. Then, for each domain in the sequence, FastIoTBot takes the domain name string as input and calculates its malicious score using long short-term memory (LSTM) model. Finally, FastIoTBot identifies compromised IoT devices through analyzing NXDOMAIN sequences with internal domains' malicious score leveraging threshold random walk (TRW) algorithm. The effectiveness of FastIoTBot is evaluate with real world DNS data of two large ISP networks. The results show that FastIoTBot perform well with over 99% accuracy.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123255640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the academic performance of students with GPcSAGE 预测GPcSAGE学生的学习成绩

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135308

Xiaochen Lai, Sixuan Zeng, Wenkai Xu, Lu Tong, Jialiu Yang

引用次数: 0

Research on “SanWei” Web Application System Based on Java Technology 基于Java技术的“三微”Web应用系统研究

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135378

Yue Chang

引用次数: 0

Understanding of Fake News Dissemination on Social Media by Comparing IPS, MF, NCF and BPR 通过IPS、MF、NCF和BPR的比较了解假新闻在社交媒体上的传播

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135245

Haotong Xin, Yimin Wei, Tianda Fan, Shang Peng, Haohua Liu, Junxiang Su

引用次数: 0

Gradient-Based Meta-Learning Using Adaptive Multiple Loss Weighting and Homoscedastic Uncertainty 基于自适应多重损失加权和均方差不确定性的梯度元学习

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135472

Lin Ding, Wenfeng Shen, Weijia Lu, Peng Liu, Shengbo Chen, Sisi Chen

引用次数: 0

Distributed Task Assignment Method for Multiple Robots Based on Dynamic Auction Rules 基于动态拍卖规则的多机器人分布式任务分配方法

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135261

Jiajie Xu, Chin-Yin Chen, Si-lu Chen, Qiang Liu

引用次数: 0

Mask Wearing Detection System Based On YOLO And Raspberry Pi 基于YOLO和树莓派的面具佩戴检测系统

2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2023-01-06 DOI: 10.1109/ICCECE58074.2023.10135213

Ruishi Liang, Yizhu Chen, Shuaibing Li, Huizhi Yang

引用次数: 0