2022 IEEE 8th International Conference on Computer and Communications (ICCC)最新文献_第2页

Adversarial Samples Defense Strategy Based on Service Orchestration 基于服务编排的对抗性样本防御策略

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065619

Mengxin Zhang, Xiaofeng Qiu

{"title":"Adversarial Samples Defense Strategy Based on Service Orchestration","authors":"Mengxin Zhang, Xiaofeng Qiu","doi":"10.1109/ICCC56324.2022.10065619","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065619","url":null,"abstract":"Deep neural networks (DNNs) are playing an increasingly significant role in the modern world. However, they are weak to adversarial samples that are generated by adding specially crafted perturbations. Most defenses against adversarial samples focused on refining the DNN models, which often sacrifice the performance and computational cost of models on benign samples. In this paper, we propose a manifold distance detection method to distinguish legitimate samples and adversarial samples by measuring the different distances on the manifold. The manifold distance detection method neither modifies the protected models nor requires knowledge of the process for generating adversarial samples. Inspired by the effectiveness of the manifold distance detection, we demonstrated a well-designed orchestrated defense strategy, named Manifold Distance Judge (MDJ), which selects the best image processing method that will effectively expand the manifold distance between legitimate and adversarial samples, and thus, enhances the performance of the following manifold distance detection method. Tests on the ImageNet dataset, the MDJ is effective against the most adversarial samples under white-box, gray-box, and black-box attack scenarios. We show empirically that the orchestration strategy MDJ is significantly better than Feature Squeezing on the recall rate. Meanwhile, MDJ achieves high detection rates against CW attack and DI-FGSM attack.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122382713","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

Low-Complexity Multiuser Hybrid Precoding with Partially-Connected Architecture 具有部分连接架构的低复杂度多用户混合预编码

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065937

Jianing Li, Yuan Jiang, Lei Zhao, Bin Wang

引用次数: 0

The Music Generation Road from Statistical Method to Deep Learning 从统计方法到深度学习的音乐生成之路

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065693

Jiayun Xu, Weiting Qu, Dixin Li, Changjiang Zhang

引用次数: 0

k-Means-Based Tactile Data Recognition Method for Sharp and Dangerous Objects for Robots 基于k均值的机器人尖锐危险物体触觉数据识别方法

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10066027

Jing Yang, Shunyu Cen, Xiangyu Zhang, Honglin Luo, Taohong Zhao, Guangshu Wei, Zukun Yu, Qinglang Li

{"title":"k-Means-Based Tactile Data Recognition Method for Sharp and Dangerous Objects for Robots","authors":"Jing Yang, Shunyu Cen, Xiangyu Zhang, Honglin Luo, Taohong Zhao, Guangshu Wei, Zukun Yu, Qinglang Li","doi":"10.1109/ICCC56324.2022.10066027","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10066027","url":null,"abstract":"A robot senses its surroundings through its “skin.” Through this tactile sensing method, a robot can obtain tactile data of various shapes and sharp objects. Then, a robot can analyze whether objects are sharp and dangerous through these tactile data. This study proposes a k-means algorithm-based tactile data recognition method for sharp and dangerous objects for robots. It develops a distributed pressure tactile sensing device that aims to simulate how the human skin layer works. By using this device, multiple sets of data are obtained after collecting the data of seven types of objects with different degrees of sharpness, namely, a hobby knife, sharp pliers, a diamond-shaped block, a square charger, a pencil-shaped block, a ping-pong ball, and a cylindrical block. The data are classified and stored using the k-means algorithm. By sensing the data of the seven types of sharp objects studied in this work in real time on the developed device for analysis, human brain's judgment on the sharpness of the objects is simulated. Through the analysis of a large amount of experimental data and algorithm optimization, experimental results show that the device can recognize relatively regular square objects, objects with curved surfaces with small changes in the radius of curvature, and objects with tips of less complexity. Recognition accuracy can reach 95%.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122815340","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

Analysis of NR Positioning Evolution: From Scenarios to Techniques NR定位演进分析:从场景到技术

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065808

Hang Yin, Bowen Wang, Nanxi Li, Jianchi Zhu, X. She

引用次数: 0

Performance Analysis of Service-Based RAN via Multi-state Markov Chain 基于多状态马尔可夫链的基于服务的无线局域网性能分析

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10066043

Huimin Zhang, Guangyi Liu, N. Li, Zhou Tong, Quan Zhao, Junshuai Sun

引用次数: 0

Fuzzy Alternate Control Delayed Systems 模糊交替控制延迟系统

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065957

Ning Tang, Yuming Feng, B. Onasanya, Wei Zhang

引用次数: 0

A Lightweight Key Agreement Scheme for UAV Network 一种无人机网络轻量级密钥协议方案

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065747

J. Liu, Lin Yuan, Zhi-Shang Feng, Xiong Chen, Zhi-Cheng Hang

引用次数: 1

Decoupled Teacher for Semi-Supervised Drone Detection 半监督无人机检测的解耦教师

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065897

Jiawei Wang, Tianyu Song, Yan Zhang, Shengmin Wang, Wenhui Lin, Zhigang Wang, Yuanyuan Qiao

{"title":"Decoupled Teacher for Semi-Supervised Drone Detection","authors":"Jiawei Wang, Tianyu Song, Yan Zhang, Shengmin Wang, Wenhui Lin, Zhigang Wang, Yuanyuan Qiao","doi":"10.1109/ICCC56324.2022.10065897","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065897","url":null,"abstract":"Aerial drones come in handy in a variety of science and research applications, sometimes even cause privacy harms during aerial surveillance. In many anti-drone situations, detection, tracking, and classification of drones are of great significance to secure the airspace. Drones are small in size, different in appearance, and have different flight attitudes for various flight environments, which makes the drone data sets too expensive to annotate and the foreground-background imbalance occurs in drone detection. Previous works on drone detection have focused on supervised learning, which depends on large labeled data set. To alleviate the problem of scarce labeled data sets, Semi-Supervised Learning can be employed to leverage unlabeled samples. In this paper, we propose a Semi-Supervised Object Detection method Decoupled Teacher to use unlabeled data and address the foreground-background imbalance issue. Specifically, Decoupled Teacher decouples the Exponential Moving Average mechanism in the general SSOD paradigm, and applies a fusion method of weak/strong data augmentation. We have bench-marked our method and the state-of-the-art SSOD methods using two publicly available drone data sets. The experiment results demonstrate the superior performance of our approach compared with the state-of-the-art methods.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122198378","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

Adaptive Beam Handover Algorithm Based on Reinforcement Learning for mmWave System 基于强化学习的毫米波系统自适应波束切换算法

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065827

Jianzhong Yi, Chao Dong, K. Niu, Qiulin Xue, Junping Zhang

引用次数: 0