2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)最新文献_第10页

Wandering Path Visualization System Prototype for Finding Wandering Elderly People Using BLE Beacon 利用BLE信标寻找流浪老人的漫游路径可视化系统原型

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00095

Tomoya Arakawa, Shun Shiramatsu, Akira Iwata

{"title":"Wandering Path Visualization System Prototype for Finding Wandering Elderly People Using BLE Beacon","authors":"Tomoya Arakawa, Shun Shiramatsu, Akira Iwata","doi":"10.1109/CANDARW.2018.00095","DOIUrl":"https://doi.org/10.1109/CANDARW.2018.00095","url":null,"abstract":"In this study, we focused on wandering behavior, which is one of symptoms of dementia patients, and developed a system for watching over such patients. In our proposed method, Bluetooth Low Energy (BLE) beacons were carried by elderly people with dementia. We installed fixed receivers in the town where we conducted this study. We visualized the wandering route by using the reception history of the BLE beacon that can be acquired from the fixed receiver. In this paper, we aimed to develop an interface that enables us to check the history of actions to narrow down the search range when an elderly person with dementia is missing. Also, by using this interface, his/her family and caregivers can check that an elderly person with dementia is in a safe place. Particularly, (1) because the signal range of the BLE beacon has been improved, it became possible for some fixed receivers to receive signal, and this solved the problem of detecting an elderly person with dementia in the vicinity. Furthermore, (2) merely displaying the shortest route is insufficient for the purpose of searching for a wandering route, and it was necessary to be able to display detour routes. To deal with these two problems, we developed a method to acquire only the reception history of the BLE beacon that is considered necessary for route visualization.","PeriodicalId":329439,"journal":{"name":"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134266500","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: 1

Detecting Distributed Cyber Attacks in SDN Based on Automatic Thresholding 基于自动阈值的SDN分布式网络攻击检测

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00083

Ryousuke Komiya, Yaokai Feng, K. Sakurai

{"title":"Detecting Distributed Cyber Attacks in SDN Based on Automatic Thresholding","authors":"Ryousuke Komiya, Yaokai Feng, K. Sakurai","doi":"10.1109/CANDARW.2018.00083","DOIUrl":"https://doi.org/10.1109/CANDARW.2018.00083","url":null,"abstract":"Distributed Cyber Attack launched from many hosts simultaneously has become one of the most sophisticated and the most dangerous attacks in the cyber world including the traditional Internet and the SDN (Software Defined Networking) environments. As a kind of centralized network environment, the SDN has been greatly developed and popularized in recent years, especially in cloud systems. Thus, how to efficiently detect distributed attacks in SDN environments has attracted great attentions in academia and industry and various researches have been done to counter such attacks. The latest related researches made attempts to exploit the information of the PacketIn packets collected in the SDN controller and those methods proved efficient for detecting distributed cyber attacks in SDN environments. However, such methods adopted a threshold for distinguishing between attacks and normal situations. The threshold must be properly determined manually in advance, which is not easy in many applications even for experts. In this study, we try to automatically extract a proper threshold from the historical data of the monitored SDN environment so that the difficult parameter-tuning (determination of the threshold) process can be removed. In addition, because the extracted threshold can well reflect the actual situations of the monitored environment, a better detection performance than the existing approaches can be expected. The detection performance of our proposal is also tested using real traffic data.","PeriodicalId":329439,"journal":{"name":"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439237","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: 1

Preconditioner Auto-Tuning Using Deep Learning for Sparse Iterative Algorithms 基于深度学习的稀疏迭代算法预调节器自动调谐

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00055

Kenya Yamada, T. Katagiri, H. Takizawa, K. Minami, M. Yokokawa, Toru Nagai, M. Ogino

{"title":"Preconditioner Auto-Tuning Using Deep Learning for Sparse Iterative Algorithms","authors":"Kenya Yamada, T. Katagiri, H. Takizawa, K. Minami, M. Yokokawa, Toru Nagai, M. Ogino","doi":"10.1109/CANDARW.2018.00055","DOIUrl":"https://doi.org/10.1109/CANDARW.2018.00055","url":null,"abstract":"In numerical libraries for sparse matrix operations, there are many tuning parameters related to implementation selection. Selection of different tuning parameters could result in totally different performance. Moreover, optimal implementation depends on the sparse matrices to be operated. It is difficult to find optimal implementation without executing each implementation and thereby examining its performance on a given sparse matrix. In this study, we propose an implementation selection method for sparse iterative algorithms and preconditioners in a numerical library using deep learning. The proposed method uses full color images to represent the features of a sparse matrix. We present an image generation method for partitioning a given matrix (to generate its feature image) so that the value of each matrix element is considered in the implementation selection. We then evaluate the effectiveness of the proposed method by conducting a numerical experiment. In this experiment, the accuracy of implementation selection is evaluated. The training data comprise a pair of sparse matrix and its optimal implementation. The optimal implementation of each sparse matrix in the training data is obtained in advance by executing every implementation and getting the best one. The experimental results obtained using the proposed method show that the accuracy of selecting the optimal implementation of each sparse matrix is 79.5%.","PeriodicalId":329439,"journal":{"name":"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868056","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: 7

Mitigating Use-after-Free Attack Using Library Considering Size and Number of Freed Memory 考虑释放内存的大小和数量，使用库来缓解释放后使用攻击

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00080

Yuya Ban, Toshihiro Yamauchi

{"title":"Mitigating Use-after-Free Attack Using Library Considering Size and Number of Freed Memory","authors":"Yuya Ban, Toshihiro Yamauchi","doi":"10.1109/CANDARW.2018.00080","DOIUrl":"https://doi.org/10.1109/CANDARW.2018.00080","url":null,"abstract":"Use-after-free (UAF) vulnerabilities, are abused by exploiting a dangling pointer that refers to a freed memory, location and then executing arbitrary code. Vulnerabilities are caused by bugs in software programs, particularly large scale programs such as browsers. We had previously proposed HeapRevolver, which prohibits freed memory area from being reused for a certain period. HeapRevolver on Windows uses the number of freed memory areas that are prohibited for reuse as a trigger to release the freed memory area. Alternatively, HeapRevolver uses the number of the freed memory areas as a threshold for releasing freed memory. However, when the size of individual freed memory area is large, HeapRevolver on Windows increases the memory overhead. In this paper, we propose an improved HeapRevolver for Windows considering the size and number of the freed memory areas. The improved HeapRevolver prohibits the reuse of a certain number of freed memory areas at a given time by considering the size and number of freed memory areas as thresholds. Evaluation results demonstrate that the improved HeapRevolver can prevent attacks that exploit UAF vulnerabilities. Particularly, when the size of individual freed memory area is small in a program, HeapRevolver is effective in decreasing the attack success rate.","PeriodicalId":329439,"journal":{"name":"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132709883","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

Tile Art Image Generation Using Conditional Generative Adversarial Networks 使用条件生成对抗网络的图像生成

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00047

Naoki Matsumura, Hiroki Tokura, Yuki Kuroda, Yasuaki Ito, K. Nakano

引用次数: 10

Application of an Online Questionnaire to Secure the Diversity of Planungszelle Participant Attributes 在线问卷的应用，以确保计划参与者属性的多样性

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00075

Yoshiomi Otsuka, T. Aramaki

引用次数: 0

Improving Performance of Transposition Algorithm of 3-D Data Array for Parallelization Using Message Passing Interface 利用消息传递接口提高并行化三维数据阵列转置算法的性能

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00094

Masahiro Arai, F. Akagi, Saneyasu Yamaguchi, K. Yoshida

引用次数: 0

DHT Clustering for Load Balancing Considering Blockchain Data Size 考虑区块链数据大小的负载均衡DHT聚类

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00022

Yudai Kaneko, T. Asaka

引用次数: 7

Design of an Incentive Decision Method for an Agriculture Information Sharing System 农业信息共享系统的激励决策方法设计

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00103

Joji Toshima, Akiko Takahashi

引用次数: 0

Incentive Driving Multipath Inter-Domain Routing 激励驱动多路径域间路由

2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) Pub Date : 2018-11-01 DOI: 10.1109/CANDARW.2018.00067

Donghong Qin, Lina Ge, Ting Lv

引用次数: 0