2019 Seventh International Symposium on Computing and Networking (CANDAR)最新文献_第3页

CANDAR 2019 Reviewers

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/candar.2019.00008

Amila Akagic, Muhammad Alfian Amrizal, Hiroaki Anada, Akram Ben Ahmed, Masaki Inamura, Y. Ito, Keisuke Iwai, Genaro Juarez Martinez, H. Kakugawa, Yoshiaki Katayama, Akane Kawaharada, Ryuta Kawano, Kyeong Soo Kim, Yonghwan Kim, A. Nagoya

{"title":"CANDAR 2019 Reviewers","authors":"Amila Akagic, Muhammad Alfian Amrizal, Hiroaki Anada, Akram Ben Ahmed, Masaki Inamura, Y. Ito, Keisuke Iwai, Genaro Juarez Martinez, H. Kakugawa, Yoshiaki Katayama, Akane Kawaharada, Ryuta Kawano, Kyeong Soo Kim, Yonghwan Kim, A. Nagoya","doi":"10.1109/candar.2019.00008","DOIUrl":"https://doi.org/10.1109/candar.2019.00008","url":null,"abstract":"Amila Akagic, University of Sarajevo, Bosnia and Herzegovina Motoki Amagasaki, Kumamoto University, Japan Muhammad Alfian Amrizal, Tohoku University, Japan Hiroaki Anada, University of Nagasaki, Japan Ritu Arora, University of Texas at Austin, U.S. Thomas Austin, San Jose State University, U.S. Akram Ben Ahmed, Keio University, Japan Antoine Bossard, Kanagawa University, Japan Chen-Mou Cheng, National Taiwan University, Taiwan Franco Chiaraluce, Università Politecnica delle Marche, Italy Stéphane Devismes, VERIMAG UMR 5104, France Nguyen Anh Vu Doan, Technical University of Munich, Germany Ryusuke Egawa, Tohoku University, Japan Toshio Endo, Tokyo Institute of Technology, Japan Martti Forsell, VTT, Finland Naoki Fujieda, Toyohashi University of Technology, Japan Noriyuki Fujimoto, Osaka Prefecture University, Japan Hiroshi Fujiwara, Shinshu University, Japan Ken-Ichi Fukui, Osaka University, Japan Masayuki Fukumitsu, Hokkaido Information University, Japan Masaru Fukushi, Yamaguchi University, Japan Junichi Funasaka, Hiroshima City University, Japan Takayasu Fushimi, University of Tsukuba, Japan Jing Gong, KTH Royal Institute of Technology, Sweden Takaaki Goto, Toyo University, Japan Andreas Grapentin, Hasso Plattner Institute, Germany Yan Gu, Carnegie Mellon University, U.S. Masafumi Hashimoto, Osaka University, Japan Kohei Hatano, Kyushu University, Japan Akihiro Hayashi, Rice University, U.S. Ramon Hermoso, University of Zaragoza, Spain Shoichi Hirose, University of Fukui, Japan Michihiro Horie, IBM, Japan Naoto Hoshikawa, National Institute of Technology, Oyama College, Japan Shuichi Ichikawa, Toyohashi University of Technology, Japan Tsutomu Inamoto, Ehime University, Japan Masaki Inamura, Tokyo Denki University, Japan Fumihiko Ino, Osaka University, Japan Takashi Ishida, Tokyo Institute of Technology, Japan","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124539441","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

Mobility-Aware Tasks Offloading in Mobile Edge Computing Environment 移动边缘计算环境下的移动感知任务卸载

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00034

Chunrong Wu, Qinglan Peng, Yunni Xia, Jia Lee

{"title":"Mobility-Aware Tasks Offloading in Mobile Edge Computing Environment","authors":"Chunrong Wu, Qinglan Peng, Yunni Xia, Jia Lee","doi":"10.1109/CANDAR.2019.00034","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00034","url":null,"abstract":"In the mobile edge computing (MEC) paradigm, the underlying business processes of mobile applications can be modeled as application graphs, and mobile users are allowed to offload mobile tasks in these graphs to nearby edge servers to speed up their mobile applications. Nevertheless, various challenges, especially the quality of such a mobile task offloading in edge computing environment, are yet to be properly tackled. Most studies and related offloading strategies based on the assumption that mobile users are fully stationary when they are offloading tasks to edge servers. However, this is not realistic in real-world where edge users are keeping moving, which has a great impact on the success of task offloading and further affects the response time of mobile applications. In this paper, we take the mobility of edge users into consideration and employ a deep learning method to predict the future trajectories of mobile users. Then we develop an online method to solve the offloading problem based on the quality-of-service (QoS) and the predicted user trajectories in real-time. Experiments based on real-world user trajectories and edge server dataset show that our proposed approach can achieve lower offloading failure count and shorter response time than traditional ones.","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122091046","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: 14

A Color Image Representation Approach for Content-Based Image Retrieval 一种基于内容的图像检索彩色图像表示方法

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00014

Cheng-Hsien Liu, Chang-Hsing Lee, Jau-Ling Shih, Chin-Chuan Han

{"title":"A Color Image Representation Approach for Content-Based Image Retrieval","authors":"Cheng-Hsien Liu, Chang-Hsing Lee, Jau-Ling Shih, Chin-Chuan Han","doi":"10.1109/CANDAR.2019.00014","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00014","url":null,"abstract":"In this paper, a color image representation approach is proposed for color image retrieval. First, an input color image is represented by a number representative images (called color degree images) consisting of chromatic (including red, green, blue, cyan, magenta, and yellow) and achromatic (including black and white) degree images. For each pixel, the degree to which its color value is similar to each representative color will be computed. For the local window centered at each detected key point, a vector representing the degree values, called local color degree (LCD) descriptor, will be computed to describe the local color property. Meanwhile, the scale-invariant feature transform (SIFT) descriptor is extracted to describe the local texture characteristics. Both LCD descriptor and SIFT descriptor are combined together to form the local descriptor. Vector of locally aggregated descriptors (VLAD) is then employed to aggregate all local descriptors to get a single VLAD vector for each input image. Experimental results on INRIA Holidays and Leeds Butterfly datasets have shown that the proposed descriptor yield higher retrieval accuracy.","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125066663","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: 3

Implementation of a Strongly Robust Identity-Based Encryption Scheme over Type-3 Pairings 基于Type-3对的强鲁棒身份加密方案的实现

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00032

H. Okano, K. Emura, T. Ishibashi, T. Ohigashi, Tatsuya Suzuki

{"title":"Implementation of a Strongly Robust Identity-Based Encryption Scheme over Type-3 Pairings","authors":"H. Okano, K. Emura, T. Ishibashi, T. Ohigashi, Tatsuya Suzuki","doi":"10.1109/CANDAR.2019.00032","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00032","url":null,"abstract":"Identity-based encryption (IBE) is a powerful mechanism for maintaining security. However, systems based on IBE are unpopular when compared with those of the public-key encryption (PKE). In our opinion, one of the reasons is a gap between theory and practice. For example, a generic transformation of weakly/strongly robust IBE from any IBE has been proposed by Abdalla et al., no robust IBE scheme is explicitly given. This means that, theoretically, anyone can construct a weakly/strongly robust IBE scheme by employing this transformation. However, this seems not easily applicable to non-cryptographers. In this paper, we first introduce the Gentry IBE scheme constructed over Type-3 pairings by employing the transformation proposed by Abe et al., and second we explicitly give strongly/weakly robust Gentry IBE schemes by employing the Abdalla et al. transformation. Finally, we show its implementation result and show that we can add strong robustness to the Gentry IBE scheme with a very few additional costs. We employ the mcl library to support a Barreto-Naehrig curve defined over the 462-bit prime. The encryption requires about 9 ms, whereas the decryption requires about 12 ms.","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129629474","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: 4

A Resource-Efficient Weight Sampling Method for Bayesian Neural Network Accelerators 一种资源高效的贝叶斯神经网络加速器权值抽样方法

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00024

Yuki Hirayama, T. Asai, M. Motomura, Shinya Takamaeda-Yamazaki

{"title":"A Resource-Efficient Weight Sampling Method for Bayesian Neural Network Accelerators","authors":"Yuki Hirayama, T. Asai, M. Motomura, Shinya Takamaeda-Yamazaki","doi":"10.1109/CANDAR.2019.00024","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00024","url":null,"abstract":"The main problems of deep learning are requiring a large amount of data for learning, and prediction with excessive confidence. A Bayesian neural network (BNN), in which a Bayesian approach is incorporated into a neural network (NN), has drawn attention as a method for solving these problems. In a BNN, the probability distribution is assumed for the weight, in contrast to a conventional NN, in which the weight is point estimated. This makes it possible to obtain the prediction as a distribution and to evaluate how uncertain the prediction is. However, a BNN has more computational complexity and a greater number of parameters than an NN. To obtain an inference result as a distribution, a BNN uses weight sampling to generate the respective weight values, and thus, a BNN accelerator requires weight sampling hardware based on a random number generator in addition to the standard components of a deep learning neural network accelerator. Therefore, the throughput of weight sampling must be sufficiently high at a low hardware resource cost. We propose a resource-efficient weight sampling method using inversion transform sampling and a lookup-table (LUT)-based function approximation for hardware implementation of a BNN. Inversion transform sampling simplifies the mechanism of generating a Gaussian random number from a uniform random number provided by a common random number generator, such as a linear feedback shift register. Employing an LUT-based low-bit precision function approximation enables inversion transform sampling to be implemented at a low hardware cost. The evaluation results indicate that this approach effectively reduces the occupied hardware resources while maintaining accuracy and prediction variance equivalent to that with a non-approximated sampling method.","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129779024","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

Evaluating a Side-Channel Resistance against Order 4 Rational Points in Arduino Cryptography Library 评估Arduino加密库中4阶有理点的侧信道电阻

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00040

Keiji Yoshimoto, Yoshinori Uetake, Yuta Kodera, Takuya Kusaka, Y. Nogami

引用次数: 1

CANDAR 2019 Conference Organization 加拿大2019年会议组织

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/candar.2019.00006

Akihiro Fujiwara, Hiroyuki Sato, J. Bordim, M. Koibuchi, Y. Kakuda, T. Nakanishi, Yasuyuki Nogami, T. Ohta, A. Asato, H. Takizawa, Akihiro Fujii, K. Nakano, Takeshi Ohkawa, K. Ootsu

引用次数: 0

A Tensor Factorization on Rating Prediction for Recommendation by Feature Extraction from Reviews 基于评论特征提取的推荐评级预测张量分解

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00036

Yang Sun, Guan-Shen Fang, S. Kamei

{"title":"A Tensor Factorization on Rating Prediction for Recommendation by Feature Extraction from Reviews","authors":"Yang Sun, Guan-Shen Fang, S. Kamei","doi":"10.1109/CANDAR.2019.00036","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00036","url":null,"abstract":"In many online review sites or social media, each user is encouraged to assign a numeric rating and write a textual review as a feedback to each item that he had gotten, e.g., a product that he had bought, a place that he had visited, a service that he had received. Sometimes, feedbacks by some users would be affected by some contextual factors such as weather, distance, time and season. In addition, the context-aware approach is being developed by utilizing the user's contextual information to produce more precise recommendations than traditional approaches. Furthermore, previous works [1], [2] have already approved the drawback of the ignorance of textual reviews would bring mediocre performance for rating prediction. In this work, we propose two rating prediction models TF-Doc2Vec and TF-LDA. Tensor Factorization (TF) is an extended version of Matrix Factorization (MF) by adding another dimension. We consider seasonal context as the additional dimension. Firstly, in TF-Doc2Vec (resp. TF-LDA), each of the reviews is characterized by a numeric feature vector via Doc2Vec (resp. a topic distribution via LDA). Secondly, TF-Doc2Vec uses TF which is trained by the proposed first-order gradient descent method for TF, named Feature Vector Gradient Descent (FVGD), while TF-LDA uses TF which is trained by extended TGD proposed in [1]. In our evaluation, we use pre-processed data of three cities in the YELP challenge dataset. We conduct experimental comparisons, and results show that TF-Doc2Vec and TF-LDA improve the performance significantly as compared to the basic TF model.","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130599952","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: 4

Threat Analysis of Fake Virus Alerts Using WebView Monitor 使用WebView监视器对假病毒警报进行威胁分析

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00012

Yuta Imamura, Rintaro Orito, Kritsana Chaikaew, Célia Manardo, P. Leelaprute, Masaya Sato, Toshihiro Yamauchi

引用次数: 1

A Label-Based Approach for Automatic Identifying Adversarial Examples with Image Transformation 基于标签的图像变换对抗样本自动识别方法

2019 Seventh International Symposium on Computing and Networking (CANDAR) Pub Date : 2019-11-01 DOI: 10.1109/CANDAR.2019.00021

D. D. Thang, Toshihiro Matsui

{"title":"A Label-Based Approach for Automatic Identifying Adversarial Examples with Image Transformation","authors":"D. D. Thang, Toshihiro Matsui","doi":"10.1109/CANDAR.2019.00021","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00021","url":null,"abstract":"Besides extraordinary results that deep neural networks are widely deployed in many fields such as computer vision, speech recognition, and natural language processing, in recent years deep neural networks have been recognized vulnerable to intentional modification of legitimate inputs called adversarial examples. These patterns are almost indistinguishable from AI models and human perception. Adversarial examples' concern is rising dramatically and is attracted by many research's apprehensiveness because of its tremendous impact. Unfortunately, until now there is none of the defenses has been shown to be very effective. In this paper, we introduce a new defense strategy against adversarial examples by using a label-based end-to-end system. Our proposed defense system can mostly distinguish adversarial samples and benign images without human intervention. We exploit the important role of spatial domain in adversarial attacks and proposing a state-of-the-art method for detecting adversarial examples based on our observation. We evaluate our system's performance on a variety of standard benchmark datasets including MNIST and ImageNet. Our proposed method reached out detection rates in a range from 94.6% to 99.2% in many settings.","PeriodicalId":300306,"journal":{"name":"2019 Seventh International Symposium on Computing and Networking (CANDAR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117339418","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