Yuki Nanjo, Masaaki Shirase, Takuya Kusaka, Y. Nogami
{"title":"Improvement of Final Exponentiation for Pairings on BLS Curves with Embedding Degree 15","authors":"Yuki Nanjo, Masaaki Shirase, Takuya Kusaka, Y. Nogami","doi":"10.1587/transfun.2020eal2046","DOIUrl":"https://doi.org/10.1587/transfun.2020eal2046","url":null,"abstract":"SUMMARY To be suitable in practice, pairings are typically carried out by two steps, which consist of the Miller loop and final exponentiation. To improve the final exponentiation step of a pairing on the BLS family of pairing-friendly elliptic curves with embedding degree 15, the authors provide a new representation of the exponent. The proposal can achieve a more reduction of the calculation cost of the final exponentiation than the previous method by Fouotsa et al.","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116931773","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}
{"title":"Unsupervised Deep Embedded Hashing for Large-Scale Image Retrieval","authors":"Huanmin Wang","doi":"10.1587/transfun.2020eal2056","DOIUrl":"https://doi.org/10.1587/transfun.2020eal2056","url":null,"abstract":"Hashing methods have proven to be effective algorithm for image retrieval. However, learning discriminative hash codes is challenging for unsupervised models. In this paper, we propose a novel distinguishable image retrieval framework, named Unsupervised Deep Embedded Hashing (UDEH), to recursively learn discriminative clustering through soft clustering models and generate highly similar binary codes. We reduce the data dimension by auto-encoder and apply binary constraint loss to reduce quantization error. UDEH can be jointly optimized by standard stochastic gradient descent (SGD) in the embedd layer. We conducted a comprehensive experiment on two popular datasets. key words: hashing, unsupervised learning, deep learning","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126631217","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}
{"title":"Sum Rate Maximization of Dense Small Cell Network with Load Balance and Power Transfer among SBSs","authors":"Xuefei Peng, Xiao-Yong Xue","doi":"10.1587/transfun.2020eal2011","DOIUrl":"https://doi.org/10.1587/transfun.2020eal2011","url":null,"abstract":"This letter proposes a load balance and power transfer scheme among small cell base stations (SBSs) to maximize the sum rate of small cell network. In the proposed scheme, small cell users (SUEs) are firstly associated with their nearest SBSs, then the overloaded SBSs can be determined. Further, the methods, i.e., Case 1: SUEs of overloaded SBSs are offloaded to their neighbor underloaded SBSs or Case 2: SUEs of overloaded SBSs are served by their original associated SBSs through obtaining power from their nearby SBSs that can provide higher data rate is selected. Finally, numerical simulations demonstrate that the proposed scheme has better performance. key words: load balance, power transfer, data rate","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124302767","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}
{"title":"On the Security of Keyed-Homomorphic PKE: Preventing Key Recovery Attacks and Ciphertext Validity Attacks","authors":"K. Emura","doi":"10.1587/transfun.2020eal2039","DOIUrl":"https://doi.org/10.1587/transfun.2020eal2039","url":null,"abstract":"Theoretically, an adversary sends a homomorphically evaluated challenge ciphertext to the decryption oracle, and can immediately break the security. One may think that this is just a theoretical result and there is no practical impact. Even thoughBleichenbacher’sCCAattack [2] has beenwidely recognized, it is also widely recognized that a weaker security level is acceptable in return for obtaining a homomorphic property. However, several CCA attacks against concrete homomorphic encryption schemes have been also shown. We introduce key recovery attacks (KRA) as follows.","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124501441","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}
{"title":"Compressed Sensing Framework Applying Independent Component Analysis after Undersampling for Reconstructing Electroencephalogram Signals","authors":"D. Kanemoto, Shun Katsumata, M. Aihara, M. Ohki","doi":"10.1587/transfun.2020eap1058","DOIUrl":"https://doi.org/10.1587/transfun.2020eap1058","url":null,"abstract":"This paper proposes a novel compressed sensing (CS) framework for reconstructing electroencephalogram (EEG) signals. A feature of this framework is the application of independent component analysis (ICA) to remove the interference from artifacts after undersampling in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In this framework, we used a random undersampling measurement matrix to suppress the Gaussian. The developed framework, in which the discrete cosine transform basis and orthogonal matching pursuit were used, was evaluated using raw EEG signals with a pseudo-model of an eye-blink artifact. The normalized mean square error (NMSE) and correlation coefficient (CC), obtained as the average of 2,000 results, were compared to quantitatively demonstrate the effectiveness of the proposed framework. The evaluation results of the NMSE and CC showed that the proposed framework could remove the interference from the artifacts under a high compression ratio. key words: EEG, compressed sensing, independent component analysis, random undersampling, artifact","PeriodicalId":348826,"journal":{"name":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748725","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}