Open Access
ARTICLE
Yuan Chen1, Liangtao Duan1, Weize Sun2, *, Jingxin Xu3
Journal on Big Data, Vol.1, No.3, pp. 107-116, 2019, DOI:10.32604/jbd.2019.07294
Abstract In this work, we address the frequency estimation problem of a complex singletone embedded in the heavy-tailed noise. With the use of the linear prediction (LP) property
and l1-norm minimization, a robust frequency estimator is developed. Since the proposed
method employs the weighted l1-norm on the LP errors, it can be regarded as an extension
of the lp-generalized weighted linear predictor. Computer simulations are conducted in the
environment of α-stable noise, indicating the superiority of the proposed algorithm, in
terms of its robust to outliers and nearly optimal estimation performance. More >
Open Access
ARTICLE
Wangdong Jiang1, Taian Yang1, *, Guang Sun1, 3, Yucai Li1, Yixuan Tang2, Hongzhang Lv1, Wenqian Xiang1
Journal on Big Data, Vol.1, No.3, pp. 117-134, 2019, DOI:10.32604/jbd.2019.08454
Abstract In order to study deeply the prominent problems faced by China’s clean
government work, and put forward effective coping strategies, this article analyzes the
network information of anti-corruption related news events, which is based on big data
technology. In this study, we take the news report from the website of the Communist
Party of China (CPC) Central Commission for Discipline Inspection (CCDI) as the
source of data. Firstly, the obtained text data is converted to word segmentation and stop
words under preprocessing, and then the pre-processed data is improved by vectorization
and text clustering, finally, after text clustering, the key… More >
Open Access
ARTICLE
Yue Cai1, Zeying Song1, Guang Sun1, *, Jing Wang1, Ziyi Guo1, Yi Zuo1, Xiaoping Fan1, Jianjun Zhang2, Lin Lang1
Journal on Big Data, Vol.1, No.3, pp. 135-144, 2019, DOI:10.32604/jbd.2019.08274
Abstract Big data technology is changing with each passing day, generating massive
amounts of data every day. These data have large capacity, many types, fast growth, and
valuable features. The same is true for the stock investment market. The growth of the
amount of stock data generated every day is difficult to predict. The price trend in the
stock market is uncertain, and the valuable information hidden in the stock data is
difficult to detect. For example, the price trend of stocks, profit trends, how to make a
reasonable speculation on the price trend of stocks and profit trends is a… More >
Open Access
ARTICLE
Danping Dong1, *, Yue Wu1, Lizhi Xiong1, Zhihua Xia1
Journal on Big Data, Vol.1, No.3, pp. 145-150, 2019, DOI:10.32604/jbd.2019.08706
Abstract This paper proposes a strategy for machine learning in the ciphertext domain.
The data to be trained in the linear regression equation is encrypted by SHE homomorphic
encryption, and then trained in the ciphertext domain. At the same time, it is guaranteed
that the error of the training results between the ciphertext domain and the plaintext domain
is in a controllable range. After the training, the ciphertext can be decrypted and restored to
the original plaintext training data. More >
Open Access
ARTICLE
Mengling Zou1, Zhengxuan Liu2, Xianyi Chen3, *
Journal on Big Data, Vol.1, No.3, pp. 151-158, 2019, DOI:10.32604/jbd.2019.09057
Abstract Image encryption (IE) is a very useful and popular technology to protect the
privacy of users. Most algorithms usually encrypt the original image into an image
similar to texture or noise, but texture and noise are an obvious visual indication that the
image has been encrypted, which is more likely to cause the attacks of enemy. To
overcome this shortcoming, many image encryption systems, which convert the original
image into a carrier image with visual significance have been proposed. However, the
generated cryptographic image still has texture features. In line with the idea of
improving the visual quality of the… More >
Login
Register
Submit a Paper
Propose a Special lssue