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Antenna Array Signal Direction of Arrival Estimation on Digital Signal Processor (DSP)

Procedia Computer Science 2015年 55卷 782-791页

作者： Ying Liu Hongyuan Cui University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Antenna arrays are used in many digital signal processing applications due to their ability to locate signal sources. Direction of Arrival (DOA) estimation is a key task of array signal processing. Although various algorithms have been developed for DOA estimation, their high complexity prevents their use in real-time applications. In this paper, we design and develop an efficient parallel implementation of DOA on DSP which is the most widely used processor in embedded system. Due to the potential parallelism in MUSIC algorithm, it is selected for 2-D DOA estimation. Two computational cores in MUSIC are identified and parallelized. Vectorization of multiple single precision floating point operations is proposed to make good use of the 128-bit vectors on DSP C6678. Then, the parallel DOA estimation algorithm is implemented on one core of DSP C6678 which is the latest version up to now. Experiments are conducted on both 1-D and 2-D antenna array signals. Considerable performance improvement is obtained.

关键词： parallel computing signal processing DSP DOA estimation

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COVID-19 Impact on Global Electricity Generation Structure-Based on Sustainable Development Perspective 9th

COVID-19 Impact on Global Electricity Generation Structure-B...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Peng, Shuyang Yang, Xian Lu, Haibo Guo, Kun School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. © 2022 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Impact of Live Streamer Characteristics and Customer Response on Live-streaming Performance: Empirical Evidence from e-Commerce Platform 9th

Impact of Live Streamer Characteristics and Customer Respons...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Zhou, Xiaohui Tian, Xin Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As an emerging form of marketing, live streaming has a positive effect on the ability of e-Commerce platforms to attract traffic and increase sales. It has become another important competitive area in the industry. Based on an alternative learning theory model, the influencing factors of live streaming on e-Commerce platforms are studied, and the data findings can be applied to brand marketing and platform performance improvement. Quantitative and qualitative analyses were conducted using tens of thousands of live-streaming data, and the findings show that live interaction and live streamer follower accumulation have a significant positive effect on the number of sold users. Through the empirical study of real data, this paper inspires participants to fully embrace the effect of live streaming and enhance the consumer shopping experience. © 2022 The Authors. Published by Elsevier B.V.

关键词： Electronic commerce

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Artificial cheerleading in IEO: Marketing campaign or pump and dump scheme

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INFORMATION PROCESSING & management 2024年第1期61卷

作者： Tian, Yingjie Xie, Yuhao Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

Initial coin offerings (ICOs) have been vulnerable to pump-and-dump schemes, where fraudsters spread false information on social media to inflate coin prices. However, initial exchange offerings (IEOs) are considered more reliable as exchanges conduct due diligence on projects. This study investigates the effectiveness of IEOs in mitigating pump-and-dump schemes by analyzing over 11,000 tweets posted by different accounts from 54 IEO projects one month prior to the IEO. We select 22 projects with at least 1000 tweets and use topic modeling to perform a descriptive analysis. We use botometer to produce a bot score for each tweet account and a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for sentiment analysis. We introduce penalized sentiment scores and interaction term and use an OLS regression model to identify the relationship between bot scores, sentiment values, and post-IEO performance. Our research finds that bot score is negatively associated with 30-day return and is positively associated with 30-day volatility, indicating the presence of pump-and -dump schemes in IEOs. We suggest that investors, exchanges, and regulators should take steps to assess the risks associated with IEOs and design appropriate interventions to prevent market manipulation.

关键词： Deep learning Social media analysis Initial exchange offering Bot detection

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Relationship between Capital Operation and Market Value management of Listed Companies Based on Random Forest Algorithm

Relationship between Capital Operation and Market Value Mana...

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International Conference on Computational Science (ICCS)

作者： Long, Wen Song, Linqiu Cui, Lingxiao Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China UCAS Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

This paper analyzes the influence of capital operations on the performance of listed companies under different market conditions by combining various capital operations with market value management. Random Forest algorithm is adopted and other machine learning methods are used to compare. We find that capital operation is significantly related to market value management and different capital operations have different effects on companies in various conditions. The result shows Random Forest algorithm has the highest classification accuracy and is more stable under different thresholds. Our findings will help to establish a market or industry benchmark which provides a scientific basis and decision support to the target companies when they operate their capitals. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science

关键词： Random Forest capital operation market value management financial strategy

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DWWP: Domain-specific new words detection and word propagation system for sentiment analysis in the tourism domain

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knowledge-BASED SYSTEMS 2018年 146卷 203-214页

作者： Li, Wei Guo, Kun Shi, Yong Zhu, Luyao Zheng, Yuanchun Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Online travel has developed dramatically during the past three years in China. This results in a large amount of unstructured data like tourism reviews from which it is hard to extract useful knowledge. In this paper, a DWWP system consisting of domain-specific new words detection (DW) and word propagation (WP) is presented. DW deals with the negligence of user-invented new words and converted sentiment words by means of AMI (Assembled Mutual Information). Inspired by social networks, the new method WP incorporates manually calibrated sentiment scores, semantic and statistical similarity information, which improves the quality of sentiment lexicon in comparison with existing data-driven methods. Experimental results show that DWWP improves seventeen percentage points compared with graph propagation and four percentage points compared with label propagation in terms of accuracy on dataset I and dataset II, respectively. (C) 2018 Published by Elsevier B.V.

关键词： Online travel DWWP Chinese new words detection Sentiment lexicon Word propagation

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Improved New Word Detection Method Used in Tourism Field

Improved New Word Detection Method Used in Tourism Field

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International Conference on Computational Science (ICCS)

作者： Li, Wei Guo, Kun Shi, Yong Zhu, Luyao Zheng, Yuanchun Chinese Acad Sci Fictitious Econ & Data Sci Res Ctr Beijing Peoples R China UCAS Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Chinese segmentation has attracted amounts of attention in natural language processing in recent years and is the basis of web text mining The article improved statistics-based method EMI, then we use improved approach to detect new words in tourism field. The result demonstrates that our method can detect new words significantly, especially in detecting proper nouns and sentiment words which will be helpful in subsequent tasks such as sentiment analysis and word embedding. In additional, this paper analyze parameters which are influential on the effects of new words detection. At last, the article discussed possible application of new word detection in sentiment analysis. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science

关键词： EMI PMI online travel new word detection

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Graph Neural Networks with Learnable and Optimal Polynomial Bases 40

Graph Neural Networks with Learnable and Optimal Polynomial ...

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40th International Conference on Machine Learning

作者： Guo, Yuhe Wei, Zhewei Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Shenzhen Peoples R China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard's Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis fromWang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis.

关键词： Graph neural networks

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Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning

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Procedia Computer Science 2016年 91卷 566-575页

作者： Ying Liu Linzhi Wu University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Geological disaster recognition, especially, landslide recognition, is of vital importance in disaster prevention, disaster monitoring and other applications. As more and more optical remote sensing images are available in recent years, landslide recognition on optical remote sensing images is in demand. Therefore, in this paper, we propose a deep learning based landslide recognition method for optical remote sensing images. In order to capture more distinct features hidden in landslide images, a particular wavelet transformation is proposed to be used as the preprocessing method. Next, a corrupting & denoising method is proposed to enhance the robustness of the model in recognize landslide features. Then, a deep auto-encoder network with multiple hidden layers is proposed to learn the high-level features and representations of each image. A softmax classifier is used for class prediction. Experiments are conducted on the remote sensing images from Google Earth. The experimental results indicate that the proposed wav DAE method outperforms the state-of-the-art classifiers both in efficiency and accuracy.

关键词： target recognition deep learning remote sensing image

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Pedestrian Detection Using Privileged Information

Pedestrian Detection Using Privileged Information

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Zhiquan Qi Yingjie Tian Lingfeng Niu Fan Meng Limeng Cui Yong Shi Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467384940

How to balance the speed and the quality is always a challenging issue in pedestrian detection. In this paper, we introduce the Learning model Using Privileged Information (LUPI), which can accelerate the convergence rate of learning and effectively improve the quality without sacrificing the speed. In more detail, we give the clear definition of the privileged information, which is only available at the training stage but is never available for the testing set, for the pedestrian detection problem and show how much the privileged information helps the detector to improve the quality. All experimental results show the robustness and effectiveness of the proposed method, at the same time show that the privileged information offers a significant improvement.

关键词： Feature extraction Image color analysis Detectors Training Support vector machines Standards Histograms

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