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Volume 4 Issue 3
Published:05 September 2021

Paromita Nitu,Joseph Coelho,Praveen Madiraju

2021, 4(3): 139-154.   doi:10.26599/BDMA.2020.9020026
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A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then u...

Zhao Tong,Feng Ye,Ming Yan,Hong Liu,Sunitha Basodi

2021, 4(3): 155-172.   doi:10.26599/BDMA.2020.9020029
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With the rapid development of human society, the urbanization of the world’s population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus p...

Shuai Zhang,Hongyan Liu,Jun He,Sanpu Han,Xiaoyong Du

2021, 4(3): 173-182.   doi:10.26599/BDMA.2021.9020002
Abstract ( 58 HTML ( 1   PDF(2313KB) ( 25 )   Save

Live streaming has grown rapidly in recent years, attracting increasingly more participation. As the number of online anchors is large, it is difficult for viewers to find the anchors they are interested in. Therefore, a personalized recommendation system is important for live streaming platforms. On live streaming platforms, the viewer’s and anchor’s preferences are dynamically changing over time. How to capture the user’s preference change is extensively studied in the literature, but how t...

Zhonghao Xue,Hongzhi Wang

2021, 4(3): 183-194.   doi:10.26599/BDMA.2021.9020001
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Density-based clustering is an important category among clustering algorithms. In real applications, many datasets suffer from incompleteness. Traditional imputation technologies or other techniques for handling missing values are not suitable for density-based clustering and decrease clustering result quality. To avoid these problems, we develop a novel density-based clustering approach for incomplete data based on Bayesian theory, which conducts imputation and clustering concurrently and ma...

Yong Bie,Yan Yang

2021, 4(3): 195-207.   doi:10.26599/BDMA.2021.9020003
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The aspect-based sentiment analysis (ABSA) consists of two subtasks'aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network (MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. ...

Zhixiang Ren,Yongheng Liu,Tianhui Shi,Lei Xie,Yue Zhou,Jidong Zhai,Youhui Zhang,Yunquan Zhang,Wenguang Chen

2021, 4(3): 208-220.   doi:10.26599/BDMA.2021.9020004
Abstract ( 57 HTML ( 0   PDF(10523KB) ( 11 )   Save

The plethora of complex Artificial Intelligence (AI) algorithms and available High-Performance Computing (HPC) power stimulates the expeditious development of AI components with heterogeneous designs. Consequently, the need for cross-stack performance benchmarking of AI-HPC systems has rapidly emerged. In particular, the de facto HPC benchmark, LINPACK, cannot reflect the AI computing power and input/output performance without a representative workload. Current popular AI benchmarks, such as ...