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- Alternative splicing (1)
- Big data (1)
- Blockchain (1)
- Cloud computing (1)
- Data analysis (1)
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- Feature selection (1)
- Gene expression (1)
- Gravitational wave (1)
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- High-dimensional data (1)
- Identity authentication (1)
- Intelligent Financial Advisor (IFA) (1)
- Intron retention (1)
- Multi-domain dialog state tracking (1)
- MultiTask Learning (MTL) (1)
- Normalizing flow (1)
- Potential client identification (1)
- Prior sampling (1)
- RNA-Seq (1)
- Slot-relevant attention (1)
- Social network (1)
- Task-oriented dialog system (1)
- Trusted service evaluation model (1)
Articles 1 - 6 of 6
Full-Text Articles in Engineering
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Big Data Mining and Analytics
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new …
Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu
Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu
Big Data Mining and Analytics
With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, …
Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang
Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang
Big Data Mining and Analytics
Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the …
Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren
Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren
Big Data Mining and Analytics
Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow …
Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu
Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu
Big Data Mining and Analytics
Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user→activated user→client and decompose this relationship into …
A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li
A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li
Big Data Mining and Analytics
Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an …