Author
Dawei Jin
Bio: Dawei Jin is an academic researcher from Zhongnan University of Economics and Law. The author has contributed to research in topics: Computer science & Value-added tax. The author has an hindex of 6, co-authored 7 publications receiving 723 citations.
Papers
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TL;DR: An App (called Healthcare Data Gateway (HGD) architecture based on blockchain is proposed to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private.
Abstract: Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.
884 citations
TL;DR: The findings show that the dataset with reviews having a short length and high readability could achieve the best performance compared with any other combinations of the levels of word count and readability and that controlling the review length is more effective for garnering a higher level of accuracy than increasing the readability.
Abstract: Cognitive computing is an interdisciplinary research field that simulates human thought processes in a computerized model. One application for cognitive computing is sentiment analysis on online reviews, which reflects opinions and attitudes toward products and services experienced by consumers. A high level of classification performance facilitates decision making for both consumers and firms. However, while much effort has been made to propose advanced classification algorithms to improve the performance, the importance of the textual quality of the data has been ignored. This research explores the impact of two influential textual features, namely the word count and review readability, on the performance of sentiment classification. We apply three representative deep learning techniques, namely SRN, LSTM, and CNN, to sentiment analysis tasks on a benchmark movie reviews dataset. Multiple regression models are further employed for statistical analysis. Our findings show that the dataset with reviews having a short length and high readability could achieve the best performance compared with any other combinations of the levels of word count and readability and that controlling the review length is more effective for garnering a higher level of accuracy than increasing the readability. Based on these findings, a practical application, i.e., a text evaluator or a website plug-in for text evaluation, can be developed to provide a service of review editorials and quality control for crowd-sourced review websites. These findings greatly contribute to generating more valuable reviews with high textual quality to better serve sentiment analysis and decision making.
75 citations
TL;DR: The experiment conducted shows that combining the anti-hypertensive drugs personalized recommendation service context ontology (HyRCO) with the optimized rule reasoning can achieve a higher-quality personalized drug recommendation service.
Abstract: The World Health Organization estimates that almost one-third of the world's adult population are suffering from hypertension which has gradually become a "silent killer". Due to the varieties of anti-hypertensive drugs, patients are interested in how these drugs can be selected to match their respective conditions. This study provides a personalized recommendation service system of anti-hypertensive drugs based on context-awareness and designs a context ontology framework of the service. In addition, this paper introduces a Semantic Web Rule Language (SWRL)-based rule to provide high-level context reasoning and information recommendation and to overcome the limitation of ontology reasoning. To make the information recommendation of the drugs more personalized, this study also devises three categories of information recommendation rules that match different priority levels and uses a ranking algorithm to optimize the recommendation. The experiment conducted shows that combining the anti-hypertensive drugs personalized recommendation service context ontology (HyRCO) with the optimized rule reasoning can achieve a higher-quality personalized drug recommendation service. Accordingly this exploratory study of the personalized recommendation service for hypertensive drugs and its method can be easily adopted for other diseases.
21 citations
TL;DR: It is proved that political engagement in social media is a bi-directional habitual process and the use of a habit formation coefficient as a new parameter to measure ‘reciprocal engagement’ insocial media provides a better understanding of the dynamic exchange between users of social media.
Abstract: This study draw upon the theory of habit formation in consumption from macroeconomics to support the evidence on the existence of habit formation in social media consumption. Treating social media ...
21 citations
TL;DR: A complex event processing (CEP) framework for an Adaptive Language Learning System that can process various inputs such as voice, video, text and other interaction events and is efficient with a processing delay of less than 1 .
13 citations
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1,995 citations
TL;DR: A comprehensive classification of blockchain-enabled applications across diverse sectors such as supply chain, business, healthcare, IoT, privacy, and data management is presented, and key themes, trends and emerging areas for research are established.
1,310 citations
TL;DR: Wang et al. as discussed by the authors conduct a systematic study on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. And they also review the security enhancement solutions for blockchain, which could be used in the development of various blockchain systems, and suggest some future directions to stir research efforts into this area.
1,071 citations
TL;DR: This paper introduces blockchain technologies, including their benefits, pitfalls, and the latest applications, to the biomedical and health care domains and discusses the potential challenges and proposed solutions of adopting blockchain technologies in biomedical/health care domains.
798 citations
TL;DR: It is argued that blockchain (BC), a disruptive technology that has found many applications from cryptocurrencies to smart contracts, is a potential solution to these challenges and is proposed a BC-based architecture to protect the privacy of users and to increase the security of the vehicular ecosystem.
Abstract: Interconnected smart vehicles offer a range of sophisticated services that benefit the vehicle owners, transport authorities, car manufacturers, and other service providers. This potentially exposes smart vehicles to a range of security and privacy threats such as location tracking or remote hijacking of the vehicle. In this article, we argue that blockchain (BC), a disruptive technology that has found many applications from cryptocurrencies to smart contracts, is a potential solution to these challenges. We propose a BC-based architecture to protect the privacy of users and to increase the security of the vehicular ecosystem. Wireless remote software updates and other emerging services such as dynamic vehicle insurance fees are used to illustrate the efficacy of the proposed security architecture. We also qualitatively argue the resilience of the architecture against common security attacks.
627 citations