With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.
A promising and practical chrome-free tanning system has been developed based on a novel Al-Zr bimetal complex tanning agent. However, to achieve satisfactory resultant leather, the retanning process that is compatible with this emerging tannage needs to be investigated systematically. This paper aims to explore the interaction between the bimetal complex tanned wet white and retanning agents. The isoelectric point (pI) of wet white was 7.2, which was nearly the same as wet blue. The electropositivity of wet white was even higher than that of wet blue during post-tanning processes, resulting in higher uptake rate of retanning agents. The distribution of various retanning agents in wet white was analyzed by pI measurement of layered leather and fluorescent tracing technique. The retanning agents were unevenly distributed throughout the cross-section, which might be an important restriction factor in obtaining satisfactory organoleptic properties of the crust leather. This fact is mainly due to the strong electrostatic interaction between anionic retanning agents and wet white. Applying a high dosage of multiple retanning agents in a proper sequence of addition benefited the full penetration of retanning agents in leather matrix and thus improved the organoleptic properties of crust leather. This work provides guidance for optimizing retanning process of the wet white leather.
In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.
Social network has provided a promising way for massive users to share their ideas and communicate with each other. A key issue in social network is to find out the prospective friends of users so as to extend the users' social cycles. Fortunately, users' thumbs-up data on web news or blogs have become an important evaluation basis in friend finding. Typically, through analyzing the thumbs-up data from different users, we can find out the friends or neighbors of a user. However, the thumbs-up data are often sensitive to users as they can disclose the private information of users, which violate the civil privacy-protection laws enacted by governments. In view of this challenge, we introduce the Simhash technique in information retrieval domain into social network and further bring forth a privacy-aware prospective friend-finding solution in social network based on the sensitive thumbs-up data. At last, we conduct a range of experiments based on well-known Movielens dataset. Experimental data demonstrate the advantages of our solution.
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