Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy

Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications.However,users’personal privacy will be leaked in the mining process.In recent years,application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method.Local differential privacy means that users first perturb the original data and then send these data to the aggregator,preventing the aggregator from revealing the user’s private information.We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s multi-attribute.The main technique has bitmap encoding for converting the user’s original data into a binary string.It also includes how to choose the best perturbation algorithm for varying user attributes,and uses the frequent pattern tree(FP-tree)algorithm to mine frequent itemsets.Finally,we incorporate the threshold random response(TRR)algorithm in the framework and compare it with the existing algorithms,and demonstrate that the TRR algorithm has higher accuracy for mining frequent itemsets.

Relationship between land use and user’s behavior along the street:A case study of Da Nang City,Viet Nam

Human activities on street spaces are affected by both physical and non-physical settings on streets.These two aspects are primarily impacted by land use which results in the uneven distribution of different activities on streets.This research investigates land use patterns and their characteristics in association to user’s behaviors.By using mixed qualitative and quantitative research methods,e.g.,place-centered behavioral map,observation,visual encounter surveys,machine learning,the relationship between user’s behavior and land use characteristics along the street is unveiled.All user behaviors along both types of streets were classified into six main categories,with 26 subcategories.The observation results show that the residential use of land along the street was transformed into the commercial use for various types of shophouses based on the resident’s ideas.There is a great correlation between land use and user’s activities.These findings give support to authorities to manage urban streets and develop a sustainable policy for improving street spaces.Further,this research contributes useful information to urban designers and planners in creating a successful street space that is appropriate for the Vietnam Community.

Use of methotrexate in inflammatory bowel disease in 2014: A User's Guide

Methotrexate has been used an immunomodulator in many autoimmune diseases,including inflammatory bowel disease. However,many physicians are unfamiliar or uncomfortable with its use in the management of inflammatory bowel disease. We summarize the data for use of methotrexate in common clinical scenarios:(1) steroid dependant Crohn's disease(CD);(2) maintenance of remission in steroid free CD;(3) azathioprine failures in CD;(4) in combination therapy with Anti-TNF agents in CD;(5) decreasing antibody formation to Anti-TNF therapy in CD;(6) management of fistulizing disease in CD; and(7) as well as induction and maintenance of remission in ulcerative colitis. An easy to use algorithm is provided for the busy clinician to access and safely prescribe methotrexate for their inflammatory bowel disease patients.

Customization Product Design Based on User's Emotion and Needs

The paper presents a design method that ensures the ingenuity of the product form as well as the whole and exact expression of user’s needs. The key idea is to establish an automatic design system which can transform the user’s language needs into the product features in real-time. A rifle was taken as a research instance and soldiers were chosen as evaluation customers. The theory of fuzzy set and semantic difference are adopted to evaluate the relationship between user’s needs and product features as well as their alternatives. FAHP (fuzzy analytic hierarchy process) is utilized to judge the user’s satisfactory forms. This method can also be applied to other product form designs.

Method of Consistency Judgment for App Software’s User Comments

With the popularity of mobile intelligent terminal, user comments of App software is viewed as one of the research interests of social computing. Faced with the massive App software, most users usually view the other users’ comments and marks to selecting the desired App software. Due to the freedom and randomness of the network comments, the inconsistence between the user’s comment and mark makes it difficult to choose App software. This paper presents a method by analyzing the relationships among user’s comment information, the user’s mark and App software information. Firstly, the consistency between user’s comment information and App software information is judged. Then, through analyzing the grammar relationships among the feature-words, adverbs and the feature-sentiment-words in App software’s feature-sentimentword- pairs, the user’s emotional tendency about App software is quantified quantified combining with the dictionary and the network sentiment words. After calculating the user’s comprehensive score of App software, the consistency of App software’s user comment is judged by comparing this score and the user’s mark. Finally, the experimental results show that the method is effective.

Calibration of a Confidence Interval for a Classification Accuracy

Coverage of nominal 95% confidence intervals of a proportion estimated from a sample obtained under a complex survey design, or a proportion estimated from a ratio of two random variables, can depart significantly from its target. Effective calibration methods exist for intervals for a proportion derived from a single binary study variable, but not for estimates of thematic classification accuracy. To promote a calibration of confidence intervals within the context of land-cover mapping, this study first illustrates a common problem of under and over-coverage with standard confidence intervals, and then proposes a simple and fast calibration that more often than not will improve coverage. The demonstration is with simulated sampling from a classified map with four classes, and a reference class known for every unit in a population of 160,000 units arranged in a square array. The simulations include four common probability sampling designs for accuracy assessment, and three sample sizes. Statistically significant over- and under-coverage was present in estimates of user’s (UA) and producer’s accuracy (PA) as well as in estimates of class area proportion. A calibration with Bayes intervals for UA and PA was most efficient with smaller sample sizes and two cluster sampling designs.

A Novel Popularity Extraction Method Applied in Session-Based Recommendation

Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.74%, 1.23%, 2.72%, and 3.48%, respectively. On Movieslen-1m, the relative improvements of P@20 are 7.41%, 15.52%, 8.20%, and 20.12%, respectively, and that of MRR@20 are 2.34%, 12.41%, 20.34%, and 19.21%, respectively.

信息型常见毒物质谱用户库的建立及其在毒物快速筛选上的应用研究

The spectrum user’s librarys containing 1533 familiar toxicants have been built; A method with high separability and sensitivity has been found,The RT and the detection limit of 210 familiar abuse drugs has been confirmed; the processing methods to analyse spectrum data automatically have been found; It is simple,quick,sensitive and reliable,and can take place of manual operation,It is very useful to screen the familiar toxicants in unkown sample.