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.

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.

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.

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user＇s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval （PIR） system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

A Dynamic Bandwidth Allocation Solution Based on User Behavior and Software Defined EPS

For telecommunication operators, how to improve the utilization of bandwidth resources is always a problem which worthy of study, nowadays, this issue has become more and more important, since the traffic load bursting in the mobile Internet. So the key to solving this problem is that we need to find a kind of traffic model to predict the traffic load that users need. According to the predicted traffic load to allocate bandwidth to each base station dynamically.But the traffic consumption behavior of a single base station is random, it is difficult to predict[ 1 ]. For this reason, we based on reality that, when the user moves, it may get traffic load from different base stations, therefore, there will have some kind of relationship between those base stations.We use this relationship to establish a kind of Spatial Collaborative Network.consequently, we make use of stability algorithm to divided those base station cluster into different communities, According to the traffic load usage rules which these communities indicated to us, we get a traffic model.At the same time, we studied bow to use this traffic model in the future networks to dynamically allocate bandwidth resources, then we propose a new kind of EPS architecture based on SDN, on this platform, we can deploy our strategy through it＇s programmable interface.Finally, we designed an experiment to test the performance of our dynamic strategy, and the result shows that our method enables bandwidth utilization has been greatly improved.

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.

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.

基于MAS的自适应智能搜索引擎的研究

随着网络信息资源的迅速增加和实时更新,准确、快捷地获取自己所需要的有用信息是现代网络信息检索技术需要解决的主要问题。本文介绍了一种基于MAS(Multi-Agent System)的自适应智能搜索引擎系统,MAS为每个Agent指派了不同的功能,其中学习Agent能够很好地对用户的兴趣进行自适应学习,过滤Agent对反馈给用户的信息进行了过滤,既保证了查全率,又提高了检索结果的查准率。

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.

用户个性化推荐系统的设计与实现

为实现个性化服务,理解用户兴趣就成了提供服务的关键任务,因此,提出了隐性采集用户浏览内容、用户浏览时间和用户操作时间的信息方法,通过对网络爬虫程序抓取的网页进行内容清洗提取出主要内容之后,利用VSM建立文档模型,并采用SVM分类方法建立推荐库。基于从客户端采集的用户兴趣信息建模,以及根据该模型和推荐库的相似度,给用户推荐信息。此外,给出了基于该模型的推荐原型系统的实现,使用查准率来评价该系统。试验结果表明,系统较好地实现了基于用户兴趣来推荐阅读的信息。

改进的访问统计方法及对用户兴趣度的计算

为解决传统的以点击量为标准的网络访问统计方法对网络访问量表示不准确的问题,分析了现有的两种统计方法,提出了一种改进的网络访问统计方法。在点击量的基础上,引入用户停留时间,并把两种统计量进行合并,设计了一个兼有点击量和停留时间两种衡量尺度的表示网络访问量的统计量。把改进的网络访问统计方法引入到对用户兴趣度的计算,并提出了改进的用户兴趣度量化定义,实验结果表明,改进的网络访问统计方法对用户兴趣度的表示更加合理有效。

基于隐式反馈的用户兴趣漂移方法

针对现有用户兴趣模型在模型建立以及更新阶段漂移策略的缺陷,设计了一种改进的基于隐式反馈的用户兴趣漂移方法。将用户兴趣分为长期兴趣和短期兴趣,对短期兴趣采用滑动窗口处理更新,对长期兴趣采用最近最少使用淘汰算法,并将该模型与遗忘策略模型、滑动时间窗口模型做了精确率的对比实验,实验结果显示该模型的性能优于其它三个模型。

应用模糊集理论的多因素兴趣度评价

互联网的信息急剧增加,用户越来越难以找到自己所需要的信息。针对目前个性化服务系统中用户兴趣难以获取的问题,提出了一种模糊综合评判评价用户兴趣的方法,实验表明具有较好的评价效果。

基于用户兴趣的数字资源的集成化研究

数字资源的集成是目前信息服务的一个研究热点。为了在集成信息和用户的兴趣之间进行关联,首先对用户的兴趣进行了分类,并基于不同的类别定义了各自所包含的兴趣值;进而结合信息集成的映射规则,针对不同的Web页面集合设计相应的页面插件,使得在动态超媒体引擎的支撑下,将用户感兴趣的数字资源和特定的兴趣值进行绑定,从而提供一种有效的基于用户兴趣的集成化服务。

分布式计算在Web信息检索中的应用

web信息检索技术是当前研究的热门课题,本文是继上一篇文章<<web信息检索系统中个性化信息模型的研究和实现>>在信息检索方面提出的分布式检索算法的研究,提出了检索的结构模型,检索算法。

基于多兴趣Agent的信息检索模型的研究

基于agent的信息检索系统在海量网络信息检索中已经得到广泛的应用。提出了一个基于多兴趣agent新的信息检索模型,该模型对查询信息进行了兴趣预测并生成了用户兴趣项权重向量。实验表明,模型在预测用户兴趣的效果和推荐搜索信息的准确率方面比传统的单兴趣Agent检索系统IRHO IA有一定的改善。

基于用户兴趣和工作背景的信息收集个性化实现方案

数字图书馆的个性化服务是基于定制动态资源中相关用户兴趣的信息检索或过滤即信息收集个性化。由于用户的兴趣是随时变化的,其工作背景也描述了用户访问数字图书馆时当前的信息需求,因此信息收集个性化的重点是要捕获基于用户兴趣和工作背景的信息需求,以提供个性化检索和过滤。

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.