User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

Deep Learning Social Network Access Control Model Based on User Preferences

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

Multi-national observational study to assess quality of life and treatment preferences in patients with Crohn’s perianal fistulas

BACKGROUND Patients with Crohn’s disease(CD)are at risk of developing complications such as perianal fistulas.Patients with Crohn’s perianal fistulas(CPF)are affected by fecal incontinence(FI),bleeding,pain,swelling,and purulent perianal discharge,and METHODS This cross-sectional observational study was conducted in patients with CD aged 21-90 years via a web-enabled questionnaire in seven countries(April-August 2021).Patients were recruited into three cohorts:Cohort 1 included patients without perianal fistulas;cohort 2 included patients with perianal fistulas without fistula-related surgery;and cohort 3 included patients with perianal fistulas and fistula-related surgery.Validated patient-reported outcome measures were used to assess quality of life.Drivers of treatment preferences were measured using a discrete choice experiment(DCE).RESULTS In total,929 patients were recruited(cohort 1,n=620;cohort 2,n=174;cohort 3,n=135).Short Inflammatory Bowel Disease Questionnaire scores were worse for patients with CPF(cohorts 2 and 3)than for those with CD without CPF(cohort 1):Mean score 3.8 and 3.7 vs 4.1,respectively,(P<0.001).Similarly,mean Revised FI and FI Quality of Life scores were worse for patients with CPF than for those with CD without CPF.Quality of Life with Anal Fistula scores were similar in patients with CPF with or without CPF-related surgery(cohorts 2 and 3):Mean score 41 and 42,respectively.In the DCE,postoperative discomfort and fistula healing rate were the most important treatment attributes influencing treatment choice:Mean relative importance 35.7 and 24.7,respectively.CONCLUSION The burden of illness in CD is significantly higher for patients with CPF and patients rate lower postoperative discomfort and higher healing rates as the most desirable treatment attributes.

User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering

The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.

Incorporating User’s Preferences into Scholarly Publications Recommendation

Over the years, there has been increasing growth in academic digital libraries. It has therefore become overwhelming for researchers to determine important research materials. In most existing research works that consider scholarly paper recommendation, the researcher’s preference is left out. In this paper, therefore, Frequent Pattern (FP) Growth Algorithm is employed on potential papers generated from the researcher’s preferences to create a list of ranked papers based on citation features. The purpose is to provide a recommender system that is user oriented. A walk through algorithm is implemented to generate all possible frequent patterns from the FP-tree after which an output of ordered recommended papers combining subjective and objective factors of the researchers is produced. Experimental results with a scholarly paper recommendation dataset show that the proposed method is very promising, as it outperforms recommendation baselines as measured with nDCG and MRR.

PREFERENCE AND EVOLUTION IN THE ITERATED PRISONER'S DILEMMA

Game theory is extensively used to study strategy-making and actions of play- ers. The authors proposed an analysis method for study the evolutionary outcome and behaviors of players with preference in iterated prisoner＇s dilemma. In this article, a preference parameter k was introduced in the payoff matrix, wherein the value of k denotes the player＇s degree of egoism and altruism （preference）. Then, a game-theoretic dynamical model was formulated using Birth-and-Death process. The authors studied how preference influences the evolutionary equilibrium and behaviors of players. The authors get the general results： egoism leads to defection, and altruism can make players build trust and maintain cooperation, and so, the hope of the Pareto optimal solution. In the end, the simulation experiments proved the efficiency of the method.

Do inflammatory bowel disease patient preferences from treatment outcomes differ by ethnicity and gender?A cross-sectional observational study

BACKGROUND Inflammatory bowel disease(IBD)patients’expectations of treatment outcomes may differ by ethnicity.AIM To investigate treatment preferences of Jewish and Arabs patients.METHODS This prospective survey ranked outcomes treatment preferences among Arab IBD patients,based on the 10 IBD-disk items compared to historical data of Jews.An anonymous questionnaire in either Arabic or Hebrew was distributed among IBD patients.Patients were required to rank 10 statements describing different aspects of IBD according to their importance to the patients as treatment goals.Answers were compared to the answers of a historical group of Jewish patients.RESULTS IBD-disk items of 121 Arabs were compared to 240 Jewish patients.The Jewish patients included more females,[151(62.9%)vs 52(43.3%);P<0.001],higher education level(P=0.02),more urban residence[188(78.3%)vs 54(45.4%);P<0.001],less unemployment[52(21.7%)vs 41(33.9%);P=0.012],higher income level(P<0.001),and more in a partnership[162(67.8%)vs 55(45.4%);P<0.001].Expectations regarding disease symptoms:abdominal pain,energy,and regular defecation ranked highest for both groups.Arabs gave significantly lower rankings(range 4.29-6.69)than Jewish patients(range 6.25-9.03)regarding all items,except for body image.Compared to Arab women,Jewish women attached higher priority to abdominal pain,energy,education/work,sleep,and joint pain.Multivariable regression analysis revealed that higher patient preferences were associated with Jewish ethnicity(OR 4.77;95%CI 2.36-9.61,P<0.001)and disease activity.The more active the disease,the greater the odds ratio for higher ranking of the questionnaire items(1-2 attacks per year:OR 2.13;95%CI 1.02-4.45,P=0.043;and primarily active disease:OR 5.29;95%CI 2.30-12.18,P<0.001).Factors inversely associated with higher patient preference were male gender(OR 0.5;95%CI 0.271-0.935,P=0.030),UC(OR 0.444;95%CI 0.241-0.819,P=0.009),and above average income level(OR 0.267;95%CI:0.124-0.577,P=0.001).CONCLUSION The highest priority for treatment outcomes was symptom relief.,Patients preferences were impacted by ethnicity,gender,and socio-economic disparity.Understanding patients'priorities may improve communication and enable a personalized approach.

Application Research of Ingroup Preference in Employee Relationship Management

With stepwise development of Chinese enterprise,management problem is increasingly prominent,especially human resource management issues. Facing international environment of entering into World Trade Organization,the country accelerates construction pace of human resource management subject in colleges and universities,which provides professional channel for management efficiency and market competition ability of Chinese enterprise,but it is still far from practice. In this paper,the concept,origin and inhibition factors of ingroup preference are elaborated. Based on the visual angle of ingroup preference,started from four dimensions( enterprise incentive system,talent management idea,psychological contract and employee communication consciousness),main problems existing in employee relationship management of modern enterprise are explored. It is specially emphasized that manager often holds the blame in front of dealing with the crisis after contradiction,which is " fatal point" neglected in employee relationship management of modern enterprise. To improve its core competitive power,enterprise must value harmonious relationship with employees.

基于S-O-R理论的新型公共阅读空间用户流失行为分析——来自大众点评网用户评论

文章以大众点评网站中新型公共阅读空间的评论数据为对象,以S-O-R模型为理论基础,利用Python爬取其“中评”与“差评”的评论数据,运用内容分析法归纳得出新型公共阅读空间用户流失行为的主要因素,并构建新型公共阅读空间用户流失行为模型。在此基础上,提出提高新型公共阅读空间服务效能的相关建议,为提高新型阅读空间的服务质量,降低用户流失率提供参考。

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.

Embedding Implicit User Importance for Group Recommendation

Group recommendations derive from a phenomenon in which people tend to participate in activities together regardless of whether they are online or in reality,which creates real scenarios and promotes the development of group recommendation systems.Different from traditional personalized recommendation methods,which are concerned only with the accuracy of recommendations for individuals,group recommendation is expected to balance the needs of multiple users.Building a proper model for a group of users to improve the quality of a recommended list and to achieve a better recommendation has become a large challenge for group recommendation applications.Existing studies often focus on explicit user characteristics,such as gender,occupation,and social status,to analyze the importance of users for modeling group preferences.However,it is usually difficult to obtain extra user information,especially for ad hoc groups.To this end,we design a novel entropy-based method that extracts users’implicit characteristics from users’historical ratings to obtain the weights of group members.These weights represent user importance so that we can obtain group preferences according to user weights and then model the group decision process to make a recommendation.We evaluate our method for the two metrics of recommendation relevance and overall ratings of recommended items.We compare our method to baselines,and experimental results show that our method achieves a significant improvement in group recommendation performance.

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.

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.

基于改进D-S证据和融合权集的安全风险评估

针对信息化系统安全风险评估过程中安全风险因素指标的重要性难以赋权的问题,本文以建筑工地施工现场为应用场景,提出一种基于改进的D-S证据理论与融合权集结合的安全风险评估模型.首先,在充分研究建筑工地安全风险评估流程和要素的基础上,建立建筑工地安全评价体系;其次,采用基于权值分配和矩阵分析的D-S合成算法改进AHP法和基于数据的熵权法计算评价体系中指标层中各指标的主、客观权重;然后,运用改进的D-S证据融合算法进行多源证据的合成,获取指标权重,避免单一赋权的片面性,得到最优综合权重;最后,根据TOPSIS评价算法计算建筑工地综合评价指数.分析表明,基于改进D-S证据理论-融合权集的安全风险评估模型,能有效评估建筑工地施工现场的安全性,降低评估结果的不确定性,提高风险评估结果可信度.

基于Spark平台的电子商务个性化信息推荐方法

互联网上数据传播量日益增加,但信息使用率却很低,消耗用户大量精力,针对这个问题,提出一种基于Spark平台的电子商务个性化信息推荐方法。Spark平台通过弹性分布式内存数据集,可将中间计算结果直接保存至内存中,建立用户喜好模型;使用评分机制计算不同个体偏好商品,形成推荐列表;引入挖掘隐含信息的矩阵分解算法,将未知参数转化为已知量,提高个性化信息推荐精准度。仿真对比实验,从用户满意度、信息熵值和运行速度三个角度,验证了所提方法可以实现优质且高效的电子商务个性化信息推荐工作。