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.

Personalized web pages ranking algorithm based on user preferences

In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.

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.

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.

User Preference Survey on Staircase Rotation

Staircase is an important means of vertical transportation. Staircase design exerts a great influence on the aesthetics, transportation efficiency, user comfort and experience level. In this paper, a survey on the staircase rotation preference was conducted, based on the environment behavior studies. Different user frequencies of a pair of scissors stairs in the 2nd teaching building of North China University of Technology were analyzed. The psychological effect was evaluated and quantified, and the user preference on the two staircase rotations was then withdrawn. The survey found that the type of staircase with clockwise upstairs was much more preferred （78%） than the other staircase rotation anti-clock upstairs. Considering different genders, the female shows a 66% higher preference inclination of this type of staircase rotation than the male. To improve the transportation efficiency of the staircase in case of fire, the result of this paper can be very constructive for the evacuation staircase rotation choice for the high-rise buildings.

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.

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.

Research on Influencing Factors of Information Dissemination Based on User Preference

The rapid development of the Intemet makes the social network of information dissemination has undergone tremendous changes. Based on the introduction of social network information dissemination mode, this paper analyzes the influencing factors of information dissemination, establishes the user preference model through CP-nets tool, and combines the AHP principle to mine the user＇s preference order, and obtain the user＇s optimal preference feature Portfolio, and finally collect the user in the microblogging platform in the historical behavior data. the use of NetLogo different users of information dissemination decision to predict.

融合巴氏系数与综合相似度的改进加权Slope One算法

针对传统加权Slope One算法因过度依赖用户共同评分项导致在过稀疏数据集中预测准确度低的问题,提出一种融合巴氏系数与综合相似度的改进加权Slope One算法(BS-WSO)。首先,引入巴氏系数和用户行为偏好对用户相似度计算方法进行改进,依此筛选出待预测的近邻集合;其次,为了优化预测评分,利用巴氏系数和项目流行度计算项目相似度,并将其作为权重因子融入评分计算;最后,将BS-WSO与几种代表性算法进行比较,仿真实验结果表明,BS-WSO算法能有效克服数据过稀疏情况下预测准确度低的缺陷,提高推荐精确度。

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.

User Behaviors Analysis in Website Identification Registration

Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese Software Develop Net (CSDN) to provide a complete perspective on this research point. We concentrate on the following three aspects: complexity, correlation, and preference. From these analyses, we draw the following conclusions: firstly, a considerable number of users have not realized the importance of identification and are using very simple identifications that can be attacked very easily. Secondly, there is a strong complexity correlation among the three parts of identification. Thirdly, the top three passwords that users like are 123456789, 12345678 and 11111111, and the top three email providers that they prefer are NETEASE, qq and sina. Further, we provide some suggestions to improve the quality of user passwords.

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.

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.

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.

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.

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.