Evaluation of Urban Park Landscape in Ma’anshan Based on Analytic Hierarchy Process(AHP)

In order to further develop the urban park landscape,the representative urban park landscape in Ma’anshan City is first evaluated and analyzed from the aspects of humanistic artistic value,social historical value and user experience value.Afterwards,the weight of each park landscape is judged by analytic hierarchy process(AHP),and it is found that the indexes in the criterion layer successively are user experience value>humanistic artistic value>social historical value;in the factor layer,excellent user experience,universal design,reasonable and perfect public services and layout suitable for urban planning are more important factors,while artistic conception creation has the lowest weight.Finally,based on the results obtained by AHP,the improvement and optimization strategy for urban park landscape in Ma’anshan City is proposed to further strengthen the development of urban parks in Ma’anshan City.

Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

Designing generation Y interactions:The case of YPhone

Background With an increasing number of products becoming digital,mobile,and networked,paying attention to the quality of interactions with such products is also becoming more relevant.Although the quality of such interactions has been addressed in several scientific studies,little attention has been paid to their implementation in real life and everyday contexts.Methods This paper describes the development of a novel office phone prototype,called YPhone,which demonstrates the application of a specific set of Generation Y interaction qualities(instantaneous,playful,collaborative,expressive,responsive,and flexible)in the context of office work.The working prototype supports office workers in experiencing new types of interactions.It was set out in practice through a series of evaluations.Results We found that the playful,expressive,responsive,and flexible qualities incur greater trust than the instantaneous and collaborative qualities.Such qualities can be grouped,although this may differ for different evaluated products,and researchers must be cautious about generalizations.Conclusions The overall evaluation was deemed positive,with some valuable suggestions provided regarding its user interactions and features.

Color and Shape efficiency for outlier detection from automated to user evaluation

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data.In these representations,data are encoded with various visual attributes depending on the needs of the representation itself.To make coherent design choices about visual attributes,the visual search field proposes guidelines based on the human brain’s perception of features.However,information visualization representations frequently need to depict more data than the amount these guidelines have been validated on.Since,the information visualization community has extended these guidelines to a wider parameter space.This paper contributes to this theme by extending visual search theories to an information visualization context.We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors.Stimuli are defined by color and shape features for the purpose of visually encoding categorical data.The experimental protocol is made of a parameters space reduction step(i.e.,sub-sampling)based on a machine learning model,and a user evaluation to validate hypotheses and measure capacity limits.The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier.When redundantly encoded,the display heterogeneity has no effect on the task.When encoded with one attribute,the difficulty depends on that attribute heterogeneity until its capacity limit(7 for color,5 for shape)is reached.Finally,when encoded with two attributes simultaneously,performances drop drastically even with minor heterogeneity.

On visualization techniques comparison for large social networks overview:A user experiment

Visualizing social networks,especially an overview emphasizing their structure,i.e.,communities and their interconnections,is known to be a challenging problem.In this paper,we present a set of design rationales to build such overview visualizations of social networks and our solution called Jasper.We evaluate its performances against two of the most wide-spread visualization techniques(matrix and node-link diagram)in a human–computer controlled experiment based on community-related tasks.While none of the techniques emerge as the overwhelming winner,Jasper appears to be one of the best methods for each task;a fact sustained by the marks given by the users.Overall,Jasper can be seen as an all-encompassing solution for quickly producing legible and compact overviews of large social networks on a single modern computer.