Information visualization analysis on Advances in Psychological Science:1983-2014

Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with the information visualization method using Citespace software from the aspects of pub- lications, cited frequency and downloads, funding, organizations, authors and keywords. Results： The results showed that the amount of literature published annually had an upward tendency, and 49.4% of the papers were supported by national or provincial projects. Institutions such as the Chinese Academy of Sciences （CAS） and the normal universities were rated in the forefront of the sci- entific research output. Xiting Huang, Hong Li and Yuejia Luo were at the top of the list of prolific authors. Conclusions： A new pattern of cooperative development of the theory and application in the field of psychological research is forming.

Science Mapping:A Systematic Review of the Literature

Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.

Bibliometrics study on the Journal of American College Health： 1994-2014

Objective： To help readers around the world comprehensively understand the development of the journal and evolution of cooperation study, we employed a bibliometrics analysis for the Journal of American College Health. Methods： One-thousand-one-hundred-forty-three articles published in this journal from 1994 were analyzed using the bibliometrics and visualization software CiteSpace. Results： The annual number of published articles and cited studies increased. The published studies by RP Keeling and H Wechsler were at the forefront. ＂College student＂ and ＂alcohol＂ were prevalent key- words. University of Wisconsin and Harvard University were the institutional leaders of contributions. Conclusions： This journal provides an important platform for sharing research achievements and promoting cooperation in this field. The level of articles published is continually improving. A research cooperative network promoted by famous scholars and institutions is developing. However, crossregional and international cooperation is relatively limited.

A bibliometric study of the Journal of School Health:1965-2014

Background：As an important international journal in the field of school health,the Journal of School Health has drawn wide attention from researchers and readers around the world.Therefore,it is important to conduct a systematic retrospective study of the journal.With the aim of understanding the development of the journal and the evolutionary process of cooperative study of this field comprehensively,we employed bibliometric analysis using the articles published in the Journal of School Health from 1965.Methods：Using bibliometrics,5242 articles published in the journal were extracted and then analyzed using the visualization software CiteSpace Ⅲ.Results：The annual published amount of literature showed a declining tendency;however,the frequency of citation displayed an increase year by year.Among prolific authors,the number of reports published by JH Price,L Kann and RJ McDermott are at the top.Among the high frequency keywords used in the research journal, ＂adolescents＂, ＂children＂ and ＂programs＂ have become popular in the journal＇s vocabulary.CDCP,Univ Texas and Univ Calif are positioned in the forefront of the involved institutions when ranked by degree of contribution.Conclusions：The Journal of School Health provides an important platform for sharing research achievements and promoting cooperation in this field.The amount of articles published in the journal is continually improving;its cooperative research network promoted by famous scholars and institutions is forming.As more researchers and institutions join,the network will grow and relationships will become increasingly close.However,limitations to cooperation at the regional or interagency scope remain.

Feature selection on probabilistic symbolic objects

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.

Unsupervised object detection with scene-adaptive concept learning

Object detection is one of the hottest research directions in computer vision,has already made impressive progress in academia,and has many valuable applications in the industry.However,the mainstream detection methods still have two shortcomings:(1)even a model that is well trained using large amounts of data still cannot generally be used across different kinds of scenes;(2)once a model is deployed,it cannot autonomously evolve along with the accumulated unlabeled scene data.To address these problems,and inspired by visual knowledge theory,we propose a novel scene-adaptive evolution unsupervised video object detection algorithm that can decrease the impact of scene changes through the concept of object groups.We first extract a large number of object proposals from unlabeled data through a pre-trained detection model.Second,we build the visual knowledge dictionary of object concepts by clustering the proposals,in which each cluster center represents an object prototype.Third,we look into the relations between different clusters and the object information of different groups,and propose a graph-based group information propagation strategy to determine the category of an object concept,which can effectively distinguish positive and negative proposals.With these pseudo labels,we can easily fine-tune the pretrained model.The effectiveness of the proposed method is verified by performing different experiments,and the significant improvements are achieved.

Visual analytics for event detection: Focusing on fraud

The detection of anomalous events in huge amounts of data is sought in many domains.For instance,in the context of financial data,the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud.Hence,various financial fraud detection approaches have started to exploit Visual Analytics techniques.However,there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences.Thus,we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions,to identify and to propose further research opportunities.In this work,fraud detection solutions are explored through five main domains:banks,the stock market,telecommunication companies,insurance companies,and internal frauds.The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics.In this survey,we(1)analyze the current state of the art in this field;(2)define a categorization scheme covering different application domains,visualization methods,interaction techniques,and analytical methods which are used in the context of fraud detection;(3)describe and discuss each approach according to the proposed scheme;and(4)identify challenges and future research topics.

Visual recognition of cardiac pathology based on 3D parametric model reconstruction

Visual recognition of cardiac images is important for cardiac pathology diagnosis and treatment.Due to the limited availability of annotated datasets,traditional methods usually extract features directly from twodimensional slices of three-dimensional(3D)heart images,followed by pathological classification.This process may not ensure the overall anatomical consistency in 3D heart.A new method for classification of cardiac pathology is therefore proposed based on 3D parametric model reconstruction.First,3D heart models are reconstructed based on multiple 3D volumes of cardiac imaging data at the end-systole(ES)and end-diastole(ED)phases.Next,based on these reconstructed 3D hearts,3D parametric models are constructed through the statistical shape model(SSM),and then the heart data are augmented via the variation in shape parameters of one 3D parametric model with visual knowledge constraints.Finally,shape and motion features of 3D heart models across two phases are extracted to classify cardiac pathology.Comprehensive experiments on the automated cardiac diagnosis challenge(ACDC)dataset of the Statistical Atlases and Computational Modelling of the Heart(STACOM)workshop confirm the superior performance and efficiency of this proposed approach.

A visual analytics system to support the formation of a hypothesis from calcium wave data

In most species,calcium waves in the oocyte are considered common phenomena in the activation of eggs.However,the mechanism of calcium waves has not yet been clarified.By collaborating with biologists studying Caenorhabditis elegans(C.elegans),which is widely used as a model organism,we observed that the following requirements must be satisfied to form a useful hypothesis based on calcium waves captured using high-speed in vivo imaging:(1)the ability to obtain an overview of how the calcium waves are propagated and(2)the ability to understand the propagation of waves in a narrow region.However,conventional visualization methods cannot satisfy these requirements simultaneously.Therefore,we propose a visual analytics system that allows users to understand and explore calcium wave images using cross-correlation analysis of the time-series data of the Ca^(2+)fluorescence intensity at each point.The interface of this system comprises an overview visualization,a detail visualization,and user interactions to satisfy these requirements and realize exploratory visualization.Some views present an overview visualization that displays the clustering results of a directed graph calculated using cross-correlation analysis.These views enable the users to understand the overview of wave propagation,thereby helping users find a region of interest.The detail visualization shows the relationship between the region of interest and other areas.Furthermore,users can use the proposed system with overview-detail and brush-link exploration to assign meaning to the region of interest and construct a hypothesis for its role.In this paper,we demonstrate how the proposed visual analytics approach works and how new hypotheses can be formed using the analysis of C.elegans calcium waves.