Augmented reality in science laboratories: The effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories

This study investigated the effects of the use of augmented reality (AR) technologies in science laboratories on university students' laboratory skills and attitudes towards laboratories. A quasi-experimental pre-test/post-test control group design was employed. The participants were 76 first-year university students, aged 18–20 years old. They were assigned to either an experimental or a control group. Qualitative and quantitative data collection tools were used. The experimental results obtained following the 5-week application revealed that the AR technology significantly enhanced the development of the university students' laboratory skills. AR technology both improved the students’ laboratory skills and helped them to build positive attitudes towards physics laboratories. The statements of the students and the instructor regarding other effects of AR technology on science laboratories, both negative and positive, are also discussed.     

Impression management concerns and support-seeking behavior on social network sites

This study investigates whether and how impression management concerns shape ways people seek social support on social network sites (SNS). The data from a laboratory experiment (N = 176) show that the importance of impression management goals change depending on the publicity of the message, and that the amount of effort in message composition increases as the severity of the problem increases. This relationship becomes greater when people use a public as opposed to a private SNS channel to communicate a stressful situation. The more effort people put into message composition, the more likely they are to produce a sophisticated support-seeking message that serves an additional goal of impression management. This study contributes to understanding supportive communication occurring on social network sites.     

The effects of social media on students’ behaviors; Facebook as a case study

Social networks are one of the most used communication methods of today's world. Their use in different fields has been examined in several research studies. This study aims to examine the effects of social media on student's behaviors which will mainly focus on Facebook. Whether there is a positive relationship between confidence, social media participation and social media related behaviors will also be assed with regard to using Facebook. In order to collect the primary data, a general scanning model was used to observe attitudes of high school students. The participants chosen were 362 high school students from level 9 to 12. The findings highlight that Facebook is used for communication entertainment and sharing news, pictures and songs. In addition, their Facebook profile picture is alone and students were aware that swearing is considered a form of misconduct, which is a good sign. The study also indicates that students were aware of protecting their social identity as their Facebook shares are not public. Furthermore, they respect privacy as they do not use their friend's Facebook account.     

Linear combination of densities and its direct estimation framework with applications

In this paper, typical learning task including data condensation, binary classification, identification of the independence between random variables and conditional density estimation is described from a unified perspective of a linear combination of densities, and accordingly a direct estimation framework based on a linear combination of Gaussian components (i.e., Gaussian basis functions) under integrated square error criterion is proposed to solve these learning tasks. The proposed direct estimation framework has three advantages. Firstly, different from most of the existing state-of-the-art methods in which estimating each component’s density in this linear combination of densities and then combining them linearly are required, it can directly estimate the linear combination of densities as a whole, and it has at least comparable to or even better approximation accuracy than the existing density estimation methods. Secondly, the time complexity of the proposed direct estimation framework is O(l ³) in which l is the number of Gaussian components in this framework which are generally viewed as the Gaussian distributions of the clusters in a dataset, and hence l is generally much less than the size of the dataset, so it is very suitable for large datasets. Thirdly, this proposed framework can be typically used to develop alternative approaches to classification, data condensation, identification of the independence between random variables, conditional density estimation and the similarity identification between multiple source domains and a target domain. Our preliminary results about experiments on several typical applications indicate the power of the proposed direct estimation framework.     

谈话节目语料库的构建与会话结构分析

口语语料库的建设是口语研究的基础工作,该文选择具有代表性的交谈式谈话节目《锵锵三人行》和对谈式谈话节目《鲁豫有约》作为语料,建立了一个小型的谈话节目语料库,并构建了包含五大类16小类的会话结构标注体系,对语料进行了会话结构的标注。统计得到打断结构309例,插入结构141例,重复结构111例,问答结构653/589例,阻碍—修正结构51/21例,反映了会话结构在数量上的不均衡分布,节目的形式、性质以及交际任务是会话结构分布的主要影响因素。会话结构组合具有模式性,该文使用Trigram方法对其组合情况进行了分析,发现语料中的高频组合是问答毗邻对,此外有大量的非毗邻性组合。会话结构组合模式不但反映出谈话节目的风格特点,还有助于分析会话中的功能性模块、会话策略的形成,进而更加深入地了解会话的运作机制。     

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

In this paper a new feature extraction method called Multi-scale Sobel Angles Local Binary Pattern (MSALBP) is proposed for application in personal verification using biometric Finger Texture (FT) patterns. This method combines Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). The resulting characteristics are formed into non-overlapping blocks and statistical calculations are implemented to form a texture vector as an input to an Artificial Neural Network (ANN). A Probabilistic Neural Network (PNN) is applied as a multi-classifier to perform the verification. In addition, an innovative method for FT fusion based on individual finger contributions is suggested. This method is considered as a multi-object verification, where a finger fusion method named the Finger Contribution Fusion Neural Network (FCFNN) is employed for the five fingers. Two databases have been employed in this paper: PolyU3D2D and Spectral 460 nm (S460) from CASIA Multi-Spectral (CASIA-MS) images. The MSALBP feature extraction method has been examined and compared with different Local Binary Pattern (LBP) types; in classification it yields the lowest Equal Error Rate (EER) of 0.68% and 2% for PolyU3D2D and CASIA-MS (S460) databases, respectively. Moreover, the experimental results revealed that our proposed finger fusion method achieved superior performance for the PolyU3D2D database with an EER of 0.23% and consistent performance for the CASIA-MS (S460) database with an EER of 2%.     

A robust projector–camera interactive display system based on finger touch control by fusing finger and its shadow

A robust projector–camera interactive display system consisting of one camera and one projector is proposed, which enables users to interact with computer by touching on arbitrary surfaces with bare hand. The system utilizes the camera to recover 3D information of fingertip for touch detection. Firstly, predicted image generated from geometric and photometric calibration matrix is used to segment hand and its shadow. Secondly, fusion degree of the hand and its shadow is detected using linear‐scanning method. Finally, a square tracking mask is used to determine the touch action on the projection surface. Experimental results show that the proposed system is robust to different lighting conditions. The touch detection accuracy is 93.0% and the average processing time of each frame is 36 ms.     

Understanding buyers' loyalty to a C2C platform: the roles of social capital,satisfaction and perceived effectiveness of e‐commerce institutional mechanisms

Drawing upon social capital theory, this study aims to investigate how different dimensions of social capital affect online buyers' satisfaction and ultimately boost their loyalty to a Consumer‐to‐Consumer (C2C) platform. Specifically, we propose that three dimensions of social capital (i.e., cognitive, structural and relational capital) contribute positively to the two types of online buyers' satisfaction (i.e., economic and social satisfaction). In addition, we posit that perceived effectiveness of e‐commerce institutional mechanisms (PEEIM) moderates the relationships between economic and social satisfaction and buyers' loyalty to the platform. Three hundred buyers on the Consumer‐to‐Consumer platform, TaoBao, were surveyed to test the proposed model. The results suggest that buyers' evaluation of social capital with the community of sellers can enhance their satisfaction with the sellers, which subsequently affect their loyalty to the platform. Furthermore, perceived effectiveness of e‐commerce institutional mechanisms negatively moderates the effect of economic satisfaction and positively moderates the effect of social satisfaction on buyers' loyalty to the platform. The theoretical contributions and practical implications are discussed.