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Digital libraries and academic search engines have developed as two important online scholarly information sources with different features. The purpose of this study is to compare digital libraries and academic search engines from the perspective of the dual-route model.

Research hypotheses were developed. Potential participants were recruited to answer an online survey distributing at Chinese social media out of which 251 responses were deemed to be valid and used for data analysis. The paired samples t-test was used to compare the means.

Both information quality (central route) and source credibility (peripheral route) of digital libraries are significantly higher than those of academic search engines, while there is no significant difference between digital libraries and academic search engines in terms of affinity (peripheral route).

In the digital information society, the important status of digital libraries as conventional information sources should be spread by necessary measures. Academic search engines can act as complementary online information sources for seeking academic information rather than the substitute for digital libraries. Practitioners of digital libraries should value the complementary role of academic search engines and encourage users to use academic search engines while emphasizing the importance of digital libraries as conventional information sources.

According to the dual-route model, this study compares digital libraries and academic search engines in terms of information quality, source credibility and affinity, which the authors believe presents a new lens for digital libraries research and practice alike.

In this study, the authors use the term “e-quality” to refer to information quality, system quality and service quality. This study aims to focus on e-quality, exploring and comparing users' perceptions of digital libraries and virtual communities in the hope that the results of this study can help lead to better understanding of the exact nature of e-quality as perceived by users.

A large-scale survey was conducted for data collection. Data collected from 334 users of digital libraries and virtual communities were used for data analysis.

The study finds that users are likely to perceive a higher level of information quality, system quality and service quality of digital libraries than of virtual communities.

The authors suggest that librarians do not need to have concerns over the challenge brought by virtual communities, which indeed have an increasing impact on the way a lot of people seek and gather information. Instead, they should encourage their users to use both digital libraries and virtual communities. The authors believe that the usage of these two types of information sources by users can efficiently inform each other, thus facilitating the e-quality of both digital libraries and virtual communities to reach excellence.

Building on the information systems (IS) success model, this study explores and compares users' perceptions of digital libraries and virtual communities in terms of e-quality, which the authors think presents a new view for digital library research and practice alike.

Flow experience is conceptualized as an optimal experience about an activity, characterized by a match between perceived challenges and perceived skills. The purpose of this paper is to explore mobile libraries by comparing users’ perceptions of web digital libraries and mobile libraries in terms of flow experience so as to obtain insights regarding the healthy development of mobile libraries.

Data collected from university digital library users were used for analysis. One figure was used to present the exact nature of users’ perceptions of flow experience in terms of data distribution. The paired samples t-test was used to present the exact mean difference between flow experience in using web digital libraries and mobile libraries.

Fewer users can experience flow and more users cannot experience flow in using mobile libraries than in using web digital libraries. The mean of flow experience in using mobile libraries is significantly smaller than that in using web digital libraries.

Digital libraries have faced severe competition in the modern information society. In China university libraries as a whole are undergoing the transition from web digital libraries to mobile libraries. It is critical to examine user experience in the initial or early stage of mobile library development. The authors believe the findings of this study regarding flow experience provide useful insights for facilitating the healthy development of mobile libraries.

This study explores and compares users’ perceptions of web digital libraries and mobile libraries in terms of flow experience, which the authors think provides a new view for university digital library research and practice alike.

The purpose of this paper is to explore the antecedents of information seeking in digital libraries from the perspectives of the Technology Acceptance Model and flow experience, as well as the consequences from the perspectives of self-efficacy in getting information and individual performance.

A research model is developed and tested using questionnaires and, partial least squares structural equation modeling.

The effect of flow experience on information seeking in digital libraries is the largest one. Meanwhile, flow experience fully mediates the effects of ease of use and usefulness on information seeking in digital libraries which further leads to self-efficacy in getting information and individual performance.

Librarians should help users to experience more stable and sustainable flow by providing dependable, prompt, personalized and professional service to them. Librarians should try their best to provide diversified user training so as to guide potential users to seek information in digital libraries.

This study contributes to the theoretical development of the structural model exploring information seeking in digital libraries, presenting a new view for digital library research and practice alike.

Individual differences are critical in determining how individuals think and behave in different ways. The purpose of this paper is to explore the effect of individual differences on users’ perceptions of print and electronic resources in terms of ease of use, usefulness and usage in the hopes that a better understanding of these effects can help Chinese university libraries to meet the diversified information needs of their users more specifically and appropriately so that the second-level capability divide and third-level outcome divide of library information resources can be much reduced.

Data collected from 273 library users were used for data analysis. The independent samples t-test, one-way analysis of variance (ANOVA) and two-way ANOVA were employed. Meanwhile, the quantitative analysis is supplemented by the qualitative interviews which present richer data about the use of specific types of print and electronic resources.

The effect of basic characteristics (gender, age, field) and experience (experience with library print resources, experience with library electronic resources, which library resources were used first) on users’ perceptions of print and electronic resources in terms of ease of use, usefulness and usage was explored and discussed. Meanwhile, the two-way interaction effect was examined and 13 significant interaction effects were presented.

Building on the digital divide, this study examines ease of use, usefulness and usage in terms of individual differences which cover not only basic characteristics but also experience and two-way interaction, which the authors think provides a new view for library information resources research and practice alike in China.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This study aims to explore whether the usability of user experience for mobile library application plays a mediating role in the relation between the success factors of information systems and net benefit.

The theoretical model of “information system success factor–user experience usability–net benefit” was constructed. A questionnaire was conducted at the Nankai University. The mediating effects of the usability of the mobile library application on information system success factors and net benefit were examined using hierarchical linear regression and structural equations.

First, the usability of user experiences is positively correlated to information quality, system quality and service quality. Second, user experience usability has a significant mediating effect on the relation between information system success factors and net benefit.

This study improves upon the DeLone and McLean model, connects the usability of user experience with the model and constructs the success factor mechanism of the mobile library application. It provides a theoretical basis for interpreting the relation between the mobile library application and users.

Multi-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment.

First, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.

In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.

This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.