Towards an Integrated Clickstream Data Analysis Framework for Understanding Web Users’ Information Behavior

Clickstream data offers an unobtrusive data source for understanding web users’ information behavior beyond searching. However, it remains underutilized due to the lack of structured analysis procedures. This paper provides an integrated framework for information scientists to employ in their exploitation of clickstream data, which could contribute to more comprehensive research on users’information behavior. Our proposed framework consists of two major components, i.e., data preparation and data investigation. Data preparation is the process of collecting, cleaning, parsing, and coding data, whereas data investigation includes examining data at three different granularity levels, namely, footprint, movement, and pathway. To clearly present our data analysis process with the analysis framework, we draw examples from an empirical analysis of clickstream data of OPAC users’ behavior. Overall, this integrated analysis framework is designed to be independent of any specific research settings so that it can be easily adopted by future researchers for their own clickstream datasets and research questions

An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons

The Study of Rock Body Damage Constitutive Model on Refracturing

In order to characterize the mechanical behavior of rock body damage evaluation and forming multiple fractures, in this paper in multiple fracturing , we have established rock body damage evaluation constitutive model, and given the point that the rock can bear secondary damage in multiple fracturing. Established the secondary damage evaluation model, and obtained the method for calculating the parameter of the crack in multiple fracturing. We have verified the model by a oil well in Jilin oilfield, the result has well anastomosis with the actual engineering.Key words: Multiple fracturing; Damage evaluation; Secondary damag

Performance of a new Candida anti-mannan IgM and IgG assays in the diagnosis of candidemia

Candida is one of the most frequent pathogens of bloodstream infections, which is associated with high morbidity and mortality rates. Rapid immunological detection methods are essential in the early diagnosis of candidemia. Anti-mannan is one of host-derived biomarkers against cell wall components of Candida. We conducted this study to evaluate the diagnostic performance of two anti-mannan assays (IgM, IgG) for candidemia through the analysis of 40 candidemia patients, 48 participants with Candida colonization and 213 participants with neither Candida colonization nor Candida infections (13 patients with other bloodstream infections, 145 hospitalized patients and 55 healthy controls). The performance of the two assays were evaluated by calculating their sensitivity and specificity. The sensitivity ranged from 0.78 to 0.80 for the IgM assay and 0.68 to 0.75 for the IgG assay. The specificity ranged from 0.97 to 0.98 for the IgM assay and 0.91 to 0.94 for the IgG assay. The diagnostic performance of the anti-mannan IgM assay was better than that of IgG, with higher sensitivity and specificity. Combining the two assays (positive results of single or both assays are both considered as positive) could improve the sensitivity up to 0.93 (0.79-0.98) and only slightly reduce the specificity (0.93(0.89-0.95)). The anti-mannan IgM, IgG assays are rapid and cost-effective assays that may be probably useful in the diagnosis of candidemia

Investigation of Tribological Performances for Porous Structure of Diatom Frustule with FSI Method

Tribological performances of the diatom frustule are investigated with the liquid-solid interaction (FSI) method. Take, for example, the representative Coscinodiscus sp. shell; the diatom frustule with the porous structure is achieved by the scanning electron microscope (SEM). Based on the frustule, the representative diatom frustule is modeled. Further, tribological performances of the diatom at its different geometry sizes and velocities are solved with FSI method and compared with corresponding values for the nonporous structure. The numerical result shows that the existence of the porous structure of the diatom helps to reduce friction between it and ambient water and to increase its load-carrying capacity

Face Recognition with Facial Occlusion Based on Local Cycle Graph Structure Operator

Facial occlusion is a difficulty in the field of face recognition. The lack of features caused by occlusion may reduce the face recognition rate greatly. How to extract the identified features from the occluded faces has a profound effect on face recognition. This chapter presents a Local Cycle Graph Structure (LCGS) operator, which makes full use of the information of the pixels around the target pixel with its neighborhood of 3 × 3. Thus, the recognition with the extracted features is more efficient. We apply the extreme learning machine (ELM) classifier to train and test the features extracted by LCGS algorithm. In the experiment, we use the olivetti research laboratory (ORL) database to simulate occlusion randomly and use the AR database for physical occlusion. Physical coverings include scarves and sunglasses. Experimental results demonstrate that our algorithm yields a state-of-the-art performance

Identification and comprehensive analyses of the CBL and CIPK gene families in wheat (Triticum aestivum L.)

The interaction analysis of wheat TaCBL and TaCIPK proteins were performed by Y2H method. (PDF 191 kb