Publindex: Aweb service to automatically evaluate research publications according to customized criteria

We introduce Publindex, a system that retrieves, classifies, and returns research publications of a given researcher according to the criteria and in the format predefined by the user

Building and Using Digital Libraries for ETDs

Despite the high value of electronic theses and dissertations (ETDs), the global collection has seen limited use. To extend such use, a new approach to building digital libraries (DLs) is needed. Fortunately, recent decades have seen that a vast amount of “gray literature” has become available through a diverse set of institutional repositories as well as regional and national libraries and archives. Most of the works in those collections include ETDs and are often freely available in keeping with the open-access movement, but such access is limited by the services of supporting information systems. As explained through a set of scenarios, ETDs can better meet the needs of diverse stakeholders if customer discovery methods are used to identify personas and user roles as well as their goals and tasks. Hence, DLs, with a rich collection of services, as well as newer, more advanced ones, can be organized so that those services, and expanded workflows building on them, can be adapted to meet personalized goals as well as traditional ones, such as discovery and exploration

Where My Books Go: Choice and Place in Digital Reading

Digital reading is a topic of rising interest in digital libraries, particularly in terms of optimizing the reading experience. However, there is relatively little data on the patterns of digital reading, including issues of where and what users read, and how they organize, plan and conduct their reading sessions. This paper reports the first data on mobile reading, combining insights from three different studies of users, including diary studies, interviews and ethnomethodological work. The data reveals that reading often depends on highly developed and rehearsed practices, especially when the reading is related to study or research. From this, we are able to identify a number of opportunities for further digital library research to better support the needs of users

Bridging the gap between digital libraries and e-learning

Digital Libraries (DL) are offering access to a vast amount of digital content, relevant to practically all domains of human knowledge, which makes it suitable to enhance teaching and learning. Based on a systematic literature review, this article provides an overview and a gap analysis of educational use of DLs.The research work presented in this paper is partially supported by the FP7 Grant 316087 AComIn ”Advanced Computing for Innovation”, funded by the European Commission in the FP7 Capacity Programme in 2012-2016.peer-reviewe

Parsing AUC Result-Figures in Machine Learning Specific Scholarly Documents for Semantically-enriched Summarization

Machine learning specific scholarly full-text documents contain a number of result-figures expressing valuable data, including experimental results, evaluations, and cross-model comparisons. The scholarly search system often overlooks this vital information while indexing important terms using conventional text-based content extraction approaches. In this paper, we propose creating semantically enriched document summaries by extracting meaningful data from the results-figures specific to the evaluation metric of the area under the curve (AUC) and their associated captions from full-text documents. At first, classify the extracted figures and analyze them by parsing the figure text, legends, and data plots – using a convolutional neural network classification model with a pre-trained ResNet-50 on 1.2 million Images from ImageNet. Next, we extract information from the result figures specific to AUC by approximating the region under the function’s graph as a trapezoid and calculating its area, i.e., the trapezoidal rule. Using over 12,000 figures extracted from 1000 scholarly documents, we show that figure specialized summaries contain more enriched terms about figure semantics. Furthermore, we empirically show that the trapezoidal rule can calculate the area under the curve by dividing the curve into multiple intervals. Finally, we measure the quality of specialized summaries using ROUGE, Edit distance, and Jaccard Similarity metrics. Overall, we observed that figure specialized summaries are more comprehensive and semantically enriched. The applications of our research are enormous, including improved document searching, figure searching, and figure focused plagiarism. The data and code used in this paper can be accessed at the following URL: https://github.com/slab-itu/fig-ir/

Fusion architectures for automatic subject indexing under concept drift:Analysis and empirical results on short texts

Indexing documents with controlled vocabularies enables a wealth of semantic applications for digital libraries. Due to the rapid growth of scientific publications, machine learning-based methods are required that assign subject descriptors automatically. While stability of generative processes behind the underlying data is often assumed tacitly, it is being violated in practice. Addressing this problem, this article studies explicit and implicit concept drift, that is, settings with new descriptor terms and new types of documents, respectively. First, the existence of concept drift in automatic subject indexing is discussed in detail and demonstrated by example. Subsequently, architectures for automatic indexing are analyzed in this regard, highlighting individual strengths and weaknesses. The results of the theoretical analysis justify research on fusion of different indexing approaches with special consideration on information sharing among descriptors. Experimental results on titles and author keywords in the domain of economics underline the relevance of the fusion methodology, especially under concept drift. Fusion approaches outperformed non-fusion strategies on the tested data sets, which comprised shifts in priors of descriptors as well as covariates. These findings can help researchers and practitioners in digital libraries to choose appropriate methods for automatic subject indexing, as is finally shown by a recent case study

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios