Capturing and Exploiting Citation Knowledge for the Recommendation of Scientific Publications

With the continuous growth of scientific literature, it is becoming increasingly challenging to discover relevant scientific publications from the plethora of available academic digital libraries. Despite the current scale, important efforts have been achieved towards the research and development of academic search engines, reference management tools, review management platforms, scientometrics systems, and recommender systems that help finding a variety of relevant scientific items, such as publications, books, researchers, grants and events, among others. This thesis focuses on recommender systems for scientific publications. Existing systems do not always provide the most relevant scientific publications to users, despite they are present in the recommendation space. A common limitation is the lack of access to the full content of the publications when designing the recommendation methods. Solutions are largely based on the exploitation of metadata (e.g., titles, abstracts, lists of references, etc.), but rarely with the text of the publications. Another important limitation is the lack of time awareness. Existing works have not addressed the important scenario of recommending the most recent publications to users, due to the challenge of recommending items for which no ratings (i.e., user preferences) have been yet provided. The lack of evaluation benchmarks also limits the evolution and progress of the field. This thesis investigates the use of fine-grained forms of citation knowledge, extracted from the full textual content of scientific publications, to enhance recommendations: citation proximity, citation context, citation section, citation graph and citation intention. We design and develop new recommendation methods that incorporate such knowledge, individually and in combination. By conducting offline evaluations, as well as user studies, we show how the use of citation knowledge does help enhancing the performance of existing recommendation methods when addressing two key tasks: (i) recommending scientific publications for a given work, and (ii) recommending recent scientific publications to a user. Two novel evaluation benchmarks have also been generated and made available for the scientific community

Understanding the Effectiveness of Diabetes Self-Management Education on Psychological Distress and Self-care Activity Measures: A Focus on Latinx Community

The psychological distress that people with diabetes experience makes them twice likely to have clinical depression. Diabetes self-management education and support is an effective strategy to improve psychological distress and increase self-care activity measures. However, there has been limited knowledge on the impact of formal diabetes self-management education and support DSME/S on psychological distress among minority populations such as Latinx. The primary objective of this study is to explore the impact of diabetes self-management education on psychological distress and self-care activity measures. This study also explores the impact of demographic variables of the Latinx respondents on psychological distress and self-care activity. Using a pre- and post-test study design, baseline and post-test assessments for the respondents were conducted at week one and week four of the intervention, respectively. Statistical analysis such as descriptive statistics of demographic variables, paired t-test of diabetes distress score and self-care activity measure score, and mixed-method analysis of variance were conducted using Intellectus Statistics. The results found that while the self-management education substantially reduce diabetes distress, it wasn\u27t statistically significant. However, it was found to significantly increase self-care activity measures among Latinx communities. Some impacts of demographic variables such as Sex and overall health were found. Implications to diabetes Education, policymakers, and existing literature have been discussed

Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications

In this paper we address the problem of providing personalised recommendations of recent scientific publications to a particular user, and explore the use of citation knowledge to do so. For this purpose, we have generated a novel dataset that captures authors’ publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, citation contexts, and citation types. Through a number of empirical experiments on such dataset, we show that the exploitation of the extracted knowledge, particularly the type of citation, is a promising approach for recommending recently published papers that may not be cited yet. The dataset, which we make publicly available, also represents a valuable resource for further investigation on academic information retrieval and filtering

Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers

With the continuous growth of scientific literature, discovering relevant academic papers for a researcher has become a challenging task, especially when looking for the latest, most recent papers. In this case, traditional collaborative filtering systems are ineffective, since they are unable to recommend items not previously seen, rated or cited. In this paper, we explore the potential of exploiting citation knowledge to provide a given user with relevant suggestions about recent scientific publications. A novel hybrid recommendation method that encapsulates such citation knowledge is proposed. Experimental results show improvements over baseline methods, evidencing benefits of using citation knowledge to recommend recently published papers in a personalised way. Moreover, as a result of our work, we also provide a unique dataset that, differently to previous corpora, contains detailed paper citation information

Resilient Machine Learning:Advancement, Barriers, and Opportunities in the Nuclear Industry

The widespread adoption and success of Machine Learning (ML) technologies depend on thorough testing of the resilience and robustness to adversarial attacks. The testing should focus on both the model and the data. It is necessary to build robust and resilient systems to withstand disruptions and remain functional despite the action of adversaries, specifically in the security-sensitive Nuclear Industry (NI), where consequences can be fatal in terms of both human lives and assets. We analyse ML-based research works that have investigated adversaries and defence strategies in the NI . We then present the progress in the adoption of ML techniques, identify use cases where adversaries can threaten the ML-enabled systems, and finally identify the progress on building Resilient Machine Learning (rML) systems entirely focusing on the NI domain

The Functional Role of sFRP3/4 in the Wnt Network that Governs Positioning of the Anterior Neuroectoderm around the Anterior Pole in the Sea Urchin Embryo

The anterior neuroectoderm (ANE) is progressively restricted along the anterior-posterior axis to a territory around the anterior pole during the blastula stages in sea urchin embryos. Information obtained from Wnt/beta-catenin, Wnt/JNK and Wnt/PKC signaling pathways is responsible for this positioning process. Several secreted Wnt modulators of the Dickkopf and Secreted Frizzled Related Protein families (Dkk1, Dkk3, and sFRP1/5) are expressed within the ANE and play important roles in the Wnt signaling network during this process. In this study, we have characterized the function of another secreted Wnt modulator, sFRP3/4, within this Wnt signaling network. Here we have shown that the maternal sFRP3/4 is necessary for the early ANE restriction mechanism during cleavage stage. We also found that Fzl1/2/7 activates zygotic sFRP3/4 during blastula stage, which possibly antagonizes ANE restriction mechanism. During the final phase of ANE restriction mechanism sFRP3/4 and Dkk1 likely works together define the final ANE territory

Citation-Context Dataset (C2D)

<p>We have released the first version of a citation-context based dataset called C2D, created while doing an experiment in the work which will be published in <a href="https://recsys.acm.org/recsys18/" rel="noopener" target="_blank">RecSys 2018</a> as a <a href="https://dl.acm.org/citation.cfm?id=3240379">short paper</a>.<br></p><p><br></p><p>C2D dataset is created by using 2 million full-text open-source research publications obtained from <a href="https://core.ac.uk/" rel="noopener" target="_blank">CORE</a>. It contains 53 million unique records of citation-information. To construct C2D, we extracted citation information from each publication. Information such as cited document's title, author(s), published date and citation-context. We will describe the assumption of extracting citation-context in a bit more detail below:</p><p><br></p><p>First of all, we extracted positions of citations where they are mentioned including citation-contexts which are texts around the cited documents. For our purpose, we created a citation-context using three sentences; the sentence where the reference has been cited, the preceding, and the following sentence. Additionally, at the start or end of a paragraph, the preceding or following sentence is not extracted respectively.</p><p><br></p><p>Therefore, the attributes of the dataset contain:</p><h3>Attributes:</h3><ul><li><strong>ReferenceID</strong> - unique identifier of cited reference in a citing document</li><li><strong>SourceID</strong> - unique identifier of a citing document.</li><li><strong>ChapterNumber</strong> - Chapter number of the citing document where the <b>ReferenceID</b> has mentioned.</li><li><strong>ParagraphNumber</strong> - paragraph number of the citing document where the reference <strong>ReferenceID</strong> has mentioned.</li><li><strong>SentenceNumber</strong> - sentence number of the citing document where the reference <strong>ReferencedID</strong> has mentioned.</li><li><strong>Title</strong> - Title of the reference <strong>ReferenceID.</strong></li><li><strong>PublishedDate</strong> - Publication date when the reference <strong>ReferenceID </strong>was published.</li><li><strong>Authors</strong> - Author(s) of the reference <strong>ReferenceID</strong></li><li><strong>TextBeforeRefMention</strong> - Sentence just before the sentence where the reference <strong>ReferenceID</strong> has been cited.</li><li><strong> TextWhereRefMention</strong> - Sentence where the reference <strong>ReferenceID</strong> has been cited.</li><li><strong>TextAfterRefMention</strong> - Sentence just after the sentence where the reference <strong>ReferenceID</strong> has been cited.</li></ul><div>Please cite our <a href="https://dl.acm.org/citation.cfm?id=3240379">paper</a> if you use this dataset. </div><p><br></p><p>Note:<br></p><ul><li>The actual size of the dataset is ~40gb however compressed size is ~6.7gb.</li><li>Requirements of different users may be different therefore we have released the raw version of the dataset. Please note, data cleansing (such as special character and stop-word removal) has not been performed.</li></ul