Specific Background Knowledge Research Articles

Health literacy, cancer literacy, comprehensions and knowledge among men attending a urology clinic

Health literacy (HL) is essential for men receiving urological treatment so that they can be involved in the shared decision making process. HL is supported by domain specific background knowledge which also informs cancer literacy and comprehension. Comprehension is in turn a determinant of HL. This study aimed to assess the level of HL among a group of men receiving urological treatment and to investigate if there were any correlations between the two different measures of HL, cancer literacy and comprehension, and prostate cancer knowledge. A survey was mailed to 200 men attending a urological clinic. The survey included: demographic questions, two validated tests of HL, (1) the Brief Health Literacy Score (BHLS) and (2) the Health Literacy Management Scale (HeLMS); a test of cancer comprehension; the Cancer Message Literacy Tests Reading (CMLT); and a prostate cancer knowledge test. Descriptive statistics were used to analyse the data. Surveys from 72 respondents, average age of 65 years, were included in the final analysis. Based on the BHLS, 22%of respondents had inadequate HL and 50% of respondents had inadequate HL in one or more of the HeLMS domains. Overall, the study participants had relatively high cancer literacy, comprehensions, and knowledge. However, for men with inadequate HL, based on the BHLS and the HeLMS, there were strong correlations with poor cancer literacy, comprehension, and knowledge. Our study highlights that many men receiving urological treatment with inadequate HL, require additional support to access health information which they can understand and act on to be part of the shared decision-making process.

Support Personalized Weighted Local Differential Privacy Skyline Query

The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.

Sememe knowledge and auxiliary information enhanced approach for sarcasm detection

Sarcasm expression is a pervasive literary technique in which people intentionally express the opposite of what is implied. Accurate detection of sarcasm in a text can facilitate the understanding of speakers’ true intentions and promote other natural language processing tasks, especially sentiment analysis tasks. Since sarcasm is a kind of implicit sentiment expression and speakers deliberately confuse the audience, it is challenging to detect sarcasm only by text. Existing approaches based on machine learning and deep learning achieved unsatisfactory performance when handling sarcasm text with complex expression or needing specific background knowledge to understand. Especially, due to the characteristics of the Chinese language itself, sarcasm detection in Chinese is more difficult. To alleviate this dilemma on Chinese sarcasm detection, we propose a sememe and auxiliary enhanced attention neural model, SAAG. At the word level, we introduce sememe knowledge to enhance the representation learning of Chinese words. Sememe is the minimum unit of meaning, which is a fine-grained portrayal of a word. At the sentence level, we leverage some auxiliary information, such as the news title, to learning the representation of the context and background of sarcasm expression. Then, we construct the representation of text expression progressively and dynamically. The evaluation on a sarcasm dateset, consisting of comments on news text, reveals that our proposed approach is effective and outperforms the state-of-the-art models.

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

CTRL+ESC: An escape/exit room to teach control and its relevance to an audience outside engineering

An escape/exit room (called CTRL+ESC) has been created at TU Berlin that aims to convey some basic understanding and awareness of automatic control. Participants are not required to have specific background knowledge of control or engineering at all such that CTRL+ESC is suitable to reach out to the general public as well as motivating local students at the university. Participants are required to solve around 10 puzzles, quizzes or riddles embedded in an overarching fictitious storyline. Each puzzle, quiz or riddle is associated to one concept in automatic control, which is briefly explained together with its importance and relevance in a brochure that participants receive after solving the puzzles.Spoiler alert: By reading this paper, some of the underlying secrets of puzzles in the scape room will be revealed and some hints will be given.

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

Mental health and the news media in Aotearoa New Zealand: Key informant perspectives

INTRODUCTION: Without personal experience or specific background knowledge of an issue, individuals exposed to news media are limited in their ability to gauge the accuracy of media content. The perception of people with mental illness as dangerous or incompetent allows for discrimination to occur, often resulting from inaccurate portrayals in the media.METHOD: A small, qualitative study was undertaken to explore key informant perspectives on the representation of mental health issues in the current Aotearoa New Zealand media context. Eight semi-structured interviews were carried out with key informants; each participant provided at least one of the following perspectives: mental health professional, mental health advocate, mental health consumer and/or media personnel.FINDINGS: The participants in this research found that news media strongly influence how the public views mental health, but believe media representation has improved in recent years. However, participants described an absence of stories illustrating successful interventions and felt that the portrayal of mental illnesses are still misrepresented and stigmatised by the media. Mental health services and mental health professionals were seen as poorly represented by the news media, which was thought to deter help-seeking and hinder trust.CONCLUSION: Public attitudes towards mental health have a significant impact on the lives of those experiencing mental distress. The news media have the ability to change public perceptions; however, this influence can be either harmful or helpful. Mental health stories need to be consistently contextualised and use inclusive language in order to a make a positive large- scale change in public attitudes.

Deep learning of mutation-gene-drug relations from the literature

BackgroundMolecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature.ResultsHere, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks.Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively.ConclusionWe believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.

Google and advertising: digital capitalism in the context of Post-Fordism, the reification of language, and the rise of fake news

Google’s dominance over the web allows it to dictate various norms and practices that regulate the state of contemporary capitalism online. The way in which Google operates as a company and generates revenue is often sidelined in academic discussions regarding the cultural implications of how its search engine functions. Almost 90% of Google’s revenue is derived from advertising, despite Larry Page and Sergey Brin’s original academic paper regarding Google in which they argue that advertising produces mixed motives that make it an unfeasible way to fund search engines. This article outlines how Google’s model of advertising reflects and encourages wider changes in capitalism as it shifts from its twentieth-century Fordist incarnation to contemporary Post-Fordist arrangements of labour. In doing so, this article analyses Google’s two main advertising systems, AdWords and AdSense, and proposes that these financial models have significant effects upon online discourse. In discussing AdWords, this article details some of the tensions between the local and the global that develop when tracing flows of information and capital, specifically highlighting Google’s impact on the decline of online language diversity. In outlining AdSense, this article demonstrates how Google’s hegemonic control prescribes which parts of the web can be monetised and which remain unprofitable. In particular, in drawing from existing studies, evidence is provided that Google’s AdSense programme, along with Google’s relationship with Facebook, incentivised the rise of fake news in the 2016 US presidential election. This work builds on existing scholarship to demonstrate that Google’s economic influence has varied and far-reaching effects in a number of contexts and is relevant to scholars in a range of disciplines. As such, this article is intended as a discursive introduction to the topic and does not require specific disciplinary background knowledge. In doing so, this article does not attempt to provide the final word on Google’s relationship to digital capitalism, but rather, demonstrate the profitability of a Post-Fordist perspective, in order to enable a wider engagement with the issues identified.

Current open questions in complete mixability

Complete and joint mixability has raised considerable interest in recent few years, in both the theory of distributions with given margins, and applications in discrete optimization and quantitative risk management. We list various open questions in the theory of complete and joint mixability, which are mathematically concrete, and yet accessible to a broad range of researchers without specific background knowledge. In addition to the discussions on open questions, some results contained in this paper are new.

English Reading Comprehension by Japanese High School Students: Structural Equation Modeling including Working Memory and L1 Literacy

The present study aims to build a model of second language (L2) reading, taking into account working memory capacity and first language (L1) literacy as variables within structural equation modeling (SEM). In this study, a total of 120 Japanese L1 high school students were given a series of tests: 1) English reading span tests, 2) English vocabulary size tests, 3) English grammar tests, 4) Japanese literacy tests, and 5) two kinds of English reading tests. One of the English reading tests was on a general topic, and the other required specific background knowledge to control for the effects of topic familiarity. SEM revealed that L2 working memory, L2 grammatical knowledge, and L2 vocabulary knowledge significantly predicted general L2 reading, while general L2 reading, along with L1 literacy, was the significant predictor of L2 reading on a specific topic. Furthermore, L1 literacy predicted L2 vocabulary knowledge, and skilled L1 readers excelled at creating situation model for L2 reading comprehension, even when they lacked topical knowledge. The present study presents a more refined model of L2 reading comprehension, which accounts for L1 literacy and working memory more comprehensively than previous research has.

Ebola – contradictions between knowledge and communication

If the possible effects of a threat are difficult to identify and seem far away it leaves a lot of space for seemingly trustworthy assessments and statements that, however, very often do not withstand a more detailed examination. The spectrum of the customary, reflexive statements can currently be seen in Europe and North America in reaction to the humanitarian catastrophe of the Ebola epidemic in the West African countries of Guinea, Liberia and Sierra Leone. A constant in the past weeks and months is the regular repetition of the statement that countries such as Germany are optimally equipped to handle this threat. Strongly worded and sweeping phrases are often heard which the normal citizen must interpret as a guarantee that there is no risk without any specific background knowledge. The representation in the media delivers a fireworks display of statements such as ‘‘risk is practically zero’’, ‘‘probability is almost zero’’, ‘‘one can definitely rule out’’ as well as ‘‘practically one hundred percent’’. The risk of dying of Ebola in Germany is certainly ‘‘extremely’’ low (to stay with this terminology). However, the risk of incorrectly communicating the risks is extremely high. The choice of words and the message between the lines must comply with petty accuracy and comprehensibly

Incidental findings on cone beam computed tomography scans in cleft lip and palate patients

Cone beam computed tomography (CBCT) is frequently used in treatment planning for alveolar bone grafting (ABG) and orthognathic surgery in patients with cleft lip and palate (CLP). CBCT images may depict coincident findings. The aim of this study was to assess the prevalence of incidental findings on CBCT scans in CLP patients. Initial CBCTs taken from consecutive patients (n = 187; mean age 11.7 years, range 6.9-45) with a non-syndromic orofacial cleft from January 2006 until June 2012 were systematically evaluated. Twenty-eight patients (mean age 19.3 years, range 13.2-30.9) had been subjected to ABG before their first CBCT was taken; 61 patients had a CBCT before and after ABG. Sinuses, nasopharynx, oropharynx, throat, skull, vertebrae, temporomandibular joint (TMJ), maxilla and mandible were checked for incidental findings. On 95.1 % of the CBCTs, incidental findings were found. The most prevalent were airway/sinus findings (56.1 %), followed by dental problems, e.g. missing teeth (52 %), nasal septum deviation (34 %), middle ear and mastoid opacification, suggestive for otitis media (10 %) and (chronic) mastoiditis (9 %), abnormal TMJ anatomy (4.9 %) and abnormal vertebral anatomy (1.6 %). In the 28 patients whose first CBCT was taken at least 2 years after ABG, bone was still present in the reconstructed cleft area except in 2 out of 12 patients with a bilateral CLP. The ABG donor site (all bone grafts were taken from the chin area) was still recognizable in over 50 % of the patients. Based on the CBCT findings, 10 % of the patients were referred for further diagnosis and 9 % for further treatment related to dental problems. Incidental findings are common on CBCTs. Compared with the literature, CLP patients have more dental, nasal and ear problems. Thus, whenever a CBCT is available, this scan should be reviewed by all specialists in the CLP team focusing on their specific background knowledge concerning symptoms and treatment of these patients. The high number of findings indicates that CBCT imaging is a helpful tool in the treatment of CLP patients not only related to alveolar bone grafting and orthognathic surgery but it also provides diagnostic information for almost all specialties involved in CLP treatment.

Validating Contradiction in Texts Using Online Co-Mention Pattern Checking

Detecting contradictive statements is a foundational and challenging task for text understanding applications such as textual entailment. In this article, we aim to address the problem of the shortage of specific background knowledge in contradiction detection. A novel contradiction detecting approach based on the distribution of the query composed of critical mismatch combinations on the Internet is proposed to tackle the problem. By measuring the availability of mismatch conjunction phrases (MCPs), the background knowledge about two target statements can be implicitly obtained for identifying contradictions. Experiments on three different configurations show that the MCP-based approach achieves remarkable improvement on contradiction detection and can significantly improve the performance of textual entailment recognition.

S-Match: An open source framework for matching lightweight ontologies

Achieving automatic interoperability among systems with diverse data structures and languages expressing different viewpoints is a goal that has been difficult to accomplish. This paper describes S-Match, an open source semantic matching framework that tackles the semantic interoperability problem by transforming several data structures such as business catalogs, web directories, conceptual models and web services descriptions into lightweight ontologies and establishing semantic correspondences between them. The framework is the first open source semantic matching project that includes three different algorithms tailored for specific domains and provides an extensible API for developing new algorithms, including possibility to plug-in specific background knowledge according to the characteristics of each application domain.

An ontological approach to focusing attention and enhancing machine perception on the Web

Today, many sensor networks and their applications employ a brute force approach to collecting and analyzing sensor data. Such an approach often wastes valuable energy and computational resources by unnecessarily tasking sensors and generating observations of minimal use. People, on the other hand, have evolved sophisticated mechanisms to efficiently perceive their environment. One such mechanism includes the use of background knowledge to determine what aspects of the environment to focus our attention. In this paper, we develop an ontology of perception, IntellegO, that may be used to more efficiently convert observations into perceptions. IntellegO is derived from cognitive theory, encoded in set-theory, and provides a formal semantics of machine perception. We then present an implementation that iteratively and efficiently processes low level, heterogeneous sensor data into knowledge through use of the perception ontology and domain specific background knowledge. Finally, we evaluate IntellegO by collecting and analyzing observations of weather conditions on the Web, and show significant resource savings in the generation and storage of perceptual knowledge.

How well prepared are medical students for their first year as doctors? The views of consultants and specialist registrars in two teaching hospitals

Objective:To evaluate (1) the extent to which first year doctors (foundation year 1 doctors, F1s) in two teaching hospitals in the Trent Deanery were rated by specialist registrars (SpRs) and...

Reexamining the relationship between working memory and comprehension: The role of available long-term memory

Two individual differences studies tested relationships between listening comprehension and two conceptualizations of working memory (WM) capacity. Recently, some theorists have stressed that the empirically indicated limits of rehearsal-based WM storage components are inconsistent with the amounts of information needed to accomplish complex cognitive tasks, including language comprehension. Accordingly, they have proposed models of WM that include available long-term memory (ALTM) as part of the cognitive workspace. We tested structural equation models (SEM) depicting relationships among factors representing ALTM, content specific background knowledge, listening comprehension, and conventional WM. The analyses revealed that ALTM mediated the relationships of both WM and background knowledge with listening comprehension. The incongruity posed by small-capacity, attention-controlled WM components and theoretical models of comprehension that depict the integration of text information and background knowledge can, at least in part, be resolved by the models presented here.

Information sources in biomedical science and medical journalism: methodological approaches and assessment

Throughout the world the public is showing increasing interest in medical and scientific subjects and journalists largely spread this information, with an important impact on knowledge and health. Clearly, therefore, the relationship between the journalist and his sources is delicate: freedom and independence of information depend on the independence and truthfulness of the sources. The new “precision journalism” holds that scientific methods should be applied to journalism, so authoritative sources are a common need for journalists and scientists. We therefore compared the individual classifications and methods of assessing of sources in biomedical science and medical journalism to try to extrapolate scientific methods of evaluation to journalism. In journalism and science terms used to classify sources of information show some similarities, but their meanings are different. In science primary and secondary classes of information, for instance, refer to the levels of processing, but in journalism to the official nature of the source itself. Scientists and journalists must both always consult as many sources as possible and check their authoritativeness, reliability, completeness, up-to-dateness and balance. In journalism, however, there are some important differences and limits: too many sources can sometimes diminish the quality of the information. The sources serve a first filter between the event and the journalist, who is not providing the reader with the fact, but with its projection. Journalists have time constraints and lack the objective criteria for searching, the specific background knowledge, and the expertise to fully assess sources. To assist in understanding the wealth of sources of information in journalism, we have prepared a checklist of items and questions. There are at least four fundamental points that a good journalist, like any scientist, should know: how to find the latest information (the sources), how to assess it (the quality and authoritativeness), how to analyse and filter it (selection), how to deal with too many sources of information, sometimes case biased by conflicting interests (balance). The journalist must, in addition, know how to translate it to render it accessible and useful to the general public (dissemination), and how to use it best.

The impact of relational markers on expository text comprehension in L1 and L2

This article reports on an experimentinvestigating the impact of causal discoursemarkers (connectives and signaling phrases) onthe comprehension of expository texts in L1 andL2. Although several psycholinguistic studieshave investigated the impact of connectives andlexical markers of text structure oncomprehension (i.e. off-line), there is noconsensus on the exact effect of explicitdiscourse markers on text understanding; threedifferent findings are reported in theliterature: markers would have a facilitatingeffect, an interfering effect or no effect atall. The first goal of this article is toclarify this problem of contradicting resultsby limiting the scope of the study to causalrelations, and to one specific text type:expository texts. Furthermore, the naturalnessof the experimental texts was controlled,readers did not need specific backgroundknowledge to understand the texts and theexperimental method consisted of open answerquestioning. Our second goal is to investigateto what extent a supposed effect of linguisticmarking depends on readers proficiency in afirst or second language.The experiment consisted in the reading of short expository texts in two languages, Dutchand French, which both functioned as L1 and L2.The results indicate that readers benefit fromthe presence of causal relational markers bothin L1 and in L2. Implications for (theoriesof) text processing are discussed, as well asfor the further insights in readingcomprehension in L1 and L2.