Different Kinds Of Information Research Articles

Orientation determination of nitrogen-vacancy center in diamond using a static magnetic field

Nitrogen-vacancy (NV) centers in a bulk diamond are often employed to realize measurement of multiple physical quantities, which depends on orientation information of NV axis. We report a fast and effective method to determine the orientation of NV axis with the aid of a static magnetic field. By measuring the optically detected magnetic resonance spectra, we can precisely extract the polar angle information between the NV axis and the known magnetic field. Combining with the polar angle information of different kinds of NV centers, we employ the Nelder–Mead algorithm to get the optimal solution of the orientation of NV axis. This method is simple and efficient, and is easily applied in NV-based quantum sensing.

Modality-DTA: Multimodality Fusion Strategy for Drug-Target Affinity Prediction.

Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.

Children intend to teach conventional but not moral norms selectively to ingroup members.

Several studies have investigated factors guiding children's decisions when learning from others, although less is known about factors that govern children's decisions when they transfer knowledge to others. Here we asked whether children would privilege ingroup members when teaching and, if so, whether this tendency would persist when transferring different kinds of information (conventional norms vs. moral norms). In Experiment 1 (N = 24), we first replicated ingroup preference based on minimal group membership with 5- and 6-year-old Turkish children. In Experiment 2 (N = 64), we examined whether children would consider group membership and the type of knowledge to be transferred in their teaching intentions. Children were introduced to two ignorant targets differing in their group membership and were asked to choose one or both of these targets to teach conventional or moral norms. Children were more likely to choose ingroup members for teaching conventional norms and both members when teaching moral norms. Further, this trend was particularly evident among girls. These results suggest that children make flexible teaching decisions considering the social attributes of the learners and raise interesting questions regarding the mechanisms underlying children's information transfer. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.

Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises.

The role of implicit social bias on holistic processing of out-group faces

Although faces of in-group members are generally thought to be processed holistically, there are mixed findings on whether holistic processing remains robust for faces of out-group members and what factors contribute to holistic processing of out-group faces. This study examined how implicit social bias, experience with out-group members, and ability to process in-group faces holistically might predict the magnitude of holistic processing for faces of two out-groups: other-race and other-age groups. In Experiment 1, Caucasian participants viewed Caucasian (own-race) and East Asian (other-race) faces. In Experiment 2, young adult participants viewed young adult (own-age) and baby (other-age) faces. Each participant completed a composite task with in-group and out-group faces, an implicit association test, and questionnaires about their experience with in-group and out-group members. We found that while the participants had relatively extensive experience with the other-race group, they had limited experience with the other-age group. Nonetheless, implicit social bias was found to positively predict the magnitude of holistic processing for both other-race and other-age faces. Exploratory analyses on the interactions among the predictors suggest that the effect of implicit social bias was primarily observed in participants with strong holistic processing ability of in-group faces but with low level of experience with members of the out-groups. These findings suggest that observers utilize different kinds of information when processing out-group faces, and that social features, such as race or age, are incorporated to influence how out-group faces are processed efficiently.

Evaluation of accuracy, reliability, quality, and readability of online patient information materials on coccyx injury.

The aim of this research is to evaluate the websites containing "coccydynia, coccyx trauma or fracture" in terms of readability, reliability, accuracy, and quality. Searches for "coccydynia, coccyx trauma, coccyx fracture" were carried out in the 3 most used search engines in the USA: Google, Yahoo, and Bing in February 2022. A total of 141 websites were rated by 2 different neurosurgeons for the "Global Quality Score" and "Alexa Popularity Rank." 97.2% of the sites examined include the definition of the disease, 66% include the importance of the disease, 92.9% include the symptoms of the disease, 88.7% include the treatment of the disease, 78% include the signs of the disease, 77.3% include the mechanism of the disease It has been determined that the websites examined within the scope of the research have high global quality score (GQS) and APR and are enriched with images to a large extent.

The Catch-22 of Forgetfulness: Responsibility for Mental Mistakes

ABSTRACT Attribution theorists assume that character information informs judgments of blame. But there is disagreement over why. One camp holds that character information is a fundamental determinant of blame. Another camp holds that character information merely provides evidence about the mental states and processes that determine responsibility. We argue for a two-channel view, where character simultaneously has fundamental and evidential effects on blame. In two large factorial studies (n = 495), participants rate whether someone is blameworthy when he makes a mistake (burns a cake or misses a bus stop). Although mental state inferences predict blame judgments, character information does not. Using mediation analyses, we find that character information influences responsibility via two channels (Studies 3–4; n = 447), which are sensitive to different kinds of information (Study 5; n = 149). On the one hand, forgetfulness increases judgments of responsibility, because mental lapses manifest an objectionable character flaw. On the other hand, forgetfulness decreases judgments of state control, which in turn decreases responsibility judgments. These two channels cancel out, which is why we find no aggregate effect of forgetfulness on responsibility. Our results challenge several fundamental assumptions about the role of character information in moral judgment, including that good character typically mitigates blame.

‘No one size fits all’ – community trust-building as a strategy to reduce COVID-19-related health disparities

BackgroundCitizens with low levels of social capital and social status, and relative poverty, seem to have been disproportionally exposed to COVID-19 and are at greater risk of experiencing poor health. Notably, the incidence of COVID-19 was nearly three times higher among citizens living in socially vulnerable areas. Experiences from the African Ebola epidemic show that in an environment based on trust, community partners can help to improve understanding of disease control without compromising safety. Such an approach is often driven by the civil society and local lay health promoters. However, little is known about the role of lay health promoters during a pandemic with communicable diseases in the European Union. This study had its point of departure in an already established Community Based Participatory Research health promotion programme in a socially disadvantaged area in southern Sweden. The aim of this study was to explore how citizens and local lay health promoters living in vulnerable neighbourhoods responded to the COVID-19 pandemic a year from the start of the pandemic. MethodIn-depth interviews with the 5 lay health promoters and focus group discussions with 34 citizens from the neighbourhood who were involved in the activities within the programme were conducted in autumn 2020. The interviews and focus group discussions were transcribed verbatim and analysed using qualitative content analysis following an inductive approach. ResultsFour themes emerged including, ‘balancing between different kinds of information’, ‘balancing between place-based activities and activities on social media’, ‘bridging between local authorities and the communities and community members’, and ‘balancing ambivalence through participatory dialogues’.ConclusionThe study highlights that a Community Based Participatory Research programme with lay health promoters as community trust builders had a potential to work with communicable diseases during the pandemic. The lay health promoters played a key role in promoting health during the pandemic by deepening the knowledge and understanding of the role that marginalised citizens have in building resilience and sustainability in their community in preparation for future crises. Public health authorities need to take the local context into consideration within their pandemic strategies to reach out to vulnerable groups.

Critical Discourse Analysis in the Education Community to Respond the Hoax Based on Technology and Information

The research looked at how people in the Education community responded to the hoax phenomenon from the point of view of technology and information. This research aims to improve the educational component's ability to analyse different kinds of information or news. This will make it less likely that fake information will be used as a talking point or a guide. The method used is qualitative with critical discourse analysis, where the program's implementation is through the ASSURE method. The number of respondents is 60 consisting of teachers, parents and students in the sixth grade of an elementary school in Medan city. The technique used by researchers to gather data involves grouping various data, processing it in accordance with the aim to be recorded, and then deconstructing it using an essential conversation investigation preparation software created using the ASSURE approach. The technique of analyzing data was by implementing the six stages in the ASSURE model, which represent three other stages: pre-program, program implementation and post-program. The results show that critical discourse analysis through the basic conversation investigation program using the ASSURE method has proven to be effective and can be used to prevent the spread of hoax information that can cause various disputes.

Perceptual measurement of articulatory goodness in young children: Relationships with age, speech sound acquisition, and intelligibility

ABSTRACT Speech language pathologists regularly use perceptual methods in clinical practice to assess children’s speech. In this study, we examined relationships between measures of speech intelligibility, clinical articulation test results, age, and perceptual ratings of articulatory goodness for children. We also examined the extent to which established measures of intelligibility and clinical articulation test results predicted articulatory goodness ratings, and whether goodness ratings were influenced by intelligibility. A sample of 164 (30–47 months) typically developing children provided speech samples and completed a standardised articulation test. Single word intelligibility scores and ratings of articulatory goodness were gathered from 328 naïve listeners; scores on a standardised articulation test were obtained from each child. Bivariate Pearson correlation, linear regression, and linear mixed effects modelling were used for analysis. Results showed that articulatory goodness ratings had the highest correlation with intelligibility, followed by age, followed by articulation score. Age and clinical articulation scores were both significant predictors of goodness ratings, but articulation scores made only a small contribution to prediction. Articulatory goodness ratings were substantially lower for unintelligible words compared to intelligible words, but articulatory goodness scores increased with age at the same rate for unintelligible and intelligible words. Perceptual ratings of articulatory goodness are sensitive to developmental changes in speech production (regardless of intelligibility) and yield a different kind of information than clinical articulation scores from standardised measures.

PP71 Lessons Learned During Analyses Of Appropriate Hearing Care: Building Bridges Between Patient Organizations, Health Professionals And Insurance Companies

IntroductionAs part of the cyclic Appropriate Care programme of the National Health Care Institute in the Netherlands, a systematic analysis of hearing health care is taking place. Parties in hearing health care are actively involved throughout the entire process. This abstract focuses on lessons learned from the cooperation as a HTA body with a diverse group of stakeholders.MethodsWe carried out an in-depth analysis for the patient journey of both children and adults with ear complaints or hearing impairment. Different kinds of information were included in the analyses, including claims data, quantitative and qualitative research, analyses of (international) guidelines and patient information. A range of strategies were used to co-operate and interact with patient organizations, hearing health care professionals, institutes/hospitals and insurance companies.ResultsClose collaboration between the project team and patient organizations turned out to be effective to comprehend patient’ perspectives. Data analyses were often found to be challenging in hearing health care, as the reimbursement data lacked sufficient information. In several cases, building bridges between parties, but also in relation to our HTA body was needed. Conclusions from the analyses were being shared and discussed with a panel of involved stakeholders, leading to support, but not always consensus on potential room for improvement. An internal review process turned out to be helpful in sharing experiences on effective multi-stakeholder management.ConclusionsWe believe that the process did influence the way stakeholders think about the appropriate use of the different available treatment options. Building bridges, and combining different perspectives from patient organizations, health-care professionals and insurance companies is necessary in a cyclic approach. The cyclic appropriate Care programme proved to be a constructive approach for collaboration with stakeholders.

Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.

What inspires us? An experimental analysis of the semantic meaning of irrelevant information in creative ideation

Past research showed that apparently irrelevant information for a creative task at hand can lead to higher creative performance, especially in open-minded individuals. Through two diverse experimental procedures, the present work investigated which type of irrelevance information can inspire (i.e., increase) the creative performance during a divergent thinking (DT) task and how open-minded individuals can be inspired by this kind of information. In Experiment 1, the attentional processing of information that was either apparently relevant or irrelevant for the execution of a verbal DT task was assessed by means of an eye-tracking methodology. In Experiment 2, creative performance was explored through a verbal priming paradigm, which forcedly introduced apparently irrelevant information during the DT task. In both experiments, the level of irrelevance was operationalized in terms of semantic distance between the different kind of information. Results from both experiments highlighted the role of the semantic meaning of the irrelevant information as one of the main determinants, along with Openness, of inspiration (i.e., enhancement) of the creative performance.

Who Talked about What Regarding Derek Chauvin’s Trial: A Work‐In‐Progress Analysis

ABSTRACTThis work‐in‐progress study investigates the information sharing behavior of different levels of Twitter influencers within the context of the #BlackLivesMatter social movement and its related discussions #AlllivesMatter and #BlueLivesMatter during the 2‐week period around Derek Chauvin’s trial. Using qualitative content analysis and quantitative machine learning methods, we analyzed over one million retweets to test if different levels of influencers tend to spread different kinds of information in the discussions around #All/Black/BlueLivesMatter on Twitter. We found out that different levels of influencers tend to spread different information within and between the #All/Black/BlueLivesMatter, and we offer some explanations through the lens of curation logics. We suggest that different levels of influencers may be exposed to different incentives, and be facing different social norms, which leads to different information behaviors. This work‐in‐progress study contributes to updating the theory of curated logics, virality, and influencers, as well as provides empirical data for the discussions of the #BlackLivesMatter social movement and its related discussions of #AllLivesMatter and #BlueLivesMatter.

Node classifications with DjCaNE: Disjoint content and network embedding

Machine learning approaches have become a crucial tool in graph analysis. Despite the accurate results of the existing approaches, most of them are not scalable enough to be used in real-world problems. Networks provide two different kinds of information, nodes contents and nodes relations (network structure). Training deep graph neural networks (GNN) over large-scale graphs is challenging due to the limitation of the message passing framework. Graph Convolutional Networks (GCN) work on all node neighbours at once. Furthermore, it is usual to transform node features with a deep neural network before the GC operation. Therefore, the deep transform operation may apply up to hundreds of times for each target node which is heavy computation and hard to batch. This paper presents an abstract framework with two embedding components, the first component embeds node relations, and the second one embeds node contents. The model makes predictions by aggregating these embeddings through a combination component. The presented approach limits the deep transform only to the target node and uses random walk-based embedding instead of the GC operator to reduce the cost. The main goal of the proposed approach is to provide a light framework for the task. To this aim, node relations are embedded based on node neighbourhood structure by a biased variant of the DeepWalk model, called GuidedWalk, and an autoencoder embeds node contents. The experimental results on three well-known datasets show the superiority of the proposed model compared to the state-of-the-art GraphSAGE and TADW models with less computational complexity. On the Citeseer, Cora, and PubMed datasets, the model has achieved 3.23%, 0.88%, and 7.63% improvement in Macro-F1 and 3.25%, 0.7%, and 6.34% improvement in Micro-F1, respectively. Although GNNs are state-of-the-art models, considering node content is their main advantage. This paper shows that even a simple integration of node content to available random walk-based methods improves their performance up to GCNs without increasing the complexity.

A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network.

Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity; Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator; Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://github.com/hanzefang/DESDGAN.

Benefits, discounts, features, and value as communication foci in selling: Exploring concepts, drivers, and outcomes

Customers in business markets face difficult choice problems. Depending on the customer’s choice construal, in terms of the customer’s criteria and the tradeoffs the customer wants to make, different kinds of information are relevant and influential. We conceptualize four communication foci in B2B selling, which differ in terms of the kinds of information that is communicated during sales interactions with customers: value communication, benefit communication, feature communication, and discount communication. Drawing on survey data from a sample of 226 B2B salespeople, we empirically investigate these constructs and specifically test for their distinctiveness. Finally, in an exploratory analysis, we identify potential drivers, outcomes, and moderators (i.e., enablers) of these communication foci. The four communication foci are driven by substantially different sets of drivers, indicating that they are employed in different settings. We find no communication focus is clearly directly related to sales performance. Instead, the effectiveness of a communication focus is likely to depend on the situation.

Expectations of how machines use individuating information and base-rates

AbstractMachines are increasingly used to make decisions. We investigated people’s beliefs about how they do so. In six experiments, participants (total N = 2664) predicted how computer and human judges would decide legal cases on the basis of limited evidence — either individuating information from witness testimony or base-rate information. In Experiments 1 to 4, participants predicted that computer judges would be more likely than human ones to reach a guilty verdict, regardless of which kind of evidence was available. Besides asking about punishment, Experiment 5 also included conditions where the judge had to decide whether to reward suspected helpful behavior. Participants again predicted that computer judges would be more likely than human judges to decide based on the available evidence, but also predicted that computer judges would be relatively more punitive than human ones. Also, whereas participants predicted the human judge would give more weight to individuating than base-rate evidence, they expected the computer judge to be insensitive to the distinction between these kinds of evidence. Finally, Experiment 6 replicated the finding that people expect greater sensitivity to the distinction between individuating and base-rate information from humans than computers, but found that the use of cartoon images, as in the first four studies, prevented this effect. Overall, the findings suggest people expect machines to differ from humans in how they weigh different kinds of information when deciding.

Automating Gamification Personalization to the User and Beyond

Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several combinations to tailor. Consequently, tools for automating gamification personalization are needed. However, which of those characteristics are relevant and how to do such tailoring are open questions. Furthermore, the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants' opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through Conditional Decision Trees to address the aforementioned tailoring process. Second, as a product of the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings present empirical evidence that LAT, geographic locations, and other user characteristics affect users' preferences, enable defining gamification designs tailored to user and contextual features simultaneously, and provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system.

Pseudo-Labeling Improves News Identification and Categorization with Few Annotated Data

News articles analysis has been the subject of numerous research papers in recent years. Tasks such as identifying fake news and classifying news into categories have been addressed, but all of them require news as the main source of data. Websites offering news articles also include different kinds of information, such as advertising and personal opinions, which should be avoided when collecting data to create a news corpus. In this paper we propose a method that identifies news and separates them from other documents (non-news), following a semi-supervised approach using NER features corresponding to who, where and when questions, along with a measure of subjectivity. We experimented with different pseudo-labeling methods to improve classifier’s performance and obtained a robust increase of 2% to 3% when adding automatically labeled data on top of manually tagged data, even for small quantities of it (20%). We also explored the use of this semi-supervised method for the task of classifying news by categories (news categorization), obtaining better performance than supervised approaches.