Text Item Research Articles

Emotional factors of causal coherence in text comprehension

The current study tested the effects of positive and negative emotion at the beginning and end of texts, as well as the consistency in valence throughout a text on readers’ response choice to items on a multiple-choice reading comprehension assessment. Multinomial logistic regressions were performed on 1,161 college participants’ assessment responses to test whether emotion in narrative and informational text items significantly predicted which distractor response options readers chose in comparison to the correct response option. Overall, consistent emotion throughout text and positive and negative emotion at the beginning and end of text were significant predictors of readers’ response choices. The results are discussed in terms of emotion being an indicator of readers’ causal processing. Specifically, findings provide a better understanding of how emotional features in narrative and informational texts may influence how readers develop causal coherence and comprehension during reading. This understanding could also help inform the development of instructional tools that encourage readers to focus on aspects of text (i.e., emotion) which could, in turn, help improve comprehension for readers who struggle.

Learning Explainable Disentangled Representations of E-Commerce Data by Aligning Their Visual and Textual Attributes

Understanding multimedia content remains a challenging problem in e-commerce search and recommendation applications. It is difficult to obtain item representations that capture the relevant product attributes since these product attributes are fine-grained and scattered across product images with huge visual variations and product descriptions that are noisy and incomplete. In addition, the interpretability and explainability of item representations have become more important in order to make e-commerce applications more intelligible to humans. Multimodal disentangled representation learning, where the independent generative factors of multimodal data are identified and encoded in separate subsets of features in the feature space, is an interesting research area to explore in an e-commerce context given the benefits of the resulting disentangled representations such as generalizability, robustness and interpretability. However, the characteristics of real-word e-commerce data, such as the extensive visual variation, noisy and incomplete product descriptions, and complex cross-modal relations of vision and language, together with the lack of an automatic interpretation method to explain the contents of disentangled representations, means that current approaches for multimodal disentangled representation learning do not suffice for e-commerce data. Therefore, in this work, we design an explainable variational autoencoder framework (E-VAE) which leverages visual and textual item data to obtain disentangled item representations by jointly learning to disentangle the visual item data and to infer a two-level alignment of the visual and textual item data in a multimodal disentangled space. As such, E-VAE tackles the main challenges in disentangling multimodal e-commerce data. Firstly, with the weak supervision of the two-level alignment our E-VAE learns to steer the disentanglement process towards discovering the relevant factors of variations in the multimodal data and to ignore irrelevant visual variations which are abundant in e-commerce data. Secondly, to the best of our knowledge our E-VAE is the first VAE-based framework that has an automatic interpretation mechanism that allows to explain the components of the disentangled item representations with text. With our textual explanations we provide insight in the quality of the disentanglement. Furthermore, we demonstrate that with our explainable disentangled item representations we achieve state-of-the-art outfit recommendation results on the Polyvore Outfits dataset and report new state-of-the-art cross-modal search results on the Amazon Dresses dataset.

ĐÁNH GIÁ KHẢ NĂNG NGHE HIỂU CÁC THÔNG TIN TƯỜNG MINH VÀ KHẢ NĂNG SUY LUẬN TRONG KHI NGHE: CÁC CÂU HỎI TRẮC NGHIỆM CÓ THẬT SỰ HIỆU QUẢ?

Multiple-choice questions (MCQs) are a widely accepted format used in second language (L2) listening tests. However, there is a lack of literature on the usefulness of this format in measuring the listening construct. This study investigates the extent to which MCQs engage two important components of the default listening construct: understanding explicit information and drawing inferences from the spoken input (Buck, 2001). Verbal reports were employed to explore the participants’ cognitive processes as they answered multiple-choice test items. Taking a qualitative approach, this study draws on the think-aloud protocols provided by 10 English majors from a university in Vietnam. The results show that while the multiple-choice format successfully measured the ability to understand explicit information, it promoted the use of test-taking strategies that compromised cognitive validity of the test items that targeted the ability to draw inferences. Practical suggestions were given so that MCQs can be better prepared to engage the inference-making process. These included input text preparation, item design, and double-play format. The challenges in measuring L2 learners’ ability to make inferences while listening were also discussed.

Transforming the generative pretrained transformer into augmented business text writer

This study uses transformers architecture of Artificial neural networks to generate artificial business text for a given topic or theme. The implication of the study is to augment the business report writing, and general business writings process with help of generative pretrained transformers (generative pretrained transformer (GPT)) networks. Main focus of study is to provide practical use case for GPTs models with help of big data. Our study model has 355 million model parameters and trained for three months on GPU enable devices using 2.3 billion text tokens(is available as open-source data now). Text tokens are collected with help of rigorous preprocessing, which includes; shortlisting of Subreddits of Fortune 500 companies and industries, listed on US-based social news aggregation online portal called “Reddit”. After shortlisting, millions of submission of users during the five years, are parsed to collect the URLs out of it. 1.8 million working URLs are scrutinized. Business text is parsed, cleaned, and converted into word embeddings out of uniform resoruce locator (URLs). The result shows that both models; conditional interactive and random sampling, generate text paragraphs that are grammatically accurate and stick to the given topic.

ArRASA: Channel Optimization for Deep Learning-Based Arabic NLU Chatbot Framework

Since the introduction of deep learning-based chatbots for knowledge services, many research and development efforts have been undertaken in a variety of fields. The global market for chatbots has grown dramatically as a result of strong demand. Nevertheless, open-domain chatbots’ limited functional scalability poses a challenge to their implementation in industries. Much work has been performed on creating chatbots for languages such as English, Chinese, etc. Still, there is a need to develop chatbots for other languages such as Arabic, Persian, etc., as they are widely used on the Internet today. In this paper, we introduce, ArRASA as a channel optimization strategy based on a deep-learning platform to create a chatbot that understands Arabic. ArRASA is a closed-domain chatbot that can be used in any Arabic industry. The proposed system consists of four major parts. These parts include tokenization of text, featurization, intent categorization and entity extraction. The performance of ArRASA is evaluated using traditional assessment metrics, i.e., accuracy and F1 score for the intent classification and entity extraction tasks in the Arabic language. The proposed framework archives promising results by securing 96%, 94% and 94%, 95% accuracy and an F1 score for intent classification and entity extraction, respectively.

Pediatricians’ Disclosure and Documentation of Sickle Cell Trait Found on Newborn Screening: Barriers and Possible Implementation Strategies

Introduction All states in the US require hemoglobinopathy testing as part of the newborn screen (NBS) which incidentally identifies individuals with sickle cell trait (SCT). Each state has its own protocol for disclosure of SCT results to families (i.e., via primary care provider (PCP), public health nurse, direct mailing to family) with no standards for documentation in the electronic health record (EHR). Our past work showed that SCT is documented 53% of the expected rate within our EHR. Though gaps in documentation may not equate to lack of disclosure, such gaps demonstrate a risk for inadequate counseling and loss of data with transitions of care. Further, the majority of individuals with SCT are Black or Hispanic. Failure to disclose SCT may contribute to inequities in healthcare and hamper informed reproductive decision making. Objective To identify barriers and facilitators to PCPs’ documentation and disclosure of SCT. Methods A cross sectional survey of outpatient PCPs at 43 pediatric offices within a pediatric health system in Delaware, Pennsylvania, and Florida was conducted. Using Likert scales, rating scales, and free text response, the survey assessed PCP practices surrounding NBS results, attitudes toward SCT disclosure and documentation, and ranking of implementation strategies from the "Expert Recommendations for Implementing Change” project intended to increase SCT documentation and disclosure. Recruitment and survey administration was via REDCap. Lunch was provided to offices with >75% participation. Free text items were analyzed using qualitative methods. Descriptive statistics were used for other items. The study was approved by the IRB. ResultsParticipants: Seventy-eight PCPs from 43 practices completed surveys (51% recruitment rate). Fourteen offices received lunch incentives. PCPs worked in their offices a mean of 7.1 years (SD 6.8y). Most (76%) identified as female and White (66%), with 17% Asian, 4% Black, and 12% Hispanic. Practice locations varied in percentages of patients insured by Medicaid (3-76%) or who identify as Black (2-91%). NBS Results: Most PCPs had a process for reviewing NBS results with ~1/3 actively seeking results (i.e., log into a separate portal). The majority "always” checked a NBS during infancy but "never/occasionally” checked for an older new patient, before sports participation, or transition to adult medicine. All PCPs endorsed the importance for families and PCPs to know about hemoglobin traits but 14% thought a family may not want to know and 30% thought a family may have trouble understanding results. Nearly half (45%) thought a non-PCP should discuss results. SCT: Almost all participants routinely discuss SCT during infancy. 16% routinely discuss it with adolescents and 56% "rarely/never” discuss SCT before transition to adult medicine. 10% "rarely/never” discuss inheritance patterns, but nearly one third "rarely/never” discuss possible medical complications and reproductive planning options. PCPs had varying confidence levels for several tasks (Table). Barriers: In free text questions, PCPs identified barriers to discussing SCT (during infancy or adolescence) including PCP knowledge/comfort, adequate time, NBS availability, lack of "good” handouts, and family health literacy. Additional barriers included believing adolescents are not interested. Potential Interventions: The highest ranked interventions to help increase the documentation and discussion of SCT included having a designated person to review the NBS, prompts within the EHR and templates for documentation, NBS program providing disclosure, longer appointment slots, educational sessions to increase PCP knowledge/comfort, and educational materials for families. Least helpful rated interventions included chart audits, incentives, leadership/society endorsements, or simulated discussions. Conclusions Despite PCPs recognizing the importance of discussing SCT, they rarely do so outside of infancy and have varying degrees of confidence in doing so. PCPs recognized several system-based (i.e. time, EHR prompts) and personal barriers (i.e. confidence, knowledge) to discussing SCT. They expressed preferences for a range of potential implementation strategies. Real world studies are needed to determine whether strategies designed to address these barriers increase discussion and documentation of SCT across childhood and enhance care equity. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Evaluating the Impact of a Year-Long External Mentorship Pilot Program in Classical Hematology

Introduction: Mentorship and access to role models in hematology/oncology (H/O), particularly in classical hematology (CH), are important factors in positively influencing trainee career decisions. However, mentorship opportunities are perceived as more accessible in oncology than hematology. Most mentorship experiences occur within an institution; therefore, creating access to external mentors may benefit trainees interested in CH if these opportunities are lacking. In April 2021, a year-long external mentorship pilot program (EMPP) developed through the ASH Medical Educators Institute was implemented to pair H/O fellows pursuing CH careers with faculty at external institutions. This mixed methods study aims to evaluate feasibility and impact of the program on personal satisfaction, academic productivity, access to networking opportunities, and career development of trainees interested in CH. Methods: Details about the EMPP were disseminated to fellowship program directors, social media, and listservs; 35 adult and pediatric H/O fellows applied, and all were accepted. Fellows were paired by 1 investigator (S.J.P.) with 34 mentors who were recruited based on hematology expertise and academic productivity (Figure 1). A 34- and 25-item survey was distributed to mentees and mentors, respectively, at the end of the program. Multiple choice, 5-point Likert-type Scale, and free text items were used to gather quantitative and qualitative data on their experiences. Participants were assumed to agree to a statement if they chose "agree" or "strongly agree" for Likert-type Scale items. Free text comments were analyzed by 2 investigators (Z.Q. and S.J.P.) with themes identified and agreed upon by both. Results: Twenty-eight (80.0%) mentees self-identified as female and 7 (20.0%) as male. Sixteen (45.7%) were White, 12 (34.3%) were Asian, 2 (5.7%) were Black, 1 (2.9%) was Arab, and 1 (2.9%) was White and Native American. Three (8.6%) were Hispanic. Thirteen (37.1%) and 22 (62.9%) were in or accepted into an adult or pediatric H/O program, respectively. There were 10 first-year (28.6%), 16 second-year (45.7%), 7 third-year fellows (20.0%), and 2 third-year pediatrics residents (5.7%); following inception of the program, all participants advanced a year in their training. Each mentee was paired with 1 mentor, with 1 mentor matched to 2 mentees. Thirty-three pairs (94.3%) completed the program. Thirty-one mentees (91.2%) and 26 mentors (78.9%) completed a post-program survey, which was distributed to 34 mentees and 33 mentors, as 1 pair started the program 4 months off cycle. Twenty-nine (93.5%) mentees and 22 (84.6%) mentors agreed that their pairing was a good match. In free text comments, mentees and mentors wrote that "similar interests" made their pairings successful. Mentees noted "approachability", "significant experience in mentoring," and similar "personality" as reasons why their mentor was a good match. Twenty mentees (64.5%) believed that this program increased their academic productivity, and 23 (74.2%) indicated that they had scholarly work published, submitted, or in progress as a result of the program (Table 1). Eighteen mentees (58.1%) connected with other faculty outside of their mentor through the program. Twenty-four mentees (77.4%) indicated that the program facilitated their career development, through obtaining "career advice" and finding a "sense of community." One participant wrote "[a]s a Black male meeting [mentor] and getting his direct mentorship provided me safety and permission to live up to my dream of being a physician scientist." Over half of mentees (54.8%) and mentors (53.8%) plan to continue their mentoring relationship beyond the required 1 year duration. Thirty mentees (96.8%) and 24 mentors (92.3%) agreed that they would recommend this program to other fellows and faculty, respectively. Conclusion: This EMPP was feasible as all applicants were matched with a mentor and most pairs completed the program. The program positively impacted the experience of fellows interested in CH in terms of personal satisfaction, academic productivity, access to networking opportunities, and career development. Factors leading to the program's success included strong mentee-mentor pairings, involvement in scholarly work, and connecting with CH faculty. Given the feasibility and positive reception, we plan to continue and expand the program beyond its pilot sites. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The Impact of the COVID-19 Crisis on the Practices and Mental Health of Psychologists in Belgium: Between Exhaustion and Resilience.

While the COVID-19 pandemic has created psychological distress in the general population and increased the need for psychological care, little research has been done on how mental health practitioners (MHP) have been affected by the pandemic, and these health professionals have received little attention from public authorities. In this article, we focus on psychologists and the impact that the pandemic has had on their mental health and practices by exploring the adaptive and innovative responses generated. This study is based on an online survey (including multiple choice questions, several validated scales, and eight free text items) completed by 187 psychologists (86% female) one year after the beginning of the COVID-19 pandemic in Belgium (February-April 2021). Most participants considered that the crisis had an impact on their well-being and mental health. However, the prevalence of symptoms of depression and anxiety was relatively low (17%; 12%). On the other hand, the majority of psychologists (72%) suffered from a medium level of burnout (BO), 7% suffered from a high level of BO, and only 21% had low levels of BO. Psychologists working in face-to-face settings had the highest scores on the "exhaustion" subscale of the BO, and those working primarily with patients in precarious situations had significantly higher scores of BO and exhaustion. Qualitative analysis of free text items showed that MHP were resilience and developed new frameworks and modes for proactive interventions in order to reach their patients, meet the psychological and social population's needs, and maintain their relationships with the network. In a crisis or pandemic context, public policies should take into account the psychological and social needs of the most socially precarious populations in reinforcing and supporting mental health professionals working in this sector.

A Deep Learning Approach for The Detection of Structured Query Language Injection Vulnerability

With the rapid development of Web 2.0 technology, network applications have gradually become an indispensable part of our lives. At the same time, Web applications are confronted with more challenges. As announced by the OWASP (open web application security project) organization, injection attack has been the first of the top 10 security vulnerabilities in 2013 and 2017, and SQL injection attack is one of the most important types among the injection attacks. Due to the rapid growth of SQL injection attacks on web application, this research developed a deep learning model in detecting SQL injection attack. The model was trained on a dataset that contains about 30,635 queries, which includes both injected and non-injected queries. The dataset was gotten from Kaggle database. The dataset was then processed by removing null and duplicate values. Further pre-processing was carried out in terms of tokenization and conversion of text to arrays. CountVectorizer () function was used for data normalization in converting the dataset to arrays in form of 0s and 1s. After the pre-processing stage, Feature selection was done on the dataset using the tfidvectoriser. The selected features were passed to the deep feed forward neural network for training. The model was trained on a step of 20 epochs, the model achieved an accuracy of 97.65%. Confusion matrix depicts the total number of correct prediction and the total number of false classifications. The confusion matrix shows that out of 590 classifications on attacks that are of normal, the model predicted correctly for 572 and predicted falsely for 16 times. Then for attacks that are of SQL injection, the model predicted correctly 251 times and predicted falsely for just 1. This shows the performance of the model is in good shape. The model was saved and deployed to web using python flask for easy testing and usage. The model was compared with other existing models and it outperformed the existing model in terms of accuracy. This research can further be extended by using combinations of deep learning algorithms. It can further be extended by deploying the model to android applications.

Procedures Used in the Translation of Culture Specific Items in Moroccan Literary Texts: Lu'bat al-Nisyan as a Case Study

In recent times, literary translation seems to have gained considerable interest among translation scholars and translators. Furthermore, translating culture-specific items (CSIs) in literary texts appear to be one of the most challenging tasks a translator must overcome. In the present article, attempts were made to describe the most and least frequent translation procedures adopted by translators to overcome cultural barriers in the translation of the literary text. The current study used Newmark’s model (1988) for cultural translation, which includes cultural categories and translation procedures to classify the data. Through this model, it was concluded that in the case studied, ‘The Game of Forgetting’, the translator used most frequently modulation, functional equivalent and cultural equivalent to render the meaning of the source text, such as expansion and notes were barely used.

Translation of Hollywood film titles: Implications of Culture-Specific Items in Greater China

In view of the lack of updated analysis on film title translation in Greater China, the present study attempted to investigate translation of culture-specific items in Hollywood film titles among three regions of Greater China: Mainland China, Hong Kong and Taiwan. From 1989 to 2018, a film title database was built, comprising of 2472 source texts and over 7410 target texts. Culture-specific items were identified and classified into five themes, namely toponym; anthroponym and fictional character; forms of entertainment; means of transportation; and social taboos. Analysis was in two tiers: First, translation methods under each theme was compared within target regions. Second, corresponding cultural implications of the three target regions were discussed using the concept of glocalisation. In a translational perspective, adaptation was highly favoured by Hong Kong under film title translation, whereas transliterations and literal translations were preferred by Mainland China. In a cultural perspective, both Mainland China and Hong Kong were found to preserve local cultures via translation. While Mainland China attempted to protect the purity of Chinese language through using transliterations and literal translations, Hong Kong used Cantonese slangs and jargons to replace culture-specific items in source text. Different from the former regions, Taiwan adopted exotic and explicit translation of social taboos. The present research sheds new light on Translation Studies research by analyzing film title translation in a sociocultural perspective, and thus can offer stakeholders in the film industry to appreciate translation in another perspective.

Stylistic Fingerprints, POS-tags, and Inflected Languages: A Case Study in Polish

ABSTRACT In stylometric investigations, frequencies of the most frequent words (MFWs) and character n-grams outperform other style-markers, even if their performance varies significantly across languages. In inflected languages, word endings play a prominent role, and hence different word forms cannot be recognized using generic text tokenization. Countless inflected word forms make frequencies sparse, making most statistical procedures complicated. Presumably, applying one of the NLP techniques, such as lemmatization and/or parsing, might increase the performance of classification. The aim of this paper is to examine the usefulness of grammatical features (as assessed via POS-tag n-grams) and lemmatized forms in recognizing authorial profiles, in order to address the underlying issue of the degree of freedom of choice within lexis and grammar. Using a corpus of Polish novels, we performed a series of supervised authorship attribution benchmarks, in order to compare the classification accuracy for different types of lexical and syntactic style-markers. Even if the performance of POS-tags as well as lemmatized forms was notoriously worse than that of lexical markers, the difference was not substantial and never exceeded ca. 15%.

Is there a gender difference in STEM students' perceived employability?

PurposeThe study sought to determine whether there are gender differences in self-perceived employability of students enrolled in Australian higher education science, technology, engineering and mathematics (STEM) programs.Design/methodology/approachUsing an online measure comprised of Likert style and open text items, STEM students (n = 3,134) reported their perceived employability in relation to nine dimensions of employability identified from the literature as having relevance to careers in STEM. Analysis determined whether student confidence differed according to gender, field of study, study mode, age, and engagement with work.FindingsFemale students in STEM reported higher mean factor scores in relation to their self- and program-awareness, self-regulated learning, and academic self-efficacy. Male students were more confident in relation to digital literacy skills; these findings were consistent both overall and across several fields of study within STEM. Gender differences were observed across study mode, age, and engagement with work.Originality/valueThe analyses of students' perceived employability provide important insights into the formation of a STEM “identity” among female students. The study has implications for policy, higher education, the engagement of girls in early STEM education, and future research.

Trialing alternative listening assessment tasks: interactions between text authenticity, item focus and item presentation condition

ABSTRACT Purpose: The current study applies an innovative approach to the assessment of second language listening comprehension skills. This is an important focus in need of innovation because scores generated through language assessment tasks should reflect variation in the target skill and the literature broadly suggests that conventional methods of assessing listening may fall short of achieving this principle (Field [2019]. Rethinking the Second Language Listening Test from Theory to Practice. Equinox). Design: The study investigated interactions between different methods of presenting listening comprehension questions, the focus of the comprehension questions and the relative authenticity of the sound file in an English-medium university entrance listening test. In a balanced design, 61 participants completed a listening test featuring both scripted and unscripted sound files by answering explicit and implicit information comprehension questions under five counterbalanced question preview and presentation conditions. Test scores were analysed using ANOVA and examined for interactions. Findings: The results revealed interesting relationships between text authenticity and item focus whereby item responses were most frequently correct overall on the explicit items on scripted tasks. However, the reverse was observed on the implicit items, which were more frequently correct on the unscripted tasks. Value: The research findings have important implications for construct definition and highlight possible directions for the development of pedagogical listening tasks and assessments for English-medium academic study.

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.

IMPROVING STUDENTS’ SKILL IN WRITING NEWS ITEM TEXT BY IMPLEMENTING SILLY NEWS REPORTS GAME

The tendency to ignore the lesson and make distraction in writing course led to bad result in writing assessment especially in news item text. This research intended to see how silly news reports game improve students’ skill in writing news item text particularly in improving students’ motivation as interest in writing. This research was a Classroom Action Research (CAR) in three cycles to 37 students at class XII IPA 6 of SMAN 3 Pontianak in academic year 2019/2020. The result of the data collection showed that implementing silly news reports game improved students’ skill in writing news item text in terms of writing composition and grammar accuracy on using reported speech, and improved students’ motivation as interest in writing. Based on the observation, the highlight of the improvement by the game implementation was on the content of silly news reports game which is ridiculous yet easy to understand supported by the guided question worksheets that made the students able to produce a well-organized writing by considering the composition of the writing such as the organization of the text and the language features used in the news item text. The improvement of each cycle differed one to another. While cycle one found the gap of the student improvement, cycle two solved the problem of students writing composition and using reported speech, and cycle three solved the problem of students’ behaviour. Therefore, the researcher recommends that the teacher implement silly news reports game to teach news item text of writing course.

SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning

Answering complex questions about images is an ambitious goal for machine intelligence, which requires a joint understanding of images, text, and commonsense knowledge, as well as a strong reasoning ability. Recently, multimodal Transformers have made a great progress in the task of Visual Commonsense Reasoning (VCR), by jointly understanding visual objects and text tokens through layers of cross-modality attention. However, these approaches do not utilize the rich structure of the scene and the interactions between objects which are essential in answering complex commonsense questions. We propose a Scene Graph Enhanced Image-Text Learning (SGEITL) framework to incorporate visual scene graph in commonsense reasoning. In order to exploit the scene graph structure, at the model structure level, we propose a multihop graph transformer for regularizing attention interaction among hops. As for pre-training, a scene-graph-aware pre-training method is proposed to leverage structure knowledge extracted in visual scene graph. Moreover, we introduce a method to train and generate domain relevant visual scene graph using textual annotations in a weakly-supervised manner. Extensive experiments on VCR and other tasks show significant performance boost compared with the state-of-the-art methods, and prove the efficacy of each proposed component.

Establishing national stakeholder priorities for quality improvement in pediatric trauma care: Consensus results using a modified Delphi process.

Quality improvement efforts within pediatric trauma centers (PTCs) are robust, but the majority of children do not receive initial postinjury care at PTCs. Disparities in access to quality trauma care remain, particularly for children who initially access the trauma system outside of a PTC. The purpose of this project was to identify unmet needs for injured children within the pediatric emergency care system and to determine national priorities for quality improvement across the continuum of pediatric trauma care. A panel of delegates representing patients and families, prehospital providers, federal funding partners, nurses, and physicians was recruited from 10 national stakeholder organizations. Potential targets were identified using an initial stakeholder meeting followed by a free text response survey. Free text items were coded and condensed as themes and then ranked by the panel using a modified Delphi approach to determine consensus priorities. Items not achieving >35% prioritization on a given iteration were dropped from subsequent iterations. Consensus was defined as 75% of members designating an item as a top-four priority. Nineteen themes were identified as potential targets for QI initiatives. Four iterations of panel ranking were used to achieve consensus, with four priorities identified: (1) creation of a toolkit and standard provider training for pediatric trauma triage, shock recognition, and early recognition for need to transfer to higher level of care; (2) development of minimum standards for pediatric trauma resuscitation and stabilization capability in nonpediatric centers; (3) facilitating creation of local nursing and physician champions for pediatric trauma; and (4) development and dissemination of best-practice guidelines to improve imaging practices for injured children. System-level quality improvement priorities for pediatric trauma care should focus resources on developing and implementing minimum pediatric standards for injury care, frontline provider training, stabilization protocols, imaging guidelines, and local pediatric champions. Therapeutic/Care Management; Level IV.

Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction.

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F2-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.

Emoji image symbol’s social function and application

<p id="p00005">Nowadays, with the rapid development of the Internet, online communication has become increasingly popular and popular. However, due to the lack of nonverbal cues in face-to-face communication, it is difficult for people to detect the emotional state of each other, which hinders normal communication. Emoji, which compensate for nonverbal cues in online communication, have been introduced into cyberspace to compensate for the absence of gestures and facial attributes in online communication, and have been developing constantly. The development of Emojis has gone through Emoticon composed of ASCII characters, Emoji of pictographic icons and now emerging more vivid and interesting stickers. This paper will mainly comb, analyze and summarize the functions, influencing factors and interaction mechanisms of Emoji in network communication, as well as the current application of Emoji in different fields, and put forward the future research direction of Emoji based on the current research status of Emoji. <br/>At the beginning, Emoji is borrowed from Japanese animation symbols, and gradually developed into a widely used image symbol system. Since the creation of Emoji by Shigetaka Kurita in 1999, it has been enriched and developed continuously At present, Emoji has become a tool commonly used around the world to replace non-verbal cues such as body gestures and facial expressions in digital communication. In the process of continuous use in Internet communication, Emoji has been equipped with many functions, including expressing emotions, enhancing expression, changing tone, maintaining or enhancing interpersonal relationship, etc. At the same time, the use of Emoji is also affected by many factors, including age, gender, culture, context and platform. In addition, we also explore the interactive mechanism of Emoji in online communication from the perspective of symbol interaction theory, so as to clearly reveal the specific interaction process of people in online communication through Emoji. <br/>At present, with the continuous development and widespread use of Emoji, its application scope has been extended to many other fields besides Internet communication. In the field of sentiment analysis, Emoji has become an important object of sentiment analysis due to its rich emotions. In psychometrics, Emoji has been developed into a nonverbal tool for evaluating personality and depression which has the same reliability and validity as text items. In the field of commercial marketing, Emoji has begun to play a role in advertising marketing and attracting consumers, and can measure consumers' food-related emotions in the form of questionnaires. In the field of legal judgment, Emoji has gradually become a powerful evidence in judicial trials due to its widespread use. Through the above analysis and summary, we put forward the future research direction of Emoji from the following aspects: (1) explore the application and future development trend of Emoji in online communication; (2) study on the application of Emoji in other fields; (3) further explore the neurophysiological mechanism of Emoji; (4) discuss the positive effect of Emoji in online communication from the perspective of cognitive processing.