Sportsman’s Mental State Evaluation and Early Warning Method Using Decision Tree

Aiming at the intelligent needs of psychological state assessment of university students, the text information-based psychological problem identification approach is investigated in the paper. This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling layer, and a fully connected layer. After the convolution is completed, the convolution result is de-linearized by the activation function, and then pooling is performed to improve the fitting ability of the network for nonlinearities. For data processing, behavioral features attribute features, content features, and social relationship features are extracted from text information as the input of the CNN by using the decision tree. The psychological lexicon of expertise (LIWC) is used to enhance the efficiency of text word frequency statistics when performing text content extraction. To evaluate the performance of the proposed method, simulations are performed in the open dataset of CLPsyh2017 ReachOut Forum, and the FastText method is used as a comparison. The results show that the CNN model achieves an accuracy of 0.71 in the full-sample domain, which is significantly higher than that of the FastText model at 0.64. In the early warning evaluation of mental states, the CNN performance is better than that of FastText.

Unfolding visual characteristics of social media communication: reflections of smart tourism destinations

Purpose This study aims to explore the visual social media (SM) (Instagram) communication and the visual characteristics of smart tourism destination (STD) communication from destination marketing/management organizations (DMOs) and user-generated content (UGC) perspectives, which refer to projected image and perceived image, respectively. Design/methodology/approach Three DMO official accounts of STDs (Helsinki, Gothenburg and Lyon) and corresponding official hashtags were selected for the sample and total 6,000 post data (1,000 × 6) were retrieved from Instagram. Visual communication content was examined with a netnographic design over a proposed four-level visual content framework using corresponding methodological approaches (thematic analysis, visual analysis, object detection and text mining) for each level. Findings Among the eight emerging themes dominating the images, communication of smart elements conveys far less than expected textual and visual signals from DMOs despite their smart status, and in turn, from UGC as well. UGC revealed three extra image themes regardless of smartness perception. DMOs tend to project and give voice to their standard metropolitan areas and neighborhoods while UGCs focus on food-related and emotional elements. The findings show a partial overlap between DMOs and UGCs, revealing discrepancies in objects contained in visuals, hashtags and emojis. Additionally, as a rare attempt, the proposed framework for visual content analysis showed the importance of integrated methods to investigate visual content effectively. Research limitations/implications The number of attributes in visual analysis and focusing on the observed elements in text content (text, hashtags and emojis) are the limitations of the study in terms of methodology. Originality/value Apart from the multiple integrated methods used over a netnographic design, this study differs from existing SM and smart destinations intersection literature by attempting to fill a gap in focusing on and exploring visual SM communication, which is scarce in tourism context, for the contents generated by DMOs and users.

Using Infographics as an Effective Technological Tool With Adult Language College Students

Infographics are like a map of visual information, and they are free great technological tools to teach visual or text content in an easy, enjoyable, engaging way. This chapter highlights the importance of using infographics with university adult learners in face-to-face and online courses. Infographics allow for higher thinking skills such as evaluation and analysis as the designer makes judgments about the content, design, and quality. They also allow for making syntheses as the designer plans and then builds their infographic. The use of higher thinking skills makes learning the materials more effective and more accessible. The chapter focuses on how to enhance learning and teaching by using visuals through infographics. The use of infographics makes teaching more culturally relevant and allows for creativity. The chapter discusses the benefits of infographics and talks about how teachers can use them in their classes. The chapter also provides some sample lessons with different ideas on using infographics and concludes with some recommendations.

The Book of Obadiah in the Septuagint

The Book of Obadiah, although short (it has only 21 verses; the shortest in the Hebrew Bible), is at the same time very difficult. The difficulties are manifested in its linguistic and textual layers, but above all in what concerns its content, theology and interpretation. The Greek translation of Obad contained in the LXX is particularly important because it represents a way of understanding the Book going back to pre-Christian, Hellenistic times, which strongly emphasised the theme of threats to Israel from other nations. In the Greek translation (LXXObad), the cursing character of the Book is radicalised and the guilt of the enemies (Edomites – Idumeans) is highlighted. The article presents the Book of Obadiah in its historical context (both the Hebrew original and the Greek version), and presents its text, content and character in the Septuagint version. It compares it with LXXJer 29 (LXX numbering) and shows how the challenging theology of the Book was understood among the Jews of Hellenistic Alexandria. The universalisation of the message of the Book by the LXX translation was later continued in its patristic and rabbinic interpretations.

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

Geospatial data is an indispensable data resource for research and applications in many fields. The technologies and applications related to geospatial data are constantly advancing and updating, so identifying the technologies and applications among them will help foster and fund further innovation. Through topic analysis, new research hotspots can be discovered by understanding the whole development process of a topic. At present, the main methods to determine topics are peer review and bibliometrics, however they just review relevant literature or perform simple frequency analysis. This paper proposes a new topic discovery method, which combines a word embedding method, based on a pre-trained model, Bert, and a spherical k-means clustering algorithm, and applies the similarity between literature and topics to assign literature to different topics. The proposed method was applied to 266 pieces of literature related to geospatial data over the past five years. First, according to the number of publications, the trend analysis of technologies and applications related to geospatial data in several leading countries was conducted. Then, the consistency of the proposed method and the existing method PLSA (Probabilistic Latent Semantic Analysis) was evaluated by using two similar consistency evaluation indicators (i.e., U-Mass and NMPI). The results show that the method proposed in this paper can well reveal text content, determine development trends, and produce more coherent topics, and that the overall performance of Bert-LSA is better than PLSA using NPMI and U-Mass. This method is not limited to trend analysis using the data in this paper; it can also be used for the topic analysis of other types of texts.

Evaluation of Drug Procurement with the E-Phurchasing System on the Availability of Drugs at the Pharmacy Installation of the Central Aceh Regency Health Office 2021

The purpose of the study was to evaluate drug procurement with the E-Purchasing system on the availability of drugs at the Pharmacy Installation of the Health Office of Central Aceh Regency. The research design used qualitative research methods through in-depth interviews accompanied by direct observation (observation). Informants of the Head of the Health Office, the Head of the Pharmacy Installation of the Health Office, the Planning and Finance Subdivision of the Health Office, the drug management officer at the Health Office and the health center drug management officer were 2 people. Data analysis is presented in the form of a text (content analysis). The results of the research on the availability of drugs at the Pharmacy Installation of the Aceh Tengan District Health Office have not been maximized, the process of ordering drugs by E-purchasing and Non-E-purchasing is in accordance with PMK No. 63 of 2014, but the fulfillment time is not in accordance with the 2014 Pharmaceutical Service Standards. The budget provided for the drug procurement process by e-purchasing is not sufficient. The conclusion of this study is that the availability of drugs in the Pharmacy Installation of the District Health Office of Central Aceh Regency has not been maximized. This drug vacancy is caused by the number of drugs that are not all realized, the time of drug delivery by the distributor.

The Gaia Explorer, a Powerful Search Platform

After two years of development, the GAIA Explorer is now ready to assist Geoscientists at Total! This knowledge platform works like a little Google, but with a focus solely on Geosciences - for the time being. The main goal of the GAIA Explorer is to save time finding the right information. Therefore, it is particularly useful for datarooms or after business acquisitions to quickly digest the knowledge, but also for feeding databases, exploration syntheses, reservoir studies, or even staff onboarding specially when remote working. With this additional time, Geoscientists can focus on tasks with added value, such as to synthesize, find analogies or propose alternative scenarios. This new companion automatically organizes and extracts knowledge from a large number of unstructured technical documents by using Machine Learning (ML). All the models relie on Google Cloud Platform (GCP) and have been trained on our own datasets, which cover main petroleum domains such as geosciences and operations. First, the layout of more than 75,000 document pages were analyzed for training a segmentation model, which extracts three types of content (text, images and tables). Secondly, the text content extracted from about 6,500 documents labelled amongst 30 classes was used to train a model for document classification. Thirdly, more than 55,000 images were categorized amongst 45 classes to customize a model of image classification covering a large panel of figures such as maps, logs, seismic sections, or core pictures. Finally, all the terms (n-grams) extracted from objects are compared with an inhouse thesaurus to automatically tag related topics such as basin, field, geological formation, acquisition, measure. All these elementary bricks are connected and used for feeding a knowledge database that can be quickly and exhaustively searched. Today, the GAIA Explorer searches within texts, images and tables from a corpus (document collection), which can be made up of both technical and operational reports, meeting presentations and academic publications. By combining queries (keywords or natural language) with a large array of filters (by classes and topics), the outcomes are easily refined and exploitable. Since the release of a production version in February 2021 at Total, about 180 users for 30 projects regularly use the tool for exploration and development purposes. This first version is following a continuous training cycle including active learning and, preliminary user feedback is good and admits that some information would have been difficult to locate without the GAIA Explorer. In the future, the GAIA Explorer could be significantly improved by implementing knowledge graph based on an ontology dedicated specific to petroleum domains. Along with the help of Specialists in related activities such as drilling, project or contract, the tool could cover the complete range of upstream topics and be useful for other business with time.

An analysis of a third-party food delivery app during the COVID-19 pandemic

PurposeThe study aims to analyze guest comments on the Uber Eats food delivery app (FDA) in the USA during the April–June 2020 COVID-19 pandemic lockdown period. Three aspects influencing customer satisfaction with the FDA were evaluated in this study: (1) performance on the app, (2) product quality and (3) service quality.Design/methodology/approachOne thousand customer comments posted on the Uber Eats Google Play app from April 1 to June 30, 2020 were analyzed in this study. The text mining technique was applied to discover the hidden, but meaningful patterns from the unstructured text. Content analysis was applied to systematically analyze the text into organized categories and themes.FindingsAmong the three dimensions evaluated in this study, the most important dimension regarding customers' perceptions toward the FDA was the service quality dimension (40.02%), followed by the FDA's performance dimension (39.43%) and the product quality dimension (20.54%) was least important. Additionally, customers' perceptions towards the three dimensions were all unfavorable and there were more negative comments than the positive comments: FDAs (P/N = 0.728), product quality (P/N = 0.60) and service quality (P/N = 0.865).Originality/valuePrevious studies investigating FDAs assessed solely the performance of the app. However, customers' experience of a food delivery service is comprised of multiple components including the app, the restaurant and the delivery driver. To fill the void, this study evaluated a third-party app performance, product quality and service quality to capture the totality of customers' food delivery service experience.

U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images

Tables are an important element in a document and can express more information with fewer words. Due to the different arrangements of tables and texts, as well as the variety of layouts, table detection is a challenge in the field of document analysis. Nowadays, as Optical Character Recognition technology has gradually matured, it can help us to obtain text information quickly, and the ability to accurately detect table structures can improve the efficiency of obtaining text content. The process of document digitization is influenced by the editor’s style on the table layout. In addition, many industries rely on a large number of people to process data, which has high expense, thus, the industry imports artificial intelligence and Robotic Process Automation to handle simple and complicated routine text digitization work. Therefore, this paper proposes an end-to-end table detection model, U-SSD, as based on the object detection method of deep learning, takes the Single Shot MultiBox Detector (SSD) as the basic model architecture, improves it by U-Net, and adds dilated convolution to enhance the feature learning capability of the network. The experiment in this study uses the dataset of accident claim documents, as provided by a Taiwanese Law Firm, and conducts table detection. The experimental results show that the proposed method is effective. In addition, the results of the evaluation on open dataset of TableBank, Github, and ICDAR13 show that the SSD-based network architectures can achieve good performance.

Interactive Innovation Research on Film Animation Based on Maya-Unity Animation Simulation of Visual Sensor

At present, enhancing the understanding of the importance of the development of cultural and creative industries and grasping the future development trends of the whole society has become the primary issue for the development of this industry in our country. The main work of this paper is based on the interactive innovation research of visual sensor Maya-Unity animation simulation in film and television animation. Maya Unity puts the required lights on the model, adds texture materials, etc., selects all objects, and uses the “Mesh- > Combine” command. Then, open “Create UV->Automatic Mapping” for these objects to perform the UV operation. Open “Window-> UV Texture Editor” and check the new UV for baking. First, the article introduces an overview of the development of cultural and creative industries and the application status of virtual simulation technology in film and television animation. Then, the article introduces the characteristics of visual sensors and interactive design ideas. The scene view displays the document from a graphical perspective, while the code view displays the text content of the document. Both views must be displayed in the split window Dreamer, and only one view can be displayed at the same time; so, it is necessary to establish a button for scene and code switching. The animation scene will change with the passage of time following the development of the story. Therefore, in the lighting design, the animation changes of light intensity and color temperature should be produced. The time to complete a frame of image matching is 135 ms, and to complete a feature measurement, 8 frames of images are used for template matching, and then the average coordinate value is calculated, which takes about 135 × 8 = 1080 ms , which meets the system requirements. The main effect of the text effect is very significant, F = 13.780 , P < 0.001 . Comparison of effect between development and middle to two sides, P = 0.02 , P < 0.05 . The results show that the use of Maya-Unity software for animation simulation can make the rendering of the animation more beautiful.