Intelligent Question Research Articles

Designing a Qualitative Model of Verbal Hypnotism in Working Relationships of Government Organizations

Verbal hypnosis as a communication technique plays a multifaceted role in strengthening working relationships in government organizations and acts as a catalyst for improving interpersonal and group communication, trust building and effective change management. The main goal of the current research is to design a qualitative model of verbal hypnotism in working relationships of government organizations. The research design in this study is qualitative and content analysis method is used. The statistical population of the present study are experts in the fields of public administration and psychology who have conducted research in the field of verbal hypnotism and verbal communication. The sampling method is snow neck, based on which 15 people were interviewed. The data collection tool was open semi-structured interview. Content validity (experts) and face validity were used to measure the instrument's validity. In order to validate the coding process and quality control of the questionnaire, Cohen's kappa index was used in the qualitative part. The findings of the research showed that the qualitative model of verbal hypnotism in the working relationships of government organizations includes 1. Synchronization and companionship with the experience and feelings of the audience (which includes: the first meeting, creating mental conflict to distract the audience, creating harmony with the audience, feeling valuable , asking intelligent questions and creating and maintaining effective communication) 2. Deviation and ambiguity in the conscious mind of the audience (including deviation in the conscious mind of the audience, ambiguity in words, downplaying the facts, attracting attention, processing social information) and 3. Accessing the subconscious mind of the audience (includes discovering unconscious stimuli, mental imagery, using special words and discovering new facts).

Research on Intelligent English Education Based on the Short Video Recommendation Algorithm.

In order to solve the problems of English education in the form of a short video, a research method of English intelligent education based on a short video recommendation algorithm was proposed. The recommendation system is a branch of artificial intelligence data mining, which improves the efficiency of short videos for English learning. The density ratio of users and video scoring matrix was 1000000/(1030 × 9394) = 10.3%. The dataset was a relatively sparse matrix. The original dataset was randomly divided into the training set and the test set, accounting for 80% and 20%, respectively. Then, the results of the short video recommendation algorithm were elaborated based on time weighting. Finally, the intelligent initial question bank of English intelligent education based on a short video recommendation algorithm was elaborated, which provided a guarantee for the promotion of short videos in English education.

Visual Question Generation From Remote Sensing Images

Visual question generation (VQG) is a fundamental task in vision-language understanding that aims to generate relevant questions about the given input image. In this paper, we propose a paragraph-based VQG approach for generating intelligent questions in natural language about remote sensing (RS) images. Specifically, our proposed framework consists of two transformer-based vision and language models. First, we employ a swin-transformer encoder to generate a multi-scale representative visual feature from the image. Then, this feature is used as a prefix to guide a generative pre-trained transformer-2 (GPT-2) decoder in generating multiple questions in the form of a paragraph to cover the abundant visual information contained in the RS scene. To train the model, the language decoder is fine-tuned on RS dataset to generate a set of relevant questions from the RS image. We evaluate our model on two visual question-answering (VQA) datasets in RS. Additionally, we construct a new dataset termed TextRS-VQA for better evaluation for our VQG model. This dataset consists of questions completely annotated by humans which addresses the high redundancy of the questions in prior VQA datasets. Extensive experiments using several accuracy and diversity metrics demonstrate the effectiveness of our proposed VQG model in generating meaningful, valid, and diverse questions from RS images. The TextRS-VQA dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yakoubbazi/TextRS</uri> .

Retracted: Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching.

[This retracts the article DOI: 10.1155/2022/5755327.].

Models and Techniques for Domain Relation Extraction: A Survey

As the significant subtask of information extraction, relation extraction (RE) aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications including knowledge graphs, intelligent question answering, text mining, and sentiment analysis. Different from general knowledge, domain knowledge is pertinent to specific fields which include a wealth of proprietary entities and relations. Besides, most of the data are formed as documents rather than sentences. In this paper, the task of domain RE is defined, and the common domains are presented. Furthermore, we provide a systematic review of state-of-the-art techniques as well as the latest trends. We survey different neural network-based techniques for RE and describe the overall framework, training procedures, as well as the pros and cons of these techniques. Then, we introduce and compare the corpus and metrics used for domain RE tasks. Finally, we conclude and discuss future research issues of domain RE.

Design of Intelligent Question Answering System for Hospital Online Triage based on Knowledge Graph

At present, people pay more and more attention to medical and health issues, and more and more Internet users are eager to obtain medical related knowledge from the Internet. However, despite the large amount of medical data spread on the Internet, it is difficult for ordinary Internet users to effectively obtain and use these data through search engines. To solve this problem, this paper designs and implements a medical question answering system based on knowledge map, so that users can quickly obtain the relevant knowledge in the medical field they want by asking questions, and realize the effective use of medical data on the Internet. At present, there are few question answering systems in the medical and health field, so the medical question answering system designed in this paper has a broad application prospect. The medical knowledge map constructed in the system can also be used in more application scenarios such as medical chat robots in the future, further promoting the process of intelligence in the medical field.

An Overview of Knowledge Graph Reasoning: Key Technologies and Applications

In recent years, with the rapid development of Internet technology and applications, the scale of Internet data has exploded, which contains a significant amount of valuable knowledge. The best methods for the organization, expression, calculation, and deep analysis of this knowledge have attracted a great deal of attention. The knowledge graph has emerged as a rich and intuitive way to express knowledge. Knowledge reasoning based on knowledge graphs is one of the current research hot spots in knowledge graphs and has played an important role in wireless communication networks, intelligent question answering, and other applications. Knowledge graph-oriented knowledge reasoning aims to deduce new knowledge or identify wrong knowledge from existing knowledge. Different from traditional knowledge reasoning, knowledge reasoning methods oriented to knowledge graphs are more diversified due to the concise, intuitive, flexible, and rich knowledge expression forms in knowledge graphs. Based on the basic concepts of knowledge graphs and knowledge graph reasoning, this paper introduces the latest research progress in knowledge graph-oriented knowledge reasoning methods in recent years. Specifically, according to different reasoning methods, knowledge graph reasoning includes rule-based reasoning, distributed representation-based reasoning, neural network-based reasoning, and mixed reasoning. These methods are summarized in detail, and the future research directions and prospects of knowledge reasoning based on knowledge graphs are discussed and prospected.

Intelligent question answering method for construction safety hazard knowledge based on deep semantic mining

Timely safety hazard management can reduce the probability of safety accidents at construction sites. However, the formulation of safety hazard management measures is a time-consuming and labor-intensive process. This paper describes a safety hazard knowledge question answering method to automatically generate safety hazard management measures. The method builds a deep learning network fusing Bidirectional Encoder Representation from Transformer (BERT), Bidirectional Gated Recurrent Unit (BiGRU), and Self-attention mechanism to extract text semantic features. Taking the text semantic feature extraction mechanism as a subnet, an answer selection model based on a Siamese neural network is built to implement the deep matching of safety hazard questions and management measures. Experimental results from hydraulic engineering construction demonstrate that the proposed model outperforms the existing answer selection model. Meanwhile, a question answering system based on the proposed model is developed to address safety hazard management problems, which verifies the reliability and applicability of the model.

Of Question

Good question manifests goodness. Great question implies greatness. Where goodness ends greatness begins. Intelligent question confirms the scholar. Witty question amuses the audience. Slang question degrades the soul. It causes quarrel between parties involved. Among all questions embarrassing question is the worst of all.

Big Questions of Artificial Intelligence (AI) in Public Administration and Policy

Teknolojik gelişmeler, insanlık tarihi boyunca dönüm noktaları yaratmıştır. İdari yapıdaki dönüşümler teknolojik keşiflerin sonucudur. Yapay zekâ (YZ) ekosistemi ve algoritmalar günlük yaşantıları, toplulukları ve hükümet yapılarını her geçen gün daha fazla etkilemektedir. Devletler teknolojik geçişin koordinatörleri ve modern kamu yönetimi faaliyetlerinin temel denetçileridir. Bu nedenle, kamu yönetimi ve politikalarına yapay zekâ teknolojisini entegre etme, yönetme ve düzenleme konusunda çeşitli sorumlulukları vardır. Yapay zekayı kamu yönetimine ve politika oluşturma sürecine entegre etmek çok sayıda fırsata olanak tanımaktadır. Öte yandan, yapay zekâ teknoloji ekosistemi uzun vadede ekonomik, sosyal ve hatta siyasi yapılarda birçok tehdit ve risk potansiyeli barındırmaktadır. Bu makale, kamu yönetimi ve kamu politikası literatüründe yapay zekanın “büyük sorularına” odaklanmaktadır. Robert Behn tarafından 1995 yılında başlatılan büyük sorular tartışması bir kamu yönetimi araştırma gündeminin birincil itici gücü olarak büyük sorulara dikkat çekmektedir. Büyük sorular yaklaşımının temel motivasyonu, “sorular cevaplar kadar önemlidir” yaklaşımıyla şekillenmektedir. Bu araştırma yapay zekâ literatüründeki büyük soruları çerçevelendirmek ve AI yönetişimi araştırma gündemi perspektifinden olası yanıtları ve çözümleri tartışmayı amaçlamaktadır.

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs.

Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.

The prospects for the use of artificial intelligence in obstetrics in Ukraine

In Ukraine, there is a steady trend towards an increase in the incidence of various forms of hypertensive disorders, both mild and severe, among pregnant women. Preeclampsia and eclampsia are severe forms of hypertensive disorders during pregnancy, the rates of which tend to increase, and lead to severe morbidity, disability and mortality in both women and newborns.The article reveals the question of the artificial intelligence (AI) use in modern midwifery in the national health care system of Ukraine in order to prevent pregnancy complications, in particular eclampsia. Implementation of AI systems in medicine is one of the most important modern trends in global healthcare. AI technologies are fundamentally changing the world health care system, allowing to radically rework the system of medical diagnostics, the development of new medicines, and also to generally improve the quality of health care services while simultaneously reducing costs for medical clinics. AI allows analyzing placenta samples and calculating the risk for a woman’s health in future pregnancies. The system is designed to help obstetrician-gynecologists, who will find it useful to forecast possible complications of future births. The use of AI in clinical practice makes it possible to identify the most serious prognostic signs of damage to blood vessels of the placenta, which is called decidual vasculopathy in preeclampsia and eclampsia. If preeclampsia is detected at an early stage, the patient can be treated before symptoms appear.AI use must be in compliance with six principles of the WHO in order to limit risks and increase the possibilities of using AI technologies: protection of human autonomy; promotion of human well-being and safety and public interests; ensuring transparency and comprehensibility; promoting responsibility and accountability; ensuring inclusiveness and fairness; promotion of AI in accordance with the principles of adaptability and sustainable development. Adherence to the ethical principles of AI gives the green light for the development of modern health care systems. It can become an effective method of preventing eclampsia in pregnant women, women in labor, and newborns and preserving their health and life.

CLIQ! Intelligent Question Classifier for the elaboration of exams

<h2>Abstract</h2> Technologies that support teachers in exam preparation are explored by academic research and real-life applications. However, few solutions help the teacher identify the complexity of questions. This article introduces the "Intelligent Question Classifier" (CLIQ), a Python Natural Language Processing (NLP) tool for identifying questions complexity in Portuguese using Machine Learning. The software is easy to run, lightweight, and able of retrieving complexity according to linguistic patterns in the educator's historical data. Regarding its contributions, we highlight that CLIQ is ready for question classification, use of personal databases, and code reuse.

Correlation of Christian ethics and developments in artificial intelligence

ABSTRACT In the traditional understanding, Christian ethics touches upon issues of morality and the general structure of a human as a person. At the same time, Christian ethics does not touch upon questions and does not impose restrictions on the development of scientific principles and methodological structures of knowledge of the surrounding world. That is, Christian ethics allows and encourages the exploration of created space. The novelty of the study is fact that the understanding of the importance of artificial intelligence is based primarily on the belief that there is a need to develop boundaries where artificial intelligence is capable of making a decision that will not be approved by humans. The authors show that the creation of such a technology runs counter to the values of a religious nature and Christianity, in particular, due to the representation of freedom of technology, which has moral convictions not based on the principles of humanism. The study compares and develops the main criteria for correlating the sufficiency of ethical norms and the requirements of technological advancement. A promising question for further discussion is the question of the artificial intelligence's deliberate lack of attachment to a particular morality, including extreme or aggressive worldviews.

Intelligent question and answer system for building information modeling and artificial intelligence of things based on the bidirectional encoder representations from transformers model

In recent years, building information modeling and artificial intelligence of things (BIM-AIOTs) in the construction industry have gained much attention. Construction engineers and researchers learn about BIM-AIOT and increase their professional knowledge through internet searches. However, the large amount of information on the internet makes it difficult to find specific information. Although some previous work of BIM-related searches exists, most still search with a combination of keywords or longer terms. This paper utilizes a machine learning model with natural language processing (NLP) technique of bidirectional encoder representations from transformers (BERT) integrated with a mobile chatbot as a question and answer (QnA) system. The dataset used for modeling contained 3334 text paragraphs that shortened to 10,002 questions. The result shows an F1 score of around 65% accuracy, which is acceptable for model prediction. Then, the system verifies to synchronize to the server and user interface. The system works well for information search and offers a supporting automation information system in the construction industry. This study achieved conversational machine understanding and a user-friendly BIM-AIOT integration information searches platform. The proposed system has a reliable research-based information source. It is verified as an effective and efficient way to produce fast decision-making. The system is deemed a future application for research-based problem-solving solutions in Architecture, Engineering, and Construction (AEC).

English Intelligent Question Answering System Based on elliptic fitting equation

Abstract The elliptic equation intelligent question answering system based on the elliptic equation intelligent question answering system is an intelligent device that combines natural language and data retrieval technology. Knowledge domains used for different purposes are divided into open domains and restricted domains. In order to simplify the existing knowledge of English intelligence answer and elliptic algebra and reduce the efficiency of the questionnaire, a similar algorithm is designed and implemented in the process of English intelligence answer, and the method of distance ellipse switching is adopted. The experimental results show that the accuracy of the questionnaire answering the questions described in this paper is 17% higher than that of general research answers, which confirms the authenticity of the questionnaire algorithm mentioned in this paper. The system not only provides accurate answers to English questions for primary school students, but also plays an important role in the cultivation of English question and answer skills in primary and secondary schools.

THEORIA TO THEORY (AND BACK AGAIN): INTEGRATING MASTERMAN'S WRITINGS ON LANGUAGE AND RELIGION

AbstractThis article explores three aspects of Masterman's language work and applies them to questions of spiritual intelligence: metaphor, coherence, and ambiguity. First, metaphor, which is ubiquitous in ordinary language, both leads and misleads in religious and scientific understanding. Masterman's case for a “dual‐approach” to thinking, both speculative and critical, is explored and tied to concepts of moral‐spiritual development per Pierre Hadot and Hannah Arendt. Second, Masterman's work on machine translation presents semantic disambiguation as an emerging coherence wherein one gradually hones in on meaning through features of ordinary language (like redundancy and repetition). This is applied to the problem of comprehending difficult spiritual language, and tied to spiritual stretching and spiritual cartography. Third, Masterman's work with thesauri, rather than relying on words as having fixed meanings, appeals to a concept of semantic spaces, nebulae of variously interconnected meanings. This is constructed into an exhortation to reambiguate overfamiliar religious language, to reinvest one's quotidian surroundings with spiritual meaning through defamilarization.

Scientometric Study of Research in Information Retrieval in Medical Sciences

Background: Mapping scientific trends is one of the most important missions of scientometric research for effective research. The main goal of this paper was to visualize and draw the intellectual and cognitive structures of information retrieval (IR) in the medical sciences using science mapping. Methods: In this cross-sectional scientometric study, we recruited all documents indexed in the Web of Science database with the topic of storing and retrieval of information in medical sciences. To analyze the results, 3 software, SciMAT-v1.1.04, VOSviewer-v1.6.14, CitNetExplorer_v1.0.0, were used. Results: Our results showed that most scientific productions in this field fall into 2 categories: (1) effective methods of organizing information and (2) application and operation of the IR system in the process of intelligent questioning and answering, and analyzing information behaviors of physicians and health professionals. The results showed that the similarity index increased over time from 0.43 to 0.71. Analysis of the findings showed that similarity measures, expert systems, concepts, experience, answers, and multimodel IR clusters were considered as mature and completely centralized clusters in the first quarter of the strategic chart. Conclusion: Because of the dramatic approximation of the vocabulary used by researchers and a relative slowdown in the growth rate of the subject's domain in the last decade, it seems necessary to pay attention to the expansion of the fields of IR and the application of its concepts in medical information sciences. Also, it can be recommended that designers of IR systems and techniques in medical information sciences pay more attention to human factors attentively to develop new technologies and tools.

Formulaic language identification model based on GCN fusing associated information.

Formulaic language is a general term for ready-made structures in a language. It usually has fixed grammatical structure, stable language expression meaning and specific use context. The use of formulaic language can coordinate sentence generation in the process of writing and communication, and can significantly improve the idiomaticity and logic of machine translation, intelligent question answering and so on. New formulaic language is generated almost every day, and how to accurately identify them is a topic worthy of research. To this end, this article proposes a formulaic language identification model based on GCN fusing associated information. The innovation is that each sentence is constructed into a graph in which the nodes are part-of-speech features and semantic features of the words in the sentence and the edges between nodes are constructed according to mutual information and dependency syntactic relation. On this basis, the graph convolutional neural network is adopted to extract the associated information between words to mine deeper grammatical features. Therefore, it can improve the accuracy of formulaic language identification. The experimental results show that the model in this article is superior to the classical formulaic language identification model in terms of accuracy, recall and F1-score. It lays a foundation for the follow-up research of formulaic language identification tasks.

Prediction of Academic Formulaic Language based on Multi-feature Fusion

&lt;p&gt;Academic formulaic language is multi-word combinations with specific functions and semantics, which are important to improve the idiomaticity, fluency and logic of machine translation, intelligent question answering, automatic summarization, etc. In order to narrow the search range of the corpus and extract academic formulaic language more efficiently, this paper proposes a prediction model of academic formulaic language based on multi-feature fusion. The semantic features and part-of-speech features of the academic formulaic language are extracted separately, and then the late fusion method is used to learn multiple features and predict whether formulaic language is included in the sentence. Experimental results show that the late fusion method based on part-of-speech features and semantic features has the best predictive effect among the four fusion methods, which lays the foundation for further efficient recognition of academic formulaic language.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;