Intelligent Question Research Articles

Design of Multimedia Teaching Software Based on Distributed Network Architecture

With the leap of network technology and the vigorous development of online teaching, many universities are actively adopting online means to optimize teaching and course management. This paper focuses on building an efficient and comprehensive auxiliary teaching platform that integrates functions such as student learning monitoring, course management, online testing, and teacher–student interaction, aiming to improve the quality of education. Designs based on distributed B/S architecture and web technology to ensure efficient resource allocation and expansion, meet the needs of large-scale concurrent learning, and achieve cross-platform access to enhance user experience. The platform features personalized learning support, enhanced interactive collaboration, and the construction of a multimedia teaching database through data analysis. The innovation lies in using neural network technology to create an intelligent question answering module, utilizing cosine similarity to automatically group teaching resources, and using graph convolution and variational autoencoder techniques to construct a student performance monitoring model. Experimental verification shows that the platform runs stably, effectively reduces the blocking rate, and significantly improves students’ academic performance, pass rate, and learning interest. This design not only successfully completed the teaching task, but also provided valuable experience for the application of online-assisted teaching systems in subject education.

A Classification Intelligent Question Answering Model for Retrieval-Based Chatbots

Abstract Intelligent question answering (QA) models or chatbots automatically provide appropriate responses to questions posed by users. In terms of generating continuous responses, they are divided into generative and retrieval-based approaches. For retrieval-based QA models, the key issue is how to reduce the search space. This research focuses on a retrieval-based approach and proposes a classification intelligent question answering (CIQA) model. The CIQA model contains two stages, namely a question classification stage and an answer prediction stage. The first stage consists of building a classification ensemble based on a training set. The second stage uses the first stage classification ensemble to determine the appropriate categories for a test set and selects an appropriate deep learning QA model based on a chosen category. A new benchmark dataset for chatbot, SQuAD (Stanford question answering dataset) 2.0, is used to evaluate performance. Based on the outcome of our experiments, the proposed CIQA model outperforms the baseline model and demonstrates the feasibility of the proposed approach. JEL classification numbers: M15, O35. Keywords: Question answering, Ensemble learning, Deep learning, Retrieval-based QA models.

Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration

Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

Utilization of transformer model in multimodal data fusion learning: Cross-modal knowledge transfer in the new generation learning space

In response to the difficulties in integrating multimodal data and insufficient model generalization ability in traditional cross-modal knowledge transfer, this article used the Transformer model to explore it in the new generation learning space. Firstly, the article analyzed the processing methods of data and models in cross-modal knowledge transfer, and explored the application of Transformer models in the learning space. This model used natural language processing to represent and extract textual features, Mel Frequency Cepstral Coefficients (MFCCs) to represent and extract audio features, and Faster R-CNN (Faster Region-based Convolutional Neural Network) to represent and extract image features. The article also discussed the implementation process of the Transformer model functionality. The experiment used data from four datasets, including Quora Question Pairs, to test the performance of the model’s cross-modal knowledge transfer through intelligent question answering and task analysis. In single type data testing, the accuracy and recall of the model in this article were better than the comparison model in the three types of data. The highest accuracy and recall in the test set were 91% and 93%, respectively. In the most challenging multimodal intelligent question answering test, the speech-image question answering method achieved an accuracy rate of 89% in answering open questions, indicating that the model had good multimodal data fusion ability. In the analysis experiment of 6 homework prone knowledge points on images with text annotations, the induction accuracy reached 85%, indicating that the model had strong generalization ability. The experimental results showed that the Transformer model had good cross-modal knowledge transfer performance, providing a reference for subsequent research on cross-modal knowledge transfer in the new generation learning space.

Neural Dynamic Principles for an Intentional Embodied Agent.

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.

Design and Implementation of Intelligent Question Answering Robot for Human Resources and Social Security Based on DCU and ChatGLM6B Model

This paper presents a human resources and social security based on DCU and ChatGLM6B model. This solution combines the high performance computing power of iguang DCU with the natural language processing technology of ChatGLM6B model, aiming to provide users with efficient and convenient voice interaction experience. This paper first introduces the research background and significance of intelligent question answering robot, then expounds the design scheme and implementation process of the system, and finally conducts the test and performance analysis of the system. The experimental results show that the intelligent question-answering robot designed in this paper has high answer accuracy and good user experience, and can provide intelligent services for the field of human resources and social security.

Intelligent Question and Answer and Dialogue System

Intelligent Question and Answer and Dialogue System

Domain-Specific Few-Shot Table Prompt Question Answering via Contrastive Exemplar Selection

As a crucial task in natural language processing, table question answering has garnered significant attention from both the academic and industrial communities. It enables intelligent querying and question answering over structured data by translating natural language into corresponding SQL statements. Recently, there have been notable advancements in the general domain table question answering task, achieved through prompt learning with large language models. However, in specific domains, where tables often have a higher number of columns and questions tend to be more complex, large language models are prone to generating invalid SQL or NoSQL statements. To address the above issue, this paper proposes a novel few-shot table prompt question answering approach. Specifically, we design a prompt template construction strategy for structured SQL generation. It utilizes prompt templates to restructure the input for each test data and standardizes the model output, which can enhance the integrity and validity of generated SQL. Furthermore, this paper introduces a contrastive exemplar selection approach based on the question patterns and formats in domain-specific contexts. This enables the model to quickly retrieve the relevant exemplars and learn characteristics about given question. Experimental results on the two datasets in the domains of electric energy and structural inspection show that the proposed approach outperforms the baseline models across all comparison settings.

Design and Implementation of AI-based Judicial Big Data Intelligent Service Platform

Based on big data and artificial intelligence technology, the judicial big data intelligent service platform provides users with efficient and accurate legal consulting services. It has many functions such as intelligent question and answer, case analysis, and law inquiry. It also analyzes the application value of the intelligent legal consulting service platform in the field of judicial big data. By building an intelligent service platform, the rapid processing and analysis of massive judicial data has been realized, and the efficiency and quality of judicial work have been significantly improved. In addition, it emphasizes the importance of data management and quality control in the application of judicial big data, and puts forward specific measures to strengthen data management and quality control. With the continuous advancement of technology and the expansion of application fields, the platform is expected to achieve deep integration with other legal services, expand new application fields, and provide users with more comprehensive and efficient legal services. At the same time, it points out the problems that need to be paid attention to in the future development, such as data security, privacy protection and so on. Through in-depth analysis and research on judicial big data, the platform can provide strong support for judicial decision-making and promote the improvement of judicial justice and efficiency

Modification and completion of geological structure knowledge graph based on pattern matching

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

GPB and BAC: two novel models towards building an intelligent motor fault maintenance question answering system

Generally, the existing methods for constructing a knowledge graph used in a question answering system adopted two different models respectively, one is for identifying entities, and the other is for extracting relationships between entities. However, this method may reduce the quality of knowledge because it is very difficult to keep contextual information consistent with the same entities in the two different models. To address this issue, this paper proposes a model called GPB (GlobalPointer + BiLSTM) which integrates the BiLSTM into GlobalPointer through concatenation operations to simultaneously guarantee the rationality of identified entities and relationships between entities. In addition, to enhance the user experience using an intelligent motor fault maintenance question answering system, a model called BAC (BiLSTM + Attention + CRF) is proposed to identify named entities in user questions, and the BERT-wwm model is used to classify user intentions to improve the quality of answers. Finally, to verify the advantages of the proposed model GPB and BAC, comparative experiments and real application effects of the developed question answering system are demonstrated on our built motor fault maintenance dataset. The experimental results indicate that the constructed knowledge graph and developed question answering system provide engineers with high-quality motor maintenance knowledge services.

An “Enterview” with Christophe Le Mouël

In this “enterview” conducted by Robert Henderson, Christophe Le Mouël talks about his initial discovery of Jungian psychology as a teenager living in France, and describes how it helped him make sense of his own experience of the unconscious, as first expressed through poetry. He became a physicist in France before relocating to the United States, where he now serves as the executive director of the C.G. Jung Institute of Los Angeles. He is currently in the midst of training in Zürich to become a professional Jungian analyst, and he provides personal reflections on the future of Jungian psychology, the question of artificial intelligence, and the status of Jung in France.

Fostering idealogical and polical education via knowledge graph and KNN model: an emphasis on positive psychology

As the primary domain of ideological and political education in higher education institutions, ideological and political courses must align with principles rooted in human psychology and education. Integrating educational psychology into ideological and political teaching in universities enhances the scientific, targeted, and forward-thinking nature of such education. The burgeoning exploration of knowledge graph applications has extended to machine translation, semantic search, and intelligent question answering. Diverging from traditional text matching, the knowledge spectrum graph transforms information acquisition in search engines. This paper pioneers a predictive system for delineating the relationship between educational psychology and ideological and political education in universities. Initially, it extracts diverse psychological mapping relationships of students, constructing a knowledge graph. By employing the KNN algorithm, the system analyzes psychological characteristics to effectively forecast the relationship between educational psychology and ideological and political education in universities. The system's functionality is meticulously detailed in this paper, and its performance is rigorously tested. The results demonstrate high accuracy, recall rates, and F1 values. The F1 score can reach 0.95enabling precise sample classification. The apex of the average curve for system response time peaks at approximately 2.5 s, maintaining an average response time of less than 3 s. This aligns seamlessly with the demands of practical online teaching requirements. The system adeptly forecasts the relationship between educational psychology and ideological and political education in universities, meeting response time requirements and thereby fostering the scientific and predictive nature of ideological and political teaching in higher education institutions.

Why Look at Toy Animals?

Abstract This article addresses the toy as a neglected cultural and technical object. The toy is neither tool nor ritual object, and its animation in children's imaginative play suggests alternative perspectives on the history and lived experience of material and technological artifacts. The concept of protopolitics is advanced to explore the implications for the cultural politics of the ambiguous articulation of power relationships in play. The article takes the long history of the toy animal as a case study, drawing attention to its creaturely, artificial facets that go beyond, or more accurately precede, familiar cultural-political binaries of authentic and inauthentic, depth and surface, knowledge and illusion, truth and lies, belief and fetishism, human and nonhuman, natural and synthetic. These other facets include dynamics of the technics of imagination, and their ambivalent articulation of relationships of control, training, care, violence, and love—a protopolitics evident in imaginative play. And, in postnatural media culture, the toy animal has migrated to digital habitats, offering an alternative animal perspective on questions of artificial intelligence. The child's toy and media environment is playfully zoomorphic, populated with artificial animals, from toys and stories to virtual pets and video-game characters, a new simulacral and postnatural trajectory in the descendance of the artificial animal and its playful and play-like behaviors.

The Role of Civilian Cybersecurity Companies in Military Cyber Operations

Abstract The Russia-Ukraine war has clearly shown that critical infrastructures are prime targets for cyber operations in addition to the physical domain. In practice, in many cases, these critical infrastructures are protected by civilian cybersecurity companies in the context of a managed security service, so their defense operations must necessarily be coordinated with military defense activities. This includes among others the sharing of information classified under different classification systems (national, EU, NATO) between different actors in national cyber defense, the inclusion of appropriate civilian experts in the armed forces, and the usage of cutting-edge cybersecurity technologies (e.g. AI-enabled solutions) that are first introduced for civilian use, with military organizations only having access to them later or possibly not encountering them at all due to procurement difficulties. The main goal of this paper is to introduce the existing opportunities and obstacles to civilians’ involvement in military cyber operations in the areas of legislation, technology, and human resources from the European perspective. Moreover, the paper deals with the actual questions of cybersecurity intelligence sharing between civilian and military entities and the European Union’s actions in order to improve the overall cybersecurity posture of the region.

Pengaruh Kecerdasan Intrapersonal Terhadap Kemampuan Pemahaman Konsep Matematika Siswa Sekolah Menengah Atas

This study aims to determine the effect of intrapersonal intelligence on the ability to understand mathematical concepts. The research method used is quantitative research with a correlational survey method. The population in this study were all students of class XII MIPA SMA Negeri 1 Sedayu. The research sample amounted to 65 students taken from 2 XII MIPA classes with sampling techniques using random sampling techniques. Research instruments in the form of intrapersonal intelligence questionnaires and questions about the ability to understand mathematical concepts. The data analysis technique was carried out by classical assumption test and simple regression test. Based on the results of the study obtained that there is a significant positive effect of intrapersonal intelligence on the ability to understand students' mathematical concepts, where the amount of contribution given by intrapersonal intelligence to understanding mathematical concepts is 27.2%.

Artificial intelligence in entrepreneurship education: a scoping review

PurposeThe study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.Design/methodology/approachA scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.FindingsEducators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.Originality/valueThis study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

The Concept of Property Between Technology, Anthropology and Ontology

The article Anthropological crisis or crisis in moral status: a philosophy of technology approach to the moral consideration of artificial intelligence questions the anthropology of properties commonly assumed in philosophical discussions about the relationship between humans and technologies and the attribution of moral status. By beginning to develop the possible link between the ontology of properties and the anthropological question aptly outlined by that contribution, this short commentary suggests that the adoption of a truly relational or non-proprietary approach in the philosophy of technology seems at once necessary and challenging. For, on the one hand, it represents a response to the demands posed by information technologies; on the other it seems to call into question some of our deeply ingrained habits of thought.

ChatGPT: Artificial Intelligence as a Potential Tool for Parents Seeking Information About Autism.

Autism Spectrum Disorder has seen a drastic increase in prevalence over the past two decades, along with discourse rife with debates and misinformation. This discourse has primarily taken place online, the main source of information for parents seeking information about autism. One potential tool for navigating information is ChatGPT-4, an artificial intelligence question and answer-style communication program. Although ChatGPT shows great promise, no empirical work has evaluated its viability as a tool for providing information about autism to caregivers. The current study evaluated answers provided by ChatGPT, including basic information about autism, myths/misconceptions, and resources. Our results suggested that ChatGPT was largely correct, concise, and clear, but did not provide much actionable advice, which was further limited by inaccurate references and hyperlinks. The authors conclude that ChatGPT-4 is a viable tool for parents seeking accurate information about autism, with opportunities for improvement in actionability and reference accuracy.