Method For Comprehension Research Articles

Об изменении подходов к формированию читательской грамотности школьников

The article highlights the results of a study of the problem of developing reading literacy among schoolchildren in Kyrgyzstan in the light of the requirements of the All-Republican Testing (ORT). ORT tasks place higher demands than subject standards in terms of students' language skills and level of text complexity, as well as in terms of depth of analysis, analytical, critical thinking and the ability to apply knowledge in practical scenarios mastered in school subject lessons. However, there are gaps between the requirements of ORT and teaching students reading and comprehension at school. To eliminate the gaps between subject standards, methods of teaching reading and comprehension at school and the requirements of ORT, the author proposes ways to solve the problem that will contribute to the development of students’ ability to read and understand a variety of texts of varying complexity, which, in turn, will contribute to the formation of students’ information skills. competence.

Applying the Task Assignment Method to Teaching Chinese Reading Comprehension Skills at the Basic Stage at Hung Vuong University

In the trend of global integration, Chinese is increasingly proving its important role as an international language, foreign language and culture subject in university programs. Chinese provides students with a new communication tool to absorb advanced scientific and technical knowledge, learn about rich cultural diversity, and easily integrate with the international community. The teaching staff of the Department of Chinese Language and Culture has been using a number of active teaching methods for Chinese Reading comprehension based on two pedagogical perspectives: "learner-centered" and “process-centric”. Among them, task-based teaching method (任务型教学法) is one of the important teaching methods. This is a task-based language teaching method in which instructors guide language learners in completing tasks in class.

Gaze-assisted visual grounding via knowledge distillation for referred object grasping with under-specified object referring

Understanding human referring intention is the key challenge in referred object grasping tasks. Current Referring Expression Comprehension (REC) and Referring Expression Segmentation (RES) methods enable robots to ground the referred object through precise language commands while ignoring the under-specified referring expressions in practical scenarios. In this paper, we propose a gaze-assisted visual grounding network, which efficiently estimates the human gaze via knowledge distillation techniques to provide visual guidance for inferring human referring intention. Specifically, a gaze auxiliary network is introduced to incorporate the human gaze to implicitly eliminate the referring ambiguity. A cross-attention-based multi-modal fusion module is designed with a balanced structure to project the diverse modalities into a common feature space to balance the inter-modal information. A tabletop object dataset with human gaze and under-specified referring expressions was established to evaluate our method. Experimental results show that our method achieves advanced results compared to the state-of-the-art visual grounding method, with a gain of 8.23% on REC precision and 10.44% on RES mIoU (mean Intersection over Union). The effectiveness and superiority of our method are also demonstrated through real-world referred object grasping experiments. The code is now available at https://github.com/robotseu/GVGNet_KD.

International Experience in Ensuring Cybersecurity in Local Self Government Management as a Component of the National Security of Ukraine

The study examines the critical relationship between cyber security as a component of national security and its provision in local government administration. The purpose of the study is to evaluate the international experience of ensuring cyber security in the management of local self-governments in Germany, Poland and Ukraine. In research were used visual and graphic methods, a set of methods of processing, comprehension and interpretation of information. This highlights the level of trust in local authorities in Poland, Germany and Ukraine, as well as a comparison of the systems of local self-government entities in the selected countries. It was established that there is a need to create and develop an adequate cybersecurity system. The result of the study is an analysis of the cyber security strategies of Germany, Poland and Ukraine. Prospects for further research are the analysis of the possibility of consolidation and differentiation of powers by subjects of local self-government in ensuring cyber security. An analysis of the real need for the powers related to providing cyber security and the ability to perform them is appropriate.

The Effect of Using the Metacognitive Learning Cycle Strategy in Developing Reading Comprehension in the Arabic Language Subject for Ninth Grade Students

The study aimed to identify “the effect of using the metacognitive learning cycle strategy in developing reading comprehension in the Arabic language subject for ninth-grade students.” The study adopted the experimental method and the quasi-experimental design, where a reading comprehension test was prepared, consisting of (17) items. The study sample consisted of (50) students who were chosen intentionally. The results of the study showed that there was a statistically significant effect on the study sample’s scores in the reading comprehension test according to the teaching method (metacognitive learning course strategy for teaching the Arabic language, regular), and the differences were in favor of those who were exposed to For the strategy of the metacognitive learning course for teaching the Arabic language compared to individuals using the traditional method, the researcher recommends preparing a guide for the Arabic language teacher regarding reading comprehension skills and methods for developing them.

Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach.

Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context. This study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately. Existing approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model's performance. We demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F1-score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases. The proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts.

DocMSU: A Comprehensive Benchmark for Document-Level Multimodal Sarcasm Understanding

Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection. However, existing MSU benchmarks and approaches usually focus on sentence-level MSU. In document-level news, sarcasm clues are sparse or small and are often concealed in long text. Moreover, compared to sentence-level comments like tweets, which mainly focus on only a few trends or hot topics (e.g., sports events), content in the news is considerably diverse. Models created for sentence-level MSU may fail to capture sarcasm clues in document-level news. To fill this gap, we present a comprehensive benchmark for Document-level Multimodal Sarcasm Understanding (DocMSU). Our dataset contains 102,588 pieces of news with text-image pairs, covering 9 diverse topics such as health, business, etc. The proposed large-scale and diverse DocMSU significantly facilitates the research of document-level MSU in real-world scenarios. To take on the new challenges posed by DocMSU, we introduce a fine-grained sarcasm comprehension method to properly align the pixel-level image features with word-level textual features in documents. Experiments demonstrate the effectiveness of our method, showing that it can serve as a baseline approach to the challenging DocMSU.

Teaching method reading and comprehension of the text “Waves” (Sóng) by Xuân Quỳnh in the high school

Literature is an important subject in school education. Maxim Gorky, a famous Russian writer, once said that "Literature is human science." Studying literature is learning how to be human. Literature provides learners with knowledge about life and the hidden things in the human soul. Moreover, literature today also directly affects human emotions, making people's lives more meaningful, optimistic, and loving. Each essay, poem, and literary work in the general education program is a moral education lesson for students. It contributes to the development and perfection of students' personalities. In order to achieve effective teaching of Literature for students, teachers need to proactively change their teaching methods and mindset. With the topic of "Teaching method for reading and comprehension of the text Waves by Xuân Quỳnh in the high school" (Literature for class 11), we want to introduce a scientific and appropriate teaching method for students to receive and memorize knowledge. At the same time, we also hope that the topic will contribute a small part to the study of lyric texts as a useful reference material for teachers and students.

„Man muss an dem Wort richtig arbeiten, bevor man es versteht“: Sprachbezogene Zugänge zum Metaphernverstehen in der Sekundarstufe I

Based on two explorative studies on metaphor comprehension (methods: thinking aloud; intervention with questionnaires), we discuss the ways students at the end of lower secondary school refer to their linguistic knowledge. In collected meaning explanations of composite metaphors, three levels of reference to grammatical knowledge can be detected: implicit references, explicit, dysfunctional references, and explicit, functional references. The article discusses the extent to which language-related concepts of students can be changed in terms of a conceptual change approach. The findings support the assumption that conceptual change occurs in stages and emphasize the need to teach terminology.

On the scientific comprehension of the subject of constitutional law: to putting the question

An attempt to putting the question of scientific comprehension of the subject of Constitutional Law, with highlighting of three parts was made: content of the subject of Constitutional Law in its historical development; method of comprehension of content using the ascent from the abstract to concrete; the starting point of the ascent, as determined by the philosophical category “relation”. The detailed consideration of the question posed and the formation of a holistic view of the subject of Constitutional Law are possible only through the efforts of the entire constitutional legal community within the framework of a broad discussion on the subject of Constitutional Law, which is long overdue.

Cleansing Islamic Culture Courses from Inauthentic Traditions and Clarifying the Problematic Ones

Objectives: This study emphasises the significance of cleansing Islamic Culture courses from certain unreliable or questionable traditions and clarifying elements that may be difficult for students to understand. It also seeks to propose general rules and guidelines in criticism and comprehension, stressing the necessity of offering Islamic Culture courses free from flaws. Methodology: This study employs an inductive approach by examining selected Islamic Culture textbooks prescribed in some Arab universities. It then briefly assesses their value according to the critical methodology of hadith scholars. The study followed an authentic approach rooted in professional methods of criticism and comprehension through inductive analysis of traditions, identifying essential rules and guidelines in dealing with them. Results: This study emphasises the importance of properly selecting the material used in Islamic Culture courses, which necessitates careful and critical scrutiny of the traditions that these courses rely on. Moreover, it identifies the major factors in textual criticism, such as the combined analysis of chains of transmission and the traditions they authenticate and adherence to the requirements for strengthening traditions through corroborative evidence, following the rules set by hadith scholars. Some Islamic Culture courses turned out not to strictly adhere to these standards. Additionally, this study highlights the importance of forming specialised academic committees from various Islamic disciplines and involving scholars in psychology, sociology, media and the Arabic language in developing and reviewing Islamic Culture courses. Collaboration through reputable academic institutions is also encouraged. Authenticity: The authenticity of this research lies in its relevance for Islamic Culture courses, significantly enhancing cognitive awareness, shaping students' behaviour, refining concepts and terminology, correcting misconceptions, ideas, and behaviours, and solidifying a culture of moderation and centrism. This requires reliance on the most reliable Islamic sources, primarily the Prophetic Sunna, and a sound interpretation of whatever has been said about the Prophet Muhammad (peace be upon him).

Roberta and BERT: Revolutionizing Mental Healthcare through Natural Language

In recent years, developments in natural language processing (NLP) have made it possible for novel applications to be developed in the field of mental healthcare. Roberta and BERT are two examples of these advances. They are examples of state-of-the-art language models that have exhibited extraordinary skills in comprehending and processing human language. Through the use of NLP, this research investigates the revolutionary role that Roberta and BERT have had in the transformation of mental healthcare. via the use of huge quantities of textual data, these models provide insights into the feelings, attitudes, and mental states of patients that are communicated via language that have never been considered before. Through the use of advanced language comprehension methods, Roberta and BERT are able to provide more precise diagnosis, individualized treatment suggestions, and therapeutic interventions that are specifically designed to meet the specific requirements of people. Furthermore, they make it easier to construct virtual assistants and chatbots that are able to provide support and intervention in real time. This allows mental health treatments to be extended to groups who are not currently receiving them and helps to eliminate obstacles that prevent access to these services. However, in order to guarantee the appropriate deployment of these technologies, it is necessary to carefully traverse difficulties such as concerns around privacy, bias in data, and ethical considerations. Despite this, Roberta and BERT have a tremendous amount of promise to revolutionize mental healthcare via the use of natural language understanding. This has the ability to improve results, better relationships between patients and providers, and eventually contribute to the overall well-being of people all over the globe.

Mixed inference machine reading comprehension method based on symbolic logic

With the rapid development of machine learning, challenging question and answer datasets have also emerged, and the machine reading comprehension technology has emerged. Traditional machine reading comprehension methods mostly focus on the understanding word level semantics, with the weak ability to extract logical relationships from text, resulting in the lower ability of logical reasoning. In order to strengthen the ability of machine reading comprehension method to extract the logical relationship of text and the ability of logical reasoning, a neural symbol model based on logical reasoning was proposed, and the logical expressions captured by the neural symbol model were converted into text input and trained in a mixed reasoning reading comprehension model based on symbolic logic. The mixed reasoning reading comprehension model based on symbolic logic is different from the traditional machine reading comprehension model. It uses symbolic definition and logical capture to extract logical symbols and generate logical expressions. The research results show that the accuracy and F-measure values of the neural symbol model based on the logical reasoning are 70.08% and 70.05%, respectively, when the training set sample size is 4000. The accuracy of the mixed reasoning reading comprehension model based on symbolic logic in the logical reasoning data set of the standard postgraduate entrance examination is 88.31%, which is higher than the 58.74% of the language perception map network model. The accuracy rate in the four-choice and one-choice question-and-answer data set is 40.92%, which is 1.58% higher than that of the language awareness graph network model. In summary, the neural symbol model and hybrid inference reading comprehension model proposed in the study have superior performance, which can capture the logical relationship of text in data sets well, improve the model feature abstraction and reasoning ability, effectively shorten the training time and improve the model efficiency.

Effectiveness of an Innovative Card Game as a Supplement for Teaching Factual Content to Medical Students: A Mixed Method Study.

Introduction Lectures and small group teaching are useful to transfer conceptual knowledge. Anatomy is the foundation of medical sciences, but it is perceived to be difficult to comprehend and recall. For such clinically relevant aspects of medical education that require memorization, educational card games can be very effective. As the complex concepts and terminology of Embryology are difficult to follow and retain, we designed a card game "MedFc"for a topic on pharyngeal arches. This study was planned to determine the effectiveness of the card game on curriculum comprehension, recall of factual topics among medical undergraduates, and its utility as a supplement to interactive lecture sessions. Methods The mixed method study was conducted in the Department of Anatomy of an undergraduate medical college. Ethical approval was obtained prior to beginning the study. Convenience sampling was done. From a batch of 50 first-year medical students, a total of 40 students consented to participate in the study, 24 (60%) were males and 16 (40%) were female participants. A lecture on the pharyngeal arches was conducted for the entire batch of 50 first-year medical students. After three weeks, the students who consented to participate in the study were randomly grouped into two groups of 20 each. The groups were the game group (which played the card game in teams of five) and the control group (which discussed the same topic in small groups of five). For both the group's pretests and posttests, 10 higher order multiple choice questions, were conducted and students' feedback regarding the effectiveness of the teaching technique was obtained. Results Students opined that playing the card game was a superb experience, a positive use of time, and a very effective method of comprehension and memorizing complex topics. The scores increased from the pretest to the posttest indicating that both methods effectively reinforced the embryological concepts, but a t test showed that card game is more effective than small group discussions, with p-value = 0.008. The improvement in scores of students who had achieved <50% in pretest for the game group was statistically significant with t-value = 0.0023, when compared with the improvement in scores of similar students from the control group. Conclusions The study has demonstrated the effectiveness of "MedFc" card game in the recall of factual topics and can be used as supplementary material for enhancing learning amongst medical graduates. This educational card game applies gamification to Anatomy education to create a fun filled learning experience and is a valuable addition to thelearning resources.

Video Turing Test: A first step towards human‐level AI

AbstractThe development of artificial intelligence (AI) agents capable of human‐level understanding of video content and conducting conversations with humans on this basis is a promising application that people expect. However, this is a challenging task that requires the holistic integration of multimodal information with temporal dependencies and reasoning, as well as social and physical commonsense. In addition, the development of appropriate systematic evaluation methods is essential. In this context, we introduce the Video Turing Test (VTT), a blind test used to evaluate human‐likeness in terms of video comprehension ability. Moreover, we propose Vincent as a video understanding AI. We explain the configuration of VTT, the architecture of Vincent to prepare for VTT and the proposed evaluation methods for video comprehension. We also estimate the current intelligence level of AI based on our results and discuss future research directions.

Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm

Chinese open-domain reading comprehension question answering is a task in the field of natural language processing. Traditional neural network-based methods lack interpretability in answer reasoning when addressing open-domain reading comprehension questions. This research is grounded in cognitive science’s dual-process theory, where System One performs question reading and System Two handles reasoning, resulting in a novel Chinese open-domain question-answering retrieval algorithm. The experiment employs the publicly available WebQA dataset and is compared against other reading comprehension methods, with the F1-score reaching 78.66%, confirming the effectiveness of the proposed approach. Therefore, adopting a reading comprehension question-answering model based on cognitive graphs can effectively address Chinese reading comprehension questions.

Scene Detection Classification and Tracking for Self-Driven Vehicle

A number of traffic-related issues, including crashes, jams, and pollution, could be resolved by self-driving vehicles (SDVs). Several challenges still need to be overcome, particularly in the areas of precise environmental perception, observed detection, and its classification, to allow the safe navigation of autonomous vehicles (AVs) in crowded urban situations. This article offers a comprehensive examination of the application of deep learning techniques in self-driving cars for scene perception and observed detection. The theoretical foundations of self-driving cars are examined in depth in this research using a deep learning methodology. It explores the current applications of deep learning in this area and provides critical evaluations of their efficacy. This essay begins with an introduction to the ideas of computer vision, deep learning, and self-driving automobiles. It also gives a brief review of artificial general intelligence, highlighting its applicability to the subject at hand. The paper then concentrates on categorising current, robust deep learning libraries and considers their critical contribution to the development of deep learning techniques. The dataset used as label for scene detection for self-driven vehicle. The discussion of several strategies that explicitly handle picture perception issues faced in real-time driving scenarios takes up a sizeable amount of the work. These methods include methods for item detection, recognition, and scene comprehension. In this study, self-driving automobile implementations and tests are critically assessed.

Robust Domain Adaptation for Machine Reading Comprehension

Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., Noisy Correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain. Undoubtedly, the noisy correspondence will degenerate the performance of MRC, which however is neglected by existing works. To solve such an untouched problem, we propose to construct QA pairs by additionally using the dialogue related to the documents, as well as a new domain adaptation method for MRC. Specifically, we propose Robust Domain Adaptation for Machine Reading Comprehension (RMRC) method which consists of an answer extractor (AE), a question selector (QS), and an MRC model. Specifically, RMRC filters out the irrelevant answers by estimating the correlation to the document via the AE, and extracts the questions by fusing the candidate questions in multiple rounds of dialogue chats via the QS. With the extracted QA pairs, MRC is fine-tuned and provides the feedback to optimize the QS through a novel reinforced self-training method. Thanks to the optimization of the QS, our method will greatly alleviate the noisy correspondence problem caused by the domain shift. To the best of our knowledge, this could be the first study to reveal the influence of noisy correspondence in domain adaptation MRC models and show a feasible solution to achieve the robustness against the mismatched pairs. Extensive experiments on three datasets demonstrate the effectiveness of our method.

Semantic Comprehension Method for Chinese Sentences Based on Minimal Semantic Structures and Its Application

Semantic Comprehension Method for Chinese Sentences Based on Minimal Semantic Structures and Its Application

Dual-Space Graph-Based Interaction Network for RGB-Thermal Semantic Segmentation in Electric Power Scene

Real-time scene comprehension is the basis for automatic electric power inspection. However, existing RGB-based scene comprehension methods may achieve unsatisfied performance when dealing with complex scenarios, insufficient illumination or occluded appearances. To solve this problem, by cooperating visual and thermal images, the Dual-Space Graph-based Interaction Network (DSGBINet) is proposed to achieve all-day time semantic segmentation of power equipment in high-voltage power transmission line and electric transformer substation scenes. Specifically, modality-specific features are first extracted via two separate backbone networks with the same architecture. Multi-modality high-level features are first fused via long-range relationship in coordinate space. Then, multi-modality features from regular grids are further clustered and assigned to vertices in feature space. Cross-graph and inner-graph regional relations are utilized for reasoning and enhancement, which could exploit the mutual benefits and extract rich contextual information in a semantic view. Furthermore, to overcome the huge scale difference and the inherent characteristic of thermal images, the idea of multi-task learning is integrated into the decoding process. The edge detection and semantic segmentation are achieved collaboratively, which could segment the different power equipment more accurately and completely. The comparative and ablation experiments on the proposed two RGB-T semantic segmentation datasets evaluate the effectiveness and robustness of the proposed network compared with existing state-of-the-art methods. The extended experiments on the public datasets further demonstrate the superiority of the proposed method. Our dataset and code will be released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hhujiang/DSGBINet</uri> .