Reviewerly/exHarmony huggingface dataset:
Named Entity Recognition Incorporating Chinese Segmentation Information

Word-level information is crucial for Chinese named entity recognition. Presently, most works have achieved better performance by extracting word-level information into character-level representations through existing lexicons, but the maintenance of lexical lists is a major challenge. In this paper, we present the NIMSI model, proposing the incorporation of multiple segmentation information to enhance recognition, using a trilogy to align character-level attention with word-level attention to construct features of segmented information in Chinese text. Also, we use a simple but effective method to directly incorporate multi-segmentation information into character-level representations. Finally, as the experiments on the three benchmark datasets show, our model effectively incorporates segmentation information and alleviates the segmentation errors.
Optimization Algorithm of Integrating Folk Art into VR Vision Under Deep Learning

With the vigorous development of social economy and science and technology, the protection and inheritance of folk art are facing great challenges. Folk art has a long history. In the context of in-depth learning, the protection and development of folk art has also reached a new level. Using advanced and new technology to serve folk art is one of the effective ways to revitalize and develop folk art. This paper analyzes the virtual reality technology, and puts forward to inherit and protect the folk art by using the visual direction optimization method in the virtual reality technology and its characteristics of perception, existence and interactive operation.
Artificial Intelligence of Internet of Things and Virtual Reality Technology in the Image Reconstruction of Film and Television Characters

The animation market structure continues to be optimized, and the animation economic cake continues to grow. Character image building is a very important work content, this part of the work is the most important part of the whole film and TV series. Animation characters from the character, characteristics, character psychological activities, action and other aspects to show their own character image. All these features are innovative elements in film and television, which provide their own services for the development of film and television plot and make the development of film and television more visual. This paper studies the research on the image reconstruction of Internet of Things artificial intelligence and virtual reality technology in film and television character reconstruction, and points out the related content of the research on the image reconstruction of film and television character reconstruction. The test confirmed that artificial intelligence of the Internet of Things and virtual reality technology had excellent performance in the image reconstruction of film and television character role shaping.
CORRELATION BETWEEN LISTENING SKILL AND SPEAKING ABILITY ENGLISH DEPARTMENT SECOND SEMESTER MADURA UNIVERSITY

One of factors for student success in speaking ability is to use their listening ability, because Listening is play important in development process of speaking ability. therefore, this research aimed to finding out the correlation between students’ listening skill and speaking ability.This research applied a correlation study and used a quantitative design, the subject were 22 students of English Department second semester Madura University. There are two instruments to collect the data on this research. (1) essay test was used to get the data of their listening skill. (2) interview was used to get the data of their speaking ability.Person correlation product moment and SPSS version 26 program is used to analysis the data. The result this research shown that there is high correlation between students’ listening skill and speaking ability, which the value of
THE CORRELATION OF STUDENTS’ INTRINSIC MOTIVATION TOWARD READING COMPREHENSION OF THE SEVENTH GRADE SMP AL-MAARIF PANGGUNG

The research are formuated to find out there is any correlation between students’ intrinsic motivation and reading comprehension in seventh grade smp al-maarif panggung. This analysis used two variable. Students’ intrinsic motivation as independent variable and reading comprehension as dependent variable.The method used in this research was a correlation method. The population of this research is seventh grade smp al-maarif panggung by systematic sampling techniques, the research takes consist of 23 students. The instrument collecting data are questionnaire and test. The questionnaire was used to get the data of students’ intrinsic motivation and test for reading comprehension. To validity test of instruments this research used content validity and to the reliability test of instrument used two method. Alpha cronbach to test the reliability of questionnaire and guttman split-half to test reliabilty of test. In the analyzing the data, the researcher used Pearson Product Moment by SPSS v.26 program. The result of this research showed that there is correlation between students’ intrinsic motivation and reading comprehension with the r value is (0,787) &gt; rt (0.413) it means that the students’ intrinsic motivation and reading comprehension coefficient correlation and the category is strong correlation.
THE POSSIBILITIES OF REDUCING EMOTIONAL BURNOUT BY MANAGEMENT TECHNOLOGIES

The article offers consideration of issues related to emotional burnout in people engaged in volunteer activities. The reasons for this phenomenon are named, and examples of solutions using a number of management technologies are described.
Scalable Text Index Construction

Abstract We survey recent advances in scalable text index construction with a focus on practical algorithms in distributed, shared, and external memory.
Gamification of Animal Recognition Learning for Children with Deaf in Elementary School Using Dance Pad Media

Madrasah Ibtiyah Muhammadiyah Special Program (MIM PK) Kartasura is an elementary school for children with special needs, one of which is the deaf, located in Kartasura. As technology develops, the MIM PK school continues to strive to improve innovation and quality in learning, one of which is using educational games. Based on the age of elementary school children, education is needed in recognizing the things around them, one of which is the introduction of animals so that they will know about the various kinds of animals that are around them. The solution that the researcher provides is using animal recognition gamification for children with special needs (ABK) with hearing impairment in 4th grade using the Media Dance Pad. The method used is divided into several stages including requirements analysis, system design, implementation and code writing, testing, evaluation, and maintenance. The making of this educational game uses Construct 2 as the software used in making the game and uses a dance pad as a connecting device for the controller and Figma as a component in the game. Based on the BlackBox and questionnaire tests that have been carried out, it is hope that the educational game is useful and easily as a learning media that makes it easier for students to receive the material, especially in animal recognition.
MSFL-Net: Multi-Semantic Feature Learning Network for Occluded Person Re-Identification

Recently, occluded person re-identification (Re-ID) has received significant interest due to its widespread real-world applications. However, most existing occluded person Re-ID methods ignore semantic granularities that indicate different levels of occluded information of the human body, leading to sub-optimal performance. To address this, we propose a Multi-Semantic Feature Learning Network (MSFL-Net) for occluded person Re-ID. Specifically, MSFL-Net involves a backbone network and a Multi-branch Feature Learning sub-network (MFL). MFL consists of two local-global branches and a global branch to learn multisemantic features in a multi-branch deep network architecture. In each local-global branch, we design a local subbranch and a semantic-guided global sub-branch to extract discriminative features at a certain level of feature granularity and semantic granularity. In the global branch, we learn global features at the largest level of semantic granularity. In particular, a patch contrastive loss is developed to explicitly encourage the semantic feature maps to capture the information from specific body parts. By extracting multi-semantic features, our method is effective in dealing with person Re-ID at different occlusion levels. Experimental results on an occluded person Re-ID dataset (Occluded-REID) and two partial person Re-ID datasets (Partial-iLIDS and Partial-REID) show the superiority of our method against state-of-the-art person Re-ID methods.
Research on User Profile based on Interest Dynamic Change Modeling

The profile study based on the modeling of the dynamic change of user interest is a research method that realizes the dynamic update of interest tags by constructing the time decay function of interest transfer, and then constructs the dynamic model to achieve more accurate personalized recommendation and other functions. This article mainly introduces the construction of user interest model based on forgetting mechanism,by comparing various interest models, it can be found that the dynamic change model of interest based on the forgetting mechanism is more capable of constructing a user profile system with accurate delivery effects.
Overview of Relation Extraction

With the rapid development of big data, information extraction has gradually become a hot research issue. As one of the sub tasks of information extraction, relation extraction is widely used in the fields of knowledge atlas, intelligent question answering and so on. According to the development history of relation extraction, this paper expounds relation extraction based on traditional machine learning, relation extraction based on deep learning and relation extraction in open domain, summarizes the representative research methods, models and achievements, and summarizes and prospects the possible research directions in the future.
The Construction of English New Words Corpus Based on Decision Tree Algorithm

An English word is a sound or a combination of sounds that we make with the vocal organs; words are representative, they represent some meaning, and they help humans communicate; words are part of the human language communication system. In short, words are the smallest and meaningful language units that humans can use independently. This paper aims to study the English neologism corpus based on decision tree algorithm. Aiming at the problems of low internal cohesion of words in the new word recognition algorithm of point mutual information and adjacency entropy, many high threshold invalid phrases and low threshold new phrases that exist in the single threshold setting of point mutual information, an improved multi-word point is proposed. A new word extraction algorithm for English of the year based on mutual information and adjacency entropy. In the preprocessing stage, according to the characteristics of English new words, it is further filtered, and the point mutual information is expanded into multi-word point mutual information, and the new words are extracted by setting double thresholds and adjacency entropy. In the recognition algorithm, this paper regards it as a classification problem to solve, analyzes English new words, and uses cosine similarity and path distance similarity to quantify word form and word meaning features. For phrase pairs containing acronyms, this paper An algorithm for identifying acronyms by rules is proposed, and a decision tree suitable for English new word recognition is constructed based on the features of word form and word meaning. Experiments show that the data of the algorithm in this paper is better than the traditional algorithm, the P value reaches 33.8%, and the accuracy rate is greatly improved. Compared with the word sense feature recognition algorithm, the method in this paper has a 4% improvement in accuracy.
Classroom Learning Situation Analysis System Based on Computer Vision

With the continuous progress of society and the change of learning methods and teaching environment, research on the analysis of College Students’ learning situation in the classroom is emerging in endlessly. The real-time learning situation analysis (LSA) system of college students mainly analyzes college students’ learning status and behavior in the classroom, which can provide college teachers with the basis and means to effectively manage the classroom. Based on computer vision (CV) technology, this paper takes the students’ listening behavior in college theory class as the research object, and uses CAMSHIFT tracking algorithm to analyze the behavior of college students in class. On this basis, the system of classroom learning situation analysis (CLSA) based on CV is designed and implemented. Through the simulation experiment, the traditional algorithm is compared with the CV CAMSHIFT tracking algorithm; The test results show that the recognition rate of this algorithm is about 10% higher in the case of facial interference, which proves the accuracy, availability and effectiveness of the classroom real-time LSA system designed and studied in this paper, and provides a new way of LSA for college teachers and students.
Design of Calisthenics Choreography and Recording System Based on Action Recognition Algorithm

Traditional algorithms of human action recognition and also have many other technical problems, for example, there are traditional human motion image acquisition processing equipment susceptible by some outside factors such as light intensity, the impact of human motion features of the function is not entirely clear, human motion feature recognition susceptible factors affected by physical deformation and so on many problems. Gymnastics complete is a to skill dominant class difficult art XiangQun constitute a comprehensive program, the combination of the contents and forms of the complete set of movements in the choreography is also directly determines the effect of aerobics game the complete set of movements in the form of combination ultimately achieve successes, athletes personal and professional coaches team in strive to produce each set of unique design of novel and practical aerobics movements, drawn to the view The eyeballs of the judges and the high attention of the expert judges. As we all know, the beauty of content and layout method of complete sets of aerobics movement fluent and novel and unique competitors such as the ability of independent action choreography, a complete set of quality is the core of competitive game is directly determined how many key, in this paper, in order to grasp the latest international development trend of new aerobics competition, arrangement of the latest development practices systematically sorting rules and international competition, After careful study and study summary, the main changes in the content of the complete set of aerobics movements of the current national aerobics athletes are systematically summarized, summarized and strengthened to learn, to improve the basic sports theoretical knowledge of the content and rules of the complete set of movements of competitive aerobics and gymnastics. In view of the above problems, this paper tries to study the human motion recognition algorithm from the three aspects of action data information collection, feature recognition extraction and action classification feature recognition, in order to reduce the influence of the above problems. Therefore, in this paper, Kinect is selected to extract human skeleton data to overcome the problem of illumination, CNN is selected to automatically extract advanced human movement features, SoftMax is selected to complete multi-classification tasks, and on this basis, the calisthenics programming system is developed.
IoT and Machine Learning-Based Covid-19 Healthcare Monitoring System Using Face Recognition

After facing the horrible COVID-19 pandemic, steadily life is getting back to normal again. This pandemic came with opportunities as well, especially for researchers to come out with novel ideas and handle the situation. Many researchers have contributed with their dedicated research work with the help of recent technology to overcome similar circumstances. This paper presents a novel idea for proper monitoring and detecting normal/abnormal health using AI-based models. Proper monitoring and detection of symptoms are essential to ensure the health of members. This model is devised using several IoTs components and various ML (Machine Learning) techniques have been used to get comparative enhanced results. The hardware used Raspberry Pi 4 model B, which is the main hardware connected to several sensors like MLX906014 non-contact thermal sensor and MAX30100 pulse oximeter and heart rate sensor to measure body temperature without contact and to calculate the level of oxygen in the blood and measuring pulse rate respectively. Additionally, a Camera module for facilitating face recognition features for devices has been used. An Alert will be sent to Admin if someone has an abnormal temperature and oxygen level. The Firebase database is used to store information and it gets updated in real-time. People’s health history can be further analyzed through graphs for visualization and monitored by the administrator.
Consistency Comparison of Machine Vision Images Based on Improved ORB Algorithm

In recent years, with the continuous development of society, people's exploration in the field of machine vision images has gradually increased the demand for machine vision and digital image processing. Only the consistency comparison research of machine vision images based on the improved ORB algorithm, in order to obtain more complete image information. Based on the improved ORB algorithm, this paper conducts research by improving the consistency comparison of machine vision images, and meets the requirements of accuracy and precision for the obtained machine vision images and so on. This paper briefly introduces the technology and development trend of machine vision image consistency comparison, studies the machine vision image consistency comparison, and through a series of experiments to prove that the machine vision image consistency comparison based on the improved ORB algorithm is effective in To a certain extent, it has certain feasibility in terms of precision and accuracy. Analysis and comparison based on different image stitching methods were carried out. The final results of the research show that the accuracy of the five-consistency comparison of machine vision images is 98.7% when the distance of image five is 83.4 km. Experimental data show that the accuracy of machine vision image consistency comparison has always been maintained at a stable level, that is, 97%. It shows that the accuracy of machine vision image consistency comparison does not decrease with the increase of distance.
Blockchain-Based Integrity Auditing with Secure Deduplication in Cloud Storage

Public auditing technology has currently been proposed to ensure the integrity of the cloud data and reduce users’ overheads, which, enables users to hire a third-party auditor (TPA) for cloud data auditing. However, most existing public auditing schemes are exposed to security problems of not completely reliable TPAs, easily manipulated challenge messages, and the convenience for external adversaries to launch side-channel attacks based on audit parameters. Additionally, a large number of redundant integrity tags caused by the auditing mechanism increase the storage burden of cloud servers, and reduce the searching and auditing efficiency. Hence, aiming to solve the concern of data security and storage efficiency in cloud storage, this paper proposes a blockchain-based cloud storage integrity auditing with secure deduplication (BIAD). We distribute a random file key between different users by employing the ciphertext-policy attribute encryption (CP-ABE), and conduct secure public auditing through the blockchain combined with a bloom filter-based random challenge generation method. In particular, by applying the random key to encrypt ciphertext and integrity tags, the existence and ownership privacy of the requested file in the auditing process can be protected. The security analysis and experimental results demonstrate that compared with the state-of-the arts, the proposed scheme achieves public auditing and deduplication in a secure and lightweight way.
3D camera augmented Autonomous Mobile Robot for intralogistics purposes

AMRs (Autonomous Mobile Robots) are driverless vehicles programmed to perform a variety of tasks and are often used to transport goods or materials. However, there can be several problems with the driving performance of this type of vehicle. Safety in the workplace is important everywhere, but there are some areas that are particularly exposed to safety risks. Limited visibility is the main cause of accidents at work. Yet, robots can map these dangerous areas with their sensors. We will present a mobile robotic sensor development that can be implemented to increase the security of robotic warehouses. Testing is necessary in order to check the quality of existing defects in safety elements before delivery and to carry out a risk assessment. Our goal was to add a safety element to the robot to prevent the most common problem, namely it is colliding with a forklift truck, hitting objects outside the sensor planes or hanging in. Based on our experience with robots over the years, this has been a big challenge, as we have had several such collisions. Therefore, we selected and installed a modern 3D camera system and tested its compliance with the safety requirements and the difference between the safety level of a robot without 3D sensor (Alpha) and a robot with 3D sensor (Beta), based on the knowledge of the installed camera.
Pelatihan penulisan artikel jurnal ilmiah bagi guru di MA Ma’arif NU Sains AL-Quran Sumbang

Scientific publication is an activity that every academician, including teachers, should carry out. Publication activities can take various forms. One form of scientific publication is the publication of articles in scientific journals. Unfortunately, the publication of articles in partner schools, namely MA Ma'arif NU Science Al-Quran Contributes, still needs to be higher. Only 9.09% of teachers have experience writing articles; the rest, or as many as 90.91% of teachers, still need to carry out article publication activities. Therefore, training activities for writing scientific journal articles are needed. Article writing training activities are carried out offline and online. The series of activities consisted of providing theory related to scientific journal articles, independent practice, and writing assistance twice. The results of the activity show that there is a slight increase in the number of teachers who can compile articles. There, 27.27% of teachers have been able to compile complete articles. There are also 72.72% of teachers who have been able to access the Open Journal System (OJS) and are able to create accounts on journals. The results of the questionnaire also showed a positive response from the teacher. The "agree" option has the highest percentage across all items in the questionnaire. The results of the questionnaire show several important conclusions: 1) The teacher feels that the whole series of training activities is helpful; 2) Writing scientific journal articles is essential; 3) Teachers think that the publication process through OJS is easy to understand. Overall, the training activities have improved teachers' abilities in compiling articles and accessing OJS, and the training activities received positive responses.