Reviewerly/exHarmony huggingface dataset:
Biomedical Disabled Rehabilitation Analysis considering Fuzzy Parameter Adaptive PID Algorithm

Fuzzy parameter adaptation refers to an idea of adjusting parameters based on parameter deviation, deviation change rate and the relationship between the parameters. The PID control algorithm incorporates three algorithms: ratio, sigma, and lead. It is one of the most commonly used algorithms and has a wide range of applications. Embedded system is an application-centric computer system based on modern computer technology that can be flexibly dedicated according to user needs. Although it is convenient to use, it cannot perform large-scale operations and storage. This article aims to explore the role of fuzzy parameter adaptive PID algorithm in the rehabilitation of biomedical persons with disabilities. It is hoped that modern technology will be used to find a new direction for the rehabilitation of the disabled, so that the disabled can better survive in the society. This paper studies the existing classic real-time scheduling algorithms and traditional adaptive scheduling algorithms and analyzes their shortcomings when applied to embedded systems; when sorting out related concepts, clarified related concepts such as disabled persons, to provide tertiary level study for people at risk and higher education policies for persons with disabilities; it combines classic and fuzzy control to form fuzzy self-adaptation, realizes fuzzy self-adaptive control of the temperature value of high temperature sterilization, and improves the automation level of the system. The experimental results of this article show that 68 persons with disabilities need medical diagnosis, accounting for 52%; 15 persons with disabilities need physical correction, accounting for 12%; 29 persons with disabilities require acupuncture treatment, accounting for 22%; 18 persons with disabilities were in mental treatment, accounting for 14%; 57 people lost the ability to learn due to memory decline, accounting for 44%; 46 people were unable to learn due to physical disabilities, accounting for 35%; but there were still 17 people who were able to study normally, accounting for 13% of the total number of people surveyed; 10 people were unable to study due to other reasons, accounting for 8%.
Deep Learning for College English Education Evaluation

With the expanding and growth of the present reform of English education and teaching, the study of English education quality is receiving more and more attention. The focus of enhancing the quality of English education is to enhance the quality of teaching, and English education assessment is a main initiative to improve the quality of English education and teaching. So, the formation and development of the English education quality assessment system are of great importance to education management. This paper is based on an in-depth study of the present condition and characteristics of English education quality evaluation in colleges and combines the characteristics of deep learning. To provide a feasible solution for the English education evaluation model, this paper constructs a deep learning-based English education quality evaluation model and theoretically analyzes and compares the training results of the deep learning algorithm and the algebraic algorithm used in the model, respectively. Through simulation analysis, we can conclude that the English education evaluation based on deep learning suggested in this paper has achieved good evaluation results.
A Hash-Based Fast Image Encryption Algorithm

Many specialists and academics have recently become interested in the security of digital images in applications for the Internet of Things. Hash-based digital image encryption algorithms with high unified average changing intensity ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>UACI</mtext> <mo>&gt;</mo> <mn>30.96</mn> </math> percent) and only one pixel difference from the plain image would therefore adjust plenty of the pixels in the cipher image and have indeed been suggested to maintain the protection of images in the Internet of Things ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>NPCR</mtext> <mo>&gt;</mo> <mn>98.77</mn> </math> percent). Theoretical study and simulation results show that the suggested approach can fix these issues while retaining all the advantages of the original. The proposed image encryption algorithm has important application value for strengthening the security of the Internet of Things.
Saturation and Hue Analysis Using Complex Hadamard Transform

This paper presents an overview in relation to observed changes in saturation and hue. The magnitude and phase components of color images are analyzed using the Complex Hadamard Transform. By considering the human visual system, CIE La*b* color space is applied to RGB images in the transform domain. It is determined that by changing the phase in a sequency domain, the hue varies when the saturation is kept the same, and the opposite occurs when the magnitude is changed. The results are compared with those of the Discrete Fourier Transform in the frequency domain. The above parameters play an integral part in the functioning of color images on different applications using the Complex Hadamard Transform.
Development of Artificial Intelligence in Activity Recognition

This essay focuses on how the technology of activity recognition has developed with the development of artificial intelligence. It illustrates three major solutions in activity recognition, including sensor-based solution, visual solutions, and Wi-Fi-based solutions, and discuss the pros and cons of each of the solutions.
Menggali Makna Akuntansi Perspektif Fresh Graduate Program Studi Akuntansi

This article explores the meaning of accounting from a fresh graduate perspective. Through an interpretive approach with a transcendental phenomenological method, forms of social experience from the point of view of the informant who experienced it directly were explored more deeply through in-depth interviews. Three informants, namely accounting alumni, have just graduated and already have work experience. The phenomenological method emphasizes the meaning that arises from the individual conscious experience. The stages of the data analysis technique begin with identifying the noema or textual analysis. Next, the researcher did bracketing or structural analysis based on noema identification. This stage is carried out as a level of deep meaning (noesis). The relationship between noema and noesis provides an understanding of how noesis forms noema, which is called intentional analysis. Finally, the researcher will get to the idea that underlies all pure consciousness called eidetic reduction. This study concludes that there are three different understandings of accounting. Namely, accounting is magic, accounting is complicated, and accounting is balanced.
Research on Improved YOLOv4-based Pedestrian Detection Model

Pedestrian safety affects many fields such as driverless vehicles. In order to improve the precision of the pedestrian detection method and distinguish people-like objects from pedestrians, an improved YOLOv4 pedestrian detection method is proposed. Firstly, RepVGG Block is introduced into the feature extraction layer and feature fusion layer of YOLOv4 to improve the feature extraction ability of the network and reduce the loss of feature information. Then the SENet attention mechanism is introduced to make the algorithm focus more on the useful information. Finally, the SIoU loss function is introduced to the regression of the pedestrian target frame, which improves the convergence speed and reduces the blindness of the target frame. Experimental results show that, compared with the original YOLOv4 algorithm, this improved algorithm has higher detection precision, and it can also distinguish people from people-like objects on the published Pedestrian Detection Data set, with a detection precision of 83.7%.
Research on small target detection technology on assisted driving road based on Yolov3

Aiming at the huge difference in the size of the bounding box of different types of targets on the road in natural traffic scenes. The existing original target detection algorithm yolov3 cannot well balance the detection accuracy of large and small targets, so the target detection algorithm based on yolov3 is redesigned. Firstly, the detection module is improved and designed, and a new feature output module for small targets is added to obtain a new road target detection method yolov3 with four detection scales_ T. At the same time, focal loss is added to the loss function to reduce the result error caused by the imbalance of positive and negative samples [1]. The results show that the improved yolov3_ The average detection accuracy of T algorithm on the data set mixed with bdd100k and Kitti is 0.465, which is 0.048 higher than that of the original yolov3, especially for small targets.
A novel approach for visual tracking based on occlusion recognition

Visual object tracking is an extremely challenging task. Many existing trackers cannot handle various challenges simultaneously. In this paper, we propose a novel tracking framework based on an occlusion recognition mechanism to improve the performance in occlusion situations. Firstly, we design an occlusion recognition mechanism based on patch pool and local correlation to describe the occlusion of objects in each frame of an image sequence. Secondly, taking advantage of the occlusion recognition mechanism, we construct a specific training set to train the filter. Thirdly, combining global correlation, we implement our own tracker based on the traditional discriminative correlation filters. Finally, we evaluate it on both OTB and VOT platforms, and the experimental results demonstrate that our design is advanced and effective.
Application of Stochastic Matrix Model with Improved GLR Algorithm in English Translation Studies

The rapid development of today’s society is accompanied by the explosive growth of information data; in the process of information transmission, language is a very important carrier. Among all kinds of communication languages, English always occupies an important position and is one of the most commonly used languages in social life. Therefore, the practical significance of English education is self-evident. With the popularization of the Internet, intelligent phrase recognition in machine translation is the key technology. With the help of natural language processing technology, an English translation corpus can be built to accurately mark the parts of speech of short words, and phrase recognition technology is used to correct grammatical ambiguity effectively. Structural ambiguity is a difficult problem in the field of English translation. Based on the random matrix model of the improved GLR algorithm, phrase structure labelling is constructed through the phrase corpus. Revised annotation can effectively improve the accuracy of academic translation, and intelligent English translation is realized through recognition technology. Simulation experiments verify the effectiveness of the model, and the results show that the English translation intelligent recognition model has a high proofreading accuracy. When the value of P is 0.95, the high accuracy can be retained to the maximum and the efficiency and feasibility of improving the GLR algorithm in machine translation can be improved.
Identifikasi Serangan Denial Of Service (Dos) Di Jaringan Dengan Algoritma Decision Tree C4.5

DoS and DDoS are attacks on computer networks that flood network traffic with continuous requests. For this reason, efforts to secure computer networks and preventive measures need to be carried out by installing firewalls, IDS / IPS devices. The IDS acts as an alarm to the admin that there is abnormal activity on the network, so that the admin can take immediate preventive action. In detecting attacks, IDS uses methods or algorithms to identify anomalies that occur in the network. The algorithm is expected to be able to classify between dangerous traffic and normal traffic. Data mining is suitable to be applied in the classification of network traffic because of the large size of the data and the various types of attacks. The C4.5 decision tree algorithm is expected to be able to be used in the traffic classification process with the aim of identifying DoS attacks. The results of the trial with dataset testing, C.45 yielded an accuracy of 90,68% in classifying traffic for the identification of DoS attacks, and yielded an accuracy of 74,99% in classifying all types of traffic. The Naïve Bayes algorithm is used as a comparison, the accuracy is 86,56% in classifying DoS attack identification traffic, and produces an accuracy of 69,50% in classifying all types of traffic. The C4.5 algorithm is superior in terms of accuracy but takes longer to build the model than the Naïve Bayes algorithm.
Fusion of Thermal and Depth Image to Improve Human Segmentation for a Mobile Robot

Abstract In machine vision, surveillance systems are a kind of security that concentrates on the safety of the human and property. One of the main tasks of a surveillance system is the detection of humans. This paper presents a system of human detection and the development of a technique of human segmentation using a combination of information thermal and depth in a real indoor setting from a mobile robot. A novel fusion of thermal-depth information (FTDI) is introduced to enhance the efficiency of the segmentation process and expedite processing. In experimental studies, evaluation of the performance for the proposed system is carried out using Ground Truth (GT), in which the proposed system yield is compared to GT. The proposed system performs well with an approximate accuracy of over 90% for all data sets as illustrated in the quantitative results and even outperformed state-of-the-art algorithms. This paper presents the novelty of the work, in which the detection method can improve the classification of persons and their occlusion. The advantages, such as being computationally inexpensive and performs well even under severe occlusion and poor illumination, show that this proposed system is robust.
Sistem Pakar Berbasis Web Diagnosa Penyakit Gigi dan Mulut di Klinik Sehati

The lack of knowledge of dental and oral health and the limited number of dentists causes low public awareness of dental and oral health. This condition makes some people put aside efforts to prevent and treat dental and oral diseases. Therefore, this study has created an expert system for early diagnosis of dental and oral diseases, so that it can help the public to find out about the dental and oral diseases they are suffering from and overcome the problem of scarcity of dental and oral experts. As for designing the decision support system are use the Expert System method for evaluating criteria and sub-criteria, with the Personal Home Page (PHP) programming language, MySQL as a database server, APPSERV as a web server. The results of this information system design are web applications. to input data on the symptoms of the disease suffered by the patient, then carry out the process of selecting the symptoms of the disease and produce a diagnosis result in the form of disease and healing solutions.
Photovoltaic potential estimation for various surface components of urban residential buildings based on Industry Foundation Classes data

Abstract Installing photovoltaic (PV) equipment on the building's surface reduces greenhouse gas emissions and additional land acquisition. However, existing methods for estimating PV potential primarily focus on roofs and the assessment of facades and windows is not detailed. This study proposes a method based on the Building Information Model and its standard Industry Foundation Classes to effectively estimate the potential area and location of PV modules on the building's surface and explore the PV potential in detail. The method was applied to residential buildings in Xinyi County, China, and the results show that the annual average energy generated through windows (47,589.793 kWh/year), roofs (141,126.304 kWh), and facades (284,060.393 kWh) constitute a significant amount, particularly the facades, which show significant potential as sources of solar electricity even under the influence of shadows. The rationality and feasibility of this method were verified by comparing it with ArcGIS and other relevant studies available in the literature. Next, we assessed whether the installation of PV panels on the building's surface still has certain operability under the serious shadows. For this purpose, a building with serious shadow shading was selected to analyze the demand and supply of energy. The electricity consumption of the selected building was found to be 131,785.898 kWh/year, which is significantly lower than PV potential (310,632.775 kWh/year), as calculated in this study. Thus, the efficient use of the building's surface for PV generation can meet not only the local electrical energy demand but also the excess energy generated can be sold for economic gain. The method proposed can provide new insights to energy software designers and developers, and the results can provide a reference for energy investors, building owners, and engineers to focus on the installation of PV panels on facades and windows, and formulate complex schemes of PV modules on different building components.
Principles of V.S. Solovyov's Positive Aesthetics in I.B. Rodnyanskaya's Literary criticism

In the most complete collection of I.B. Rodnyanskaya's works, The Movement of Literature, the name of V.S. Solovyov appears more than 50 times in different contexts. The influence of the founder of philosophical criticism on the strengthening of this direction in Rodnyanskaya's works is provided both by theoretical and methodological works ("Beauty in Nature", "The General Meaning of Art", "The First Step to Positive Aesthetics", etc.) and by private literary and critical observations ("The Fate of Pushkin", "Poetry of F.I. Tyutchev", "Buddhist mood in Poetry", etc.). The purpose of this article is to identify and analyze those principles of positive aesthetics that have most influenced Rodnyanskaya's critical method. The absence of a holistic teaching about aesthetics in Solovyov did not prevent the fact that the basic principles were used by Rodnyanskaya in private literary and critical observations. The main conclusions of the study are the data that allow us to talk about the rapprochement of Rodnyanskaya and Solovyov not only at the level of methodology, but also in the aspect of the poetics of literary and critical utterance. Common to Solovyov and Rodnyanskaya should be considered the plot, which consists in solving a problem related to the sphere of spirituality or, in a narrow sense, with the study of the artist's soul. The works of representatives of the philosophical direction of criticism are distinguished by the presence of two time layers.
Modern techniques in organizing the individual work of students-future teachers

The ability to organize one's independent work is the key to successful professional activity in the future. The purpose of this study is to develop a system for organizing students' independent work, taking into account their individual characteristics and ensuring the required level of practical training. The research works in the context of the experience of students-future teachers of social sciences, humanities, and fine arts in the formation of their ethno-artistic culture. The main methods for achieving this goal were comparison, systematization of the obtained material, and applying critical thinking skills. An analysis of the experience of higher educational institutions in Europe and the United States was carried out; the essence, duration, and practical orientation of students' self-study were systematized. It has been determined that the organization of students' independent work can be divided into the following components: subject learning, practice-oriented learning, student research work. &#x0D; &#x0D; Keywords: Critical thinking; education reform; future teachers; humanities; independent learning
Research on the Tracking Registration Method of Markerless Augmented Reality for Product Assembly

Abstract Background There are many problems in the assembly of complex products in the industrial production process, such as the complex assembly process, the variety of parts involved, and the assembly process mainly depends on the familiarity of assembly personnel with the assembly process. The emergence of augmented reality technology can well solve the above problems. Using the characteristics of virtual real fusion of augmented reality technology can enable the assembly personnel to receive the virtual assembly guidance information corresponding to the operation in real time during the assembly operation. Methods In order to improve the applicability and robustness of the augmented reality tracking registration method in the product assembly scene, combined with the weak texture of the product assembly operation object and the complex scene, a real-time and robust unmarked tracking registration method is proposed. Firstly, the improved linear parallel multi-modal LineMod template matching method is used to detect the weakly textured assembly target base, obtain the initial pose of the camera and solve the problem of re-initialization of tracking registration interruption. Then, the ICP registration algorithm is improved to improve the registration speed while accurately positioning the camera pose, so as to determine the benchmark for virtual-real fusion. Finally, according to the pose relationship between the assembly and the assembly guidance information established in the offline stage, the visual guidance of assembly process is realized. Results Firstly, the performance of the tracking registration method proposed in this paper is verified on the public weak texture data set. Then take the motor as the assembly target to realize the visual guidance of the assembly process. Conclusions The method proposed in this paper has good real-time performance and robustness in the application scene of product assembly, which has chaotic environment and the lack of texture of operating objects.
Vehicular Self-media: A Value-based Secure Data Trading Scheme in HetVNets

With the advancement of smart cities and the development of heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm in HetVNets. However, in the HetVNets with potential security risks, the vehicular self-media market lacks the consideration of the values of the data owned by the media data producers (MDPs) and the capabilities of the media data sellers (MDSs) to improve their utilities. To this end, we propose a value-based secure self-media data trading scheme in the HetVNets. Specifically, we first design a vehicular self-media trading mechanism based on smart contracts to provide participants with a safe and reliable transaction environment. Then, we model the interactions between the MDPs and the MDSs as a Stackelberg game by considering the values of various media data and the sales capabilities of different MDPs. After that, we design an iterative method to obtain the optimal game strategies for the MDPs and the MDSs to maximize their utilities. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.