Reviewerly/exHarmony huggingface dataset:
Research on Education Development Based on Artificial Intelligence Technology

Artificial intelligence is a strategic technology that leads the future of the world and changes the global landscape. The rapid development of artificial intelligence urgently needs the timely follow-up of artificial intelligence education. The level of artificial intelligence education determines the position of a country in the new round of scientific and technological revolution in the world, which is of great significance. Constructing the first-mover advantage of the development of artificial intelligence in my country by strengthening artificial intelligence education has become an urgent problem to be considered and solved. To examine the future development of artificial intelligence education from the perspective of discipline construction has both theoretical value and practical significance, and is more practically feasible. In view of the knowledge characteristics and development status of artificial intelligence education, this article proposes a practical path to promote the future development of artificial intelligence education from the four levels of planning discipline layout, perfecting discipline establishment system, building academic community, and cultivating discipline culture.
Ontology modeling and case database construction of subway emergency plan knowledge

Abstract In order to improve the effectiveness and timeliness of the subway emergency plan knowledge, it is necessary to standardize and semantically express the emergency plan knowledge and promote the emergency response work to play a more efficient and timely role. Firstly, an ontology-based emergency planning knowledge modeling is proposed, and a unified and standardized metrology database for the emergency planning ontology is constructed. Secondly, drawing on the case reasoning method MyCBR, combined with the established subway operation accident case knowledge base, a case retrieval method based on semantic similarity is proposed to achieve efficient retrieval of subway emergency accident cases. Finally, Protégé software and MyCBR are used to realize the emergency reasoning knowledge reasoning and case retrieval function, which significantly improves the application efficiency of emergency response knowledge.
Equilibrium of Blockchain Miners with Dynamic Asset Allocation

We model and analyze blockchain miners who seek to maximize the compound return of their mining businesses. The analysis of the optimal strategies finds a new equilibrium point among the miners and the mining pools, which predicts the market share of each miner or mining pool. The cost of mining determines the share of each miner or mining pool at equilibrium. We conclude that neither miners nor mining pools who seek to maximize their compound return will have a financial incentive to occupy more than 50% of the hash rate if the cost of mining is at the same level for all. However, if there is an outstandingly cost-efficient miner, then the market share of this miner may exceed 50% in the equilibrium, which can threaten the viability of the entire ecosystem.
Implementasi Teorema Bayes Dalam Diagnosa Penyakit Ayam Broiler

Broiler chickens are a type of chicken produced from the cultivation of animal husbandry technology which has a characteristic of fast growth, as a meat producer with low feed conversion and is ready to slaughter at the age of 28-45 days. Chicken health affects the benefits that will be obtained by the farmer, therefore the Expert System for diagnosing chicken disease using the Bayes Theorem method was developed to help users, especially broiler chicken breeders, in diagnosing diseases along with suggestions or recommended countermeasures. The application of the Bayes Theorem method in diagnosing disease in broilers is by entering the calculation algorithm of the Bayes Theorem method into the system, so that the expert system can perform calculations using the Bayes Theorem method and provide diagnostic results and correct solutions to the specified disease.
Hand-Motion-guided Articulation and Segmentation Estimation

In this paper, we present a hand-motion-based method for simultaneous articulation-model estimation and segmentation of objects in RGB-D images. The hand-motion information is first used to calculate an initial guess of the articulated model (prismatic or revolute joint) of the target object. Subsequently, the hand trajectory is used as a constraint to optimize the articulation parameters during the ICP-based alignment of the sequential point clouds of the object from the RGBD images. Finally, the target regions are selected from the cluster of aligned point clouds that move symmetrically with respect to the detected articulation model. The experimental results demonstrate the robustness of the proposed method for various types of objects.
Job Offer Analysis Using Convolutional and Recurrent Convolutional Networks

We present the possibilities of using convolutional and convolutional recurrent network structures to classify large text sets on the example of job offer descriptions. In the case of recruitment agencies and job offer web pages, it is essential to have a consistent database of offers. Unfortunately, different employers use very different names for the same positions and various descriptions. In this article, we classify job offer texts using feedforward and recurrent convolutional neural networks. We present also two ways to input text data for neural networks. The research was based on five randomly selected work positions. Based on the analyzed texts, the considered neural networks can recognize the type of position with relatively high accuracy.
Cross Modality Domain Adaptation To Generate Pelvic Magnetic Resonance Images From Computed Tomography Simulation For More Accurate Prostate Delineation In Radiotherapy Treatment

Radiotherapy treatment (RT) planning for localized prostate cancer is conventionally based upon computed tomography simulation (CT-SIM) which provides an accurate tissue dose estimation. However, the superior soft tissue contrast in magnetic resonance imaging (MRI) offers more accurate delineation of the prostate with some geometric uncertainty. Ideally, both modalities are used simultaneously in practice to ensure the geometric and dosimetric accuracy. In practice, however, only CT-SIM scans are often available, due to accessibility, cost of MR imaging, and speed of CT image acquisition. For improved RT planning, it is useful to have MR images alongside the CT-SIM. Herein, we propose to use a cycle Generative Adversarial Network (CGAN) to produce pelvic MR images from the provided CT-SIM scans in localized prostate cancer patients. For this project, we utilized the CT-SIM and MR images of 34 patients with histopathologically proven prostate cancer treated with RT. After anonymization, each CT-SIM image was windowed with intensity values limited to an interval of (-2000 HU, 2000 HU), and normalized at a patient-level. Similarly, after anonymization, we removed the bias field existing in MR images from the same patient cohort and standardized all intensity distributions. A train-validation split equal to 85%-15% on the patient-level for this study was utilized. As a result, MR and CT slices of 29 patients were used for training the CGAN to learn how to perform a dual cross modality adaptation between CT and MR domains. The C-GAN was trained with a loss function that maintains a spatial registration, between the input CT and the generated MR while transforming. The extracted slices from N = 29 training patients, with 100±28 slices per patient were used to optimize the algorithm (135 epochs, lr = 8e-5). The optimal model was then applied to all CT images in patients from independent validation set. In order to assess the utility of our generated MR images, we asked an expert radiologist to assign a score between 1 to 5 to each slice with higher score indicating the higher quality. Our qualitative investigation showed that we achieved an average score of 4.16 out of 5 in performing CT to MR transformation. MR images generated from CT-SIM images have complete 1:1 spatial correspondence with the CT scans, which facilitates the combined use of MR-CT for a better RT planning. Future work will produce a more generalized model by extending our experiment to our new data-set that incorporates 183 consecutive patients with histopathologically proven prostate cancer. In scenarios in which MRI cannot be obtained due to limited resources, time, or availability, this methodology may hold promise for enhanced target delineation.
Smartphone Based Authentic Learning Design Model by Paying Attention to the Characteristics of Learners in Increasing the Ability to Write, Communicate, and Learners'

Designing learning is oriented towards real / authentic things, to be synergized with the information structures that students already have. Everything related to information / messages to be conveyed is always associated with real experiences or events in the field which will give students more challenges to be able to find out more about what they already know and even experience. The message / information to be conveyed is of course not only done face-to-face, but also by using learning media. Communication media that are easy to use and fun and can be done at any time are the main part of this research design. Today, smartphone media is no longer a luxury item and is even a necessity for every individual. This learning design uses a smartphone as a communication tool between learners and learners, between learners and learners, and between learners and other learning sources. This research will facilitate learners' willingness to learn by considering the characteristics of the learners and the ease with which learners participate in the learning process, so that the learning process will not be boring and have fun. Other characteristics in this study will be considered such as achievement motivation and learning styles. This research is a type of development research that refers to the development research steps of Borg &amp; Gall (2003). Meanwhile, the steps in developing a learning design model refer to the Dick &amp; Carey (2001) development design. The results of product validation found that the attractiveness of the learning design model presentation program for students was found to be 90% said it was feasible, the relevance of the material studied said 95% said it was feasible, the benefits of smartphone application media and the material studied said 95% was feasible, the suitability of the material being studied with easy access to the material says 90% feasible, and Communications in online tutorials by learners say 95% feasible. The conclusion obtained after the implementation in the field is that authentic learning products can be used by lecturers and teachers in designing learning at schools and colleges.
Optimasi Klasifikasi Bayesian Network Melalui Reduksi Attribute Menggunakan Metode Principal Component Analysis

Dimensionality reduction is a hot topic being discussed in its development has been carried out in various fields of research one of which is machine learning by reducing can reduce the capacity of dimensions without reducing (eliminating) information contained in the data. Principal Component Analysis is one of the proven reduction techniques capable of reducing data capacity without significantly eliminating the information contained in the dataset. In this research attribute reduction using principal component analysis using a dataset of factors affecting employee absence was taken from the University of California repository at Irvine (UCI). Combination with Bayesian Network to classify data as a comparison between before and after attribute reduction. This can be seen in the initial results before the reduction with an accuracy of 100% and after the fifth attribute reduction there is a decrease in accuracy by 89,7%
Healthy diet recommendation via <scp>Food‐Nutrition‐Recipe Graph</scp> mining

Abstract Good nutrition and balance dietary pattern play vital roles of leading a healthy lifestyle. Prior studies showed that the healthy diet can successfully reduce the risk of chronic diseases (e.g., type 2 diabetes and cancer) and bring other well‐documented benefits. Existing food recommendation models, however, often solely rely on user's feedback (e.g., click and purchase data), which aims to optimize Click‐Through Rate (CTR) but ignores the importance of health needs of users. Intuitively, a healthy diet recommendation requires a comprehensive consideration of different kinds of information, such as nutrition, ingredients and cooking methods. In this study, by collecting the data from FoodData Central (FDC), recipe websites, and scientific literature, we construct a heterogeneous graph, Food‐Nutrition‐Recipe Graph (FNRG), by integrating information of nutrition, food (ingredients), and recipes. A random walk based graph mining approach is proposed to meet the health needs of users. Experiments results show that the proposed method can successfully address the health information needs for people who suffer from chronic diabetes.
Service API Modeling and Comparison: A Technology-Independent Approach

When service-based applications are used in systems where context varies in time or location (mobile, adaptive systems), clients may need to switch service providers for various reasons like temporary outage or geographical relocation. To prevent negative impacts on overall functionality, both the client and the provider need a way of discovering whether the application programming interface (API) of an alternative web service is compatible with client's expected contract. However, the diversity of API specification technologies poses a challenge in such evaluation. Building on previous results, in this paper we present a general model of (web) service APIs, a method for creating corresponding service API representations for several technologies, and a method for comparing these representations to evaluate API compatibility from client's point of view. Both the model and the comparison method have been implemented and verified on real-world services.
The effect of the credit query in reducing credit risks from the viewpoint of workers in Jordanian banks

The study aimed to identify the impact of the credit query in reducing credit risks from the viewpoint of workers in Jordanian banks, and to achieve the goal of the study, the researchers adopted the descriptive analytical approach, where a questionnaire is designed and distributed to a sample of (176) individuals within the upper and middle administrative levels in Jordanian banks. The study reached a set of results, the most important of which is the existence of a negative correlation between the level of use of the credit query system and each of the financial, administrative and legal credit risks, in addition a statistically significant effect of using the credit query system in reducing the financial, administrative and legal credit risks. The study recommended that Jordanian banks should continue to use the credit system because of its positive effects on the efficiency and effectiveness of granting credit to business organizations or individuals alike.
Penerapan Data Mining Pada Penerimaan Dosen Tetap Menggunakan Metode Naive Bayes Classifier dan C4.5

Recruitment is an important step in creating professional HR (Human Resources). The application of classification methods such as the Naive Bayes method and C4.5 can be used in the classification of potential lecturers and can be accepted by the campus by calculating the equations for each criterion. The difficulty experienced is the ineffective use of the method to generate the required lecturer acceptance so that it is not in accordance with the applicant's expertise. One of the classification methods applied to data mining is the naive Bayes method and C4.5. The purpose of this study is to determine the level of accuracy of the two methods used by using the Weka 3.8 tool based on the calculation of Correctly Classified Instance and Incorrectly Classified Instance. The accuracy results obtained with the naive Bayes method are 83.7838% and the C4.5 method is 91.8919% from 37 training data. So the C4.5 method is a more appropriate method to use than naive Bayes.
Are Direct Links Necessary in Random Vector Functional Link Networks for Regression?

A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems. The big advantage of RVFL is fast training without backpropagation. This is because the weights and biases of hidden nodes are selected randomly and stay untrained. Recently, alternative architectures with randomized learning are developed which differ from RVFL in that they have no direct links and a bias term in the output layer. In this study, we investigate the effect of direct links and output node bias on the regression performance of RVFL. For generating random parameters of hidden nodes we use the classical method and two new methods recently proposed in the literature. We test the RVFL performance on several function approximation problems with target functions of different nature: nonlinear, nonlinear with strong fluctuations, nonlinear with linear component and linear. Surprisingly, we found that the direct links and output node bias do not play an important role in improving RVFL accuracy for typical nonlinear regression problems.
Single Speaker Recognition Using Deep Belief Network Gender Classification Voices

Recently, the algorithm of machine learning are used to able to train, enhance, characterize and anticipate the data results accurately. In a way to training, the process on the algorithm can be able to produce an appropriate model based on that data; it's like supervised and unsupervised data. In this paper, we tried to trace the gender (male and female) from acoustic data, i.e., pitch, median, frequency etc. The gender that would like to implement is classified on the basis of the intensity of their utterances. To analyze the utterances, the voice intensity measuring by the hamming window to make a normalize curve to obtain the peaks of the utterances where peaks are found from each frame of speech utterance when it is divided into frames of the length of 20 milliseconds. At certain amplitude levels it can be considered to find a peak. As well as making decisions about gender use a thresholds that are adapted are adjusted. If the area of an utterance is above the threshold the gender type is a female otherwise male. After that, we handle the feature learning from the utterance into deep belief network as a machine learning tool to predict single speech by gender classification voices with optimization (taboo search) to train several neurons in the initial weight vector for the accuracy of female and male voices 75.67% and 80.83% precisely.
Reduksi Atribut Pada Dataset Penyakit Jantung dan Klasifikasi Menggunakan Algoritma C5.0

Coronary heart disease, commonly referred to as cardiovascular, heart disease is a disease with a high mortality rate. Thus diagnosis is very important and is an important area of medical research. In the diagnostic process, the most frequently encountered problems are time in making decisions and the lack of accuracy in the classification process. Attributes are important in making decisions on heart disease so it is necessary to know the main attributes of heart disease. Often different results are obtained in the diagnostic process due to the many attributes used in decision making. So it is necessary to do a reduction process in the attributes of heart disease. Principal Component Analysis (PCA) method can be used for data reduction with large dimensions and ranking the attributes to be reduced. The classification process can be done using the C5.0 Algorithm and getting a level of accuracy in the classification process. The results obtained in this study reduce the 12 attributes of the heart disease dataset and classify them with a combination of attributes after the reduction process is carried out. The results obtained with the highest level of accuracy when classifying with 11 attribute combinations where there is 1 attribute that is reduced, the accuracy rate obtained is 89.11%.
Data integration as the major mode of data reuse

Abstract Data sharing has been emphasized in scientific research in the era of open science; however, the concept of data sharing is ambiguous, and the real situation of data sharing is unclear. One reason is that most studies focused on researchers' recognition, motivation, and other contributing factors and did not thoroughly discuss the actual practice of data sharing for preparing research articles. To explicitly understand the current status of data sharing, the authors considered and investigated primary reuse, data integration, and data release distinctively as the actual practices of data sharing. A total of 237 articles published in 2018 and registered in the Web of Science global citation database were manually checked. Unlike the scarcity of data reuse (8.0%) or data release (5.1%), existing data were most frequently reused in the form of data integration (20.7%). The result implies that further studies on detailed data reuse are required to clarify the concept of data sharing.
RETRACTED: Innovation and Practice of College English Teaching Model Based on Computer Network

Abstract This article analyzes the current status of the application of the college English teaching model, including students’ low interest in learning, strong unity of teaching model, and a large proportion of theoretical courses. The author combines the application characteristics and application value of the computer network, through studying how to do a good job of innovation in the way of thinking, innovative supervision methods and teaching methods, make full use of the advantages of the network, establish a teaching resource database, and improve the security of data information. The purpose of this article is to give full play to the advantages of computer network applications and to improve the applicability and effectiveness of the college English teaching model.
Design-flexible entanglement of two distant quantum dots bridged by dark whispering gallery modes

We present a flexible design to realize the entanglement between two distant semiconductor quantum dots (QDs) embedded in separated photonic crystal nanobeam cavities. When bridged by a largely detuned microring cavity, photonic supermodes between two distant nanobeam cavities are formed via whispering gallery modes (WGMs). Due to the large detuning, WGMs in the microring exhibit almost no photonic excitation, showing the “dark WGMs.” With the dyadic Green’s functions of the nano-structure and the resolvent operators of the Hamiltonian, we numerically investigate the entanglement dynamics of two distant QDs. Furthermore, we prove that the entanglement can be tuned by adjusting the distances between the cavities. Such a scheme paves an efficient way for realizing a scalable quantum network in a solid-state system.