Reviewerly/exHarmony huggingface dataset:
Deep Just-In-Time Consistent Comment Update via Source Code Changes

During software development and maintenance, code comments are often missing, inadequate, or they do not match the actual code content. In response to this problem, the research community has proposed a method for updating natural language comments based on code changes. However, there are two major limitations of this method that must be addressed: the long-term and non-temporal dependencies in the source code. To address these limitations, we propose a new model called code semantic learning–comment update (CSL2CU). The code semantic learning component of CSL2CU uses a self-attention mechanism and a positional encoding mechanism. It also uses a relative positional representation to model pairwise relationships between source code tags, thereby improving its ability to capture long-term dependencies and non-temporal dependencies of source code tagging ability. The comment-update component of CSL2CU is used to generate new comments based on old comments and code editing. The experimental results show that the CSL2CU model outperforms the three baselines used in exact match, BLEU, METEOR, and SARI.
Sim-to-real via latent prediction: Transferring visual non-prehensile manipulation policies

Reinforcement Learning has been shown to have a great potential for robotics. It demonstrated the capability to solve complex manipulation and locomotion tasks, even by learning end-to-end policies that operate directly on visual input, removing the need for custom perception systems. However, for practical robotics applications, its scarce sample efficiency, the need for huge amounts of resources, data, and computation time can be an insurmountable obstacle. One potential solution to this sample efficiency issue is the use of simulated environments. However, the discrepancy in visual and physical characteristics between reality and simulation, namely the sim-to-real gap, often significantly reduces the real-world performance of policies trained within a simulator. In this work we propose a sim-to-real technique that trains a Soft-Actor Critic agent together with a decoupled feature extractor and a latent-space dynamics model. The decoupled nature of the method allows to independently perform the sim-to-real transfer of feature extractor and control policy, and the presence of the dynamics model acts as a constraint on the latent representation when finetuning the feature extractor on real-world data. We show how this architecture can allow the transfer of a trained agent from simulation to reality without retraining or finetuning the control policy, but using real-world data only for adapting the feature extractor. By avoiding training the control policy in the real domain we overcome the need to apply Reinforcement Learning on real-world data, instead, we only focus on the unsupervised training of the feature extractor, considerably reducing real-world experience collection requirements. We evaluate the method on sim-to-sim and sim-to-real transfer of a policy for table-top robotic object pushing. We demonstrate how the method is capable of adapting to considerable variations in the task observations, such as changes in point-of-view, colors, and lighting, all while substantially reducing the training time with respect to policies trained directly in the real.
A Study on the Scoring Method of Oral English Test in College English Online Computer Test

With the change of English teaching methods, English examination methods have gradually changed from paper examination to computer examination. The online scoring of oral English test is more in line with the needs of teaching reform, and how to score online reasonably is more important. At present, the domestic scoring system technology for Chinese has been quite perfect, and the international scoring method technology for spoken English is not mature. Even if some scoring systems evaluate the scores, there are still some doubts about the accuracy of the scores. In view of the above background and the needs of English proficiency test teaching reform, this paper mainly studies the scoring method of College English online computer-based oral test. At present, most of the scoring methods of spoken English at home and abroad are based on HMM model, which gives a simple machine probability score through the training of corpus, but does not give an objective and specific score. The scoring method of parameter comparison mainly reflects the difference between standard spoken English and standard spoken English, which can not represent the specific score. In view of the above problems, based on the existing achievements and technologies, this paper studies the scoring method of College English online computer-based oral test.
Research on Feature Picking of Domestic Waste Sorting Based on Neural Network Training

Through the analysis of the status quo of domestic waste treatment, three common and recyclable domestic wastes of plastic bottles, cardboard and cans are selected as the classification samples to study the classification of the overall domestic waste. Based on MATLAB, a nerve that can be used for domestic waste sorting is designed. The network model realizes the effective classification of the domestic garbage images after real-time acquisition of the images by the camera, and borrows the MATLAB GUI toolbox to design a GUI that is easy to operate and has strong practicability. The research provides an implementation method for the effective sorting and treatment of domestic waste.
Macro-Site Selection for Wind and Solar Power Systems with VIKOR Method

Currently, wind and solar power generation systems have many drawbacks.Wind and solar power generation Full use of new energy will break the barrier to growth.Location for wind/solar hybrid power plant the primary problem is how to choose scientifically.This article selects six wind/solar hybrid power plants and implements them as a case study and evaluates these six areas via VIKOR by weighting the indicators through the MCDM method.Conclusions Related research findings and better valid, this demonstrates the feasibility and effectiveness of the method.This macro-site selection plants may provide some theoretical basis.In this statistical methods in the literature and established by statistical analysis.Tamil Nadu, Rajasthan, Maharashtra, Gujarat, Andhra Pradesh, Karnataka taken this alternative in this method and evaluation parameters are Total investment, Wind direction, Wind speed and speed change, sunshine stabilization, wind power density, energy saving, Environmental factors.Gujarat is on 2nd rank, Rajasthan is on the 1st rank, Tamil Nadu is on the 3rd rank, Indian Karnataka is on the 4th rank, Maharashtra is on the 5th rank and finally Andhra Pradesh is on the 6th rank.
A Shannon-Theoretic Approach to the Storage–Retrieval Trade-Off in PIR Systems

We consider the storage–retrieval rate trade-off in private information retrieval (PIR) systems using a Shannon-theoretic approach. Our focus is mostly on the canonical two-message two-database case, for which a coding scheme based on random codebook generation and the binning technique is proposed. This coding scheme reveals a hidden connection between PIR and the classic multiple description source coding problem. We first show that when the retrieval rate is kept optimal, the proposed non-linear scheme can achieve better performance over any linear scheme. Moreover, a non-trivial storage-retrieval rate trade-off can be achieved beyond space-sharing between this extreme point and the other optimal extreme point, achieved by the retrieve-everything strategy. We further show that with a method akin to the expurgation technique, one can extract a zero-error PIR code from the random code. Outer bounds are also studied and compared to establish the superiority of the non-linear codes over linear codes.
Background and Miscellaneous Wait Types

SQL Server has different internal processes, and so far we have discussed quite a few of them. Some of these internal processes are constantly running inside SQL Server, waiting until there is work for them to do. While these processes, frequently called background processes, are waiting for work to arrive, SQL Server will record the time they are waiting for work as specific wait types related to these background processes. While these background wait types are not directly related to performance problems, they frequently have the highest wait time and will show up at the top of the list when you query for top wait types ordered by wait time.
Evaluation of Business English Practical Teaching Based on Decision Tree

Business English practical teaching evaluation based on decision tree is a tool to help teachers evaluate their teaching effect. This helps them identify and prioritize areas for improvement. Decision trees are used by many companies, educational institutions and governments as evaluation tools to improve the quality of education provided. The tool can be applied to any type of business environment that requires English skills, such as retail, banking or tourism. It can be applied to any type of educational institutions, such as schools and universities. The main purpose is to understand whether the students have mastered all the necessary skills for effective English communication during their college years. Since its first introduction in 2003, this method has been adopted by many institutions. The evaluation process includes three stages: pre-test, post test and analysis. In these three stages, students are required to complete the task of testing their specific knowledge. Based on the advantages of decision tree in data processing, this paper makes an effective evaluation of the problems existing in the current business English practice teaching in Colleges and universities, and improves the practice teaching strategies according to the evaluation results, so as to cultivate more qualified English talents for the society.
Design of English Teaching Resource Management System Based on Collaborative Recommendation

This study uses a system approach by relying on collaborative recommendation to provide teachers and students with the best information. The system is designed by using various tools such as LMS, e-learning platform and different types of software. It provides an integrated environment that helps provide users with a better learning experience. These systems are based on the concept of collaborative recommendation, and everyone can contribute to everyone involved in this process. This tool is used by many schools and universities around the world. The main goal is to determine how to make recommendations, how teachers use them, and what factors will affect their use. The design collaboration recommendation system requires us to first determine who will participate in making suggestions (designers), and then develop the process of design suggestions (developers). We need to understand the roles and responsibilities of the two groups so that we can ensure that the work of each group complements the efforts of the other. In addition, it is important to know what kind of information will be collected. In view of the current English teaching resources “lost” and “waste” problem. In order to realize the system, the use case of the system is analyzed firstly. Based on the use case analysis, the function and overall architecture of the system are designed respectively, and the collaborative recommendation module is designed. In order to improve the accuracy of recommendation, combined with the professional attributes and other attributes of old users, the hybrid recommendation algorithm is used to complete the recommendation of learning resources.
Formation of critical thinking of students in the process of teaching the subject “pedagogical anthropology”

The position of the problem and the purpose of the study: Today, modern society needs an education that will be able to meet the needs of society as much as possible, which will allow each member of society to step in a developing and changing world in parallel with these changes -constantly develop mental abilities, be active and proactive, show creative and critical approach in various spheres of life. Among the numerous pedagogical technologies used in modern pedagogy in order to solve the above-mentioned most important tasks, critical thinking technology occupies a special place. In the presented article, critical thinking is considered as an important quality necessary for a modern person, and the use of critical thinking technology as a proven, effective means of solving existing pedagogical problems. In the article we touched upon the issue of the development of critical thinking of students in the framework of the course "Pedagogical anthropology", and also presented the results of our research work.
Infocommunication tools: A new learning environment

The role of infocommunication devices in non-school activities is increasing and children sitting at school desks should be prepared by education to use technology and navigate in a technology-driven world (Burgeois et al. 2019).Gamification, including so-called serious games, can be one solution to the problem, but without a change in approach and methodology, they will not become a panacea.Mobile devices, for example, would perfectly support cooperative learning and personalised tasks, but teachers use them mostly for giving homework and tests and do not exploit any more possibilities (Molnár et al. 2020).In the following, the paper will review the changes in the learning environment, students' and teachers' roles, and will also intend to shed light on which areas are the shortcomings that hinder the wider use of ICT in schools.
Pembuatan alat pemurni air laut skala besar untuk memenuhi kebutuhan air bersih di SMA Negeri 1 Rupat daerah pesisir Provinsi Riau

Rupat Island located at a coastal area of Riau Province, generally coastal areas are identical with the availability of clean water. The low availability of clean water has a negative impact on all sectors. The water used by students and teachers at SMA Negeri 1 Rupat comes from a ring well which tastes salty and brackish. Problems in the health sector are the availability of clean water that is suitable for consumption, education is insufficient equipment for Physics Experiments, in the economic field, family spending increases due to the availability of bottled water, and the field of community empowerment has not maximized the use of natural resources. The solution for this aspect is technological innovation that is able to meet the need for clean water in the long term by utilizing existing potential, one of which is the manufacture of a sea water purifier, a giant measuring 4x4 meter. The tool's fundamental working principle is to evaporate the air first and then collect the condensed water to separate the mineral salts and contaminants from the seawater. The author has made a seawater purifier measuring 48x38 cm as a medium for high school physics learning in 2021 with a TKT 5.
Adoption of Blockchain Technology in Education: Application &amp; its Challenges

Abstract: Blockchain Technology has other applications outside of Bitcoin. This technology has the potential to be used in a variety of industries, including education, finance, insurance, healthcare etc. This article not only provides a broad overview of Blockchain Technology, but also provides descriptions of how it could be used in educational institutions. School records management, such as validation and transfer of student transcripts, degrees, test scores and records, including those related to college admissions; educator credentialing/certification/re-certification; management and tracking of school assets, such as buses, library books; management of student privacy and parental permissions.
PS-B07-7: MACHINE LEARNING CLASSIFICATION OF CORONARY HEART SYNDROME BASED ON ELECTROCARDIOGRAM IMAGE USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM

Background and Aim: Electrocardiogram (ECG) is one of the most important examination models used to diagnose various abnormal heart rhythms. An EKG records the electrical activity of the heart through wires and electrodes that are attached to the skin of the arms, legs, and chest wall. Early examination of heart defects can improve quality of life through appropriate treatment. One of the abnormalities in the heart is coronary heart disease. CHD is a heart disease that is mainly caused by the narrowing of the coronary arteries due to atherosclerosis or spasm or a combination of both. In this study, a system was designed to classify coronary heart disease from the results of ECG images. Methods: The study consisted of several stages, starting with the collection of ECG image data for cardiac abnormalities which were used for training images and testing images. The next step is pre-processing using a grayscale process to show the intensity value. Furthermore, at the segmentation stage, a thresholding process is used which aims to divide the image into two parts, namely between the object and the background by forming a binary image. The next stage is the process of taking feature values from the segmentation results using Invariant Moment. The final stage is classification using the Convolutional Neural Network. The data used in this study are images obtained from Beecardia physiobank. There are four classifications of images processed in this system, namely Normal, Asymptomatic, Angina Pectoris, and Acute Myocardial Infarction. The amount of data used is 126 where the image is divided into two, namely, 101 training data and 25 testing data. Results: The Convolutional Neural Network method is able to classify coronary heart disease from the results of ECG images very well. The results of the classification process for coronary heart disease from the results of ECG images are able to achieve an accuracy of 92%. Conclusion: The classification system can perform its performance well, as evidenced by the percentage obtained, including a height exceeding 90%. Suggestions for the future, more training data can be used so that at the time of testing, the test data can get higher accuracy.
LR-ProtoNet: Meta-Learning for Low-Resolution Few-Shot Recognition and Classification

For the few-shot classification problem of low-resolution(LR) images, we propose a meta-learning method based on prototypical networks called LR-ProtoNet. The metric-based meta-learning algorithm mainly extracts the features of the support and query samples through the feature encoder and obtains the prediction categories from a metric module. Our core idea is to add feature-affine layers in the feature encoder to increase the feature distribution of LR images, and use Brownian Distance Covariance(BDC) in the metric module to capture the joint distribution and nonlinear relationship between different affine transformations. We down-sample standard few-shot image datasets to simulate LR images and conduct extensive ablation experiments and comparative studies of other meta methods in general image recognition and fine-grained classification. Experimental results demonstrate that our proposed model can effectively utilize low-resolution image information, achieving state-of-the-art performance compared to baseline works.
Synthesis and application of CaO/Fe3O4 as a catalyst of transesterification reaction of kapok seed

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ega Silvia Lutviana, Muhammad Rafli, Fauziatul Fajaroh, Sumari Sumari, Adrian Nur; Synthesis and application of CaO/Fe3O4 as a catalyst of transesterification reaction of kapok seed. AIP Conf. Proc. 10 January 2023; 2569 (1): 070007. https://doi.org/10.1063/5.0131221 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Soft-Margin Ellipsoid Generative Adversarial Networks Based on Gradient Regularization

This paper presents the results on the designing and applying of a kind of generative adversarial networks(GANs) with soft-margin Ellipsoid GAN which are used in a variety of applications including computer vision and image processing. However, GANs are often subjected to problems linked to instability and non-convergence in training process. Defined an integral probability metric (IPM) on hypersphere, Sphere Generative Adversarial Network(Sphere GAN) makes discriminator have the characteristics of Lipschitz continuity to ensure the convergence in the training process. Thus, developed from Sphere GAN, we propose a new GAN model called Ellipsoid Generative Adversarial Networks(Ellipsoid GAN), in which IPM defined on hypersphere is generalized to hyperellipsoid. The hyperellipsoid is realized to relax the upper bound of IPM by extending measurable functions space, and induce a more sensitive Wasserstein distance to improve the quality of generated samples. In addition, in order to improve the stability of discriminator, the idea of soft-margin is introduced into the designed Ellipsoid GAN to prevent the discriminator from gradient vanishing and exploding on the classification boundary. Comparison experimental results on CIFAR10 and LSUN datasets show that the designed soft-margin Ellipsoid GAN is superior to Sphere GAN with better quality of generated samples.
An Improved Marine Predators Algorithm Based on Group Learning

To address the shortcomings of the marine predators algorithm (MPA) in solving complex problems, such as low optimization accuracy and easily falling into local optimization, this paper proposes an improved marine predators algorithm based on group learning (GLMPA). An opposition-based learning method is adopted to enhance the quality of the initial solutions. Then, a group learning strategy is used to diversify the population. Two subgroups are produced by fitness evaluation and employing different updating mechanisms. In addition, a new position-updating rule is used to help the proposed algorithm escape from the local optima in the later stage of iteration. Finally, six test functions are utilized to test the GLMPA, and the simulation results verify the effectiveness of the proposed algorithm when compared with other famous algorithms.
An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fitting Model

Nowadays gaze tracking systems have several common drawbacks: low suitability, high complexity and low accuracy. To solve these problems, this paper presents a novel gaze tracking method, which improves the performance of gaze tracking system in two aspects. A 3D ocular quadratic surface model is constructed with eyeball’s point cloud data, which are collected by a pair of binocular cameras in a free space, allowing head movement and without any wearable devices. Geometrical features of ocular surface, including principal direction, Gaussian curvature and radius are exploited to calculate ocular optical axis direction and person-specific parameters, and then to estimate ocular visual axis direction, i.e. gaze direction without any personal calibration procedures. For this purpose, two person-specific parameters, the radius of cornea curvature and the distance between the center of cornea curvature and the pupil on the ocular model, are inputted into a GRNN network to estimate the value of kappa angle, which is an angle between ocular optical axis and visual axis and has important effect on estimation accuracy of the ocular visual axis direction by the ocular optical axis direction. Compared with the pupil center cornea reflection method, the following experimental results show that the method presented in this paper is effective and accurate in kappa angle estimating and gaze tracking.