Reviewerly/exHarmony huggingface dataset:
Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.
Discussion on the Combination Mode of College English Production, Education and Research Based on Online Education Platform

research-article Discussion on the Combination Mode of College English Production, Education and Research Based on Online Education Platform Share on Author: Qian Peng Wuhan Donghu University, China Wuhan Donghu University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 264–268https://doi.org/10.1145/3482632.3482687Online:22 November 2021Publication History 0citation4DownloadsMetricsTotal Citations0Total Downloads4Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research and Implementation of Cross Platform Technology of Domestic Mobile GIS System

The combination of geographic information system and Internet shows the advantages that traditional GIS does not have by utilizing the fas, convenient and huge user group of Internet. With the changes brought by 21st century, the innovation and application of information technology made all countries stand on the same starting line to face new development opportunities and challenges. In addition, the rise of the domestic system of independent operation and rapid development made various types of domestic complex of terminal, in order to ensure network security and information security, the GIS system and independent control of computing devices, more and more important, the combination of requirements can be found in a variety of terminal equipment on whether online or offline mode can provide the basis of geographic information services. Based on this purpose, this paper develops a GIS kernel by using standard C++ language and ADAPTS it to various domestic mobile terminals to verify the integrity and stability of GIS software functions. The results show that the developed GIS can run stably in multiple terminals and provide basic services.
A Load Modeling Method Based on Machine Learning

Abstract More and more high-power impact loads in the power grid are put into use. When the power is impacted, these loads will cause the power grid bus voltage fluctuations and reduce the power quality. Therefore, in order to accurately analyze the changes in the power system, it is particularly important to establish a reasonable impact load model. This paper takes the impact load data generated by the CT machine during exposure as an example to analyze the characteristics of the impact load. Based on the active power characteristics of the impact load, machine learning methods (such as support vector machines and long and short-term memory networks) are introduced into the impact load modeling, and the power waveform change law of the impact load is accurately established by this method. Finally, the effectiveness of the method is verified by simulation.
Improvement Google Classroom Feature to Make It More Comfortable to Use with Google App Script

The government supports the development of learning models by adjusting to the era of the industrial revolution 4.0. Universitas Pendidikan Indonesia (UPI) is one of the leading universities in Indonesia has implemented a curriculum that is adaptive to the dynamic changing times. Industrial revolution 4.0 impacted the learning patterns of teachers in managing the classroom to the introduction of learning 4.0 by the government currently at the Indonesian Education University, the distance learning process realized for approximately one year. Some of the problems encountered when online learning succeed are the various learning media those distracting students by adjusting the learning platform by the lecturer who teaches. This research is one of the implementations of the research roadmap on distance learning in the 4.0 learning era. UPI has provided a SPOT Learning Management System (LMS)where lecturers and students use it in the learning process. One of the other LMS available at UPI is Google Suits such as google calendar, classroom, meet, form, youtube, drive Documents, and spreadsheet where features in it make it easier for lecturers and students to study online. The weakness of this G-Suits is that the features it provides are separate so that when the lecturer is going to use it, they must be skilled in using it. This study aims to designing and developing a practical learning system that can support the online learning process at UPI.
An Efficient Interactive Multilayered Approach for Segmentation of Complex Images

This paper presents an interactive algorithm that is fast and accurate for segmentation of multiclass images called GMMLC (Gaussian Mixture Model in Laplacian Coordinates). The proposed framework has an imperative feature of classification of complex images with similar background and foreground features. This paper refers to such images as Indistinct and Indistinguishable Boundary images. GMMLC combines the prevalent features of the multivariate Gaussian Mixture Model and the weighted valency graph based approach of Laplacian Coordinates. Commendable results are obtained by combining an efficient but suboptimal solution defined in probability space and the average neighborhood preservation properties of LC. This work is inspired by Casca’s seed based image segmentation approach. However, this technique presents significantly improved results in terms of boundary fitting and efficiency with only a small amount of seeds due to the homogenous regions obtained from GMM preprocessing. The proposed framework outperforms all existing advanced techniques by means of greater efficiency and better boundary fitting with minimum user intervention. The algorithm is applied on 500 images from the Berkeley datasets (BSD500 and BSD300) and 50 images from the Grab cut dataset (PASCAL VOC 2007). The paper culminates with a qualitative boundary and efficiency evaluation in comparison with five prominent algorithms BPT, SRG, GC, SIOX and LC.
Optimized VCST Algorithm for Connect6

Since the concept of Connect6 was first introduced by Professor Wu at National Chiao Tung University in Taiwan, there has been massive progress in the study of Connect6. Recently VCF(victory by continuous four) algorithm has become a well-known game search method for Connect6. This method performs well in attacking the opponent and finding offensive ways to win. Based on the VCF method of Gomoku, this paper proposes an algorithm called VCST (victory by continuous single-threat-or-more moves) for Connect6. For any feasible solution searched out, the solution is a winning strategy, and there is no need to continue traversing the tree. This method uses the AND/OR tree strategy to improve the search efficiency of the algorithm, and a parallel implementation method is designed for this strategy to speed up The running speed of the algorithm. The experimental result shows that our improved VCST algorithm improves the program robustness and leads to an increment of winning chance. Our gaming program won second place in the 14th National Undergraduate Computer Game Competition.
The Place of Painting in the Problem of Cinema Frame Composition

I offer a glimpse at the essence of the visual arts, which is related to the conceptual and the visual constructions through which the image is constructed. Although very different from painting, cinema, as a visual art, is influenced by it at a depth that is hard to imagine. I am taking into account the views of Sergei Eisenstein – one of the proponents of the formal theory of cinema, displayed in his theoretical works, as well as several of his films. Changes in the visual arts that result from the advent of photography give new meaning and role of painting. Artists occupied in painting are beginning to look for new ways to solve the problem of three-dimensional space in a two-dimensional surface. In the time that cinema develops own language, it sets to itself similar tasks. At that moment, cinema turns to the familiar visual-artistic experience and its forms in order to construct its own imaginative language. Cinema, like painting, gives us the joy of looking at the form in which the correlation of the elements is sensed. Accordingly, in order to bring pleasure and meaning, this form is created in constructions that we know from the field of painting. Editing, the basis of cinematic language, is built by putting together frames that serve as building blocks of the film. In this capacity they are of utmost importance. It is frame’s compositional organization, which at first takes place in a two-dimensional space as in picture that plays a major role in the approach to the editing. The composition of the frame which is the core of the editing process is built in the parameters of the main points through which the composition in painting can be built – rhythm, dynamics, statics, symmetry, asymmetry, analysis and synthesis of form, and at a later stage of the development of cinema – even color. The visual arts are based on similar structures and ways of building the form though they do it through different languages. There are unifying principles that define the world of images. Whether they are moving or static, their construction is often done on the basis of similar intentions and through similar instruments. Knowing them, understanding them, is a tool for creating quality images.
The evaluation of bank credit based on the improved decision tree model

With the rapid development and globalization of the economy, this article investigates the effectiveness of decision tree model for banks' decision making in granting personal credit loans, unlike the previous traditional bank credit analysis model. By collecting user data of lenders and analyzing it through decision tree model, this paper finds that decision tree model can effectively process user information and analyze it, thus increasing prediction accuracy and reducing risk, and finally helping banks to make decisions.
Jointly smoothing word embedding and text representation

Word embedding techniques have gained a lot of attention in recent years. Mapping words to distributions rather than points has tremendous advantages for language modeling and text documents classification. This paper presents a density word embedding based on categorical data distribution and Fisher information metric. Taking advantage of context information included in the embedded words, we derive a new latent topic model defined on a statistical manifold for the purpose of representing texts. We propose the mixture of smoothed Dirichlet distributions and we introduce the learning of mixture parameters using a likelihood-based approach. The proposed framework is validated through three gold-standard datasets: BBC News, Reuters 21578, and 20 Newsgroups. We compare the performance of the proposed approach with related works and the obtained results prove the robustness of our combined embedding approach with the smoothed latent topic model.
Research on Mongolian-Chinese Translation Model Based on Transformer with Soft Context Data Augmentation Technique

As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.
Small-scale Convolutional Neural Networks with Learnable Gabor Filter for Image Classifications

Recently, small-scale convolutional neural networks (CNNs) have become a hot research topic due to the fewer training parameters and shorter training time, while their accuracy is generally degraded compared with large-scale CNNs. To this end, in this paper, we propose a small-scale CNN with sparse representation (a sparse small-scale CNN) for image classification. First, the Gabor filter is a representative sparse representation, so we integrate it into the standard AlexNet to establish a sparse Gabor-AlexNet architecture to better extract the feature of images. Second, to improve further the performance of the above Gabor-AlexNet., a learnable Gabor-AlexNet with only the increase of a few learning parameters is constructed for image classification. Finally, we test the Gabor-AlexNet on small-scale datasets including Extended Yale B, CASIA-FaceV5., Original Brodatz and, DTD. The proposed method outperforms the standard AlexNet in the precision and speed as classification benchmark. Then, our learnable Gabor-AlexNet is also superior to the Gabor-AlexNet for classification performance on the CIFAR-10 dataset.
Research on Intelligent Management Model of Classroom Teaching Based on Data Analysis

As the existing problems in the classroom teaching process of colleges and universities, and the trend of all kinds of intelligent devices station in the classroom and based on the triad teaching management concept of "teaching, learning and management", this paper puts forward an intelligent management model of classroom teaching based on data analysis. To promote the improvement of classroom teaching effect and students' learning efficiency.
Designing the Virtual Rosetta: A Tool for Exploring Historical Drawings in VR

We present the results of a comparative analysis of various layout and design displays for historical drawings. This involved developing a new visualization environment, known as the ”Virtual Rosetta” for a collection of early twentieth century Vietnamese drawings known as Technique du Peuple Annamite (Mechanics and Crafts of the People of Annam) compiled by French colonial administrator Henri J. Oger (1872-1929). In this paper, we discuss similar work that has been pursued by researchers in digital humanities, specifically working in virtual displays of artwork, the design of the Virtual Rosetta, including text readability, wall and text contrast, and organization of drawings, as well as the associated results. We used an analysis technique known as hierarchical clustering utilizing sentence embeddings, a technique in natural language processing in which sentences are mapped to numerical vectors, to organize a subset of the drawings so that drawings with similar text descriptions are located near each other. Performing this on an entire dataset would allow humanities researchers to most effectively and efficiently visualize their findings. We report on user experience and efficacy for a number of virtual environment layouts for displaying drawings and visual information for research analysis in virtual reality.
Analysis of Decrease in Accuracy of Two-tier Trees without Using Feature Selection

It has been shown in previous papers that traditional approaches to feature selection can lead to learning a two-tier decision tree, which significantly loses in accuracy to a enumerate of all non-empty subsets of features containing no more than three features. In this paper, on a real, public dataset, we calculated how often such an accuracy loss can occur. It is shown that there is a statistically significant correlation between the coefficient of determination of a two-tier decision tree, constructed without any feature selection, and the magnitude of such a loss in accuracy. The characteristic values for the loss of accuracy in the construction of a two-tier tree without any feature selection, compared to a full enumeration of all nonempty subsets of features containing no more than three features, for a real dataset in some domain are calculated.
Safe Mobile Robot Path Planning Based on an Improved JPS Algorithm

Jump point search algorithm is an improved A * algorithm aiming at the problem in the computation, where the quality of path is not considered and even shows worse performance in security than traditional A * because of the rules imported to speed up the path-finding procedure. This paper proposed an algorithm to improve the security and smoothness of path planned by jump point search algorithm with the modification of its pruning rules. This improved way is not only based on the conceptions of JPS, but also combined with the method to achieve similar improvements of traditional A *, which was realized by a modified evaluation function and recursive check of nodes. To prove the improvements of path quality and consistent performance in computation time, simulation is carried out in grid map. The result demonstrates that the improved jump point search algorithm could effectively increase the quality of the final path especially in security, while the performance in speed of the proposed method is as good as the original jump point search algorithm and much more effective than A *.
Weight Term Document in Clustering Algorithm for Classification a Final Project in Online Learning

The problem in all online learning is that all assessment such as final project is uploaded in it and lecture must evaluate all final project in a specific course that has different topics and subjects so it makes difficult for the lecture. This research built an application that makes a classification of final project documents from the student based on the same subjects and topics. The application takes data from database online learning in a specific course that the database of the final project has a different scope and broad topic. Classification is carried out based on the similarity of topics from the final project document for certain subjects. The document is in the form of text, so a text-mining algorithm is needed to determine some of the topics contained in the final project document. Determination of the final project document according to a particular topic requires a similarity algorithm. This research takes the final project file from Google Drive and the online learning database and implements it in a mobile application. The average result of testing is that the accuracy is 72.49&#x0025;.
Complete Test Set Generation for Control Flipping Faults in Reversible Circuits

The newly developedHandique, Mousum fabrication process reduces the sizePrasad, Amrit of the chips and increases the speed of a system. Simultaneously, powerSarma, Hiren, Kumar, Deva dissipation is also increased, which causes a major issue in semiconductor devices. Therefore, reversible computation is gaining interest in the low-power circuit design and the fast computation system. Moreover, the mechanism of reversible computation is widely used in quantum computing nanotechnology and also in digital communication systems. These inspire the researchers to give more attention to the reversible circuit. Thus, reversible circuit becomes more alternative than the conventional circuit. Testing these circuits is an essential aspect of ensuring these circuits’ high reliability and integrity performance. This paper introduced a new fault model labeled as the control flipping fault model (CFF) in a Multiple-Control Toffoli (MCT)-based reversible circuit. Specifically, we target the positive control flipping faults (PCFFs) under the proposed control flipping fault model. The reported works present a scheme to detect fault to construct a complete test set (CTS), which is capable of detecting all the PCFFs in a reversible circuit. The paper also presents an experimental evaluation in order to show the efficiency of the CTS that covers 100% faults.
Improved nonlinear diffusion denoising method based on wavelet transform and contrast sensitivity function for low-light-level images

The electron-multiplying charge-coupled device (EMCCD) is widely used in low-light-level (LLL) imaging field. In order to solve the problems of low signal-to-noise ratio (SNR) and low contrast of EMCCD LLL images under low illumination, a denoising method based on the nonlinear diffusion filter with wavelet transform and contrast sensitivity function (CSF) is proposed. Aiming at the problems existing in the P-M diffusion model, that is, the diffusion coefficient is easily disturbed by noise, and the direction affects the noise reduction process, the method introduces the wavelet transform and the CSF, then a new diffusion function is proposed to reduce the noise of LLL images. The experimental results show that, compared with other denoising methods, the proposed method has better denoising effect on EMCCD LLL images.
Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy

Handwriting recognition, a pervasive assistive technology for a variety of consumer electronics and Internet of Things (IoT) systems does not provide meaningful privacy guarantees. Hacking of a device, malware, and compromise of handwriting recognition-enabled application's cloud service can lead to leakage of sensitive personal information with serious consequences. In this paper, we propose an algorithm to deform a user's original handwriting by adding adjustable statistical noise before sharing it with a specific application to achieve local differential privacy (LDP). To assess and quantify the algorithm's impact on recognition, we generate a shape-deformed test set by applying the algorithm to the MNIST dataset and implement a visualization tool to demonstrate the impact of the proposed algorithm. Then, we train a convolutional neural network, Naive Bayes, random forest, support vector machine, and decision tree classifiers on the MNIST dataset and evaluate them with the privatized test set. Our experiment reveals that without tuning the CNN for privatized images, it is possible to achieve around 80% accuracy in local differential privacy settings, which lays the ground for further research in this direction.