Reviewerly/exHarmony huggingface dataset:
Research and Development of Learning Early Warning System Based on Artificial Intelligence Technology

Aiming at the current situation of staff shortage and low efficiency in learning quality assessment, this research conducted in-depth research on artificial intelligence and data analysis technology, and designed an intelligent and automated learning quality analysis and early warning system. The system is divided into four functional modules: evaluation index management, evaluation management, result management and system management, which can complete the questionnaire design intelligently and quickly according to the evaluation index. After the user completes the entry of the evaluation results, the system database can quickly generate data tables, use the embedded analytic hierarchy process algorithm module to sort out the evaluation index system, and finally use the artificial neural network model to get the evaluation results. The system has complete functions and simple interaction, which can greatly improve the efficiency of learning quality evaluation.
Lightweight Non-local High-Resolution Networks for Human Pose Estimation

Human pose estimation is one of the fundamental tasks in computer vision, applied in areas such as motion recognition, games, and animation production. Most of the current deep network models entail deepening the number of network layers to obtain better performance. This requires computational resources that exceed the computational capacity of embedded and mobile devices, thereby limiting the practical application of these approaches. In this paper, we propose a lightweight network model that incorporates the idea of Ghost modules. We design Ghost modules to replace the base modules in the original high-resolution network, thus reducing the network model parameters. In addition, we design a non-local high-resolution network that is fused in the 1/32 resolution stage of the network. This enables the network to acquire global features, thus improving the accuracy of human pose estimation and reducing the network parameters while ensuring the accuracy of the model. We verify the algorithm on the MPII and COCO datasets and the proposed model achieves a 1.8% improvement in accuracy while using 40% fewer parameters compared with the conventional high-resolution network.
Finding Effective Abstractions

chapter Share on Finding Effective Abstractions Authors: Cliff Jones Search about this author , Jayadev Misra Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 23–40https://doi.org/10.1145/3477355.3477358Online:02 October 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
Deep High-order Tensor Convolutional Sparse Coding for Stereo Matching

With the rapid development of science and technology, high-dimensional data emerge one after another. High-order tensors can be used to describe high-dimensional data structure, which can retain the hidden structure of data, but cannot obtain the deep features. Therefore, it is very important to establish a deep high order tensor model. In this paper, we propose a deep high-order tensor convolutional sparse coding model, which can automatically learn the deep convolutional kernel. Based on the learned deep convolutional kernel, a two-layer deep dictionary learning model is established. Then, the sparse representation coefficients are respectively solved, and a new weighted matching cost method is constructed, which combines shallow and deep features. The experimental results on the Middlebury 2014 dataset show that the proposed deep high-order tensor convolutional sparse coding is effective for stereo matching.
Single Image Super-Resolution Network Based on Multiple- Scale Self-Similarity Module

Abstract Recently, single image super-resolution (SISR) based on deep convolutional neural networks (CNNs) has been widely studied and achieved remarkable results. However, most of these methods mainly widen or deepen the network to get better results, ignoring the self-similarity in natural images. Some works have successfully leveraged this self-similarity by exploring non-local attention modules, but they only use non-local attention modules at one scale or two scales. And they need to calculate the self-similarity in each residual module, which makes the network more complex and takes a long time to train. We propose a multiple-scale self-similarity module (MSSS) that can be flexibly inserted into existing various super-resolution networks. MSSS uses convolution with different kernels and the trailing dilated convolution to obtain feature maps at different scales, and then calculates the correlation between multiple feature maps at different scales through two-head region-level recurrent criss-cross attention modules (TRRCCA), finally gets fusion output through channel attention. So MSSS can mine multiple-scale self-similarities. We only insert a MSSS module into the basic residual SISR network, and the result is significantly improved.
Visualization and comparison of aircraft trajectories using Gaussian-Smoothed heatmaps

Thousands of airplanes traverse the national airspace every day. Each flight has a unique trajectory in space and time that is typically pre-determined by a flight plan and can be inflexible. With air traffic density expected to increase in the future, NASA has been conducting research on technology that allows flexibility for airplanes to fly more optimal trajectories that can save time and fuel. A recent computer simulation study was conducted to investigate differences in an airspace with and without the technology. A visual comparison of the trajectories in each airspace was done using heatmaps. Initial inspection showed the heatmaps were quite sensitive to small differences among trajectories. In this paper, we demonstrate the use and efficacy of kernel smoothing for improved visual comparisons of high density and overlapping aircraft trajectories via heatmaps.
Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots

Since the emergence of deep learning as a dominant technique for numerous tasks in the computer vision domain, the robotics community has strived to utilize its potential. Deep learning represents a framework capable of learning the most complex models necessary to carry out various robotic tasks. We propose to integrate deep learning and one of the fundamental robotic algorithms—visual servoing. Fully convolutional neural networks are used for semantic segmentation, which represents the process of labeling every pixel within the image. The obtained information from labeled (categorical) images can be crucial for mobile robot control in dynamic environments. To adequately utilize semantic segmentation for mobile robot control, the segmented images acquired at the desired and the current pose need to be registered (aligned). Since the accuracy of visual servoing depends on the accuracy of the image registration process, we propose to increase the accuracy of mobile robot positioning by analyzing three different optimization algorithms devoted to the registration of categorical images. The standard gradient descent algorithm is compared to the OnePlusOneEvolutionary algorithm, and simulated annealing. Moreover, different cost functions such as Mattes mutual information, global accuracy, and mean intersection over union are also investigated. All the algorithms are tested on our own wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition) developed within the Laboratory for robotics and artificial intelligence. The results indicate that the algorithm with a larger exploration to exploitation ratio provides better results. Moreover, the cost function with the steepest convex domain is more advantageous.
Image steganography Based on Chaos Permutation, Authentication and Wiener Deconvolution

Image steganography refers to hiding a secret image into another meaningful one. Researchers have employed different algorithms such as wavelet transform, discrete cosine transform functions to transform secret image and different image processing algorithms like image encryption utilized to reach better stego images in case of imperceptibility and robustness. In most of researches, higher robustness methods have lower impeccability and vice versa. In this paper, we propose an adaptive LSB method using chaos permutation, DCT, and secret image authentication to increase both imperceptibility and robustness. Chaos permutation is a key feature in our work to improve the robustness of secret image against attacks and wiener filter deconvolution is employed to extract a clear secret image in receiver side, which has the lowest noise in comparison with the original secret image. The improvement of our proposed method have been analyzed and evaluated by different criteria such as PSNR, SSIM and Histogram plots.
Automatic method to calculate the storage volume of refrigerator assembly based on binary voxel images

The storage volume of a refrigerator is an important consideration for design and purchase. However, the inner space of a refrigerator assembly is not delimited, because of the holes and gaps between parts. The traditional calculation method for the storage volume reconstructs the boundaries of the inner space by artificially filling holes and gaps, which is labor-intensive and time-consuming. We present a boundary surface reconstruction method based on binary images for automatically calculating the storage volume of a refrigerator assembly. First, the components facing the inner space are represented by a voxel model. Then, holes and gaps are filled on the basis of a binary image to form a closed voxel boundary so that the inner space can be determined. The boundary surface enclosing the storage space is reconstructed by using a modified dual contouring algorithm constrained by intersection points between the boundary voxels and assembly. Finally, the storage volume is calculated from the reconstructed surface. A prototype system was developed to verify the proposed algorithm. The results showed that the reconstructed boundary surface clung to the inside wall of the refrigerator assembly, and the transitions across the holes and gaps were smooth and continuous.
The principles of quality management in the Russian vocational education system

The authors study the problem of improving the level of training of university and college graduates by using modern approaches, based on the principles of quality management, in the system of professional education. In order to develop creative economy, the government of Russian Federation focuses on scientific and educational centers including regional ones which work on the basis of organizations of professional education. According to many experts, the level of training in regional higher educational institutions and branches of those located in the capital of the country does not comply with innovation requirements. There is a need for building modern material base in the regional higher educational institutions and colleges; there is a need for decent salaries for the staff. Educational institutions should accept the ideology of quality to be followed by all the employees not in words, but in deeds. The learning process used for centuries and based on the transmission of knowledge from a teacher to a student has exhausted itself. The basic principle of quality management is the process approach. The basis for students’ high level of training is organization and implementation of the educational and training process in educational institutions. The main subjects of the process are teachers and students. The efficiency of the educational process will increase if such approaches as consumer-orientedness, leadership and personnel interaction are used. The authors suggest making analysis of interdisciplinary links of the content of academic disciplines set out in the working academic programs. They also point out the advisability of a system of training in the classes of leading teachers. The readers’ attention is attracted to changing the ideology of controlling students’ knowledge and skills from punitive to diagnostic. The article contains recommendations to conduct regular surveys among the students and teachers to discover the areas of educational improvement. The results of the surveys should become the basis for making decisions. Implementation of the principles of quality management does not require large material costs.
Network security in the new world of work

The largest networking security risks in the past year have been a direct result of organisations having to change the way they do business in an incredibly short space of time. According to a McKinsey study, in 2020 alone, digital offerings accelerated by approximately seven years. 1 While IT teams worked overtime to move nearly all operations online, cyber criminals monitored their every move, ready to exploit any issues stemming from the global disruption caused by new technology deployments and dispersed workforces.
Distributed Coherent Absorption in Quantum Networks for Deterministic Entanglement Generation

We show theoretically and demonstrate experimentally that coherent absorption of a single photon in a quantum network allows deterministic generation of entanglement between the network’s nodes. Quantum networks, consisting of multiple nodes connected by quantum channels, are in the basis of quantum technology allowing protocols of quantum computation and quantum communication. To implement these protocols, quantum entanglement should be distributed across the network. A conventional approach of entanglement generation is realized by a sequential splitting of a single photon on a series of beamsplitters and independent coupling of each ‘part’ of the photon into the corresponding node of the network [1] , Fig. 1(a) . Efficient entanglement generation in a large scale quantum network is a challenging task as a highly efficient light-matter interaction in each node of the network is required: even in a network of 100 nodes the probability of successful entanglement generation would be around 10 −5 .
Hierarchical Cluster Labeling of Software Requirements using Contextual Word Embeddings

The popularization of social media has motivated research into machine learning methods for software requirements extraction from user comments and reviews. These methods analyze software review datasets to classify some textual excerpts as software requirements, which allows the management and monitoring of the evolution of software quality directly from the crowd users’ perspective. However, the existing methods have two major limitations. First, several duplicate requirements are extracted from reviews because users write the same requirement in different ways by using synonyms and non-technical language, often with misspellings and ambiguity. Second, requirements extraction methods do not deal with different granularity levels, thereby ignoring hierarchical relationships between software requirements. This paper presents a hierarchical cluster labeling approach for software requirements based on contextual word embeddings to address these challenges. We explore neural language models to obtain a more semantic and robust representation of the software requirements, in which the texts are represented by contextual word embeddings. Our approach organizes the software requirements into clusters and sub-clusters according to requirement similarities in the embedding space. Finally, we select representative software requirements to label each cluster and sub-cluster, thereby dealing with both duplicate and different granularity levels of the software requirements. An experimental evaluation using review datasets from 8 mobile apps shows that our approach obtains promising results and presents new ideas and research directions for data-driven requirements engineering.
A Deep Transfer Learning-Based Object Tracking Algorithm for Hyperspectral Video

Deep convolutional neural networks (CNNs) have been proved effective in color video visual tracking task. Compared with color video, hyperspectral video contains abundant spectral and material-based information which increases the instance-level discrimination ability. Therefore, hyperspectral video has huge potential for improving the performance of visual tracking task. However, deep trackers based on color video need a large number of samples to train a robust model, while it is difficult to train a hyperspectral video-based CNN model because of the lack of training samples. To tackle with this problem, a novel method is designed on basic of transfer learning technique. At first, a mapping convolutional operation is designed to embed high dimensional hyperspectral video into three channels as color video. Then, the parameters of CNN model learned on color domain are transferred into hyperspectral domain through fine-tuning. Finally, the fine-tuned CNN model is used for hyperspectral video tracking task. The hyperspectral tracker is evaluated on hyperspectral video dataset and it outperforms many state-of-the-art trackers.
Autofhm: A Python Library for Automated Machine Learning

After the introduction of machine learning, it has gone through lots of research and development which resulted in an explosion of usage in many fields. Developing such a model is not an easy task and it requires extensive domain knowledge and skills. This paper presents Autofhm, a python library used for automated machine learning. This tool automates the steps followed for the machine learning model creation such as feature engineering, model selection, and hyperparameter optimization. For a given dataset, Autofhm generates new deeper features which could increase the performance of the model. Then it selects the best performing model along with the suitable hyperparameter combinations based on the feature engineered dataset. The Autofhm is tested on 5 classification tasks and 5 regression tasks and the results demonstrate that, Autofhm gives good results with lesser time when compared to state-of-the-art frameworks like TPOT.
Fine-Tuning of the Measure for Full Reference Image Quality Assessment

In this paper, we proposed a new measure to solve the full reference image quality assessment problem. The core of the approach is known as peak signal-to-noise ratio improved with the estimation of local block-wise distortions, contrast, and saturation differences between test and referenced images. A measure that includes these values into a common quality score has been proposed. The proposed measure includes factors and thresholds which allow tuning the measure according to the specific features of the particular image dataset. The iterative numerical partial optimization algorithm for the fine-tuning of these factors and thresholds has been proposed, implemented, and tested as well as start optimization point selection has been described. The dependency of quality measure on parameters fine-tuning has been investigated. The usage of the proposed quality metric for the processing of TID2013 and CSIQ datasets as well as its computational complexity has been investigated. The results of modeling have been shown that it is possible to build the image quality measure in a fraction of a second preserving the average comparison quality in terms of the mean opinion score provided by humans.
Analysis of Motivation Structure of Pharmacy Students Belonging to Generations Y and Z

&lt;b&gt;Introduction.&lt;/b&gt; The article provides data from the analysis of the motivational profiles of students of different generations. The introduction describes the features of the teams of pharmacy organizations and modern approaches to staff motivation. &lt;br&gt;&lt;b&gt;Materials and methods.&lt;/b&gt; The study was carried out on the basis of the methodology for diagnosing the motivational profile of the personality by S. Ritchie and P. Martin among students of a pharmaceutical university - representatives of generations Y and Z during their 1&lt;sup&gt;st&lt;/sup&gt;, 3&lt;sup&gt;rd&lt;/sup&gt;, and 5&lt;sup&gt;th&lt;/sup&gt; years of studies. &lt;br&gt;&lt;b&gt;Results.&lt;/b&gt; In the course of the analysis of these motivational profiles, the main directions and differences in the motivation of these generations and the peculiarities of the motivation of generation Z specialists were identified. Their main incentives were determined, as well as the list and focus of measures to attract new generation employees. The dominant motivational factors of generation Z are: high wages and material rewards, clear job structuring and feedback. Also, representatives of generation Z revealed such qualities as: relatively low creativity, a high need for multiple social contacts, and high competitive assertiveness. &lt;br&gt;&lt;b&gt;Conclusion.&lt;/b&gt; On the basis of the results obtained, possible changes in the motivational profiles of generation Z at the beginning of their career were identified and the main recommendations for attracting such employees to the company were outlined. Further studies are needed to evaluate the changes of motivational profiles over time.
Pengaruh Minat Belajar dan Pemberian Tugas terhadap Prestasi Belajar Mata Pelajaran Fiqih

This study aims to determine the effect of interest in learning on learning achievement in fiqh subjects, giving assignments on learning achievement in Fiqh subjects, and the influence of interest in learning and assignment to learning achievement in Fiqh subjects. This research is quantitative. The population in this study were students in grades 7 and 8 of MTs Negeri 3 Sukoharjo for the 2020/2021 academic year, totaling 239 students. The sample of this study was 150 students of MTs Negeri 3 Sukoharjo for the academic year 2020/2021 with the sampling technique of proportionate stratified random sampling. Data collection techniques using a questionnaire (questionnaire) and documentation. Testing the instrument using validity and reliability tests. Classical assumption test using normality test and linearity test. The data analysis technique used Multiple Linear Regression, F test, t-test, and coefficient of determination (R2). The results showed that the influence of interest in learning on the learning achievement of students in fiqh subjects at MTs Negeri 3 Sukoharjo for the 2020/2021 academic year proved to have a significantly positive effect. Giving assignments in Fiqh subjects to Fiqh Learning Achievements has been proven to have a significantly positive impact. The influence of learning interest and Fiqh subject assignments at MTs Negeri 3 Sukoharjo for the 2020/2021 academic year together has a significant effect.